1

UNDERSTANDING PURCHASE BEHAVIOUR AND ANALYZING MARKETING MIX STRATEGIES: A

STUDY OF BOTTOM OF THE PYRAMID (BOP) CONSUMERS

Dissertation

Submitted to the Punjab Agricultural University in partial fulfillment of the requirements

for the degree of

DOCTOR OF PHILOSOPHY in

BUSINESS MANAGEMENT

(Minor Subject: Economics)

By

Amanpreet Singh

(L-2012-BS-54-D)

School of Business Studies College of Basic Sciences and Humanities

© PUNJAB AGRICULTURAL UNIVERSITY LUDHIANA-141 004

2016

2

CERTIFICATE I

This is to certify that the dissertation entitled, “Understanding Purchase Behaviour

and Analyzing Marketing Mix Strategies: A Study of Bottom of the Pyramid (BOP)

Consumers” submitted for the degree of Ph.D., in the subject of Business Management

(Minor subject: Economics) of the Punjab Agricultural University, Ludhiana, is a bonafide

research work carried out by Amanpreet Singh, Admission No: L-2012-BS-54-D under my

supervision and that no part of this dissertation has been submitted for any other degree.

The assistance and help received during the course of investigation have been fully

acknowledged.

Dr. Lalit Mohan Kathuria Major Advisor

3

CERTIFICATE II

This is to certify that the dissertation entitled, “Understanding Purchase

Behaviour and Analyzing Marketing Mix Strategies: A Study of Bottom of the

Pyramid (BOP) Consumers” submitted by Amanpreet Singh, Admission No: L-

2012-BS-54-D to the Punjab Agricultural University, Ludhiana, in partial fulfillment

of the requirements for the degree of Ph.D, in the subject of Business Management

(Minor subject: Economics) has been approved by the Student’s Advisory

Committee after an oral examination on the same.

________________________ ________________________ (Dr. Lalit Mohan Kathuria) External Examiner

Major Advisor

________________________ (Dr. Pratibha Goyal)

Director and Professor

________________________ (Dr. Neelam Grewal)

Dean, Postgraduate Studies

4

ACKNOWLEDGEMENTS

I would like to extend my heartiest thanks to the Almighty for His blessings that

enabled me to undertake the research and write this dissertation. I have been blessed by Him

in virtue of providing kind human beings around me to receive best possible support,

whenever and wherever I needed. I may fall short of words for expressing my gratitude for

such incredible persons. I thank my parents and siblings who always pray for my good luck. I

feel myself fortunate to have a supporting wife who takes care of my naughty twin sons with

utmost love and affection. Her endless and unconditional support motivated me to undertake

research work comprehensively.

I am indebted to Dr. Lalit Mohan Kathuria, Professor, School of Business Studies,

PAU for his generous support, constant direction and mentoring at all stages during the Ph.

D. programme. His selfless and excellent supervision, continuous encouragement and

constructive criticism during the course of this investigation made me to do my best. Sincere

note of thanks are also extended to Dr. Pratibha Goyal, Director and Professor, School of

Business Studies for her persisting support and constant concerns. I also extend my gratitude

to members of my advisory committee, Dr. Sandeep Kapur, Professor, School of Business

Studies, Dr. Jasdev Singh, Senior Farm Economist, Deptt. of Economics and Sociology, Dr.

Mohammad Javed, Professor, Deptt. of Maths, Stats and Physics, Dr. Khushdeep Dharni,

Associate Professor, School of Business Studies for their valuable inputs and also for

reviewing the manuscript critically.

I also acknowledge the contributions of Dr. B S Mann, Department of Business

Management, GNDU and Dr. Narinderpal Singh, Department of Agricultural Journalism,

Languages and Culture, PAU for their assistance in finalization of the questionnaire. Further,

I also thank faculty members of the department for their valuable teachings during the

doctoral degree. I am also thankful to University Grants Commission, a statutory body

established by Government of India to promote higher education, for providing me fellowship

for undertaking the research work.

At the end, I would like to convey my sincere appreciation and heart-felt gratitude to

one and all that helped me during the doctoral degree.

Date:

Place: Amanpreet Singh

5

Title of the Dissertation : Understanding Purchase Behaviour and Analyzing Marketing Mix Strategies: A study of Bottom of the Pyramid (BOP) Consumers

Name of the Student : Amanpreet Singh and Admission No. L-2012-BS-54-D

Major Subject : Business Management

Minor Subject : Economics

Name and Designation : Dr. Lalit Mohan Kathuria of Major Advisor Professor, School of Business Studies

Degree to be Awarded : Ph. D.

Year of award of Degree : 2016

Total Pages in : 171 + Annexure + VITA Dissertation

Name of University : Punjab Agricultural University, Ludhiana+141004, Punjab, India.

ABSTRACT

The present study was undertaken with the objectives: to investigate purchase behaviour of bottom of the pyramid consumers; to examine the willingness to purchase branded products; to explore the influence of social networks on purchase behaviour; to study marketing mix strategies of companies; and to recommend changes in marketing mix strategies. First three objectives were achieved by collecting primary data through a survey of 600 respondents held across two states of northern India viz. Punjab and Haryana. Findings highlighted that female members in the households play a greater role while taking purchase decisions related to food and FMCG; whereas male members were found to play a major role in purchase decisions related to durable products. The study also provided empirical evidence regarding differences in the purchase behavior of rural and urban consumers towards durable products. For example, perceived behavioral control emerged as the strongest predictor to purchase durable products among urban consumers; whereas subjective norms were found to be the most important predictor of purchase intention among rural consumers. Further, results highlighted attitude as the strongest driver of intention to purchase branded food followed by perceived behavioral control and subjective norms. Factor ‘product appearance, price and brand’ was found to be significant in the prediction of willingness to purchase branded FMCG; whereas three factors ‘familiarity and convenience’; ‘appearance and price’; and ‘quality and brand name’ emerged significant predictors of willingness to purchase branded durable products. The results highlighted trust as the strongest predictor of intention to purchase a product recommended by network members. Fourth objective of the study was achieved by interviewing 50 managers of selected companies. Findings indicated that pricing strategies emerged as the strongest predictor of company’s performance followed by promotion strategies, distribution intensity and product strategies. Based on these findings, the present study also suggested modifications in marketing mix strategies of companies serving BOP consumers. Keywords: Bottom of the pyramid; Purchase behaviour; branded food; Social network;

Marketing mix __________________________ _________________________ Signature of Major Advisor Signature of the Student

6

Koj gRMQ dw isrlyK : KrIddwr ivvhwr nUM smJxw Aqy mwrkIitMg imks rxnIqIAW dw ivSlySx : iprwimf dy ql qy KpqkwrW dw ie`k AiDAYn

ividAwrQI dw nW Aqy dw^lw nMbr

: AmnpRIq isMG (AY~l-2012-bIAY~s-54-fI)

pRmu`K ivSw : ibjnys mYnyzmYNt sihXogI ivSw : ArQ SwSqr mu`K slwhkwr dw nW Aqy Ahu`dw

fw. lilq mohn kQUrIAw pRoPYsr, skUl AwP ibjnys st`fIz

ifgrI : pI.AY~c.fI. ifgrI nwl snmwinq krn dw swl : 2016 Koj pqr iv`c ku`l pMny : 171 + AMioqkwvW + vItw XUnIvristI dw nwm : pMjwb KyqIbwVI XUnIvristI, luiDAwxw–141 004,

pMjwb Bwrq

incoV

mOjUdw AiDAYn iehnW audySW dI pRwpqI leI kIqw igAw – iprwimf dy ql qy KpqkwrW dy KrId ivvhwr dI pVqwl krnw, iprwimf dy ql qy KpqkwrW dy mwrky vwly auqpwd KrIdx dI ie`Cw dw muAwienw krnw, smwijk nYtvrk dy iprwimf dy ql qy KpqkwrW dy KrId ivvhwr au~qy pRBwv dI pVcol krnw, kMpnIAW dI mOjUdw mwrkIitMg imks rxnIqIAW dw AiDAYn krnw Aqy kMpnIAW dI mwrkIitMg imks rxnIqIAW iv`c qbdIlI dI isPwirS krnw [ pihly iqMn audyS au~qrI Bwrq dy do pRWqW (pMjwb Aqy hirAwxw) iv`coN 600 KpqkwrW dy srvyKx nwl ie`kTy kIqy hoey fytw dy ivSlySx duAwrw pRwpq kIqy gey[ Koj ny aujwgr kIqw ik GrW iv`c AorqW Bojn Aqy jldI Kpq hox vwly auqpwdW dy KrId sbMDI PYsilAW iv`c mrdW nwloN v`fI BUimkw inBwauNdIAW hn [ jdik mrd lMby c`lx vwly auqpwdW dy KrId sMbMDI PYsilAW iv`c AorqW nwloN vfI BUimkw inBwauNdy hn[ Koj ny pyNfU Aqy SihrI KpqkwrW dy lMby c`lx vwly auqpwdW dI KrId sbMDI ivvhwr ivcly Prk au~qy vI AnuBvI sbUq muh`eIAw krvwieAw [ audwhrn dy qOr qy ‘vqIry au~qy kMtrol dI smJ’ SihrI KpqkwrW dy lMby clx vwly auqpwdW sbMDI KrId-ierwdy dw sB qoN mjbUq PYktr au~Br ky swhmxy AwieAw, jdik ‘AMqrmuKI-inXm’ pyNfU KpqkwrW dy KrId-ierwdy dw sB qoN mh`qvpUrn PYktr pwieAw igAw [ Agy, nqIijAW ny aujwgr kIqw ik KpqkwrW dw rveIAw mwrky vwly Bojn pdwrQW pRqI KrId-ierwdy dw sB qoN mjbUq inrDwrk hY [ ‘pdwrQ dI idK, kImq Aqy mwrkw’ KpqkwrW dy mwrky vwly jldI Kpq hox vwly auqpwdW pRqI KrId-ie`Cw dw sB qoN mh`qvpUrn PYktr pwieAw igAw[ jdik KpqkwrW dy lMby c`lx vwly auqpwdW pRqI KrId-ie`Cw sMbMDI iqMn mh`qvpUrn inrDwrk auBr ky swhmxy Awey [ nqIijAW ny ieh vI aujwgr kIqw ik ‘KpqkwrW dy smwijk nY~tvrk dy mYNbrW pRqI Brosw’ nY~tvrk mYNbrW duAwrw isPwirS kIqy gey auqpwdW pRqI KrId-ie`Cw dw sB qoN mh`qvpUrn inrDwrk hY [ ies AiDAYn dw cOQw audyS cuxIAW geIAW 50 kMpnIAW dy mYnyjrW dI ieMtrivaU duAwrw pRwpq kIqw igAw [ nqIijAW duAwrw sbUq imilAw ik ‘kImq rxnIqIAW’ kMpnIAW dI kwrgujwrI dw sB qoN v`fw inrDwrk hn[ iehnW swry nqIijAW dy AwDwr qy, mOjUdw AiDAYn ny kMpnIAW dI mwrkIitMg imks rxnIqIAW iv`c soD dy vI kuJ suJwA idqy [

muK Sbd: iprwimf dw q`l, smwijk nY~tvrk, mwrkIitMg imks, mwrky vwly Bojn pdwrQ [ __________________ ________________ muK slwhkwr dy hsqwKr iv`idAwrQI dy hsqwKr

7

CONTENTS

CHAPTER TOPIC PAGE NO.

I INTRODUCTION 1-14

II REVIEW OF LITERATURE 15-40

III MATERIALS AND METHODS 41-66

IV RESULTS AND DISCUSSION 67-144

V SUMMARY 145-154

REFERENCES 155-171

ANNEXURES i-xv

VITA

8

LIST OF TABLES

Table No.

Table title Page No.

1.1 Population, income and expenditure at bottom of the pyramid 4

1.2 Buying power index of bottom of the pyramid 5

1.3 Percentage and number of poor in India 7

3.1 Selection of districts and villages 44

4.1 Demographic profile of the respondents 69

4.2 Products owned by households 70

4.3 Sources of information regarding product purchase 72

4.4 Sources for availing credit to purchase products 73

4.5 Purchase frequency for selected food products 74

4.6 Frequency of purchase for selected FMCG 75

4.7 Sources of purchase of selected food products 75

4.8 Sources of purchase of selected FMCG 76

4.9 Sources of purchase of selected durable products 77

4.10 Expenditure on selected product categories 78

4.11 Purchase decisions for selected product categories 79

4.12 Hypotheses proposed to test the model ‘consumer choice towards durable products’ 81

4.13 Consumers’ perceptions towards various constructs for purchase of durable products 83

4.14 Reliability and validity statistics of the model ‘consumer choice towards durable products’ 85

4.15 Structural results of the model ‘consumer choice towards durable products’ 86

4.16 Mediation results of the model ‘consumer choice towards durable products’ 87

4.17 Multi-group results of the model ‘consumer choice towards durable products’ 88

4.18 Consumers’ perceptions towards various constructs for purchase of branded food 93

4.19 Reliability and validity statistics of the model ‘consumer choice towards branded food’

95

4.20 Hypotheses proposed to test the model ‘consumer choice towards branded food’

96

4.21 Structural results of the model ‘consumer choice towards branded food’ 97

4.22 Mediation results of the model ‘consumer choice towards branded food’ 98

4.23 Multi-group results of the model ‘consumer choice towards branded food’

99

9

4.24 Perceptions towards parameters influencing the purchase of branded FMCG

103

4.25 Differences in perceptions towards parameters influencing branded FMCG purchase with respect to selected demographic variables

105

4.26 Factor affecting the purchase of branded FMCG: Resultant output 106

4.27 Model statistics for examining willingness to purchase branded FMCG 107

4.28 Estimates of the model willingness to purchase branded FMCG 109

4.29 Perceptions towards parameters influencing the purchase of branded durables 110

4.30 Difference in perceptions towards parameters influencing branded durables purchase with respect to selected demographic variables 112

4.31 Factor affecting the purchase of durables products: Resultant output 113

4.32 Model statistics for examining willingness to purchase branded durables 115

4.33 Estimates of willingness to purchase branded durable products 116

4.34 Respondents’ affiliation to different types of social networks 118

4.35 Hypotheses proposed to test the model ‘influence of social network on purchase behavior’

119

4.36 Reliability and validity of the model ‘influence of social network on purchase behavior’

121

4.37 Structural results of the model ‘influence of social network on purchase behavior’ 122

4.38 Mediation results of the model ‘influence of social network on purchase behavior’ 123

4.39 Multi-group results of the model ‘influence of social network on purchase behavior’ 124

4.40 Profile of the selected companies 127

4.41 List of items and reliability statistics 129-130

4.42 Managerial perceptions towards BOP markets 131

4.43 Managers’ perceptions towards marketing mix strategies 132

4.44 Cross-sector collaborations by companies 134

4.45 Differences in managers’ perceptions across different sectors 136

4.46 Differences in managers’ perceptions towards effectiveness of promotion mix elements

137

4.47 Influence of marketing mix on company’s performance 140

10

LIST OF FIGURES

Figure No. Figure Title Page No.

1.1 The world economic pyramid

3

2.1 The ‘pure form’ of Theory of Planned Behaviour

22

2.2 The conceptual model for examining ‘consumer choice towards branded food products’

27

2.3 The conceptual model for exploring ‘influence of social networks on purchase behavior’

32

4.1 The conceptual model for examining ‘consumer choice towards durable products’

82

4.2 A conceptual model for examining willingness to purchase branded FMCG

107

4.3 A conceptual model for examining willingness to purchase branded durables

114

11

LIST OF ABBREVIATIONS

Abbreviations Full-form

AD Administrative Division

ANCOVA Analysis of Covariance

ANOVA Analysis of Variance

AVE Average Variance Extracted

BOP Bottom of the Pyramid

CFA Confirmatory Factor Analysis

CR Composite Reliability

EFA Exploratory Factor Analysis

FMCG Fast Moving Consumer Goods

HUL Hindustan Unilever Limited

INR Indian National Rupee

MNC Multi-National Corporation

NCAER National Council of Applied Economic Research

NGO Non-Government Organization

NPO Non-Profit Organization

P & G Procter & Gamble

SEM Structural Equation Modeling

SHG Self-Help Group

S-O-R Stimulus–Organism–Response

TPB Theory of Planned Behavior

WOM Word-of-mouth

1

CHAPTER I

INTRODUCTION

Mobilization of technology and capital has resulted in globalization and encouraged a

paradigm shift in international business. Many multinational corporations (MNCs) have turned to

emerging markets due to stagnation and sluggish growth in developed markets. Companies have

shifted their focus from western countries to emerging markets like Brazil, Russia, India, China,

and South Africa as majority of multinational companies’ growth in the past several years has

come from these markets. In recent years, the emerging-market stock indices (including countries

like Brazil, Russia, India, and China) outperformed the indices of developed economies (Shaw

2011). These markets have been less affected in the global meltdown and have recovered more

strongly than western markets. In a report, International Monetary Fund indicated that emerging

markets continued to grow by 5.0 per cent in 2013, in contrast to the stagnation experienced in

major developed markets (1.4 per cent). In 2014, these markets grew by 4.6 per cent, with respect

to countries like USA (2.4 per cent) and Japan (−0.1 per cent) (Tanusondjaja et al 2015).

Despite inadequate infrastructure, limited income and resources, emerging markets have

been the most attractive destinations for multinational corporations due to higher growth

prospects. The most fundamental reason for investing in emerging markets is that these countries

constitute around 80 per cent of world’s population and just 20 per cent of the World’s economic

activity (Atale 2012). About 75 per cent of the expected growth in world trade over the next 20

years is expected to derive from these markets (Prahalad 2006). Emerging markets account for $7

trillion opportunity and this is expected to grow to $20 trillion within a decade (Chakravarthy and

Coughlan 2012). Indian economy has emerged as one of the largest economies among these

markets. For the year 2013-14, the Gross Domestic Product (GDP) of Indian economy has been

estimated at INR 10,472,807 crores. The rate of GDP growth has also been more than seven per

cent in the last two years. For instance, for the year 2013-14 and 2014-15, the GDP growth rate

has been able to touch 7.24 and 7.57 per cent respectively. These markets, therefore, represent a

source of growth drivers and innovation amid slow-down in developed economies.

Consumers in the emerging markets have different characteristics with respect to their

counterparts in the developed world. A huge chunk of the population in these markets have low-

incomes, thereby sales promotion like low-priced or discounted products have the potential to be

a useful strategy for achieving marketing objectives. Empirical evidence also suggests that

consumers in emerging markets perceive quality as a less influential determinant of satisfaction

than in developed countries. In these markets, consumers’ perceptions of value assume more

2

importance in their satisfaction (Morgeson et al 2015). Thereby, marketing based on pricing is

likely to generate positive results for companies operating in emerging markets. For example,

Dell introduced a wide range of low-cost desktops and notebooks with its famous ‘Vostro’ line in

these economies. Dell’s low-price strategy, aimed at deeper penetration of these markets keeping

in view country-specific conditions, helped the company to gain a larger share in this segment

(Kumar et al 2013). Emerging market consumers live in under-developed economic, social,

government and marketing institutions with inadequate transportation and communication

technologies. These consumers have different set of choices and require unique products tailored

according to their distinct needs. For dealing with these challenges, companies need to develop

appropriate products and require huge resources like large workforce and big amount of working

capital for distributing products in markets with hostile circumstances (Dawar and Chattopadhyay

2002).

Further, more regional diversity in economic conditions, consumer income patterns and

buying preferences is observed among consumers in emerging markets, unlike developed market

consumers. Such diversity has encouraged companies like Unilever and PandG to develop

regional advertising strategies using local celebrities, locations and even local dialects to achieve

the desired results (Kumar et al 2013). Multinationals corporations use local knowledge to

develop products not only for meeting unique needs of underdeveloped societies and also for

marketing same products in developed countries (Kahn 2008; Singal and Jain 2012). For instance,

Citibank developed mobile banking system in India and later brought to the USA. This system

allows users to undertake banking operations like storing money electronically, sending funds

from one region to another and withdrawing cash (Kahn 2008). Considering another example

from China, Haier, a appliance maker, learned that rural Chinese consumers use their washing

machines to clean vegetables, the company modified its product to accommodate this need. Haier

collaborated with American retailers and established design and manufacturing operations in

several US cities that helped the company to obtain 26 per cent share in the US market for

compact durable products (Khanna and Palepu 2006).

In India, doors of economy were opened for foreign multinational corporations in 1990s

to invest in the domestic market. Initially, it appeared that multinational companies from

developed countries like U.S., Europe, and Japan would quickly surpass local competitors and

grab the market for almost every product. Despite availability of sophisticated technologies and

products, huge financial resources, strong brands, and best management skills and systems, such

companies have not been able to do so in emerging markets. In countries like India, China, Brazil

and Indonesia domestic firms have been able to thrive in the stiff global competition. These firms

3

have faced tough challenges from multinational corporations in their core businesses and

managed to become market leaders. Many of them dominate the local markets today not because

of protectionist economic policies, but because of their strategies and execution (Bhattacharya

and Michael 2008). For instance, Bharti Airtel in India has taken on one of the biggest global

telecom company Vodafone and emerged as the market leader in the industry. Further, several

Western and Japanese companies have withdrawn their products and services from emerging

markets. For example, Yahoo and eBay have quit from Chinese market, and Panasonic have

pulled out its cellular handsets from Chinese market. Bhattacharya and Michael (2008)

highlighted strategies that domestic companies use to succeed in emerging markets. These are:

creating customized offerings; developing business models to overcome obstacles; deploying

cutting-edge technologies; taping low-cost labor; building scale quickly and using management

talent to sustain growth.

Within emerging markets, academicians and managers have successfully identified an

under-served consumer segment with distinct set of choices. Prahalad and Hammond (2002) have

been the first among academicians who touched upon a new consumer segment in an article

‘Serving the world’s poor profitably’. In the book ‘The Fortune at the Bottom of the Pyramid’,

Prahalad (2006) presents several cases where multinational companies serve four billion

individuals at the Bottom of the Pyramid (BOP) as customers living on $2 a day that constitute

the largest untapped market. Prahalad and Hammond (2002) highlighted world’s economic

pyramid that include four distinct tiers with different consumer income-groups (Fig. 1.1).

Source: Prahalad and Hart (2002); income in purchasing power parity terms on annual per capita basis; population in millions

Fig. 1.1: The World economic pyramid

Tier 1: Income = More than

$20,000 Population = 75-100

Tier 2 and 3: Income = $1500 - $20,000 Population = 1500 - 1750

Tier 4:

Income = Less than $1500 Population = 4,000

4

At the top of the economic pyramid, there are about 75-100 million affluent consumers,

mainly middle- and upper-income consumers from developed countries and the few rich people

from developing countries. This segment has annual per capita income more than $20,000 (based

on purchasing power parity in US dollars). In tier 2 and 3, the middle of the economic pyramid,

there are about 1,500-1,750 million middle-income consumers, with annual per capita income

from $1,500-$20,000, that mostly belong to developing nations. At bottom of the pyramid,

tier 4, there are about 4,000 million people who have annual per capita income less than $1,500

(in purchasing power parity terms).

Though, there is no consensus on actual size and classification of the BOP market but it

cannot be denied that it is an important market which requires increased research on its dynamics

and behavior of its consumers. This underserved consumer segment consists of four billion

consumers that are estimated to be six billion by 2045 (Hammond et al 2007). BOP markets tend

to be concentrated in Asia, Africa and Latin America, with an estimated 60 per cent in India and

China (Mohr et al 2012). In Asia and the Mid-East (Table 1.1), there are about 2.86 billion BOP

consumers with total income of $3.47 trillion; in Eastern Europe, 254 million BOP consumers

with total income of $458 billion; in Latin America, 360 million BOP consumers with total

income of $509 billion, and in Africa, 486 million BOP consumers with total income of $429

billion – a combined total income of $5 trillion (Hammond et al 2007). The United Nations

Multidimensional Poverty Index, 2011 reports South Asia as home to 435 million severely poor

people (Kochhar 2014).

Table 1.1: Population, income and expenditure at bottom of the pyramid

Area Population Income

(US$ PPP) Expenditure (million US$)

Population (%)

Africa 486 million 429 billion 524,744.8 95

Asia 2.86 billion 3.47 trillion 4,284,816.3 83

Eastern Europe 254 million 458 billion 572,228.7 64

Latin America and Caribbean

360 million 509 billion 631,371.7 70

Source: The next 4 billion (Hammond et al 2007)

5

However, these gross estimates do not provide adequate information about purchasing

power of BOP consumers across different geographical areas of the world. Guesalaga and

Marshall (2008) calculate buying power index of BOP consumers that considers three dimensions

i.e. size of the population, income and consumers’ expenditure. Table 1.2 presents the buying

power index of BOP consumer segment in comparison to the mid- and high-income segments

across different geographical areas.

Table 1.2: Buying power index of bottom of the pyramid

Area

Buying power index of BOP relative to mid and high-income segments

Distribution of BOP buying power index by geographic area

Population (%)

Income (%)

Expenditure (%)

BPI (%)

Population (%)

Income (%)

Expenditure (%)

BPI (%)

Africa 95.1 70.5 75.6 76.9 12.3 8.8 8.7 9.5

Asia 83.4 41.7 44.7 50.9 72.2 71.3 71.3 71.5

Eastern Europe

63.8 36.0 38.6 42.3 6.4 9.4 9.5 8.8

Latin America

and Caribbean

69.9 28.2 30.2 37.1 9.1 10.5 10.5 10.2

Total 82.4 42.3 45.3 51.2 100 100 100 100

Note: BPI: Buying power index; Source: Guesalaga and Marshall (2008)

The total buying power index of BOP segment stands at 51.2 per cent. This means that

BOP segment represents more than 50 per cent of the buying power in respect to the mid- and

high-income consumer segments. It is interesting to note that buying power index is highest in

Africa with 76.9 per cent; followed by 50.9 per cent in Asia; 42.3 per cent in Eastern Europe and

37.1 per cent in Latin America and Caribbean. However, these figures do not reveal information

about buying power index of BOP consumers with respect to different geographical areas of the

world. For making comparison among different geographical regions, table 1.2 also presents the

distribution of buying power index by considering BOP as a whole. It is worth to note that Asia

assumes a large majority of buying power index (about 72 per cent) of the total buying power at

BOP. In contrast, Latin America and Caribbean has buying power index of about 10 per cent

only. The other two geographical such as Africa and Eastern Europe assume buying power index

of about nine and eight per cent only.

6

Government of India has adopted a different methodology proposed by Tendulkar

committee (based on consumption patterns) for estimating poverty standards. As per this

methodology, for 2011-12, Planning Commission (name of the commission has been changed to

‘Niti Aayog’), Government of India, estimated INR 816 per capita per month and INR 1,000 per

capita per month as national poverty line in rural and urban areas respectively. Thus, for a family

of five, national poverty line in terms of consumption expenditure would amount to about INR

4,080 per month in rural areas and INR 5,000 per month in urban areas. Using these standards,

planning commission estimated the percentage and number of people living below poverty line in

India for the year 1993-94, 2004-05 and 2011-12 (table 1.3). In 2011-12, 25.7 per cent of the rural

population, 13.7 per cent of the urban population and 21.9 per cent of the country’s population,

was living below poverty line. About a decade ago, in 1993-94, it was 50.1 per cent in rural areas,

31.8 per cent in urban areas and 45.3 per cent for the country as a whole. In 2004-05, these ratios

for the rural and urban areas were 41.8 per cent and 25.7 per cent; and 37.2 per cent for the

country as a whole. In numbers, in 2011-12, India had 270 million persons below the poverty line

as compared to 407 million in 2004-05, that indicates a significant reduction of 137 million

persons over the seven years period. From 1993-94 to 2004-05, the average decline in the poverty

ratio has been estimated at 0.74 percentage points per year. It accelerated to 2.18 percentage

points per year from 2004-05 to 2011-12. It implies that the rate of decline in the poverty ratio

from 2004-05 to 2011-12 has been about three times of that experienced from 1993-94 to 2004-

05.

Since several representations were made suggesting that the Tendulkar Poverty Line was

too low, the Planning Commission, in June 2012, constituted an Expert Group under the

Chairmanship of Dr. C. Rangarajan to once again review the methodology for the measurement

of poverty. This committee submitted its report in 2014 and suggested that monthly per capita

consumption expenditure of INR 972 in rural areas and INR 1,407 in urban areas is treated as the

poverty line at the all India level. This implies a monthly consumption expenditure of INR 4,860

in rural areas or INR 7,035 in urban areas for a family of five at 2011-12 prices.1 Based on the

methodology proposed, the poverty ratio at all India level for 2011-12 comes to 29.5 per cent

(table 1.3); in contrast to 21.9 per cent as estimated by Tendulkar methodology for 2011-12. As

per proposed methodology, the poverty ratio and number of poor increased considerably. For

instance, in urban areas, the number of poor almost doubled to 102.4 million based on Rangarajan

estimates, from 52.8 million based on Tendulkar estimates in 2011-12. Similarly, the poverty

ratio rose to 29.5 per cent from 21.9 per cent in 2011-12 after revising the poverty standards.

1 http://planningcommission.nic.in/reports/genrep/pov_rep0707.pdf

7

Table 1.3: Percentage and number of poor in India

Poverty ratio (%) Number of poor (million) Rural Urban Total Rural Urban Total

1993-94 50.1 31.8 45.3 328.6 74.5 403.7

2004-05 41.8 25.7 37.2 326.3 80.8 407.1

2011-12 (Tendulkar committee) 25.7 13.7 21.9 216.5 52.8 269.3

2011-12 (Rangarajan committee) 30.9 26.4 29.5 260.5 102.4 362.9

Annual Average Decline: 1993-94 to 2004-05 (percentage points per annum)

0.75 0.55 0.74

Annual Average Decline: 2004-05 to 2011-12 (Tendulkar) (percentage points per annum)

2.32 1.69 2.18

Source: Anonymous (2013), Poverty estimates by Planning Commission, Govt. of India

In a report by National Sample Survey Office, Ministry of Statistics and Programme

Implementation, Government of India estimated that food accounts for 52.9 per cent of the value

of consumption for an average rural Indian during 2011-12. This includes 10.8 per cent for

cereals and cereal substitutes, 8 per cent for milk and milk products, 7.9 per cent on beverages,

refreshments and processed food, and 6.6 per cent on vegetables. For urban consumers food

accounts for 42.6 per cent of the value of household consumption, including 9 per cent by

beverages, refreshments and processed food, 7 per cent by milk and milk products, and 6.7 per

cent by cereals and cereal substitutes.2 However, consumers below the poverty line in India incur

a higher percentage of their income on food and food related products. Estimates by planning

commission indicated that poor population in rural areas spends 57 per cent of their income on

food and food related products. This includes 14.6 per cent on cereals and substitutes, 8.4 per cent

on vegetables, 6.3 per cent on milk and milk products, 4.8 per cent on egg, fish and meat; and 4.5

per cent on edible oil. Urban poor consumers incur 46.7 per cent of their income on food and food

related products that include 10.3 per cent on cereals, 6.4 per cent on milk and milk products, 6.0

per cent on vegetables, 4.0 per cent on egg, fish and meat, and 3.8 per cent on vegetable oil.3 The

data highlights a considerable variation in the expenditure pattern of poor consumers in relation to

non-poor consumers in India. The difference in consumption between poor and non-poor is more

in urban areas in comparison to rural areas. During 2009-10, monthly per capita consumption was

2 http://mospi.nic.in/Mospi_New/upload/press-release-68th-HCE.pdf 3 http://planningcommission.nic.in/reports/genrep/pov_rep0707.pdf

8

5.6 times less for the bottom ten per cent of the population than the top 10 per cent in rural areas;

whereas this gap increases to 9.8 times between these two classes in urban areas.4

BOP consumers face complexity in product choice due to a number of persisting

constraints. First, majority of them generally have limited literacy standards, inadequate

nutritional information and severe psychological deprivation. Second, they live in remote areas

with poor transportation infrastructure. Third, they live in subsistence markets that are

characterized by underdevelopment of social, economic, political and market institutions. Fourth,

they have limited access to media and market related information (Hammond et al., 2007;

Viswanathan and Rosa, 2007). These constraints are likely to influence consumer behavior in

number of ways. For example, lack of consumer rights protection institutions may lead to

marketing of counterfeited products and price skimming. Such constraints also inhibit subsistence

consumers from checking prices and evaluating quality of the products. These hostile

circumstances cause consumers in this segment to have distinct set of abilities and choices in

contrast with the developed world’s consumers. As a consequence, these consumers show a

divergent mechanism in their buying preferences. Accordingly, companies need to develop

quality products at affordable prices to serve unmet needs of BOP consumers that give

disadvantaged consumer an opportunity to make a better choice. Literature suggests that these

marketplaces can be rewarding both for multi-national corporations and consumers at large

(Weidner, Rosa, and Viswanathan, 2010). However, research on understanding BOP buying

behavior and analyzing marketing mix strategies to successfully serve these consumers is still

limited.

1.1 Purchase of branded products by BOP consumers

Branding plays an important role in marketing that has been well researched among

consumers in the developed economies (Woodside and Walser 2007; Yorkston et al 2010).

However, researchers have paid little attention to branding issues at bottom of the pyramid. The

role of brands in BOP consumer choice has recently become a matter of interest to marketing

practitioners in India. In the past, most of the BOP consumers have not been able to recognize the

importance of brands in consumer choice. The introduction of liberalization in Indian economy

resulted in the entry of multinational corporations into the domestic market. Recently, many of

these companies have launched and promoted international brands for BOP consumers that have

shifted consumers’ preference towards branded products.

4 http://www.business-standard.com/article/economy-policy/spending-pattern-points-to-india-s-rich-poor-divide-111071100058_1.html

9

American Marketing Association defines brand as ‘A name, term, design, symbol, or a

combination of them, intended to identify the goods or services of one seller or group of sellers

and to differentiate them from competitors.’ Davidson (2009) opines that brands are often

expensive vis-à-vis their generic counterparts due to addition of advertising and promotion cost.

Due to low-incomes, BOP consumers may not prefer to buy such costly products. Most of the

disadvantaged consumers are exploited by low-quality vendors and intermediaries in subsistence

markets. For these reasons, majority of the BOP consumers buy low-quality, less healthy,

unbranded products and often pay higher prices for purchasing necessity products such as staple

food rice (Prahalad and Hammond 2002, Talukdar 2008). However, despite of persisting

constraints BOP population has an intention and capacity to purchase branded products that

serves as a basis for freshness, safety and health (Montgomery and Wernerfelt 1992; Bredahl

2004; Akbay and Jones 2005; Rajagopal 2009). Brands can potentially engender positive

associations and a sense of community that improves adoption rates among BOP consumers

(Chikweche and Fletcher 2010; Weidner et al 2010). BOP consumers expect a good quality

product at a price they can afford. The challenge to large firms at BOP is to make quality brands

that deliver value to disadvantaged consumers at affordable prices.

By using branded products, disadvantaged consumers feel dignified that helps them to

gain a reputed position in society (Ordabayeva and Chandon 2011). Companies selling small unit

sizes at affordable prices make money, expand markets, and generate broader access to goods and

services that improve people's quality of life (Hammond and Prahalad 2004). Low priced sachets

became a popular packaging in many other product categories like detergents, jams, jellies,

toothpaste, ice cream, butter and beauty creams. Small packs enhance the value delivery to the

customer as these are convenient to carry, give consumers the satisfaction of using branded

products at low cash-out, increases trial purchase, offer control over consumption and provide

more variety for a similar outlay (Dubey and Patel 2004; Changco and Pornpitakpan 2011).

Recently, branded or packaged food has witnessed an increased demand across the world

including emerging countries like India (Narayanan 2000; Goyal and Singh 2007; Wells et al

2007). A recent report of Associated Chambers of Commerce and Industry of India highlighted

that the Indian consumers’ average annual spending on packaged food increased by 22.5 per cent

in the last five years and this growth rate is expected to reach about 30 per cent by 2017. In India,

packaged food industry is worth about $30 billion that is likely to touch $50 billion by 2017.5

Keeping in view low-affordability of bottom tier consumers, a number of Fast Moving

Consumer Goods (FMCG) companies have been offering branded products in sachets (Changco 5 http://www.assocham.org/newsdetail.php?id=4992

10

and Pornpitakpan 2011). For instance, Hindustan Unilever Limited (HUL) started distributing

small packs through its flagship project ‘Shakti’ in 2001 and successfully penetrated into interior

rural markets with sachets of shampoos, detergents and other consumer brands (Davidson 2009,

Bang and Joshi 2012, Dolan et al 2012). In India, sachets contribute more than 95 per cent of

total shampoo unit sales with more than 60 per cent of the value of the shampoo market

(Hammond and Prahalad 2004). Similarly, Britannia Industries Limited, the top biscuit

manufacturing company in the country, developed smaller packs of fortified cookies under the

brand name ‘Tiger’ for iron-deficient consumers. These packs were available in the markets at

INR 3 and 5 that helped the company to sell 3.5 billion packets per annum in India.6 On these

lines, Nestle India Limited also launched a whole grain based variant of instant noodles ‘Maggi

vegetable atta7 noodles’ in an affordable pack using a slogan ‘Taste bhi, health bhi’ (Taste and

health too). A small pack of 80 g at $0.15 (INR 10) has fiber equal to three chapattis.8 Such a

strategy helped the company to make it into top five most trusted brands of India.9

1.2 Influence of social networks on purchase behavior

BOP consumer choice is largely influenced by members of the social networks, as in

many cases consumers have to share social feelings and comply with expectations of others. The

disadvantaged consumers support each other in day to day works due to hostile circumstances.

These consumers are more connected with members of the social networks in order to cope up

with poverty constraints. For instance, at least four out of five BOP consumers have been found

to be involved in one or other forms of social network (Chikweche and Fletcher 2010). In another

study conducted in India, Viswanathan et al (2010) found more than three-fourth of the BOP

consumers connected with social networks that include labor groups, business associations,

neighborhood committees, religious/spiritual groups, ethnic groups, political groups, savings

groups, self help groups and non-government organizations etc.

Social institutions dominate in subsistence markets as members of this community often

exchange their opinions about products and services with members of the social networks. Use of

social networks to gather information is fundamental to BOP purchase behavior that occurs

among network members having established relationships and common interests. Being unable to

make optimum product-choice, subsistence consumers seek brand recommendations and purchase

related information from people on the street or with whom they have typical face to face 6 http://www.britannia-biscuits.com/bnf/media/britannia-in-health-nutrition.pdf 7 An unrefined variant of wheat flour which contains high fiber 8 A popular variant of Indian bread 9 https://www.nestle.in/Brands/MAGGIVegetableAttaNoodles

11

interactions (Viswanathan et al 2010). These recommendations help disadvantaged consumers to

get an insight into past purchase experiences (DiMaggio and Louch 1998). This also helps BOP

consumers to obtain a better understanding of the product and reduce risk associated with its

purchase. For instance, Corporation Bank marketed its mobile banking product in India, Green

Money, in collaboration with social groups like ‘Nutan Mumbai Tiffin Box Suppliers Trust’ and

‘Kshetriya Gramin Financial Services (KGFS)’. These groups have a deeper understanding of its

network members that are associated with them. The collaboration helped the bank to tailor its

services according to unique needs of their customers in an economically viable manner. KGFS

uses street plays in densely populated areas in cities to connect to un-banked customers. Other

social networks like Financial Inclusion Network and Operations (FINO) operated by ‘bandhus’

(friends and local slum dwellers) also helped the bank to connect with BOP consumers (Thakur

2015).

The social networks have established relationships with key stakeholders of business at

BOP such as consumers, in general, that also facilitates business transactions (Nitzan and Libai

2011; Chikweche 2013; Dey et al 2013; Park et al 2014). Companies need to understand ins and

outs of relationships grounded in social rather than legal networks in these markets (London and

Hart 2004; Weidner et al 2010). Companies must navigate the intricate relationships and confront

opportunities and challenges in negotiating well-established social networks to be successful at

BOP markets (Weidner et al 2010). BOP producers must reconsider the traditional marketing

approaches viz. competitive advantage, intellectual property and ownership applied to western

consumers (London and Hart 2004; London et al 2010; Chikweche and Fletcher 2012), that are

unlikely to generate desired results for resource-constrained consumers. Social networks such as

self-help groups, non-government organizations have the potential to eliminate productivity

(value creation) and transactional (value capture) constraints faced by subsistence producers by

connecting local consumers with non-local manufacturers (London et al 2010).

1.3 Marketing mix for BOP consumers

The original consumer-oriented BOP perspective (Prahalad and Hammond 2002;

Prahalad 2006), mainly highlighted poverty issues by considering BOP individuals as potential

consumers of products and services offered by multinational corporations. This approach has

been criticized by many other authors that focused more on producer-oriented BOP perspective

(e.g. Karnani 2007; Gold et al 2013). These authors suggest to consider BOP population as

potential producers and to involve them in the value-generating supply chain operations by

building their skills and capabilities. While operating in BOP markets, corporations face tough

12

challenges in dealing with weak government institutions, informal regulatory mechanisms and

inadequate communication structures. In such hostile circumstances, consumer-oriented BOP

perspective does not seem to offer desired results in these markets. Researchers have called for

more innovative, however sustainable, business strategies that would serve as a basis for

operating successfully in impoverished markets.

The BOP market in itself is not a homogenous segment due to varying incomes across

regions and countries. For example, BOP consumers in India live in more than 6,00,000 villages

scattered across the country, speaking about 500 different dialects belonging to 16 official

languages. Such heterogeneity poses significant challenges in precisely identifying and

understanding under-served needs of the customers. Many companies in BOP markets failed may

be due to the poor understanding about such heterogeneity among consumers. Therefore,

participation of local communities in developing and distributing products not only provides

access to these customers but also offers a platform to interact with them in the process of

understanding consumer preferences (Thakur 2015). Hence, companies need to re-examine its

existing marketing mix strategies in BOP markets that may be adapted as per local conditions.

Due to wealthy markets becoming saturated, multi-national corporations (MNCs) have

turned increasingly to emerging markets in the developing world (London and Hart 2004). Sheth

(2011) points out that this century will be all about marketing to the emerging countries in

contrast to the last century that has been dominated by marketing to the rich world. Multinational

corporations have experience in marketing products to developed world consumers that have

different characteristics with respect to consumers in emerging markets. Consequently, the

effectiveness of the marketing mix tools depends on the differences between emerging and

developed market consumers (Bahadir et al 2015). In India, these companies have extensively

used personal selling, whereas in China, Unilever collaborates with local companies to introduce

its new brands (Arnold and Quelch 1998). In 1960, McCarthy proposed a set of four Ps (product,

price, place and promotion) that include various activities to achieve marketing goals. Since then

this concept is being used by marketers throughout the world. The main aim of marketing mix

remains at satisfying needs of the selected markets and accomplishing specific marketing

objectives (Low and Tan 1995). Marketing orientation based on traditional 4 Ps helped many

companies to attract good number of customers and earn higher profits in western markets.

However, researchers and practitioners have partly neglected and overlooked the large majority

of the population that constitutes BOP consumers in the emerging markets. Literature highlights

an increase in number of companies offering products to BOP consumers. There has been a

13

limited research on how the firms should operate in these markets in order to effectively deliver

products and services at affordable prices.

Advocates of BOP markets argue that challenge of serving BOP consumers just does not

lie in huge numbers of consumers but in the revising established western business models that

meet local needs and requirements (Prahalad and Hart 2002). Marketing mix developed in

western markets has proved to be appropriate for top of the pyramid (TOP) market serving to the

wealthy elite. In these markets resources are abundant with higher purchasing power parity

(Prahalad 2006). Implementation of similar marketing mix at BOP markets often fails to bring

desired results. A standardized western marketing mix offering may not work well with BOP

consumers that require a highly customized approach keeping in view uncertain circumstances.

Literature suggest major modifications in traditional marketing mix for BOP markets (Fletcher

2005; Kirchgeorg and Winn 2006; Pitta and Ireland 2008; Guesalaga and Marshall 2008;

Sridharan and Viswanathan 2008; Chikweche and Fletcher 2012). Business leaders need to focus

on providing enduring value by developing product and packaging designs, distribution systems

and other aspects of market offerings that enhance consumers’ quality of life and sustainable

behavior (Viswanathan and Rosa 2010). Business strategies like collaborating with non-

traditional partners, co-inventing customer solutions, building local capacity and creating social

embeddedness are likely to be successful in these markets (London and Hart 2004).

Chikweche and Fletcher (2012) argue that the products offered to BOP consumers should

consider the degree of essentiality and potential value added to it. Essentiality means if a product

having an additional feature, which is not related to the core functionality of the product, is likely

to increase cost of the product. Such additional features would make the products less affordable

for BOP consumers. Therefore, companies need to focus more on value addition in the basic

functionality of the product. Some companies tend to reduce the quality of the product by limiting

the features that make the product affordable for BOP consumers (Karnani 2007). The reason

behind may be that they are unable to afford the high quality products as the affluent consumers

do. However, previous studies point out that BOP consumers prefer to purchase quality products

at somewhat higher price (Barki and Parente 2010; Chikweche and Fletcher 2010). The

companies like Bata in Bangladesh and HUL in India have also corroborated these findings by

generating high profits through selling quality products to BOP consumers. Further, traditional

promotion activities predominantly include advertising through TV and internet in developed

countries. These channels have proved to be cost effective with ability to have a wider reach at a

lower cost in such resource-rich markets. In recent years, sophisticated technology has facilitated

a sharp increase in two-way communication channels such as social media that could be seen as

14

an evolution of the conventional one-way communication. However, within BOP markets, there

is a big challenge in reaching the consumers due to limited literacy and low access to radio, TV

and internet (Chikweche and Fletcher 2012). Since traditional promotion media is not largely

accessible in BOP markets, firms need to find new ways to promote and generate awareness of

their products.

In view of the above discussion, the present study has been undertaken in order to

achieve following objectives:

1) To investigate purchase behaviour of bottom of the pyramid consumers

2) To examine the willingness of bottom of the pyramid consumers to purchase branded

products

3) To explore the influence of social networks on purchase behaviour of bottom of the

pyramid consumers

4) To study existing marketing mix strategies of companies for bottom of the pyramid

consumers

5) To recommend changes in marketing mix strategies for bottom of the pyramid

consumers

15

CHAPTER II

REVIEW OF LITERATURE

The present study aims to understand purchase behavior and analyze marketing mix

strategies for bottom of the pyramid consumers. The concept of BOP was mainly highlighted by

Prahalad (2006), after that not many empirical studies on BOP purchase behavior have been

undertaken. However, researchers have made several attempts to conceptually analyze consumer

behavior of this population and strategies required to tap underserved consumers. The present

study used theory of planned behavior to analyze consumers’ choice of branded products; and

stimulus-organism-response framework to explore the influence of social networks on purchase

behavior. The literature pertinent to the study was extensively reviewed and has been presented

below under the following heads:

2.1 Purchase behaviour of BOP consumers

2.1.1 Demographics and purchase behavior

2.2 Purchase of branded products

2.2.1 Research framework and hypotheses development

2.2.1.1 Theory of planned behavior

2.2.1.2 Attitude and purchase intention

2.2.1.3 Perceived usefulness and attitude

2.2.1.4 Subjective norms and purchase intention

2.2.1.5 Normative beliefs and subjective norms

2.2.1.6 Perceived behavioral control and purchase intention

2.2.1.7 Perceived availability and perceived behavioral control

2.2.1.8 Perceived affordability and perceived behavioral control

2.2.1.9 Perceived awareness and perceived behavioral control

2.2.1.10 Control variables

2.3 Influence of social networks on purchase behavior

2.3.1 S-O-R framework and hypotheses development

2.3.1.1 Relationship orientation and word-of-mouth

2.3.1.2 Relationship orientation and trust

2.3.1.3 Similarity and word-of-mouth

2.3.1.4 Similarity and trust

2.3.1.5 Expertise and word-of-mouth

16

2.3.1.6 Expertise and trust

2.3.1.7 Word-of-mouth and purchase intention

2.3.1.8 Trust and purchase intention

2.3.1.9 Control variables

2.4 Marketing mix strategies

2.4.1 Product strategies

2.4.2 Pricing strategies

2.4.3 Distribution strategies

2.4.4 Promotion strategies

2.1 Purchase behavior of BOP consumers

The critical investigation of purchase behavior of consumers within a segment is

prerequisite for firms to be marketing oriented and thus being successful in the underlying

segment. Belch and Belch (2007) considered ‘purchase’ as an important part of consumer

behavior as these authors defined consumer behavior as “the process and activities people engage

in when searching for, selecting, purchasing, using, evaluating, and disposing of products and

services so as to satisfy their needs and desires”. Consumer behavior also examines how

consumers make decisions by spending the available resources such as time, money and efforts

while purchasing different products or services. In order to survive in a marketplace, marketers

need to understand when, where and how the potential consumers make purchase related

decisions.

Purchase behavior is a multi-dimensional, complex and diverse process that is largely

determined by a number of demographic, economic, psychological, social and cultural factors

(Prescott et al 2002; Choo et al 2004; Share and Stewart-Knox 2012; Olsen and Tuu 2013).

Purchase behavior of consumers also tends to vary across different regions of the world; for

example Bradley (2003) indicated that consumers in eastern countries response differently to that

of western consumers where product life-cycle is much shorter. Schutte and Ciarlante (1998)

revealed that Asian consumers’ needs and way of satisfying needs are different as that of western

consumers. Such studies did not tend to differentiate among various socio-economic classes

within Eastern or Asian countries. These countries have different consumer classes like wealthy

elite, growing urban middle class and low-income urban or rural class where buyer behavior is

expected to differ substantially. High income consumer segment in one of the fastest growing

Asian country like India has already been well researched (Ling et al 2004; Bijapurkar 2007;

Goyal and Singh 2007; Ali and Kapoor 2009; Ali et al 2010; Kathuria and Gill 2013). However,

17

purchase behavior of consumers in BOP markets of India has not been much explored by

researchers and marketing practitioners. The disadvantaged consumers represent a large potential

market with unmet desires of using new products and services with good quality at affordable

prices.

Existing literature on BOP suggested that business models developed and tested in

western markets needs to be modified when doing business in subsistence markets. BOP

consumers have different tastes, preferences and habits that differentiate them from their high-

income counterparts (Fletcher 2005; Viswanathan et al 2010). These consumers also have unique

culture that determines degree of collectivism among community members, which consequently

influence adoption of new products and services. BOP consumer segment counters several

enduring constraints like low-income, little access to formal markets and limited social

development (Prahalad and Hammond 2002). Hence, there is a need for having deeper

understanding of purchase behavior of BOP consumers so that marketing to such marginalized

consumers can be redesigned as per unique circumstances.

2.1.1 Demographics and purchase behavior

Previous studies highlighted several demographic variables such as age, gender and

education influence the purchase of different products. For instance, consumers’ evaluations

towards food products differ according to gender, age, and education level (Sánchez et al 2012).

Literature also indicated that gender causes divergent mechanisms resulting variation in the intake

of spicy foods. Particularly, male consumers respond more to extrinsic factors, while female

consumers respond more to intrinsic factors associated with the product (Byrnes and Hayes

2015). Previously, studies also found age and education to be associated with consumption of

food products (Meiselman et al 2010; Siegrist et al 2013). Education was also found to influence

consumers’ awareness towards food ingredients (Bornkessel et al 2014).

Different members of the family play different roles in buying products. Herbst (1952)

proposed different roles structures in the family: husband dominated, wife dominated and joint

decisions. These roles tend to differ according to the product category. For example, purchase of

durable products like mobiles and televisions have been assumed to be male dominated; whereas

repeat purchases of FMCG products have been assumed to be wife dominated (Sproles and

Kendall 1986). Further, purchase decisions related to expensive products like jewellery and

furniture are often taken jointly by husband and wife. These roles do not tend to be static but keep

on undergoing substantial changes overtime. Traditionally, women have been the most influential

decision maker in purchase of food and daily need products. However, with more number of

18

women joining the workforce, male members of the family have started assuming greater buying

roles (Wut and Chou 2009).

Spence (1984) in its multi-factorial gender identity theory stated that gender differences

include personality, attitudinal and behavioral differences. Empirical studies also highlighted that

gender is a key variable in consumer purchase behavior. Noble et al (2006) found several

significant gender differences in consumer preferences to purchase products. Previous literature

suggested that male consumers have higher self-confidence than female consumers in some

situations (Maccoby and Jacklin 1974). For food and grocery shopping, housewives still remain

the key decision maker in 95 per cent of cases (Kapoor and Saraiya 2012).

There are a few opportunities for females to work at BOP, thus females play a central

role in household purchase (Viswanathan and Rosa 2010; Chikweche and Fletcher 2011). In a

study conducted in Zimbabwean BOP market; Chikweche et al (2012) found that women play an

overriding role in the purchase of food and personal hygiene products whereas husbands and

children have an insignificant participation in purchase. However, husbands and wives share

responsibility of purchasing products in BOP urban households. In rural areas, women assume a

lead role in purchasing products from local shops due to proximity and convenience in buying.

This finding was also supported by a study conducted in south India by Viswanathan (2007)

indicating that husband delegates the responsibility of purchasing household products to his wife.

Considering women playing a central role in purchasing products in BOP households, Grammen

Bank offered micro-loans to saving circles (a circle contains 12-15 BOP women) who lent money

to other BOP women in Bangladesh. The bank dispersed $6 billion to 7 million borrowers

without collateral and reported 99 per cent repayment rate (Weidner et al 2010). In contrast,

London et al (2014) provided empirical evidence that women are less likely than men to become

customers in subsistence markets of India. This may be due to the fact that women in some of the

BOP markets lack control over financial resources and are restricted by social norms to travel

independently (Banerjee and Duflo 2007). Hence, it may be argued that role of female and male

members in the household, differs as per the cultural norms of the society and type of the product

being purchased.

In emerging markets like India, there exists a significant divide among rural and urban

consumers. The economic disparity among rural and urban regions of the country is considerable,

given the fact that average monthly per capita expenditure of urban consumers (INR 2,477) has

been estimated to be double than the rural consumers (INR 1,287) in India (Anonymous 2013).

Further, average household investment in financial assets in urban areas is three times that in rural

areas (Ghosh et al 2013). Previously, rural and urban consumers were found to have different

19

attitude towards products, distribution and brand names. As a result of these differences, rural and

urban consumers were also found to use different products (Sun and Wu 2004). Further, in urban,

retail and transport infrastructure including roads, railways and airports have been developed

more than the rural areas (Uncles et al 2010; Tanusondjaja et al 2015). However, rural areas in

these markets till lack basic amenities like nutritional food, proper sanitation and uninterrupted

electricity. Rural consumers demonstrate a collectivist behavior unlike their urban counterparts

that show an individualistic behavior (Triandis 1993; McCarty and Shrum 1994). Such socio-

economic differences in rural and urban areas also influence Indian consumers’ preferences, goals

and aspirations to purchase products (Sinha 1994). Due to relative low-income, rural consumers

in India prefer to purchase small packs of products; for instance 86 per cent of total shampoo

sales in rural areas come in the form of sachets, however this figure is 69 per cent in urban areas

(Mahalingam 2007).

Based on the above discussion, the present study intends to analyze sources of

information regarding product purchase, sources for availing credit to purchase products,

frequency of purchase for selected towards, sources of purchase of selected products, expenditure

on selected product categories and role of gender and purchase decisions. This study also aims to

analyze consumer choice towards durable products by providing empirical evidence on

differences in purchase behavior of rural and urban consumers towards durable products.

2.2 Purchase of branded products

The emergence of international brands in global markets has generated the issue of how

brands should be managed in emerging markets. Till 1970s, a consumer major company in India,

Hindustan Unilever Limited (HUL) focused more on urban middle class segment while paying no

or little attention towards majority low-income consumers in rural markets of the country. An

Indian entrepreneur developed and marketed a low-cost good quality brand of washing powder

‘Nirma’ for bottom tier consumers. This brand became the second largest seller in India by 1977

(Sabharwal et al 2004). In other categories, HUL lost market share to Cavincare in skincare, Tata

tea in packaged food, Amul in ice creams etc (Rajagopal 2009). The stiff competition from local

brands forced the company to market low-cost brands for BOP consumers living in remote areas

that were hard to reach through traditional distribution channels. Company, therefore, launched a

project ‘Shakti’ to distribute top brands of its products like shampoos, washing powder, tea and

soaps etc. by appointing rural women as retailers of their products. This project was a big success

and presently the company has its presence in about majority of the rural and tribal areas of the

country.

20

American Marketing Association defined brand as “name, term, sign, symbol or design

or a combination of them intended to identify goods and services of one seller or a group of

sellers and to differentiate them from those of competitors” (Keller 1993). Previously, several

studies were undertaken in emerging markets like India and China that examined buyers’

behavior towards brands from different perspectives (Magnusson et al 2008; Ni and Wan 2008;

Jin and Kang 2011). These studies provided insights into the issues of branding and brand

management in emerging countries by treating these markets as homogeneous and tend to focus

on high-income consumers. There has been limited research on how the insights gleaned from

previous research on branding can be applied to BOP markets (Rajagopal 2009; Chikweche and

Fletcher 2011). In a research on BOP consumers of South America, D’Andrea (2006) highlighted

that low-income consumers prefer branded goods despite the price premium and perceive branded

products as a symbol of quality and confidence. Chikweche and Fletcher (2011) also found that a

large majority of BOP consumers attach high importance to branding while purchasing food and

personal hygiene products. Given the limited income, BOP consumers make sure that they

purchase a right product at a right price. However, disadvantaged consumers have been found

unable to recall brands during the purchase of personal hygiene products (Chikweche and

Fletcher 2011).

Many consumers in India prefer to purchase unbranded and unpackaged food over

branded and packaged food products (Ling et al 2004; Mukherjee and Patel 2005). In recent

times, a major shift from loose and unbranded buying to packaged and branded products was

observed due to consumers paying more attention to hygiene that resulted in increased demand

for branded products in the country (Narayanan 2000). Estimates by Euromonitor International,

ranked India as the second fastest growing packaged food market in Asia Pacific in 2007

(Euromonitor 2009 cited in Ali and Kapoor 2009). BOP consumers in most of the emerging

countries have food products as a larger share in the shopping basket (Anderson and Billou 2007),

for example, share of food products in total shopping basket remains at 78 per cent for low-

income rural consumers in India (Hammond et al 2007). India provides a suitable low-income

population as this country is a home to more than 700 million poor individuals with a combined

income of $378 billion (Anderson and Billou 2007) that offers an attractive market for branded

food.

The above discussion suggested that low-income consumers in India represent a large

part of the population with high expenditure on food where branded food choice is still

understudied. Thereby, it was important to take a closer look at what motivates such consumers to

make branded food choice. The present research, therefore, aimed to investigate drivers of

21

branded food choice among BOP consumers with application of theory of planned behaviour.

This study contributed to the existing literature in two ways. First, to the best of our knowledge

this was the first study that empirically analyzes branded food choice among BOP consumers in

an emerging country like India. Second, the study contributed to the literature by providing

evidence on how existing buyers’ behavior differs from non-buyers with respect to branded food

purchase. For this purpose, the study compared the proposed relationships across two groups of

consumers: group 1: consumers who have already purchased the branded food (buyers); and

group 2: consumers who have not yet purchased the branded food (non-buyers). Results from the

analysis suggested useful implications for food marketing companies in retaining existing or

acquiring potential consumers of branded food.

2.2.1 Research framework and hypotheses development

In this section, an attempt has been made to throw light on the theory used for analyzing

consumer choice towards branded food. In addition, conceptual framework based on extensive

review of the existing literature has also been provided. The proposed relationships among the

variables under study have been mentioned in the forms of directional hypotheses. Drawing on

these hypotheses, the present study has empirically tested the proposed model that clearly depicts

how consumers make branded food choice (see Fig. 2.2).

2.2.1.1 Theory of planned behavior

Theory of Planned Behavior (TPB) is an extension of the theory of reasoned action

(Fishbein and Ajzen 1975). According to the theory of reasoned action, behavioral intention is

determined by two constructs namely, attitude and subjective norms (Fishbein and Ajzen 1975).

Ajzen (1991) added one more construct, perceived behavioural control, in the original model

proposed in theory of reasoned action to account for situations where an individual has less than

complete control over the behavior (Fig. 2). Perceived behavioural control involved beliefs

regarding access to the resources and opportunities needed to perform a behavior (Taylor and

Todd 1995). This construct appears to include two components. The first component is

‘facilitating conditions’ that refers to the availability of resources like time and money needed to

perform a behavior. The second component is self-efficacy that may be conceptualized as an

individual's self-confidence required to engage in a behavior (Ajzen 1991). TPB has been

successfully applied in the prediction of a wide range of food choice such as health food (Astrom

and Rise 2001; Chan and Tsang 2011), junk food (Shahanjarini et al 2012), halal food (Alam and

Sayuti 2011), genetically modified food (Spence and Townsend 2006), organic food (Tarkiainen

22

and Sundqvist 2005), functional food (O’Connor and White 2010), and imported food (Ren et al

2011). However, no study using TPB in investigating branded food choice among low-income

consumers was found in the existing literature.

Theory of planned behavior posits individual’s purchase intention as a function of

attitude, subjective norms and perceived behavioral control (Ajzen 1991). The attitude measures

the extent to which a person displays a favorable or unfavorable evaluation of the behavior which

in turn is predicted by sum of the products of person’s behavioral beliefs (bi) and subjective

evaluation of the desirability of outcomes (ei).10 Subjective norms represent the perceived social

pressure to perform or not to perform a certain behavior that is formed as the individual's

normative beliefs (nbj) concerning a particular referent weighted by motivation to comply (mcj)

with that referent. Finally, perceived behavioral control may be conceptualized as the perceived

easiness or difficulty of performing a behavior which is equated with individual's control beliefs

(cbk) weighted by the perceived facilitation (pfk) in either inhibiting or facilitating the behavior

(Ajzen, 1991).

Fig. 2.1: The ‘pure form’ of Theory of Planned Behaviour (Ajzen 1991)

10 refer to Annexure I

Perceived behavioral control

Attitude

Subjective norms

∑ bi x ei

bi: behavioral beliefs

ei: desirability of outcome

Behavioral intention

Behavior ∑ nbj x mcj

nbj: normative beliefs

mcj: motivation to comply

∑ cbk x pfk

cbk: control beliefs

pfk: perceived facilitation

23

2.2.1.2 Attitude and purchase intention

Attitude may be defined as the extent to which a person displays a favorable or

unfavorable evaluation of the behavior. Literature suggests attitude as a multidimensional

construct which is considered to be composed of three components viz. affective, cognitive, and

conative (Bagozzi 1978; Kothandapani 1971). There is substantial empirical literature available

on attitude influencing food choice. Previous literature highlighted a positive relationship

between attitude and purchase intention for grocery (Hansen et al 2004), organic food (Tarkiainen

and Sundqvist 2005), halal food (Bonne et al 2007) and genetically modified food (Spence and

Townsend 2006; Prati et al 2012). Thus, attitude is one of the most important predictors of

purchase intention. Therefore, it is hypothesized as follows:

H1: Consumers with a positive attitude towards branded food will have a higher intention

to purchase branded food.

2.2.1.3 Perceived usefulness and attitude

Davis (1989) defined perceived usefulness as the degree to which one believes that

undertaking behavior will be useful to him/her. In the context of present study, it may be defined

as the extent to which low-income consumers believe that buying or consuming branded food

would be useful for them, including health in particular. Davis (1989) suggested that attitude

mediates the influence of perceived usefulness on purchase intention. Empirical research also

supported this relationship by highlighting a significant influence of health benefits on

consumers’ attitude towards food products (Magnusson et al 2003; Chaniotakis et al 2010;

Nocella and Kennedy 2012). Further, a study on low-income African-American consumers

reported health as a priority factor in food choice (Antin and Hunt 2012). Bruschi et al (2015)

found that consumers having information about health-enhancing characteristics of bakery

products, value these products over base products. Therefore, it is believed that low-income

consumers’ usefulness perception for branded food helps to develop a favorable attitude towards

branded food.

H2: Consumers perceiving branded food as more useful will have a positive attitude

towards branded food.

2.2.1.4 Subjective norms and purchase intention

Subjective norms reflect perceived social pressure to perform an underlying behavior.

Several studies reported subjective norms as a positive predictor of consumers’ intention to

engage in certain food choices (Hansen et al 2004; Spence and Townsend 2006; Bonne et al

24

2007). In contrast, a few studies found an insignificant relationship between subjective norms and

purchase intention (Mahon et al 2006; Ren et al 2011). Further, food choice among low-income

consumers is not always an individual decision as they tend to comply with expectations of

members of the social networks (Chikweche and Fletcher 2010; Weidner et al 2010), local

retailers (Viswanathan et al 2010), and family members (Chikweche et al 2012). These social

groups seem to considerably influence such consumers’ choice of branded food. Thus, the

following hypothesis is proposed:

H3: Consumers perceiving more social pressure to buy branded food will have a higher

intention to purchase branded food.

2.2.1.5 Normative beliefs and subjective norms

Normative beliefs may be conceptualized as the likelihood that important referent

individuals or groups approve or disapprove the behavior (Ajzen 1991). Low-income consumers

gather product related information from members of social networks, local retailers and family

members (Weidner et al 2010; Chikweche et al 2012). Such disadvantaged consumers prefer to

purchase products from local retailers due to good relationships, credit facility and location

convenience (Chikweche and Fletcher 2010). Viswanathan et al (2010) found that low-income

consumer’s top two sources of product information are- social group and family. Also,

Chikweche and Fletcher (2010) highlighted that social networks and family have a higher

influence on low-income consumers’ food choices than firms’ promotion activities. The above

discussion suggested that social networks, local retailers and family members constitute important

referents that are expected to exert social pressure on low-income consumers to buy branded

food.

H4: Consumers with higher normative beliefs will perceive higher social pressure to

purchase branded food.

2.2.1.6 Perceived behavioral control and purchase intention

Perceived behavioral control refers to persons’ perceived easiness or difficulty to perform

a behavior (Ajzen 1991). Previous research indicated perceived behavioral control as a significant

predictor of intention to purchase genetically modified food (Spence and Townsend 2006), health

food (Chan and Tsang 2011), and organic food (Tarkiainen and Sundqvist 2005; Zhou et al

2013). However, Mahon et al (2006) reported an insignificant relationship between perceived

behavioral control and purchase intention. Perceived behavioral control can be further categorized

into two components: perceived self-efficacy, an individuals’ confidence in his/her abilities; and

25

perceived controllability, an individuals’ judgment of control over external resources. Lack of

basic skills and resources such as knowledge, money and transport discourages low-income

consumers from evaluating, comparing and purchasing the optimum product. Therefore, it is

expected that a higher control over buying branded food would help low-income consumers to

purchase branded food.

H5: Consumers perceiving higher control to buy branded food will have a higher

intention to purchase branded food.

2.2.1.7 Perceived availability and perceived behavioral control

Availability was found to be one of the important factors influencing purchase behavior

among economically disadvantaged consumers (Richards and Smith 2009; Grutzmacher and

Gross 2011). The product must be available where consumers shop or within a reasonable

distance for it to be considered within the decision-making (Schiffman and Kanuk 2009). Lack of

availability was also found to be a barrier for purchasing organic (Tarkiainen and Sundqvist 2005;

Paul and Rana 2012); and healthy food (Fila and Smith 2006). Empirical literature reported a

significant association between availability of food and its purchase (Tarkiainen and Sundqvist

2005; Taylor et al 2005; Sharkey et al 2010; Mukherjee et al 2012). Henson (1992) also found

‘near to home’ as the most important factor of food choice among low-income consumers.

Similarly, Ali et al (2010) found Indian consumers as distance sensitive who prefer to purchase

food that are available within one kilometer radius. However, availability of food near to home or

workplace does not seem to be under an individuals’ control (Tarkiainen and Sundqvist 2005).

Therefore, it represents one of the resources required to control over purchasing food products.

Given these findings, it is believed that availability of branded food would positively influence

low-income consumers’ perceived easiness to purchase branded food.

H6: Consumers perceiving branded food as more easily available will have a higher

control over the purchase of branded food.

2.2.1.8 Perceived affordability and perceived behavioral control

Affordability is another key factor influencing food choice, particularly at lower-end of

the economic strata. Previous studies revealed inconclusive association between affordability of

fruits and vegetables and its intake (Flint et al 2013), a positive association with increased intake

(Zenk et al 2005). Briz and Ward (2009) indicated that demand for food products decreases when

these are perceived as expensive. Several studies highlighted level of income as a major factor

influencing food choice among low-income consumers (Roux et al 2000; Ali et al 2010;

26

Chikweche and Fletcher 2010). Due to low affordability, poor consumers may perceive lesser

control over the purchase of branded food products. Therefore, it is suggested that low-income

consumers’ affordability perceptions would determine perceived easiness or difficulty to purchase

branded food.

H7: Consumers perceiving branded food as more affordable will have a higher control

over the purchase of branded food.

2.2.1.9 Perceived awareness and perceived behavioral control

Consumer behavior theorists (Mowen and Minor 2001; Schiffman and Kanuk 2009),

argued that brands with high awareness are far more likely to be considered as compared to those

with low awareness. Empirical studies also demonstrated that brand awareness positively

influences consumers’ response to product choice (Srinivasan et al 2010; Huang and Sarigollu

2012; Narteh et al 2012; Anselmsson et al 2014). In subsistence markets, a large majority of low-

income consumers were found to be unaware and unable to recall a single brand of sugar

(Kathuria and Gill 2013). Partial penetration of print and broadcasting media in subsistence

markets may be attributed as a reason for low brand-awareness among disadvantaged consumers

(Prahalad 2006; Chikweche and Fletcher 2011). On this basis, the following hypothesis has been

proposed:

H8: Consumers with higher awareness towards branded food will have a higher control

over the purchase of branded food.

2.2.1.10 Control variables

The study included control variables (Fig. 2.2) that were potentially correlated with

independent variables in the proposed model. Omission of such theoretically important variables

may cause endogeneity in the model that renders estimates inconsistent (Antonakis et al 2010).

Previous studies highlighted socio-demographic factors such as age, gender, education and

children in household influence food choice. For example, Sánchez et al (2012) found that

consumers’ food evaluations differ according to gender, age, and education level. Byrnes and

Hayes (2015) highlighted that gender causes divergent mechanisms leading to the intake of spicy

foods. Specifically, men respond more to extrinsic factors, while women respond more to

intrinsic factors. Also, age and education were found to be associated with consumption of novel

food (Meiselman et al 2010; Siegrist et al 2013). Bornkessel et al (2014) also reported significant

influence of education on consumers’ awareness towards food ingredients. Further, Michaelidou

and Hassan (2010) indicated that children in household significantly predict rural consumers’

27

intention to purchase food. Thus, the present study included age, gender, education and children

in household as control variables to help rule-out possible endogeneity in the model.

Fig. 2.2: The conceptual model for examining ‘consumer choice towards branded food’

2.3 Influence of social networks on purchase behavior

Persisting constraints at BOP make disadvantaged consumers to obtain purchase related

information from members of the social networks. Network members also help disadvantaged

consumers to get an insight into the past purchase experiences. The brand recommendations from

members of the social networks help subsistence consumers to reduce risk associated with its

purchase. Previous literature considerably highlighted the role of social networks in purchase

behavior of BOP consumers (Sridharan and Viswanathan 2008; Chikweche and Fletcher 2010;

Viswanathan et al 2010; Weidner et al 2010). However, these studies were not focused on

different characteristics of social networks that may vary in tendency to influence purchase

behavior of subsistence consumers.

The present study, therefore, aimed to investigate the influence of characteristics of social

networks on purchase behavior of BOP consumers. This study contributed to the existing

H3: +

H6: + Perceived availability

H2: +

H1: + H4: +

H7: +

H8: +

H5: +

Control variables: Age

Gender Education

Children in household

Moderator: Buyers/non-buyers

Perceived behavioral control

Attitude

Subjective norms

Perceived affordability

Perceived awareness

Normative beliefs

Perceived usefulness

Purchase intention

28

literature in two ways. First, the study contributed to the existing literature (Chikweche and

Fletcher 2010; Viswanathan et al 2010; Weidner et al 2010) by empirically analyzing the

influence of characteristics of social networks on purchase behavior of BOP consumers. In India,

majority of the BOP consumers are affiliated to formal social networks and members of these

networks meet regularly and maintain stronger relationships. Affiliation to social networks helps

them to acquire power from membership, equal rights within the group, leadership skills, higher

self-efficacy and acquaintance with numerical skills (Jacobs and Goodman 1989; Viswanathan et

al 2010). Second, the study provided evidence on how the affiliation to formal social networks

such as self-help groups (SHGs), non-government organizations (NGOs), religious groups and

community groups etc. renders divergent mechanisms of purchase behavior. For this purpose,

present study compared the proposed relationships across two groups of consumers: group 1:

consumers affiliated to formal social networks and group 2: consumers unaffiliated to formal

social networks.

2.3.1 S-O-R framework and hypotheses development

The study used Stimulus–Organism–Response (S–O–R) model to build conceptual

framework of the study. This model posited that environmental stimuli influence an individual's

emotional state, which in turn affects response (Mehrabian and Russell 1974). The stimulus is

external to a person and consists of various elements of physical atmosphere. Organism refers to

an individual’s cognitive and affective states like internal processes that structures intervening

between stimuli and response (Bagozzi 1986). Response includes behaviors like acquisition,

usage and exploration of the environment (Jacoby 2002). Following the S–O–R framework, the

present study operationalized stimulus as characteristics of the social networks (i.e. relationship

orientation, similarity and expertise); organism as word-of-mouth and trust among network

members; and response as the intention to purchase a product recommended by network

members.

2.3.1.1 Relationship orientation and word-of-mouth

Relationship orientation refers to the propensity to enter long-term relationships with

members of a social network. Informal communications take place within a network with strong

or weak social relationships (Brown and Reingen 1987). The strength of communications

appeared to be influenced by the relationship orientation of network members (Bansal and Voyer

2000). Members of a BOP social network maintain strong relationships since disadvantaged

consumers often seek help from each-other in undertaking day-to-day activities (Prahalad 2006;

Viswanathan 2007; Weidner et al 2010). Previous literature also demonstrated that word-of-

29

mouth within a network with stronger relationships is more influential in determining receiver’s

decision making than a network with weaker relationships (Arndt 1967; Leonard-Barton 1985;

Brown and Reingen 1987). Therefore, the following hypothesis has been proposed:

H9: Consumers perceiving network members as more relationship orientated will have a

higher word-of-mouth with network members.

2.3.1.2 Relationship orientation and trust

Trust has been widely accepted as a basis of relationships (Morgan and Hunt 1994; Rich

2000). Long-term relationships with continuous interactions help to develop trust among

impoverished consumers. BOP individuals place trust upon network members regarding general

matters including purchase related decisions (Viswanathan et al 2010). Moreover, subsistence

consumers also prefer to purchase products from socially-embedded shopkeepers due to

committed relationships with them (Chikweche and Fletcher 2010). Such relationships assist in

developing trust within the social network as a whole. Thus, the study proposed the following

hypothesis:

H10: Consumers perceiving network members as more relationship orientated will place

higher in network members.

2.3.1.3 Similarity and word-of-mouth

Similarity among group members is a basic characteristic that structures network

relationships. It refers to the similarity of network members’ external (e.g. gender, race or age) or

internal characteristics (e.g. values, beliefs or norms) (Lazarsfeld and Robert 1954; Rogers 1983).

This characteristic has been analyzed across literatures in marketing and social psychology

(Brown and Reingen 1987; Rajaobelina and Bergeron 2009; Park et al 2014). An empirical study

highlighted a positive relationship between level of similarity and word-of-mouth (Smith 1998).

This relationship has strong implications for network members as interpersonal interactions

among similar people occur at a higher rate than what would be expected among dissimilar

members (Brown and Reingen 1987; McPherson et al 2001; Ruef et al 2003; Park et al 2014).

The strength of these interactions, also, appeared to be influenced by how similar or dissimilar

network members are in terms of language, culture and background (Gilly et al 1998; Mark

2003). On this basis, it is hypothesized that:

H11: Consumers perceiving network members as more similar will have a higher word-

of-mouth with network members.

30

2.3.1.4 Similarity and trust

Disadvantaged consumers have a collectivist approach to life and tend to form groups

with similar others (Kirchgeorg and Winn 2006; Chikweche and Fletcher 2010). A higher level of

trust among them is a key conduit for their survival in uncertain environment. Persistence of

several problems like unemployment, hyperinflation and corruption decreases BOP consumer’s

trust towards government or market related institutions (Peterson et al 2010). Thereby,

subsistence consumers tend to trust social sources of information that generally constitute

members of the social networks. Previously, empirical studies also found that similarity among

network members results in higher level of trust among them (Smith 1998; Ruef et al 2003). The

present study, therefore, assumes as follows:

H12: Consumers perceiving network members as more similar will place a place a higher

trust in network members.

2.3.1.5 Expertise and word-of-mouth

The expertise of a sender refers to the degree to which the source is perceived as being

capable of providing correct information (Bansal and Voyer 2000). It is typically assessed by

level of knowledge and experience with regard to a focal product or service (Rajaobelina and

Bergeron 2009). Receivers perceiving senders as experts will more actively seek information

from sender through face-to-face communications (Lin et al 2005; Karakaya and Barnes 2010).

Also, previous studies established expertise as one of the factors influencing word-of-mouth

(Sweeney et al 2008; Ganguly et al 2010; Ku 2012). Word-of-mouth increases when a receiver

lacks the required information to make an accurate purchase decision (Sweeney et al 2008).

Given these findings, it is expected as follows:

H13: Consumers perceiving network members as experts will have a higher word-of-

mouth with network members.

2.3.1.6 Expertise and trust

BOP consumers rely on a network member on the basis of experiences which an

individual has made in the past (Viswanathan 2007). A few members of the group provide

truthful information due to relevant experience in a specific product purchase (Sridharan and

Viswanathan 2008). BOP consumers perceive such members as more informative and reliable in

recommending others to purchase specific products. Such individuals are more likely to be trusted

within the network. Thereby, the study proposed that consumers perceiving network members as

experts will have a higher trust on network members.

31

H14: Consumers perceiving network members as experts will place a higher trust in

network members.

2.3.1.7 Word-of-mouth and purchase intention

Word-of-mouth may be conceptualized as an oral, face-to-face communication between a

receiver and a sender whom the receiver perceives as non-commercial regarding brand, product

or a service (Katz and Lazarsfeld 1955; Arndt 1967). Such communications often flow through

informal social networks, in which early adopters play an opinion leader’s role (Czepiel 1974).

Empirical literature reported a significant role of word-of-mouth in developing and predicting

consumer’s purchase intentions (Chevalier and Mayzlin 2006; Lin and Lu 2008; Sweeney et al

2008; Conley and Udry 2010; Chan and Ngai 2011; Jalilvand and Samiei 2012). However, a few

studies found that negative word-of-mouth reduces purchase intention (Smith and Vogt 1995;

Arndt 1967). Word-of-mouth within BOP networks is largely common, as individuals rely on this

type of communication to identify reliable retailers and to evaluate products to be purchased.

Further, word-of-mouth seems to be much more effective than mass media promotions (e.g. T.V.)

due to fragmented exposure to traditional media in impoverished markets (Weidner et al 2010;

Viswanathan et al 2010). Disadvantaged consumers, therefore, seek product recommendations

from social sources to make a better purchase decision (Chikweche and Fletcher 2010). Based on

these considerations, it is believed as follows:

H15: Consumers with high word-of-mouth with network members will have a higher

intention to purchase a product recommended by network members.

2.3.1.8 Trust and purchase intention

Arnott (2007) defined trust as a belief in the reliability of a third party, particularly when

there is an element of personal risk. Trust seems to be important because customers face

uncertainty in terms of both the quality and consistency while making purchase decisions (Chiou

and Drog 2006). There has been extensive investigation of trust in- retailing of food (Rampl et al

2012); clothes (Sirdeshmukh et al 2002); fast-moving consumer goods (Lymperopoulos et al

2010); tourism services (Lin and Lu 2010); and corporate brands (Sichtmann 2007). Surprisingly,

researchers have largely neglected trust within the social networks that explicitly influences

subsistence purchase behavior. Prahalad (2006) also recognized trust as a key factor to establish

long term relationship with BOP population, because it has potential impact on consumer’s

continued usage behavior. Viswanathan et al (2010) indicated that BOP consumers trust more on

social networks than family members. Therefore, such high level of trust determines consumer’s

32

Characteristics of social networks

preferences in subsistence markets. Based on above considerations, the following hypothesis has

been derived:

H16: Consumers with a higher trust on network members will have a higher intention to

purchase a product recommended by network members.

Stimulus

Organism

Response

Fig. 2.3: The conceptual model for exploring ‘influence of social networks on purchase

behavior’

2.3.1.9 Control variables

The study included control variables (Fig. 2.3) that were potentially correlated with

independent variables in the proposed model. Omission of such theoretically important variables

may cause endogeneity in the model that renders estimates inconsistent (Antonakis et al 2010).

London et al (2014) indicated that prior ownership of a specific product significantly predicts

likelihood to repurchase the product among BOP consumers. Previous studies also highlighted

socio-demographic factors such as education, gender, age and children in household influence

purchase behavior of subsistence consumers. For example, limited education level was found to

limit BOP consumer’s intention to purchase new products (Rosa and Viswanathan 2007;

Viswanathan 2010). Empirical studies also found gender as a significant determinant of purchase

behavior of consumers in different BOP markets of the world (Chikweche et al 2012; London et

al 2014). Also, age was found to be significantly associated with the purchase of product (Siegrist

et al 2013). Further, children also play an important role in the purchase decisions related to a

H13: +

H14: +

H9: +

H11: +

H10: +

H12: +

H15: +

H16: +

Word-of-mouth

Trust

Relationship orientation

Similarity

Expertise

Control variables: Age

Gender Education

Children in household Prior ownership of mobile

Purchase intention

33

variety of products in BOP markets. For instance, Chikweche et al (2012) found that the role of

children in purchase decisions in poor households increases with age. Thus, the present study

included prior ownership of mobile, education, gender, age and children in household as control

variables to help rule-out possible endogeneity in the model.

2.4 Marketing mix strategies

Marketing mix developed and tested in western markets has proved to be inappropriate

for emerging markets with low-income consumers that constitute a major part of the population.

In these markets resources are scarce that are often operated by informal market and consumer

institutions. Implementation of similar marketing mix in BOP markets often fails to bring desired

results. A standardized western marketing mix may not work well within these markets that

require a highly customized approach keeping in view hostile circumstances. Therefore, the

present study has been undertaken to analyze existing marketing mix strategies being used by

companies at BOP markets. The marketing mix comprises of 4Ps (product, price, place and

promotion). The main aim of marketing mix remains at satisfying the needs of selected markets

and accomplishing specific marketing objectives. This section has been divided into four sub-

sections viz. product strategies, pricing strategies, distribution (place) strategies and promotion

strategies, given as below.

2.4.1 Product strategies

As majority of the BOP consumers earn and spend on daily basis, companies have started

offering products to BOP consumers in small packs that provide the quality product at a constant

level as of developed markets (Changco and Pornpitakpan 2011). Products sold in bulk packs

may not find adequate number of customers in BOP market. Such large packs are often modified

by BOP retailers to break bulk packaging in order to make it affordable for economically

disadvantaged consumers. For instance, these retailers sell single cigarettes from open boxes and

individual cookies from large packs (Weidner et al 2010). Small packs of shampoo ‘Sachets’

contribute more than 95 per cent of total shampoo unit sales with more than 60 per cent of the

value of the shampoo market in India. Companies selling small unit sizes at affordable prices

make money, expand markets, and generate broader access to goods and services that improve

quality of life at BOP (Hammond and Prahalad 2004). Small packs enhance the value delivery to

the customer as these are convenient to carry, give the consumer the satisfaction of using branded

products at low cash-out, increases trial purchase, offer control over consumption and provide

more variety for a similar outlay (Dubey and Patel 2004; Changco and Pornpitakpan 2011). Low

priced sachets have become a popular packaging in many other product categories like detergents,

34

jams, jellies, toothpaste, ice cream, butter and beauty creams. In Egypt, P & G also undertook the

this strategy to boost the sales of its washing powder brand Ariel, and downsize the package size

from 200 grams to 150 grams (Kotabe and Helsen 2013).

Karnani, (2006) advocated co-creation of products where BOP individuals are treated as

co-producers rather than consumers of their offerings. Here, co-creation refers to the companies

strengthening their internal resource base by incorporating external resources into the company

by recruiting local BOP consumers and entrepreneurs in order to co-create solutions for BOP

markets (London and Hart 2004; Schuster and Holtbrugge 2014). It is primarily concerned with

developing methods to attain understanding of consumers’ needs and wants so that firms can co-

shape consumer expectations and experiences. The co-creation process must be centered on the

consumer and encourage active participation in all aspects, including information search,

configuration of products and services, fulfillment, and consumption (Prahalad and Ramaswamy

2000). Companies using local content in their products provide livelihood opportunities for local

community members and increase awareness of its products, which results in an expanded

informal network (Weidner et al 2010). For these reasons, firms operating in subsistence markets

engage disadvantaged individuals in the product design or manufacturing process to gain market

acquaintance. These customers can also be involved as distributors of products and services in the

value chain process generating prospects of increased incomes. For example, Shri Mahila Griha

Udyog, a cooperative organization started manufacturing ‘papad’11 in 1959 by employing seven

BOP women with a loan of INR 80. These women used to prepare and distribute ‘papads’ in the

nearby BOP areas. Over the years, the society grown exponentially and now employs 42,000

BOP women workers with annual turnover of INR 300 crores including INR 10 crores of exports.

Co-creation enables BOP consumers to earn livelihood, meet basic needs and makes

them informed, aware and knowledgeable (Bharti et al 2014). Prahalad and Ramaswamy (2000)

suggested four building blocks of co-creation, namely: dialogue, access, risk reduction and

transparency. Dialogue helps the firms to understand the emotional, social, and cultural contexts;

access provides consumers an opportunity to participate in product development process without

transferring ownership rights. Co-creation also helps firms to reduce risk of failure as product

meets consumer expectations well. Transparency is also necessary for consumers to become

value co-creators (Prahalad and Ramaswamy 2000). For instance, rural sales program is a

partnership between CARE and several MNCs, domestic companies of Bangladesh, including

Unilever, Bata, Danone, Bic, Square Toiletries and Lalteer Seeds. Through this program firms

distributed a range of consumer goods door-to-door across rural Bangladesh, through a network

11 A kind of large spicy chips used in India

35

of BOP women sales agents- ‘aparjitas’12 (Dolan et al 2012). A shoe major company, Bata also

engaged ‘aparjitas’ in co-creation process for designing suitable shoes for target market

conditions. The ‘aparjitas’ informed the company that BOP people prefer to wear a shoe with

hard sole because they walk on rough and uneven ground. Bata also came to know that BOP

people intend to buy high quality shoes even at somewhat higher prices. Hence, co-creation by

the firm was instrumental in developing low-cost and high-quality shoes suitable for BOP market

conditions. The products developed through co-creation assisted the company to provide low-cost

affordable shoes to poor people that helped to prevent illness and enhanced self-legitimacy

(Dolan et al 2012).

Co-creation also helps the MNCs operating in informal markets to work in partnership

for the creation of shared value (Khalid et al 2015). For this, companies attempt to create two

different types of values in order to achieve sustainability in business operations: economic and

social value. Creating economic value is necessary for the companies to generate profits and to

keep investing and selling products in these markets (Mukerjee 2012). Social value creation is

also a central objective of production process that helps to achieve economically viable results.

For example, Nokia’s basic phone with longer battery life and flashlight provided social value to

its users at an economical price of $15. The company also created economic value through the

huge volumes sold. Therefore, creating both economic value and social value simultaneously, is

called shared value (Kramer 2011).

Further, packaging is a vital part of product that also needs some changes as per hostile

circumstances of subsistence markets. Consumers with limited literacy may find it difficult to

read product information, guidelines to use, price and product ingredients from the packaging

containing such information in a non-local or foreign language. BOP consumers are more into

pictographic thinking, view brand names as objects, match patterns to identify products and

visualize amounts to buy (Viswanathan et al 2009). Firms, therefore, need to develop some

sustainable packs that are inexpensive and easily acceptable by BOP consumers. Lifebuoy, the

low-cost and largest selling soap brand of Hindustan Unilever Limited (HUL), has been made

using inexpensive local ingredients and packaging material (Dawar and Chattopadhyay 2000).

Based on the above discussion, the present aims to analyze product strategies of selected

companies for BOP consumers. Managers of the selected companies would be enquired about

development of customized products in terms of size, features and design to suit BOP consumers;

co-creation of products with BOP consumers by seek their advice on product development; and

development of products keeping in view the unique circumstances of BOP consumers. 12

a woman who does not accept defeat

36

2.4.2 Pricing strategies

Price is the only element in the marketing mix that produces revenue; the other elements

produce costs (Kotler 2009). Companies need to modify pricing mechanisms in order to create an

optimal mix between market share, revenue and profit (Hakansson and Waluszewski 2005).

London and Hart (2004) stated that multinational corporations usually impose their same pricing

formula in developing countries with little success at bottom of the pyramid markets. A

significant cost reduction is required for companies to focus on low margin-high volume strategy.

Firms that have been successful at subsistence markets have been able to learn that millions of

small sales can generate huge profits.

Companies also need to focus on enhancing affordability of consumers living with low-

income that still varies with season. Being unable to afford durable products in a single cash lay-

out, firms attempt to create capacity to consume through innovative financing schemes. For

instance, Casas Bahia, a large retailer of appliances in Brazil, provides credit to consumers with

low and unpredictable incomes. The firm sells appliances to BOP consumers with sophisticated

credit rating system coupled with counseling. The default rate of the firm remains at as low as 8.5

per cent compared to 15 per cent for competitors (Prahalad 2006). Many firms have come up with

innovative financing schemes that have been proved to be successful for subsistence consumers.

For example, Grameen Phone in Bangladesh lends money to a woman entrepreneur to buy a

mobile hand-set and to further offer telecommunication services to other people. The women

entrepreneurs, thus, got an opportunity to work and earn a reasonable amount of money (Fletcher

2005). Also, Grameen Shakti, the largest company worldwide selling Solar Home Systems

(SHSs), offers solar lights and lamps to BOP consumers. The company collects monthly

installments through technicians’ monthly service visits over up to three years that includes cost

of the equipment, maintenance and financing.13

Consumers with limited financial resources seek affordable products with essential

functionalities that may be made of simpler, local and cheaper materials (Zeschky et al 2011).

Such low-cost products developed in BOP markets also find market in developed countries. For

instance, General Electric (GE) developed a simplified portable electrocardiogram (ECG) device

‘MAC 400’ in 2008 at a low-price of $800 (vs $2,000 for traditional ECG). This device costs

only $1 per test against $5-$20 with traditional models (Woolridge 2010; Govindarajan and

Trimble 2012). GE also developed its first compact ultrasound machine in 2002 in China and

found market in other countries, including US. Just after six years of its launch, the compact

13https://static.squarespace.com/static/51bef39fe4b010d205f84a92/t/51f237c4e4b07e4e5ac4e0f6/1374828484103/Full_report_Maketing_for_the_BOP.pdf

37

ultrasound machine generated $278 million in global sales with an 50-60 per cent growth in

annual revenues (Govindarajan and Ramamurti 2011, p. 33).

The above discussion suggests to examine company’s low-margin high-volume pricing

strategy for BOP consumers. Managers of the selected companies also need to be enquired about

tailoring of pricing mechanisms to suit BOP market conditions; and setting the product price

keeping in view low-income of BOP consumers.

2.4.3 Distribution strategies

Distribution is generally concerned with the sets of interdependent organizations involved

in the process of making a product or service available for use or consumption (Kotler 2009). The

main task of distribution channels is to link existing supply with existing demand (Hakansson and

Waluszewski 2005). Distribution strategies can be categorized as exclusive, selective and

intensive that are concerned with how deeper a company wants to penetrate in targeted markets.

Exclusive distribution refers to offering products and services in a few profitable markets only.

Selective distribution is the middle way, where the products and services are offered in selected

markets. Intensive distribution deals with offering product and services to almost all the potential

markets that is mostly used with low-price/high-volume strategies. Distribution of products and

services at impoverished markets remains a key challenge for firms due to the dearth of

transportation and communication means that adds to channel costs. However, a number of

opportunities also exist for distributing new product and services to these markets. The urban

BOP markets constitute about 15,000 people per hectare that allows for intense distribution

opportunities (Prahalad 2006). In rural areas, distribution causes a bigger challenge where cost of

reaching per consumer may be higher. The poor transport infrastructure and inadequate basic

amenities imply that retail outlets close to BOP consumers’ area of living offer a potential

channel in distributing products to remote areas.

Formal distribution channels are expected to be ineffective at subsistence markets as

existing local partners lack adequate knowledge and capabilities that inhibits in having an easy

access to BOP consumers (London and Hart 2004). The formal channels include family owned

local grocery shops, supermarkets and wholesalers whereas informal channels include tuck-shops,

open market stalls and members of the social networks (Chikweche and Fletcher 2012). The local

shopkeepers and vendors sell sub-standard products at a somewhat premium price to ill-informed

consumers. Further, large retailers and wholesalers are generally away from BOP consumers’

area of residence and are often situated in urban areas. These retailers normally do not offer

products on credit to BOP consumers and they also have to incur an extra cost in transport to shop

38

from these outlets (Chikweche and Fletcher 2011). Thus, traditional local as well as modern large

retail stores may not provide an appropriate alternative for effective distribution at BOP markets.

Companies have been experimenting with innovative informal methods of distributing goods and

services. The MNCs like HUL in India, Bata Shoe in Bangladesh and Avon in Brazil have really

come out with considerable results by undertaking such experiments successfully. One common

point in these experiments is that these companies employed local rural women for distributing

daily need products to BOP population. HUL employed ‘Shakti Ammas’ (the powerful lady);

Bata engaged ‘Aparjitas’ (one who never accepts defeat); and Avon leveraged ‘Avon ladies’ to

reach remote areas in the subsistence markets of India, Bangladesh and Brazil.

However, coalition with non-traditional partners like social networks provides a possible

avenue for distributing products and services in resource-constrained markets. This partnership

not only provides access to products but also provide employment and business opportunities for

marginalized consumers. For this, companies may offer technology, expertise, production

facilities and methods of distribution whereas social networks like self-help groups (SHGs) and

non-government organizations (NGOs) leverage and understand economic, social and political

contexts and systems at local level (Follman 2012). London and Hart (2004) argue that coalition

with non-traditional partners at BOP will not only foster a superior understanding of the local

needs, but also enables firms to combine commercial and social dimensions. For instance,

keeping in view the problem of regular power supply shortage and fluctuation in India’s BOP

market, Godrej and Boyce, a premier Indian consumer durable company, launched an innovative

small-sized, battery-powered refrigerator; named it as ‘ChotuKool’ (Chakravarthy and Coughlan

2012). The final product was outcome of co-creation process. ChotuKool was developed in close

interaction with targeted BOP consumer segment to get insight into their needs, desired solutions

and barriers to consumption (Tiwari and Herstatt 2012). Distribution of ChotuKool to BOP

consumers was still a challenge for Godrej; because many of its potential consumers live in

remote areas (Whitney 2010). Company partnered with BOP rural people and trained them as

salespersons for distributing product to the end consumers. These intermediaries earned a

commission of roughly $3 (approx. INR 180) per unit sold (Chakravarthy and Coughlan 2012).

Moreover, the company also collaborated with the Indian Postal Department in Gujarat state. As

per this collaboration, a customer can order ChotuKool at the local post-office and product is

shipped within one week directly to the customer’s doorstep even in remote areas (Saiyed 2011).

It eliminated the need for BOP customers to go to the city for buying ChotuKool.

Based on the extensive literature review, the present study identified the non-traditional

partners such as suppliers, logistic service providers, financial institutions, local retailers, non-

39

profit organizations, non-government organizations, local communities, self-help groups, centre

government institutions and state government institutions with whom companies often collaborate

to distribute products among BOP consumer. Managers of the selected companies would be

enquired about company’s collaborations with these identified partners.

2.4.4 Promotion strategies

Promotion is the key element of marketing mix, concerned with ensuring that customers

are aware of the products that the organization makes available to its customers (Rowley 1998).

From a marketing viewpoint, changes in the promotional mix are of vital significance in

developing effective and cost-effective promotional campaigns in a turbulent environment

(Kitchen 1996). Companies communicate well with upper tier consumers with use of traditional

promotion media like television and magazines. However, television has limited capability to

communicate with final consumers in these markets due to erratic and unreliable electricity

supply. Limited literacy level also hampers penetration of print media at these markets.

Companies, therefore, need to relook into existing promotion media in such dark zones. Rather

firms may rely more on informal promotional channels like live demonstrations and road-shows.

For example, Chikweche and Fletcher (2012) revealed that firms predominantly use direct

marketing methods for interacting face to face with BOP consumers. Community road shows,

product demonstration, women’s clubs and mobile advertising have been found to be the common

media of promotion among such consumers. For instance, Procter and Gamble (P&G) launched a

low-cost water purifier with brand name ‘PuR’ in developing countries for targeting bottom tier

consumers having inadequate access to clean water. Company trained its workforce to

demonstrate use of the product in interior parts of the country that resulted in good response from

the consumers. HUL also engaged its sales agents to conduct water purification demonstrations

for SHGs, through its partner ‘Integrated Village Development Project’ (IVDP).14

BOP consumers have low and varying incomes and often purchase daily need products

from small retail stores on credit. Owner of the retail store is the sole sales-person interacting

with rural BOP consumers as villagers have minimal access to other retail outlets. Such retailers

give brand recommendations to consumers having low-awareness about different brands of a

specific product. When such an ill-informed consumer asks retailer for a washing soap, the

retailer decides which brand to offer (Alur and Schoormans 2013). Retailers have the potential to

be an opinion leader and play an important role in promotion related communications at BOP.

14https://static.squarespace.com/static/51bef39fe4b010d205f84a92/t/51f237c4e4b07e4e5ac4e0f6/1374828484103/Full_report_Maketing_for_the_BOP.pdf

40

Promoting the products through social networks also has a considerable impact on purchase

behavior of BOP consumers because such consumers rely heavily on social sources of

information (Viswanathan et al 2010). Fletcher (2005) showed that low-income consumers prefer

local presenters of messages, like appeals directed to group rather to the individuals and prefer for

non-verbal stimuli and messages. In contrast, consumers in upper-middle segment prefer foreign

presenters, like individual appeals and prefer verbal stimuli.

MNCs have been increasingly experimenting with innovative promotion methods that

also aim to create social value for BOP consumers. Enduring constraints and dominance of social

connections make social value creation at these markets distinctive from developed markets

(Vega and Kidwell 2007). Sinkovics et al (2014) defined social value as an activity that leads to

realization of any of the core values of development at BOP i.e. sustenance, self-esteem and

freedom from servitude. Creating and promoting social value helps companies to gain a better

understanding of business model formulation at BOP. Firms believing in promoting products

coupled with social value creation prioritize social mission over financial goals and attain more

than full cost recovery (Yunus et al 2010). Several companies operating at these markets have

successfully created social value among disadvantaged consumers; consequently such firms also

achieved greater visibility of its brands. For instance, keeping in view death of more than two

million children in India due to an easily preventable disease- diarrhea, HUL approached village

schools and educated children on causes and prevention methods for the underlying illness. The

company educated the rural population about cleanliness and regular hand washing with soap.

This helped the company to promote sales of its low-cost soap brand ‘Lifebuoy’ in rural markets

of the country (Prahalad 2006).

Therefore, managers of the selected companies would be enquired about providing

promotional budget to encourage sales at BOP markets, and promoting products through social

networks like self-help groups and non-government organizations in BOP markets.

41

CHAPTER III

MATERIALS AND METHODS

The present study was undertaken with the objectives: to investigate purchase behaviour

of bottom of the pyramid consumers; to examine the willingness of bottom of the pyramid

consumers to purchase branded products; to explore the influence of social networks on purchase

behaviour of bottom of the pyramid consumers; to study existing marketing mix strategies of

companies for bottom of the pyramid consumers; and to recommend changes in marketing mix

strategies for bottom of the pyramid consumers. For achieving aforementioned objectives, the

present study used two different methodologies viz. consumer survey and survey of managers

from selected companies. The first three objectives were achieved through consumer survey in

which primary data were collected from 600 respondents with the help of a self-designed

questionnaire. For achieving fourth objective, managers from selected companies were surveyed

with the help of a structured interview schedule. The fifth objective was accomplished by

critically analyzing findings of first four objectives. The details about methodology used in the

present study have been presented below under the following heads:

3.1 Consumer survey

3.1.1 Selection of the respondents

3.1.2 Questionnaire development

3.1.3 Measures of the study

3.1.4 Selection of the products

3.1.4.1 Products selected to investigate purchase behaviour

3.1.4.2 Product selected to examine willingness to purchase branded products

3.1.4.3 Product selected to explore influence of social network on purchase

behavior

3.1.5 Data analysis

3.1.5.1 Data analysis to investigate purchase behaviour

3.1.5.2 Data analysis to examine willingness to purchase branded products

3.1.5.3 Data analysis to explore influence of social network on purchase behavior

3.2 Managers’ survey

3.2.1 Selection of the companies

3.2.2 Data analysis to study marketing mix strategies of companies

42

3.1 Consumer survey

This survey was undertaken to collect primary data from respondents with the help of a

self-designed structured questionnaire (annexure I). The data were collected through on-field

surveys that aimed to analyze- purchase behavior of BOP consumers; consumers’ willingness to

purchase branded products; and influence of social networks on purchase behavior. Consumers

with a monthly family income between INR 2,000 - 8,000 were included in the sample. In total,

600 consumers were surveyed from both rural and urban areas of the two states of northern India.

Keeping in view lower literacy standards of the target population, questionnaire used to collect

data from the respondents was translated into two local languages of the region viz. Punjabi and

Hindi. Multi-stage sampling was used to undertake consumer survey, and judgment sampling was

used to select first participant from a selected village or city. The criteria for selecting

respondents, income level, sample size and data analysis tools and techniques have been

presented as below:

3.1.1 Selection of the respondents

There exists no consensus for defining income level of BOP consumers. Prahalad (2006)

defined BOP consumers as those who live on $2 a day. As per World Bank estimates, individuals

living below $1.25 a day are considered to be the bottom tier consumers which accounts for 41.6

per cent of India’s population (Esposito et al 2012). A study conducted by World Resource

Institute set annual incomes up to and including $3000 per capita per year (2002 PPP) as cut-off

for BOP population segment (Hammond et al 2007). NCAER (National Council of Applied

Economic Research) estimated that absolute BOP households earn below INR 45,000 per annum

and account for 35 million households, and another 80 million households are in income level

between INR 45,000 to INR 90,000 per year which comes out to be INR 3,750 to INR 7,500 per

month for a household. Some of the marketers talk of their success in bottom of the pyramid

market but they usually end up redefining the bottom that for them actually starts somewhere

above these 35 million poorest households (Singhal 2008). Another study conducted in India,

Viswanathan et al (2010) found highest family income of INR 8,000 per month with a mean

income of INR 4,493 per month for BOP consumers. Jaiswal and Gupta (2015) highlighted that

BOP household income in India ranges from INR 2,500 per month to INR 8,000 per month. For

present research, therefore, consumers having family income between INR 2,000 to INR 8,000

per month were considered as potential respondents of the survey.

Using multi-stage sampling, the participants of this cross-sectional study were drawn

from two states of northern India viz. Punjab and Haryana. As per poverty standards defined by

43

Planning Commission (name of the commission has been changed to ‘Niti Aayog’), Government

of India, about 8 per cent of the total population in Punjab and nearly 11 per cent of the total

population in Haryana lives below poverty line. Average monthly per capita expenditure for rural

consumers of Punjab is higher than the rural consumers of Haryana (INR 2,136 for Punjab and

INR 1,925 for Haryana); in contrast average monthly per capita expenditure for urban consumers

of Punjab is lower than the urban consumers of Haryana (INR 2,743 for Punjab and INR 3,346

for Haryana)15. In total, 600 respondents were surveyed comprising 300 from each state. These

two states have been officially divided into four administrative divisions. The four administrative

divisions (AD) of Punjab are: Patiala (AD I), Jalandhar (AD II), Ferozpur (AD III) and Faridkot

(AD IV). Two districts from each administrative division were selected randomly. The selected

districts from each AD were, AD I: Ludhiana and Sahibzada Ajit Singh Nagar (Mohali); AD II:

Jalandhar and Kapurthala; AD III: Ferozpur and Moga; AD IV: Faridkot and Bathinda. Further,

two villages from each selected district were chosen resulting in selection of total 8 cities and 16

villages from Punjab. As per Census 2011, about two-third and nearly one third of the total

population in Punjab lives in rural and urban areas respectively (Jain 2011). Proportionately, 200

participants from rural and 100 participants from urban areas were surveyed. Equal numbers of

respondents (approximately 12) were selected from each selected city and each selected village of

the state.

Similarly, the state Haryana has also been divided into four administrative divisions

namely, Ambala (AD I), Gurgaon (AD II), Hisar (AD III), and Rohtak (AD IV). Two districts

from each administrative division were selected randomly such as: AD I: Ambala and

Kurukshetra; AD II: Gurgaon and Faridabad; AD III: Sirsa and Fatehabad; AD IV: Sonipat and

Jhajjar. As per Census 2011, Haryana has also about one-third urban and two-third rural

population (Jain 2011). The same methodology was followed in selecting number of respondents

from each selected city and each selected village from the state of Haryana. The number and

names of the selected districts from each AD and number of villages selected from each selected

district have been provided in the table 3.1.

The first participant from a selected city or village was chosen through non-probability

judgment sampling because there is no standard sampling frame of BOP consumers having the

specific income level. The data were collected with the help of personal interviews conducted at

respondent’s home. Standard representative sampling approach with poor as participants is quite

difficult; therefore, researchers need openness and flexibility in sampling (Ozanne et al 2011;

15 http:// planningcommission.nic.in/news/pre_pov2307.pdf

44

Crockett et al 2013). Therefore, additional participant was recruited with the help of snowball

sampling by asking the preceding informant to recommend others who met the eligibility

criterion of household income between INR 2,000 – INR 8,000 per month. Individuals qualifying

this criterion were asked for being part of the survey. This sampling approach was suitable for

studying BOP population because such consumers are rich in their network relationships

(Ingenbleek et al 2013).

Table 3.1: Selection of districts and villages

Administrative division

Selected districts No. of villages selected from each selected

district Number Name

State: PUNJAB

AD I Patiala 2 Ludhiana

Sahibzada Ajit Singh Nagar

2

2

AD II Jalandhar 2 Jalandhar

Kapurthala

2

2

AD III Ferozpur 2 Ferozpur

Moga

2

2

AD IV Faridkot 2 Faridkot

Bathinda

2

2

State: HARYANA

AD I Ambala 2 Ambala

Kurukshetra

2

2

AD II Gurgaon 2 Gurgaon

Faridabad

2

2

AD III Hisar 2 Sirsa

Fatehabad

2

2

AD IV Rohtak 2 Sonipat

Jhajjar

2

2

Total 16 32

3.1.2 Questionnaire development

Not many studies have empirically investigated BOP consumers’ purchase behavior. Due

to the lack of an existing validated scale; measures used to operationalize the constructs in the

questionnaire were mainly developed from pre-existing conceptual studies. Constructs in the

questionnaire included multi-scale items as single items tend to be less reliable (Churchill 1979).

45

The initial draft of the questionnaire was screened by two academic experts and suggested

changes were incorporated. The responses on statements were obtained on a 5-point interval scale

(1 for strongly disagree and 5 for strongly agree). However, statements related to desirability of

outcome (ei), motivation to comply (mj), and perceived facilitation (pfk) were measured with help

of a 5-point scale (1 for very bad idea and 5 for very good idea). Usage of interval scale was

advisable as it allows to apply a number of statistical tools in addition to techniques applicable to

nominal and ordinal scale data (Malhotra, 2008). The final questionnaire used for collecting data

from respondents has been given in annexure I.

Keeping in mind the low-literary level of target population, questionnaire was translated

into two regional languages (i.e. Punjabi and Hindi) with the help of language experts. While

conducting the survey, due consideration was given to collect data from respondents with

different demographics such as different educational backgrounds, age groups, people with

different buying roles etc. A pilot study was also undertaken to check reliability of the scales used

to operationalize constructs in the questionnaire. Responses of 62 respondents (more than 10 per

cent of the total sample size) were included to test reliability of the scales. The value of Cronbach

alpha (α) was found to be more than the minimum cut-off score of 0.7 (Nunnally 1978) that

indicated good reliability of the scales used.

3.1.3 Measures of the study

The measures used to operationalize various constructs in the study were developed by

reviewing empirical and conceptual literature related to the present research. The model

‘consumer choice towards branded food’ (Fig. 2.2) included several constructs like perceived

usefulness, attitude, subjective norms, normative beliefs, perceived behavioral control, perceived

affordability, perceived availability, perceived awareness and purchase intention. The items were

developed with the help of previous studies as given below:

Perceived availability of branded bakery products (Honkanen and Frewer 2009):

• Most of the branded bakery products are available near to place where I live or work

• Branded bakery products are easily available in the nearby local market

• I need to travel less for buying branded bakery products

Perceived affordability towards branded bakery products (Notani A S 1997; Pavlou and

Fygenson 2006):

• My household income permits me to buy branded bakery products

• I have the money needed to purchase branded bakery products

• It is within my budget to purchase branded bakery products

46

• I can afford to purchase branded bakery products on credit from nearby retailer

Perceived awareness towards branded bakery products (Taylor and Todd 1995; Laroche et

al 1996):

• I have enough information to make a good decision about purchasing branded bakery

products

• I have enough information about various brands of bakery products

• I know enough to buy branded bakery products on my own

• I am well informed about prices of branded bakery products

• I am well informed about benefits of branded bakery products

Perceived behavioural control (Pavlou and Fygenson 2006; Ajzen 2013):

• I have all the resources for purchasing branded bakery products

• I have complete control while purchasing branded bakery products

• Purchasing branded bakery products is up to me

Normative beliefs (Taylor and Todd 1995):

• Members of my social network think that I should buy branded bakery products

• Members of my social network think that I should consume branded bakery products

• My family members think that I should buy branded bakery products

• My family members think that I should consume branded bakery products

• Local retailers think that I should buy branded bakery products

• Local retailers think that I should consume branded bakery products16

Subjective norms (Taylor and Todd 1995):

• Most people who are important to me would think that I should purchase branded bakery

products

• Most people who are important to me would think that I should consume branded bakery

products

• Most people who influence my decisions would think that I should purchase branded

bakery products

• Most people who influence my decisions would think that I should consume branded

bakery products

Perceived usefulness (Sun and Collins 2004; Heenam et al 2009; Honkanen and Frewer

2009):

16 Deleted during analysis

47

• Branded bakery products are nutritious

• Branded bakery products are hygienic

• Branded bakery products are fresh

• Branded bakery products are convenient to eat.

Attitude (Canniere MH De et al 2009; Chung et al 2012):

• Buying branded bakery products is healthy

• Buying branded bakery products is beneficial

• Buying branded bakery products is worth17

• Buying branded bakery products is good

Purchase intention (Chung et al 2012):

• I intend to purchase branded bakery products

• I want to purchase branded bakery products

• I will continue purchasing branded bakery products

• I plan to increase quantity bought of branded bakery products.

For analyzing the influence of social networks on purchase behavior, three characteristics

of social networks were identified by extensively reviewing the existing literature. The identified

characteristics were: relationship orientation, similarity and expertise. The study examined

influence of these characteristics on intention to purchase a product recommended by network

members through two mediators such as word-of-mouth and trust (see Fig. 2.3). The items were

developed with the help of previous studies as given below:

Relationship orientation (Battor and Battor 2010; Park et al 2014):

• Members of social networks assist me in buying mobiles

• Members of social networks have old ties with me

• Members of social networks maintain a close relationship with me18

• Members of social networks are willing to provide me monetary help

Similarity (Rajaobelina and Bergeron 2009; Park et al 2014):

• Members of social networks have similar interest • Members of social networks share same culture • Members of social networks have comparable income • Members of social networks have alike standard of living19.

Expertise (Rajaobelina and Bergeron 2009):

• Members of social networks are good in having mobile related information

17

Deleted during analysis 18 Deleted during analysis 19 Deleted during analysis

48

• Members of social networks usually know more about mobile features

• Members of social networks generally recommend me about retailers to buy a mobile

• Members of social networks are specialist enough to guide me about which brand of

mobile to buy

Word-of-Mouth (Walsh and Mitchell 2010; Jalilvand and Samiei 2012):

• I often seek information about mobile purchase from members of social networks to

make sure that I buy the right product

• I like to gather information from members of my social networks before I buy a mobile

• I consider members of my social networks as a good source of information while

purchasing a mobile

• Members of my social networks like introducing new mobiles with me.

Trust (Sichtmann 2007; Peterson et al 2009):

• Members of social networks are reliable

• Members of social networks are trustworthy

• I trust members of social networks to offer credible information about mobiles

• I trust members of social networks to educate me about mobiles

Purchase intention (Chung et al 2012):

• I intend to purchase a mobile as suggested by members of the social networks

• I plan to purchase a mobile as suggested by members of the social networks

• I want to purchase a mobile as suggested by members of the social networks

3.1.4 Selection of the products

Different products were selected for achieving first three objectives of the study. The

criterion of product selection has been presented as below:

3.1.4.1 Products selected to investigate purchase behaviour

Consumers in most of the emerging countries including India, have food products as a

larger share in the shopping basket. On average, the share of food products in total shopping

basket remains at 41.2 per cent in India (Sengupta 2008). The food market for BOP households

has been estimated to be $2.89 trillion globally (Hammond et al 2007). BOP consumers

individually spend less but actually spend a greater percentage of their income on daily

necessities like food and FMCG (Karnani 2006; Anderson and Billou 2007; Viswanathan et al

2010; Chikweche and Fletcher 2013). Rural consumers in India spend 54 per cent of their income

on food whereas their urban counterparts spend 42 per cent of their income on food products

49

(Anonymous 2013). As per a study undertaken by World Resource Institute, rural BOP

consumers in India have been estimated to incur a whopping of about 78 per cent of their income

on food (Hammond et al 2007).

Durable products have not been able to penetrate deep into the subsistence markets of the

country due to poor affordability of consumers. Economically disadvantaged consumers are often

left with insufficient funds to purchase durable products. Studies estimate that BOP consumers

spend about eight per cent of their income on durable products. In absolute terms, it generates a

market potential of about INR 7,200 crore for durable products (Singhal 2008). A survey

conducted by National Council of Applied Economic Research (NCAER) revealed a marginal

ownership of durable products by Indian bottom tier households. For instance, eight per cent of

poor households owned color television sets, four per cent of poor households owned telephones;

and three per cent of these households were found to own refrigerators (Rao and Shukla 2008).

The above discussion highlighted that there is huge market potential for food, FMCG and

durable products in BOP markets of the country. The present study, therefore, selected food,

FMCG and durable products to investigate purchase behavior of BOP consumers.

3.1.4.2 Product selected to examine willingness to purchase branded products

In the present study, an attempt has been made to examine consumers’ willingness to

purchase branded food, FMCG and durable products. One specific product within the each

selected product category was chosen. The study selected branded bakery products to examine

consumer choice towards branded food; beauty products to examine consumers’ willingness to

purchase branded FMCG; and mobile phone to examine consumers’ willingness to purchase

branded durable products. The theoretical grounds for selecting these products within the

aforementioned category have been provided as under:

Product selected to examine consumer choice towards branded food

Packaged food industry in India is worth about $30 billion that is estimated to touch $50

billion by 2017.20 Branded bakery segment in this industry is estimated to be worth $284 million

in 2012, and is expected to grow at 13-15 per cent in the next three to four years. The branded or

organized bakery industry accounts for about 60 per cent of the total production, and the

remaining 40 per cent is contributed by unbranded or local manufacturers.21 Two major

categories, such as bread and biscuits, account for 82 per cent of the total bakery production.

20 http://www.assocham.org/newsdetail.php?id=4992 21 http://www.thehindubusinessline.com/companies/fortified-biscuits-britannia-bakes-a-plan-for-a-healthy-business/article6423558.ece

50

Biscuits are consumed by more than 90 per cent of the country’s population, comprising low-

income population accounting for its predominant consumption. More than 50 per cent sale of

largest selling glucose cookies brand ‘Parle G’ comes from rural and low-income markets of the

country. In view of nutrient value, low-price and changing consumption patterns, branded bakery

products have acquired a growing prominence among economically disadvantaged population.

Therefore, the present study selected branded bakery products for examining consumer choice

towards branded food.

Product selected to examine willingness to purchase branded FMCG

In the recent decades, consumers in India have undergone substantial changes in lifestyle,

due to rising per capita income and introduction of new brands in the market. Preferences of

Indian consumers have shifted from functional products to more advanced and specialized

products. Recently, Indian markets have been bombarded with a plenty of new brands in FMCG

category. Beauty products like face creams and face wash have assumed more importance among

both male and female consumers. Despite a sharp increase in the popularity of such products, an

Indian consumer, on an average spends much less on beauty products than consumers from

developed markets (Desai 2014). This indicates that the beauty products have a greater potential

for sale and growth in India. Companies like AVON, HUL and P&G have started offering small

packs of beauty products like face wash and face creams to subsistence consumers of the country.

The trend of offering FMCG products in sachets started in 1983, when Cavin Kare

introduced such single-served small packs of ‘Chik’ shampoo for INR 1. This innovative launch

transformed the entire FMCG industry; presently sachets contribute more than 75 per cent of the

total volume of the shampoo sales. A few years back HUL launched its flagship fairness cream

brand ‘Fair and Lovely’ in sachets at INR 5. Presently, sachets account for more than 50 per cent

of Fair and Lovely sale in India. Similarly, Gujarat-based company, Zydus Wellness recently

launched facewash in sachets at INR 1 under the brand name ‘Everyuth’. The sachets of face

wash provided company an advantage over competitors, especially in the BOP markets where

face wash is not a regular product. Due to the low-price, poor consumers who have never used the

product are more likely to purchase it just for giving it a try.22 It seems evident that branded

beauty products are increasingly being developed and marketed by companies to serve BOP

consumers in India. Therefore, the present study selected beauty products for examining

consumers’ willingness to purchase branded FMCG.

22

http://retail.economictimes.indiatimes.com/news/food-entertainment/personal-care-pet-supplies-liquor/how-are-sachets-changing-the-game-in-skin-and-haircare/44957602

51

Product selected to examine willingness to purchase branded durable products

In both developed and developing countries, mobile phones have wide spread presence

among consumers. Recently, low-income consumers in developing countries have also started

using branded mobile phones. Usage of mobile phones among BOP consumers has provided

socio-economic benefits like providing access to knowledge and information, enhancing material

well-being and assisting in business activities (Hammond et al 2007; Porter 2012; Jaiswal and

Gupta 2015). Mobile phones also provide an ability to establish a strong network with friends,

develop a sense of wellbeing, enhanced income, consumer empowerment and improvement in

market efficiency (Scott et al 2004; Shankar and Balasubramanian 2009). Companies have added

innovative features and substantially decreased prices of mobile phones that resulted in a market

of more than four billion mobile phones in impoverished markets (Rangan et al 2007). For

instance, Nokia launched mobile phones equipped with innovative features that enabled BOP

consumers to cope up with poverty constraints. For instance, long battery back-up helped rural

consumers to make calls and a flash light assisted in illuminating the village streets during

frequent electricity cuts. Moreover, value added services related to health, weather and

agriculture were provided on mobiles by the company that helped potential consumers to prefer

the Nokia over competitors. Therefore, the present study selected mobile phone for examining

consumers’ willingness to purchase branded durable products.

3.1.4.3 Product selected to explore the influence of social network on purchase behavior

The proliferation of information and communication technologies, particularly mobile

phones, offers several benefits to users around the world (Ilahiane and Sherry 2011). Generally,

mobile phones are used not only by the high-income consumers, but also by the poor who were

unable to afford hand-sets previously (Kang and Maity 2012; Dey et al 2013). The reduced

tariffs, affordable devices and low service charges have resulted in a market of more than four

billion mobile phones in impoverished markets (Rangan et al 2007). Recently, Telecom

Regulatory Authority of India (TRAI) reported that there are more than 980 million wireless

telephone subscribers in India.23 Presently, total numbers of mobile phone users in India stood at

about 400 million and this figure is expected to cross 800 million by 2019.24 Adoption of mobile

phones at BOP has potential to enhance socio-economic well-being of users by providing access

to knowledge, information; and delivering services at lower costs and assisting in business

activities (Zainudeen and Ratnadiwakara 2011; Jaiswal and Gupta 2015). However, collection of

information through social networks, before purchasing such a critical product, is fundamental to 23 http://www.trai.gov.in/WriteReadData/WhatsNew/Documents/PR-No=47.pdf 24 http://www.statista.com/statistics/274658/forecast-of-mobile-phone-users-in-india/

52

purchase behavior of BOP consumers. Thus, understanding antecedents of intention to purchase a

mobile recommended by network members seems important in helping us to better comprehend

the influence of social networks on purchase behavior.

3.1.5 Data analysis

The present study used several multivariate data analysis techniques such as structural

equation modeling, exploratory factor analysis, analysis of variance, logistic regression and

analysis of covariance to generate meaningful results out of the data collected. Appropriate data

analysis techniques were applied according to different scales used for collecting the data. Details

of data analysis techniques used in the present study have been mentioned as below:

3.1.5.1 Data analysis to investigate purchase behavior

The study used various tools and techniques to investigate purchase behavior of BOP

consumers. The demographic profile of the respondents was analyzed by using frequency and

percentage method. Consumers’ preferences for availing credit to purchase products were

analyzed by using percentage method. Mean scores for sources of information for purchasing

different products like food and FMCG; and consumer durables were calculated. On the basis of

mean scores, consumers’ preference for sources of information for purchasing these products was

calculated. Percentage method was used to present consumers’ expenditure proportion for food

and FMCG. Independent samples t-test was used to examine the role of gender in purchase

decisions for food and FMCG; and durable products.

Structural Equation Modeling

The present study proposed several hypotheses and developed a model to examine

consumer choice towards durable products (see section 4.3.7). The proposed model was tested by

using structural equation modeling in AMOS 18.0. Structural equation modeling (SEM) is

considered to be an alternate to multiple regression because it is equipped to analyze not only a

simple or multiple linear regression, but a system of regression equations simultaneously. In

SEM, a single variable may represent a predictor (exogenous, independent or regressor) in one

equation and a criterion (endogeneous, dependent or regressand) in another equation. The

collection of such a system of regression equations is called a structural model (Nachtigall et al

2003). SEM has the capability to include latent variables (unobserved variables) like factors

underlying observed variables in the specified model. The latent variables are connected to

observed variables (indicators, statements or items) with a single-headed arrow in a structural

model (Bollen and Paxton 1998). Boomsma (2000) stated that SEM consists of two types of

53

models: measurement model and structural model. The measurement model represents the

relationships between the unobserved variables and their manifest or observed indicators that are

tested for having a specified level of reliability and validity through confirmatory factor analysis

(CFA). Further, the structural model represents the relationships among the latent variables of

interest as specified in the model (Boomsma 2000). In SEM, researchers most commonly use

maximum likelihood method of estimation for computing structural estimates and model fit

statistics that aim to find the parameter values that make the observed data most likely (or

maximize the likelihood of the parameters given the data) (Brown 2015).

Confirmatory factor analysis

When theoretical and empirical basis to specify a model is sufficiently available in the

literature, confirmatory factor analysis is likely to be a suitable technique because it allows for

focused testing of the hypotheses proposed in the model (Finch and West 1997; Fabrigar et al

1999). Unlike EFA, CFA specifies the number of latent factors and the number of indicators that

are expected to load on to these latent factors. CFA deals particularly with measurement models

that aim to test the relationships between indicators (observed measures) and latent variables or

unobserved factors. This analysis helps to establish the number and nature of indicators loading

onto their theoretical latent factors that account for the variation and covariation among a set of

indicators (Brown 2015). In other words, the indicators are influenced by the same latent factor

and these indicators are generally intercorrelated because they share a common cause. Thus, a

measurement model provides a more comprehensive understanding of the relationships among a

set of indicators within a latent factor and with indicators across the latent factors. Output of CFA

provides a number of model fit statistics such as Chi-square, Tucker-Lewis index (TLI), Root

Mean Square Error of Approximation (RMSEA), Comparative Fit Index (CFI), Normed-fit Index

(NFI), and Incremental Fit Index (IFI). Results of CFA also provide modification indices that

represent a value by which the overall chi-square value of the model is expected to decrease if the

constrained parameter is freely estimated that suggests a significant improvement in the overall

model fit. Chi-square value represents the difference between the expected and observed

covariance matrices; and lower the value of chi-square indicates the model to be better fit. The

value of other fit indices such as TLI, CFI, IFI should be greater than 0.9 (Hair et al., 2006).

Invariance, reliability and validity

Relationships proposed in the model consumer choice towards durable products (Fig. 4.1)

were tested across two groups of consumers: rural and urban consumers. In such cases, it is

important to determine whether the scale is appropriate for making comparisons across groups

54

(Brown 2015). Therefore, the measurement model was tested for two forms of invariance such as

configural and metric invariance. Configural invariance refers to same pattern of factor loadings

of measurement items across different groups (Steenkamp and Baumgartner 1998). It is

diagnosed by conducting confirmatory factor analysis without constraint in which all factors are

set free across two subgroups. Metric invariance implies that variables are measured according to

the same scale intervals across different groups (Steenkamp and Baumgartner 1998). It is tested

by comparing model fit with factor loadings constrained to be equal across subgroups, to

configural invariance model. An insignificant change in the fit measures like chi-square value

indicates the model to be metric invariant. Further, CFA results provide estimates of correlations

among constructs, standardized factor loadings and error variances that can be further used for

assessing reliability, convergent and discriminant validity of the model. Reliability of factors can

be diagnosed by calculating the value of cronbach alpha (α) and composite reliability (CR).

Values of CR were calculated by using the following formula (Fornell and Larcker 1981).

�� = (∑ λ�)�� �(∑ λ�)�� � +(∑ δ�)�� �

where λ = standardized factor loading δ = error variance n = number of indicators within a construct

Convergent validity of a latent factor means that different indicators of a factor are

strongly interrelated. A construct with a good convergent validity should have the value of

average variance extracted (AVE) higher than 0.5. Discriminant validity of the model shows that

indicators of the different latent constructs are not highly inter-correlated. The correlations among

constructs should be lower than the square root of AVE that supports discriminant validity

(Fornell and Larcker 1981). AVE value of a specific construct was calculated by using the

following formula:

��� =∑ λ��� ��

where λ = standardized factor loading

n = number of indicators within a construct

Control variables, mediation and moderation

The study specified causal paths between each of the four control variables- and three

endogeneous constructs: attitude, subjective norms and perceived behavioral control; and one

55

outcome variable: purchase intention. All control variables were also correlated with each other

and with the predictor constructs: perceived usefulness, normative beliefs, perceived availability,

affordability and awareness. Error terms of the endogeneous constructs were mutually correlated

in order to reduce potential bias in the model (Antonakis et al 2010). Further, mediation was

examined by evaluating bias-corrected bootstrap confidence intervals for indirect effects at 0.05.

According to this method, indirect effects are significant if bias-corrected bootstrap confidence

interval for the estimates does not contain zero (Shrout and Bolger 2002; MacKinnon et al 2007).

For undertaking moderation analysis, a multi-group model in AMOS was created to test

differences between proposed relationships across two subgroups: rural and urban consumers.

The full sample (n = 600) and sub-sample of each group (n1 = 400 and n2 = 200) met the

minimum condition of 100 cases required and at least five times the total number of items to test

a complex structural equation model (Bagozzi and Yi 1988). The study used SPSS 16.0 to

calculate and compare mean scores of various constructs across two subgroups. For comparing

statistical differences between estimates of relationships across two subgroups, unstandardized

estimates and standard errors were used to calculate z-score (Hair et al 2006; Iglesias and

Vázquez 2001). The formula used for calculating z-score is given as under:

� = �� −����. �. (��)� + (�. �. (�))

where β1 = unstandardized regression estimate of a relationship in group 1

β2 = unstandardized regression estimate of the same relationship in group 2

S.E. = Standard error

Handling endogeneity in the model

The study adopted appropriate measures to diagnose and control endogeneity, the most

common problem in causal models that renders estimates inconsistent. Antonakis et al (2010)

highlighted how the endogeneity caused by omitted variables, simultaneity, measurement error

and common-method variance can be potentially minimized. The first two causes of endogeneity

were handled by including relevant control variables in the model (see Fig. 4.1). Measurement

error was not detected as estimates of reliability and validity of the measures were found to be

acceptable (see section 4.3.7). Further, common-method variance problem arises where a

common scale or common rater is used and it is likely that source strives to maintain consistency

between two types of ratings. The study used Harman’s single factor test to diagnose potential

56

common method bias in the data (Podsakoff et al 2003). According to this technique, exploratory

factor analysis is applied to all the statements used for measuring different constructs in the study

and an unrotated factor solution is examined. The results highlighted neither a single factor nor a

general factor accounting for the majority of the variance explained emerged. Therefore, common

method bias was not found to be a problem in the model.

3.1.5.2 Data analysis to examine willingness to purchase branded products

For examining consumer choice towards branded food (Fig. 2.2), structural equation

modeling was used. For examining consumers’ willingness to purchase branded FMCG and

branded durable products, logistic regression was applied. Before applying logistic regression, the

present study used exploratory factor analysis to explore factors affecting the purchase of branded

FMCG and branded durable products. Factors, thus, generated were used as independent

variables in the logistic models.

Consumer choice towards branded food (Fig. 2.2) was examined with the application of

structural equation modeling in AMOS 18.0. In SEM, measurement model was tested for two

forms of invariance viz. configural and metric invariance by a method as mentioned in the

preceding section. Further, this model was diagnosed for assessing reliability and validity among

all constructs included in the study. The study included control variables like age, education,

gender and children in the household to reduce confounding relationships in the model. Structural

model specified causal paths between each of the control variables- and three endogeneous

constructs: attitude, subjective norms and perceived behavioral control; and one outcome

variable: purchase intention. In structural model, all control variables were correlated with each

other and with the predictor constructs: perceived usefulness, normative beliefs, perceived

availability, perceived affordability and perceived awareness. Error terms of the endogeneous

constructs were mutually correlated in order to reduce potential bias in the model (Antonakis et al

2010). Further, mediation was analyzed by evaluating bias-corrected bootstrap confidence

intervals for indirect effects. According to this method, indirect effects are significant if bias-

corrected bootstrap confidence interval for the estimates does not contain zero (Shrout and Bolger

2002; MacKinnon et al 2007).

For undertaking moderation analysis, a multi-group model in AMOS was created to test

differences between proposed relationships across two subgroups: buyer and non-buyers. The full

sample (n = 600) and sub-sample of each group (n1 = 406 and n2 = 194) met the minimum

condition of 100 cases required and at least five times the total number of items to test a complex

structural equation model (Bagozzi and Yi 1988). For comparing estimates of relationships across

57

two subgroups, unstandardized estimates, standard errors and critical ratios obtained for

differences were used (Iglesias and Vázquez 2001). Study used z-test to examine statistical

significance of the differences between structural estimates of relationships by using a formula

mentioned in the preceding section (Hair et al 2006).

The study adopted appropriate measures to diagnose and control endogeneity in the

causal model. Antonakis et al (2010) highlighted how the endogeneity caused by omitted

variables, simultaneity, measurement error and common-method variance can be potentially

minimized. The first two causes of endogeneity were handled by including relevant control

variables in the model (see section 2.2.1.10). Measurement error was not detected as estimates of

reliability and validity of the measures were found to be acceptable. Further, common-method

variance problem arises where a common scale or common rater is used. Here, literature proposed

a statistical remedy to minimize potential effects of this problem by including a common latent

factor in the model (Podsakoff et al 2003). According to this technique, items were allowed to

load on their theoretical constructs, as well as on a common latent factor, and significance of the

structural parameters was examined both with and without a common latent factor. Therefore,

any shared variance based on the source of rating was controlled when assessing the significance

of the structural paths (Moorman and Blakely 1995; Podsakoff et al 2003). Results highlighted

relationships to be significant with and without a common latent factor. Nevertheless, results

indicated a slight decrease (less than 0.2) in the estimates of structural paths with and without a

common latent factor since some part of the loading was shared by common latent factor. The

study, therefore, supported that the estimates were robust to common-method variance effects.

Independent samples t-test

Consumers’ perceptions towards parameters influencing the purchase of branded FMCG

and branded durable product were measured on a 5-point likert scale. For analyzing statistical

differences in the mean scores of consumers’ perceptions towards these parameters across two

categories of demographic variables such as age, income, area and gender, independent samples t-

test was used. In this test, null hypothesis assumed that there is no significant difference in mean

scores of consumers’ perceptions across two categories (Malhotra 2008). P-value less than 0.05

rejected the null hypothesis and conclude that there is statistical difference in the mean scores

across two categories of demographic variable.

Analysis of variance

Analysis of variance (ANOVA) is used to compare the statistical differences among

mean scores of a dependent variable across two or more categories of independent variable.

58

Dependent variable in ANOVA should be metric whereas the independent variable must be non-

metric or categorical (Malhotra 2008). For analyzing statistical differences in the mean scores of

consumers’ perceptions towards parameters influencing the purchase of branded FMCG and

branded durable products across categories of education, one-way ANOVA was used. Null

hypothesis in ANOVA assumed that there is no significant difference in the mean scores of

consumers’ perceptions across categories of education. F-value less than 0.05 rejected the null

hypothesis and conclude that there is statistical difference in the mean scores across education

categories. Further, Tukey’s post hoc test was used to examine the statistical differences in the

mean scores across two categories of education at a time.

Exploratory factor analysis

The study also applied exploratory factor analysis (EFA) to explore factors affecting the

purchase of branded FMCG and branded durable products. This technique was first introduced by

Spearman (1904) and later modified by Thurstone (1931). When literature lacks substantial

theoretical or empirical basis to make strong assumptions about the number of common factors or

what specific measured variables these common factors are likely to influence, EFA emerges as a

useful technique (Churchill 1979; Goldberg and Velicer 2006). Empirical literature on factors

affecting the purchase of branded FMCG and branded durable products among BOP consumers is

in nascent stage; thereby the present study used EFA for achieving the aforementioned purpose.

EFA is an interdependence technique that examines the entire set of interdependent relationships

which is in contrast to other statistical techniques where one variable is dependent and the others

are independent variables. It is also regarded as a variable reduction technique, in which several

observed variables are contained in a few factors that assume to underlie the original variables

(Goldberg and Velicer 2006; Hair et al 2006). The reduced factors include smaller sets of

observed variables that retain as much information from the original variables as possible.

Selection of variables is one of the most important decisions before the data collection in EFA;

therefore, the variables included in the present study were developed from the existing theory and

findings from past research. Selection of appropriate method for extracting factors in EFA is

another important decision. Commonly, two basic methods are used such as principal component

analysis and common factor analysis (Fabrigar et al 1999; Goldberg and Velicer 2006). If the

primary objective of EFA is to reduce the variables to minimum number of factors that will

account for maximum variance in the data, Principal Component Analysis (PCA) is more suitable

method of factor extraction. The first factor generated through principal components analysis

provides a measure of whatever is most in common to the observed variables in the study. The

59

first factor extracted through PCA explains the maximum variation in the data. On the other hand,

if the primary goal of EFA is to identify the underlying dimensions, common factor analysis is a

recommended method of factor extraction on the basis of common variance in the data (Goldberg

and Velicer 2006). In the present research, principal components analysis was used for extracting

the underlying factors.

Before rotation, EFA generates as many factors as there are number of observed variables

in the study. In order to obtain lesser number of factors that account for same variance explained

as in unrotated solution, the axes of the factor matrix are rotated by a specified method. The

present study used ‘varimax’ method of factor rotation which is the most commonly used

procedure as suggested by Kaiser (1958). After rotation, the numbers of factors are reduced

considerably that depends upon the criteria adopted for selecting number of factors. The factors

with eigen values of 1 or above were retained in the final analysis (Kaiser 1960). The correctness

of EFA was checked through Kaiser-Meyer-Olkin (KMO) test for examining sampling adequacy

and Bartlett’s test of sphericity for diagnosing correlations among variables. The KMO statistic is

based on the principle that the partial correlations among variables should be small if variables

share common factors when the effects of other variables are controlled (Munro 2005). A small

value of KMO test for sampling adequacy (below 0.5) indicates that the partial correlations

between pairs of variables are high and therefore factor analysis is inappropriate. A high value of

KMO (0.5-1.0) indicates that factor analysis is appropriate. Bartlett's test of sphericity is used to

investigate the null hypothesis that the variables in the correlation matrix are uncorrelated. A

value of test statistic less than 0.05 results in the rejection of null hypothesis that indicates a

strong correlation among variables in the study and recommends further application of factor

analysis (Hair et al 2006).

Logistic regression

Logistic regression is an appropriate technique when dependent variable is dichotomous

and the independent variables are metric or non-metric (Hair et al 2006). Similar to linear

regression, logistic regression may include several predictors. Despite the similarities between

linear regression and logistic regression, it is not advisable to apply linear regression when the

dependent variable is non-metric or categorical. In linear regression analysis, the relationships

between variables are assumed to be linear. However, when the dependent variable is categorical,

the assumption of linearity of relationships is violated (Berry 1993). In this case, literature

suggests transforming the data using the logarithmic transformation which is a way of expressing

a non-linear relationship in a linear way (Field 2009). Similarly, the logistic regression represents

60

the linear regression equation in logarithmic terms (called ‘logit’) and helps to rule out the

violation of the assumption of linearity. Mathematically, a logistic model with several predictors

may be represented with the help of following formula:

�(�) = 11 + !("#$"%&%'$"(&('$____$"*&*')

where P (Y) = probability of occurring of dependent variable (Y)

e = base of natural logarithms

b0 = constant

bi = coefficient attached to predictor Xi

The present study reported findings of the logistic model in terms of odds ratio that is an

indicator of the change in odds resulting from a unit change in the independent variable. The odds

of occurring an event may be defined as the probability of occurring an event divided by the

probability of not occurring that event (Field 2009). Odds ratios less than ‘1’ suggest that increase

in a particular predictor is associated with decrease odds of an event occurring. Whereas odds

ratios more than ‘1’ suggest that increases in a particular predictor is associated with increase

odds of an event occurring. The formulae for calculating odds and odds ratio are given as under:

+,,- = �( . �/�)�(�0 . �/�)

�( . �/�) = 11 + !("#$"%&%'$"(&('$____$"*&*')

�(�0 . �/�) = 1 − �( . �/�) For calculating odds ratio, one calculates the odds after the predictor variable has

changed by one unit. It is easy to calculate the proportionate change in odds by dividing the odds

after a unit change in the predictor by the odds before that change (Field 2009). The formula for

calculating odds ratio is given as follows:

+,,-12/30 = +,--24/ 125�3/6ℎ2�8 3�/ℎ 91 ,36/0101383�2:0,,-

In the present study, the dependent variable was measured with the help of a dichotomous

question by asking respondents to reveal their willingness to purchase selected branded product

within FMCG and durables category. This dichotomized variable took a value of ‘1’ for

61

respondents who were willing to purchase branded product and ‘0’ for those who did not wish to

purchase. Factors generated through exploratory factor analysis were included as independent

variables to examine consumers’ willingness to purchase branded FMCG and branded durable

products. The study incorporated four control variables to rule out possibility of confounding

relationships in the model. First, the study controlled for whether the respondent lives in a rural

area or urban area. Respondents living in rural area took a value of ‘0’ and respondents from

urban areas took the value of ‘1’. Second, the study controlled for gender of the respondent due to

the reason that existing circumstances in subsistence markets significantly decreases the extent to

which female influence overall family decision making (Mair et al 2012). Female consumers

were assigned the value of ‘0’ whereas male consumers were assigned the value of ‘1’. Previous

studies also indicated that age and children in household significantly predict consumers’

evaluations and willingness to purchase products (Sánchez et al 2012; Michaelidou and Hassan

2010). Accordingly, the study also controlled for age and children in the household. Older

respondents with the age more than 35 years took the value of ‘0’; in contrast respondents with

the age 35 or less took the value of ‘1’. Finally, the study assigned ‘0’ to households without child

and ‘1’ to households with child.

3.1.5.3 Data analysis to explore the influence of social network on purchase behavior

The study tested the proposed model (Fig. 2.3) with the application of structural equation

modeling in AMOS 18.0. In SEM, the measurement model was tested for two forms of invariance

viz. configural and metric invariance by a method as mentioned in the preceding section. Further,

this model was diagnosed for assessing reliability and validity among all constructs included in

the study like relationship orientation, similarity, expertise, word-of-mouth, trust and purchase

intention. The study included five control variables namely prior ownership of mobile, age,

education, gender and children in the household. The structural model specified causal paths

between each of the five control variables- and two endogeneous constructs: word-of-mouth and

trust; and one outcome variable: purchase intention. In structural mode, all control variables were

correlated with each other and with the predictor constructs: relationship orientation, similarity

and expertise. Error terms of the endogeneous constructs were mutually correlated in order to

reduce potential bias in the model (Antonakis et al 2010). Further, mediation was analyzed by the

same method mentioned in the preceding section.

For undertaking moderation analysis, a multi-group model in AMOS was created to test

differences between proposed relationships across two subgroups: consumers affiliated to formal

social networks (group 1) and consumers unaffiliated to these social networks (group 2). The full

62

sample (n = 600) and sub-sample of each group (n1 = 290 and n2 = 310) met the minimum

condition of 100 cases required and at least five times the total number of items to test a complex

structural equation model (Bagozzi and Yi 1988). Similarly, z-test was used to examine statistical

differences between structural estimates of the relationships by using a formula mentioned in the

preceding section (Hair et al 2006).

Handling endogeneity in the model

In order to potentially minimize the effect of endogeneity caused by omitted variables,

simultaneity, measurement error and common-method variance, several measures were adopted

as suggested by Antonakis et al (2010). The first two causes of endogeneity were handled by

including relevant control variables in the model that were previously influencing consumers’

purchase behavior (see section 2.3.1.9). Measurement error was not found to be a threat for the

model as estimates of reliability and validity of the measures were found to be acceptable.

Further, common-method variance problem arises where a common scale or common rater is

used and it is likely that source strives to maintain consistency between two types of ratings. To

reduce and diagnose the potential common method bias, the study followed various

recommendations (Podsakoff et al 2003). The constructs in the questionnaire were separated

under apparent headings that provided respondents clarity while rating different statements within

a construct or across various constructs. Harman’s single factor test was used to diagnose

potential common method bias in the data (Podsakoff et al 2003). According to this technique,

exploratory factor analysis is applied to all the statements used for measuring different constructs

in the study and an unrotated factor solution is examined. The results highlighted neither a single

factor nor a general factor accounting for the majority of the variance explained emerged.

Therefore, common method bias was not found to be a problem in the model.

3.2 Managers’ survey

The main aim of the managers’ survey was to study existing marketing mix strategies of

companies for BOP consumers. For accomplishing aforesaid objective, managers of companies

were interviewed with the help of a structured interview schedule (Annexure II). Due to lack of

literature, no existing validated scale could be found; thereby measures used to operationalize the

constructs in the schedule were mainly developed from pre-existing studies. Multi-scale items

were used to design the constructs as single items tend to be less reliable (Churchill, 1979). The

interview schedule contained the statements related to various marketing mix strategies of

companies in India along with some other important issues like customer orientation of the

company, top-management’s commitment towards BOP consumers, performance of the company

63

and managers perceptions about BOP markets. The responses on statements were obtained on a 7

point likert scale (1 for strongly disagree and 7 for strongly agree). Usage of interval scale was

advisable as it allows to apply a number of statistical tools in addition to techniques applicable to

nominal and ordinal scale data (Malhotra 2008). The initial draft of the schedule was screened by

managers of two major MNCs that have considerable presence in BOP markets of India. The

changes suggested by managers were incorporated and interview schedule was thus finalized.

Managers of the companies were contacted face-to-face, by e-mails and telephone calls to take

appointments for scheduling interviews. Managers in some of the companies did not agree to get

interviewed face-to-face, in this case, a web link of the interview schedule was sent to managers

electronically and managers responded to the interview schedule through internet.

As suggested by Saunders et al (2009), some measures were adopted to avoid or to

mitigate potential threats to reliability of the interview schedule. These authors indicated two

types of bias in interviews. First, interviewee bias, that emerges when an interviewee perceives

that he/she must avoid revealing strategic information that cannot be disclosed to people outside

the company. The study attempted to avoid this bias by assuring the selected interviewee that

name of the respondent will be kept anonymous throughout the study. Therefore, any confidential

information provided by the interviewee was not revealed throughout this document. Second,

observer bias often arises when more than one researcher conduct the interviews. In this case the

same information can be asked or interpreted in different ways by different researchers. In the

present study, only one researcher conducted all the interviews that resulted in uniformity in

conducting and achieving a high degree of structured interviews (Saunders et al 2009).

3.2.1 Selection of the companies

In total 50 managers were interviewed to study marketing mix strategies of companies for

BOP consumers. Companies listed on BSE FMCG index and consumer durables index were

selected on the basis of annual sales turnover. Top 30 companies from food and FMCG sector

and top 20 companies from consumer durable sector were selected. There were two main reasons

for selecting these sectors. First, spending a large share of income on food and FMCG points to a

huge market potential of such products in these markets (Prahalad and Hammond 2002;

Hammond et al 2007). Second, durable products like two-wheelers, mobile phones and

refrigerators enhance productivity of BOP consumers (Bang and Joshi 2012). Companies dealing

in business-to-business channels and companies not offering the products for BOP consumers

were not considered to be part of the survey.

64

3.2.2 Data analysis to study marketing mix strategies of selected companies

Managers were requested to rate statements on a 7-point scale measuring their

perceptions towards product strategies, pricing strategies and promotion strategies. Further,

managers were also asked to provide information about company’s collaborations with non-

traditional partners like financial institutions, government institutions, SHGs and NGOs through

which companies distribute products among BOP consumers. Managers’ perceptions towards

BOP markets, customer orientation of the company, top-management’s commitment towards

BOP consumers and performance of the company were also measured on a 7-point scale.

Managers’ perceptions towards effectiveness of promotion mix elements were measure on a 7-

point scale where ‘1’ represented very ineffective and ‘7’ represented very effective. Managers’

perceptions towards BOP markets, product strategies, pricing strategies and promotion strategies

were analyzed by calculating mean scores. Independent samples t-test was used to examine

statistical differences in the mean scores of managers’ perceptions towards customer orientation

of the company, top-management’s commitment towards BOP consumers and company

performance across two sectors viz. food and FMCG; and consumer durables. Similarly,

differences in mean scores of managers’ perceptions towards effectiveness of selected promotion

mix elements were examined by applying independent samples t-test. Further, influence of

marketing mix strategies on company’s performance was examined by using analysis of

covariance.

Analysis of covariance

Analysis of covariance (ANCOVA) was used for examining the influence of marketing

mix on performance of the company. Statements used to measure the constructs like product

strategies, pricing strategies and promotion strategies were develop by reviewing existing

literature related to the study. Product strategies were measured with the help of five items:

• Our company attempts to develop customized products in terms of size, features and

design to suit BOP consumers

• Our company gives special emphasis to co-create products with BOP consumers

• Our company constantly seeks to develop need-satisfying products for BOP consumers

• Our company actively engages with BOP consumers to seek their advice on product

development

• Our company seeks to develop products keeping in view low-literacy of BOP consumers

Pricing strategies were measured by using four items:

• Our company offers low-priced products particularly for BOP consumers

65

• Our company focuses on low-margin high-volume pricing for BOP consumers

• Our company tailors pricing mechanisms to suit each BOP market

• Our company attempts to set price keeping in view low-income of BOP consumers

Promotion strategies of the selected companies were measured by using four items:

• Our company provides a separate promotional budget to encourage sales at BOP

market

• Our company attempts to communicate with BOP consumers in local or regional

language

• Our company attempts to promote products through social networks at BOP

• Our company attempts to promote products through non-traditional/informal media

(NGOs, SHGs etc.) at BOP

Performance of the company was measured with the help of following statements:

• Our company has achieved higher profits than expected

• Our company has been able to attain growth targets

• Our company has been able to attract new customers

• Our company has been able to achieve expected market share.

The mean scores of the statements in the constructs product strategies; pricing strategies;

and promotion strategies were used as covariates whereas mean score of the statements in

‘performance of the company’ was used as a dependent variable in the analysis. Pertinent

literature was extensively reviewed to identify various partners with whom multi-national

corporations collaborate to distribute products among consumers in subsistence markets. The

identified partners were: suppliers, logistic service providers, financial institutions, local retailers,

non-profit organizations, non-government organizations, local communities, self-help groups,

centre government institutions and state government institutions. Respondents were asked to

provide information whether the company has collaborated with these cross-sector partners for

distributing products among BOP consumers. The companies having cross-sector collaborations

with six or more partners were referred to as ‘high-intensity distribution’ and companies having

collaborations with five or less partners were referred to as ‘low-intensity distribution’. The

variable called ‘distribution intensity’ was used as a dichotomous variable which is referred as a

factor in ANCOVA. Companies with low-intensity distribution took the value of ‘0’ and

companies with high-intensity distribution took the value of ‘1’ in the analysis. Levene’s test was

used to check the assumption of homogeneity of error variance across groups. This test examines

66

the null hypothesis that the error variance of the dependent variable is equal across groups. The

rejection of null hypothesis indicates homogeneity of error variance across groups and supports

appropriateness of further application of ANCOVA. Variation inflation factor (VIF) score was

calculated to check the assumption of multi-collinearity among independent variables included in

the analysis.

The effect size of independent variables in ANCOVA may be calculated by using eta

square (η2) which is similar to r2 in linear regression. Eta square is calculated by dividing the

effect of interest by the total amount of variance in the data. However, rather than using eta

square, the present study used a measure called partial eta square (partial η2) for estimating effect

size of independent variables. This differs from eta square in a way that it does not consider the

proportion of total variance explained, but considers the proportion of variance explained by a

variable which is not explained by other variables in the analysis. For instance, partial eta-squared

was used to estimate the proportion of variance in the dependent variable (i.e. performance of the

company) explained by specific covariates (product strategies, pricing strategies and promotion

strategies) and a factor (distribution intensity). The formulae for calculating effect size and partial

effect size are given as below:

; = ��<==>?@��AB@CD

�21/32:; = ��<==>?@��<==>?@ +��E>F�GHCD

where SS = Sum of squares

Effect sizes between 0.01 to 0.06 are considered as small, between 0.06 to 0.13 are

considered as medium, and greater than 0.13 are considered as large (Harlow, 2005). This

measure provided a better interpretation of the p-value for which very low values can be obtained

from large samples as in the present study.

67

CHAPTER IV

RESULTS AND DISCUSSION

The present study was undertaken to achieve the following objectives: to investigate

purchase behaviour of bottom of the pyramid consumers; to examine the willingness of bottom of

the pyramid consumers to purchase branded products; to explore the influence of social networks

on purchase behaviour of bottom of the pyramid consumers; to study existing marketing mix

strategies of companies for bottom of the pyramid consumers; and to recommend changes in

marketing mix strategies for bottom of the pyramid consumers. This chapter presents findings of

the study objective-wise. Section 4.3 reveals findings about purchase behaviour of BOP

consumers; section 4.4 presents findings regarding consumers’ willingness to purchase branded

products. Next, section 4.5 includes findings related to influence of social networks on purchase

behaviour. Section 4.6 focuses on findings of fourth objective that intended to study marketing

mix strategies of companies. Lastly, section 4.7 suggests modifications in marketing mix

strategies that may be useful for managers to offer products among potential consumers in

subsistence markets. Findings of the study have been provided under the following heads:

4.1 Demographic profile of the respondents

4.2 Products owned

4.3 Investigating purchase behaviour of consumers

4.3.1 Sources of information regarding product purchase

4.3.2 Availing credit to purchase products

4.3.3 Frequency of purchase

4.3.4 Sources of purchase of selected products

4.3.5 Expenditure on different product categories

4.3.6 Gender and purchase decisions

4.3.7 Consumer choice towards durable products

4.4 Willingness to purchase branded products

4.4.1 Consumer choice towards branded food

4.4.2 Factors affecting the purchase of branded FMCG

4.4.3 Willingness to purchase branded FMCG

4.4.4 Factors affecting the purchase of branded durable products

4.4.5 Willingness to purchase branded durable products

68

4.5 Influence of social networks on purchase behaviour

4.5.1 Affiliation to different types of social networks

4.5.2 Influence of social networks on purchase behaviour

4.6 Marketing mix of selected companies

4.6.1 Profile of the selected companies

4.6.2 Reliability of the scales

4.6.3 Managers’ perceptions towards BOP market

4.6.4 Managerial perceptions towards marketing mix strategies

4.6.5 Distribution strategy of the companies

4.6.6 Managers’ perceptions towards customer orientation, top-management’s

commitment and company performance

4.6.7 Managerial perceptions towards effectiveness of promotion mix elements

4.6.8 Influence of marketing mix strategies on company’s performance

4.7 Suggested modifications in marketing mix

4.1 Demographic profile of the respondents

The respondents were asked to provide information about several demographic variables

like gender, age, education level, type of occupation, income level, number of adults and number

of children (less than 18 years) in the household. The findings in this regard (table 4.1) show that

sample included slightly higher proportion of males than females (i.e. about 54 per cent and 46

per cent respectively). About 37 per cent of the respondents were found to be in the age category

of 36-50 years; whereas nearly 35 per cent of the respondents were in the age category of 26-35

years; and only 6.5 per cent of the respondents in the age category of more than 50 years.

Educational profile of the respondents reveals that majority (55 per cent) were below

matriculation; whereas 30 per cent of the respondents were found to be matriculate. A few (12 per

cent) respondents were having education up to senior secondary level and only about two per cent

of the respondents were graduates. Maximum (43 per cent) of the respondents were found to be

daily wagers; whereas 27 per cent of the respondents were house-wives. A few (nine per cent)

respondents were found to be small entrepreneurs. Income profile of the respondents highlight

that majority (54 per cent) of the households had monthly income between INR 6,001-8,000;

whereas 45 per cent of the households were found to have monthly income between

INR 4,001-6,000. Surprisingly, only one household was found to have monthly income between

INR 2,000-4,000. Further, a large majority of the households (83 per cent) were found to have

four or less adults. More than 90 per cent of the households were found to have upto three

69

children in the household; whereas about nine per cent of the households were found to have 4-6

children in the household.

Table 4.1: Demographic profile of the respondents

Category Frequency

(n=600) Per cent

Gender Male

Female

326

274

54.3

45.7

Age (in years)

18-25

26-35

36-50

more than 50

131

208

222

39

21.8

34.7

37.0

6.5

Area Rural

Urban

400

200

66.7

33.3

Education

Below higher secondary

Higher secondary

Senior secondary

Graduate

Post graduate

332

179

72

14

03

55.3

29.8

12.0

2.3

0.5

Occupation

Student

Daily wagers

House-wife

Small entrepreneurs

Other

32

258

163

52

95

5.3

43.0

27.2

8.7

15.8

Monthly income

INR 2,000-4,000

INR 4,001-6,000

INR 6,001-8,000

01

273

326

0.2

45.5

54.3

Number of adults in household

4 or less

5-7

More than 7

496

98

06

82.7

16.3

1.0

Number of children in household

Upto 3

4-6

More than 6

545

53

02

90.8

8.8

0.3

70

4.2 Products owned

Respondents were enquired about various products owned by them. The penetration of

products like two-wheeler, refrigerator, television, mobile, LPG stove and air-cooler is expected

to differ in rural and urban areas due to variation in income-level and availability of products

across these areas. Therefore, an attempt was made to present the findings about products owned

by households with respect to rural and urban areas.

Table 4.2: Products owned by households

Product owned

Frequency

Rural (n=400) Urban (n=200) Total (n=600)

Yes No Yes No Yes No

Mobile handset 333 (83.2) 67 (16.8) 172 (86.0) 28 (14.0) 505 (84.2) 95 (15.8)

Television 319 (79.8) 81 (20.2) 171 (85.5) 29 (14.5) 490 (81.7) 110 (18.3)

Refrigerator 101 (25.2) 299 (74.8) 48 (24.0) 152 (76.0) 149 (24.8) 451 (75.2)

Two-wheeler 38 (9.5) 362 (90.5) 09 (4.5) 191 (95.5) 47 (7.8) 553 (92.2)

LPG stove 209 (52.2) 191 (47.8) 125 (62.5) 75 (37.5) 334 (55.7) 266 (44.3)

Air-cooler 60 (15.0) 340 (85.0) 29 (14.5) 171 (85.5) 89 (14.8) 511 (85.2)

Note: Values in parentheses represent percentage

Findings (table 4.2) revealed that a large majority (84 per cent) of the households owned

mobile handsets. Findings also indicated some differences in product ownership for rural and

urban households. For instance, a higher proportion of urban households (86 per cent) were found

to own mobile handsets; whereas 83 per cent of rural households were found to own mobile

handsets. Similarly, about 86 per cent of the urban households were found to own television;

whereas nearly 80 per cent of the rural households owned television. About one fourth of the

households were found to own refrigerator; and a few of the households (about eight per cent)

owned two-wheelers. Further, majority of the households (55 per cent) owned LPG stoves.

Approximately 44 per cent of the households were not found to own a LPG stove; such

households may be using conventional sources like ‘chula’25 for cooking food. Study also found

that about 62 per cent of the urban households owned a LPG stove; whereas only 52 of the rural

households owned a LPG stove. Surprisingly, a higher number of rural households were found to

25 A type of cooking stove which is heated by burning wood, charcoal, animal dung or crop residue

71

own two-wheelers than urban households. This may be due the fact that road and transport

infrastructure in rural area is not as good as in urban areas, and rural consumers also have to

cover large distances to reach urban areas for finding job opportunities. Therefore, rural

consumers need a two-wheeler to meet their daily needs.

4.3 Investigating purchase behaviour of consumers

The first objective of the present study was to investigate purchase behaviour of bottom

of the pyramid consumers. The purchase behavior of such consumers is likely to differ from top

of the pyramid consumers due to low-income, limited literacy standards, little access to formal

markets and limited social development. These consumers also have a unique culture and a

greater degree of collectivism among community members that helps to cope up with persisting

poverty constraints. Such circumstances render BOP consumers to use new products and services

in a way dissimilar with privileged consumers. There has been limited research on understanding

purchase behavior of BOP consumers in India. Therefore, an attempt has been made to add new

insights into the existing conceptual and empirical understanding on purchase behavior of BOP

consumers in India. This section presents empirical findings of a consumer survey in which

respondents were enquired about sources of information regarding product purchase, availing

credit to purchase products, frequency of purchase, sources for purchasing products, expenditure

pattern, gender and purchase decisions and consumer choice towards durable products. Findings

in this regard have been presented as below:

4.3.1 Sources of information regarding product purchase

For making a good product purchase, consumers tend to obtain information about the

underlying product from several sources like advertisements in TV, radio and print media. There

is a partial penetration of electronic and print media in subsistence markets; therefore,

disadvantaged consumers tend to obtain purchase related information from different sources like

members of the social networks, friends and family members, and local nearby retailers. During

the survey, respondents were requested to rank selected sources of information regarding product

purchase where ‘1’ represented the most preferred source of information and following rank

represented a less preferred source. Mean score of the ranks given by respondents was calculated

and the least mean score represented the most preferred source of information for purchasing

products in a specific category. In contrast, the maximum mean score represented the least

preferred source of information. Findings in this regard have been presented below:

72

Table 4.3: Sources of information regarding product purchase

Sources Mean Rank

Food and FMCG Preference Durable products Preference

Members of the social networks 2.54 (0.96) 2 1.18 (0.47) 1

Friends and family members 3.71 (1.03) 4 3.77 (1.04) 4

Television advertisements 2.91 (1.25) 3 2.73 (1.19) 3

Radio advertisings 4.64 (0.92) 7 4.29 (0.91) 6

Print media 4.34 (0.92) 6 4.31 (0.82) 7

Point-of-purchase display 4.03 (1.03) 5 4.28 (0.77) 5

Retailers' recommendations 1.38 (0.71) 1 2.66 (0.80) 2

Note: Values in parentheses represent standard deviation

Findings (table 4.3) indicated differences in the consumers’ preference for purchasing

products in selected categories like food and FMCG; and durable products. For example,

‘retailer’s recommendations’ emerged as the most preferred source of information for purchasing

food and FMCG; however ‘members of the social networks’ were found to be the most preferred

source of information for purchasing durable products. Further, respondents ranked ‘members of

the social networks’ as the second most preferred source of information for purchasing food and

FMCG. For this product segment, ‘television advertisements’ were found to be the third most

preferred source of information. This finding is in line with the fact that due to intermittent

electricity supply and low-penetration of electronic media, such consumers have less exposure to

television advertisements. For purchasing durable products, ‘retailer’s recommendations’ were

found to be the second most preferred source of information; whereas ‘television advertisements’

were found to be the third most preferred source of information. Based on these findings,

companies offering such products in BOP markets may motivate members of the social networks

and local retailers to provide product related information to potential customers. The information,

thus, provided may be help BOP consumers to purchase a specific brand in given category.

4.3.2 Availing credit to purchase products

Due to low-affordability, majority of the subsistence consumers are unable to purchase

products in cash. Such economically disadvantaged consumers tend to purchase products on

credit from local shopkeepers; sometimes they also borrow money from members of the social

networks and micro-finance institutions for purchasing durable products. Some consumers may

also prefer to purchase products on cash because shopkeepers or money lenders charge a higher

73

rate of interest from these consumers. Respondents were enquired about their most preferred

source for availing credit to purchase products in food and FMCG; and consumer durables

category. Findings in this regard have been presented below:

Table 4.4: Sources for availing credit to purchase products

Sources for availing credit

Food and FMCG Consumer durables

Rural (n=400)

Urban (n=200)

Total (n=600)

Rural (n=400)

Urban (n=200)

Total (n=600)

Members of the social networks

1 (0.2) 2 (1.0) 3 (0.5) 112 (28.0) 48 (24.0) 160 (26.7)

Local retailers 258 (64.5) 126 (63.0) 384 (64.0) 217 (54.2) 119 (59.5) 336 (56.0)

Local money lenders

--- --- --- 10 (2.5) 4 (2.0) 14 (2.3)

Do not avail credit 141 (35.2) 72 (36.0) 213 (35.5) 61 (15.2) 29 (14.5) 90 (15.0)

Note: Values in parentheses represent percentage

Findings (table 4.4) highlight that majority (64 per cent) of the respondents preferred to

avail credit from local retailers for purchasing products in food and FMCG category. About 35

per cent of the respondents did not prefer to avail credit to purchase products in food and FMCG

category. Findings also highlight that a marginal proportion of respondents (0.5 per cent)

preferred to avail credit from members of the social networks to purchase products in food and

FMCG category. Further, local retailers also emerged as the most preferred source for availing

credit to purchase durable products. More than one fourth of the respondents preferred to avail

credit from members of the social networks for purchasing consumer durable products. A higher

proportion of rural consumers (28 per cent) than urban consumers (24 per cent) preferred to avail

credit from ‘members of the social networks’ for purchasing consumer durable products. A few

(two per cent) respondents preferred to avail credit from ‘local money lenders’ for purchasing

consumer durable products. This may be due to high rate of interest being charged by local

money lenders from illiterate consumers in BOP markets. Overall, 65 per cent of the respondents

preferred to purchase products in food and FMCG category on credit whereas a large majority

(85 per cent) preferred to purchase durable products on credit.

4.3.3 Frequency of purchase

Majority of the bottom of the pyramid consumers are daily-wagers; therefore such

consumers buy lesser quantity of products but more frequently. Companies operating in

subsistence markets have launched small packs of products for low-income consumers. For

instance, for the first time, dairy major Parag Milk Foods Private Limited introduced ‘ghee’ in

74

sachets of 18ml and 9ml at INR 20 and INR 10 respectively. BOP consumers prefer to purchase

small packs that offer an opportunity to purchase high-quality products with a minimum cash

outlay. Respondents were enquired about their purchase frequency for different products in food,

and FMCG category. Findings in this regard have been presented below:

Table 4.5: Purchase frequency for selected food products

Products Daily Once a week

Fortnightly Once a month

Do not purchase

Cereals --- 511 (85.2) 50 (8.3) 39 (6.5) ---

Dairy products 364 (60.7) 208 (34.6) --- --- 28 (4.7)

Fruits and vegetables 18 (3.0) 582 (97.0) --- --- ---

Bakery products 7 (1.2) 246 (41.0) 102 (17.0) 111 (18.5) 134 (22.3)

Meat and related products

--- 34 (5.6) 55 (9.2) 226 (37.7) 285 (47.5)

Grocery products --- 513 (85.5) 59 (9.8) 28 (4.7) ---

Note: Values in parentheses represent percentage

Findings (table 4.5) reveal the purchase frequency of products under consideration. A

large majority (85 per cent) of the respondents purchased cereals once a week; this finding

indicates that BOP consumers recoup their requirements more frequently due to scarce

availability of wages. About nine per cent of the respondents purchased cereals fortnightly;

whereas only six per cent of respondents purchased cereals once a month. It is worth to note that

majority (61 per cent approximately) of the respondents purchased dairy products daily; whereas

35 per cent of the respondents purchased dairy products once a week. About five per cent of the

respondents did not purchase dairy products, possibly such consumers lack sufficient funds to

purchase such products. Further, a large majority of the respondents (97 per cent) were found to

purchase fruits and vegetables once a week. Approximately, 22 per cent of the respondents did

not purchase bakery products. Similarly, about 48 per cent of the respondents did not purchase

meat and related products. A sizeable chunk of respondents (38 per cent) purchased meat and

related products once a month. A large majority of the respondents (85 per cent) purchased

grocery products like sugar, tea and spices once a week.

75

Table 4.6: Purchase frequency for selected FMCG

Products Once a week Fortnightly Once a month Do not

purchase

Soaps 510 (85.0) 69 (11.5) 21 (3.5) ---

Detergents 509 (84.8) 71 (11.8) 20 (3.3) ---

Shampoos 520 (86.7) 61 (10.2) 19 (3.2) ---

Beauty products 15 (2.5) 55 (9.2) 232 (38.7) 298 (49.7)

Note: Values in parentheses represent percentage

Findings (table 4.6) indicate that respondents followed a similar purchase frequency for

different fast-moving consumer goods except beauty products. For instance, majority of the

respondents purchased soaps, detergents and shampoos once a week. A large majority (85 per

cent) of the respondents purchased soaps once a week; about 85 per cent of the respondents

purchased detergents once a week and about 87 per cent of the respondents purchased shampoos

once a week. Surprisingly, about 50 per cent of the respondents did not purchase beauty products

as such consumers may not be left with adequate amount of money after meeting basic food

requirements. However, a sizeable chunk (about 39 per cent) purchased beauty products once a

month; whereas about 10 per cent of the respondents purchased beauty products fortnightly.

4.3.4 Sources of purchase of selected products

Respondents were enquired about various sources of purchase of selected products in

food, FMCG and durables category. Findings in this regard have been presented below:

Table 4.7: Sources of purchase of selected food products*

Products Local nearby

shops Street

vendors Government

depots Local mandis

Cereals 589 03 378 10

Dairy products 476 175 --- ---

Fruits and vegetables 388 549 --- 138

Bakery products 458 91 --- 10

Meat and related products 306 06 --- 28

Grocery products 591 87 --- 32

Note: *Multiple response

76

Findings (table 4.7) indicate that maximum respondents purchased selected food products

from local nearby shops, except for fruits and vegetables. Such cash-strapped consumers purchase

food products from local shops due to convenience and credit offered to them. Majority of the

respondents (589) purchased cereals from local nearby shops; whereas 378 of the respondents

purchased cereals from government depots as low-income consumers in India are provided

cereals for free or at subsidized rates through such depots. Further, maximum 476 respondents

purchased dairy products from local nearby shops; whereas 175 respondents purchased dairy

products from street vendors. Majority (550) of the respondents purchased fruits and vegetables

from street vendors; whereas 388 respondents purchased fruits and vegetables from local shops.

A large majority (458) of the respondents purchased bakery products local shops. Also, a large

majority of the respondents (591) purchased grocery products from nearby shops.

Table 4.8: Sources of purchase of selected FMCG*

Products Local nearby shops Street vendors Local mandis

Soaps 576 237 26

Detergents 576 141 20

Shampoos 581 106 16

Beauty products 242 202 12

Note: *Multiple response

Findings (table 4.8) highlight that maximum respondents purchased selected FMCG from

local nearby shops. A large majority (576) of the respondents were found to purchase soaps from

local nearby shops; whereas a sizeable number (237) of respondents purchased soaps from street

vendors. Maximum (576) of the respondents purchased detergents from local nearby shops;

whereas 141 respondents purchased detergents from street vendors. A large majority (581) of the

respondents were found to purchase shampoos from local shops; whereas 106 respondents

purchased shampoos from street vendors. Majority (242) of the respondents purchased beauty

products from local nearby shops; a sizeable chunk (202) of the respondents purchased beauty

products from street vendors. A few (12) respondents were found to purchase beauty products

from local mandis.

Findings related to sources of purchase of different durable products (table 4.9) reveal

that about 50 per cent of respondents purchased a second-hand two-wheeler. Possibly, such

consumers need to commute long distances as their place of work is usually far from their area of

residence where public transportation system remains inaccessible. Due to insufficient funds to

77

purchase a new two-wheeler, these consumers may purchase a second-hand two-wheeler. A large

majority (76 per cent) of the respondents was found to purchase mobile handsets from local

nearby shops; whereas a sizeable proportion (about 20 per cent) purchased second-hand mobile

phones. Further, about 48 per cent of the respondents purchased television from local nearby

shops; whereas about one fourth of respondents purchased television from exclusive showrooms.

Results also highlighted that maximum proportion (46 per cent) of respondents purchased second-

hand refrigerators. A large majority (75 per cent) of the respondents was found to purchase LPG

stoves from local nearby shops.

Table 4.9: Sources of purchase of selected durable products

Products Local nearby shop Exclusive showroom Second-hand

Two-wheeler (n=47)

9 (19.1) 15 (31.9) 23 (49.0)

Mobile (n=505)

386 (76.4) 22 (4.4) 97 (19.2)

Television (n=490)

233 (47.5) 126 (25.7) 131 (26.8)

Refrigerator (n=149)

30 (20.2) 50 (33.5) 69 (46.3)

LPG stove (n=334)

249 (74.6) 22 (6.6) 63 (18.8)

Air-cooler (n=89)

54 (60.7) 12 (13.5) 23 (25.8)

Note: Values in parentheses represent percentage

4.3.5 Expenditure on selected product categories

Respondents were requested to mention the percentage of their income they spend on

selected product categories like food and FMCG. Findings in this regard (table 4.10) suggest that

majority of the respondents (58 per cent) spent between 61-80 percent of their income on food

products. Nearly 30 per cent of the respondents were found to spend between 41-60 per cent of

their income on food products; whereas approximately 12 per cent of the respondents spent more

than 80 per cent of their income on food products. Further, a large majority (72 per cent) was

found to spend less than 20 per cent of their income on FMCG; whereas about 28 per cent of the

respondents spent between 21-40 per cent of their income on fast moving consumer goods.

78

Table 4.10: Expenditure on selected product categories

Expenditure (%) Food FMCG

Below 20 --- 433 (72.2)

21-40 --- 167 (27.8)

41-60 176 (29.3) ---

61-80 350 (58.3) ---

More than 80 74 (12.3) ---

Note: Values in parentheses represent percentage

4.3.6 Role of gender in purchase decisions

Gender plays an important role in purchase related decisions in the households. Studies

have provided empirical evidence regarding significant differences in consumer preferences to

purchase products due to gender (Noble et al 2006). Role of male and female consumers in

purchasing products differs as per the cultural norms of the society and type of the product being

purchased. For example, purchase of durable products like mobiles and televisions have been

assumed to be male dominated; whereas repeat purchases of non-durable products have been

assumed to be wife dominated (Sproles and Kendall 1986). Empirical studies highlighting gender

differences in purchase decisions with respect to BOP consumers in India remain nearly

nonexistent. Respondents were requested to rate a 5-point scale (where 1– only male decides, 2 –

male decides more than female, 3 – male and female both equally decide, 4 – female decides

more than male and 5 – only female decides) regarding purchase decisions for selected product

categories. Mean scores for purchase decisions were calculated and one-sample t-test was used to

examine the differences between mean scores and scale mid-value ‘3’.

Findings (table 4.11) highlighted some differences in purchase decisions taken by male

and female in the households with respect to selected product categories. The mean scores for the

purchase decisions related to food and FMCG were found to be significantly greater than the mid-

value ‘3’. This finding implies that female members play a greater role in purchasing low-

involvement products in food and FMCG category. This may happen due to the fact that females

in subsistence markets have fewer opportunities to work and they share greater responsibility of

purchasing low-involvement products. However, male members were found to take purchase

related decisions more than female members with respect to durable products. This finding

indicates that male members in BOP households have more influence on purchasing high-

79

involvement durable products. The reason may be that females in subsistence markets lack

control over financial resources and are restricted by social norms to travel independently.

Table 4.11: Purchase decisions for selected product categories

Purchase decisions Mean score (Food and FMCG)

p-value Mean score

(consumer durables) p-value

When to buy 3.82 (1.17) 0.000 1.64 (0.74) 0.000

From where to buy 3.76 (1.17) 0.000 1.59 (0.71) 0.000

How much to buy 3.89 (0.84) 0.000 2.20 (0.81) 0.000

How frequently to buy 3.77 (0.81) 0.000 2.32 (0.82) 0.000

Which brand to buy 3.29 (0.68) 0.000 2.79 (0.77) 0.000

At what price to buy 3.25 (0.83) 0.000 2.33 (0.83) 0.000

How to buy (cash or credit) 3.25 (0.96) 0.000 2.01 (0.79) 0.000

Budget of buying 3.30 (0.91) 0.000 2.06 (0.65) 0.000

Overall decision for buying 3.49 (0.76) 0.000 2.15 (0.47) 0.000

Note: Values in parentheses represent standard deviation; Mean score calculated on 5-point scale where 1– only male decides, 2 – male decides more than female, 3 – male and female both equally decide, 4 – female decides more than male and 5 – only female decides.

4.3.7 Consumer choice towards durable products

Consumer choice towards durable products has been found to be influenced by a number

of factors. Due to persisting constraints like low-income and limited knowledge, BOP consumer

choice towards high-involvement products becomes more complex. Still, there is limited

penetration of durable products among BOP consumers of India that indicates at a big potential

for such products in these markets (Singhal 2008). In emerging markets like India, there exists a

significant divide among rural and urban consumers. NCAER’s National Survey on Household

Income and Expenditure provided an interesting insight on this issue by reporting that average

household income in urban areas is almost double that in rural area. The economic disparity

among rural and urban regions of the country is considerable, given the fact that average monthly

per capita expenditure of urban consumers (INR 2,477) has been estimated to be double than the

rural consumers (INR 1,287) in India (Anonymous 2013). Further, average household investment

in financial assets in urban areas is three times that in rural areas (Ghosh et al 2013). In urban

areas of the emerging markets, retail and transport infrastructure including roads, railways and

airports have been developed more than the rural areas (Uncles et al 2010; Tanusondjaja et al

2015). However, rural areas in these markets till lack basic amenities like nutritional food, proper

80

sanitation and uninterrupted electricity. Rural consumers demonstrate a collectivist behavior

unlike their urban counterparts that show an individualistic behavior (Triandis 1993; McCarty and

Shrum 1994). Such socio-economic differences in rural and urban areas also influence Indian

consumers’ preferences, goals and aspirations to purchase products (Sinha 1994). Due to relative

low-income, rural consumers in India prefer to purchase small packs of products; for instance 86

per cent of total shampoo sales in rural areas come in the form of sachets, however this figure is

69 per cent in urban areas (Mahalingam 2007).

In emerging markets like China, rural and urban consumers were found to have different

attitude towards products, distribution and brand names. As a result of these differences, rural and

urban consumers were also found to use different products that reflect the improvement in their

living standards (Sun and Wu 2004). Keeping in view such differences in rural-urban

circumstances, the present study aims to provide evidence on how rural consumers’ purchase

behavior differs from their urban counterparts with respect to durable purchase. The potential

differences in purchase behavior would offer new insights to MNCs wishing to cater to rural

consumers of the country. Due to poor road infrastructure and inadequate public transport system

in BOP markets of the country, durable product like two-wheelers (bike or motor-cycle) are likely

to enhance productivity of disadvantaged consumers (Bang and Joshi 2012). Ownership of two-

wheelers among subsistence consumers help to uplift their social status and save time in

undertaking day-to-day activities. The present study, therefore, selected two-wheeler to examine

consumer choice towards durable products.

The present applied theory of planned behavior (Ajzen 1991) to examine consumer

choice towards durable products. Theory of planned behavior posits purchase intention as a

function of attitude, subjective norms and perceived behavioral control. The attitude measures the

extent to which a person displays a favorable or unfavorable evaluation of the behavior which in

turn is predicted by sum of the products of person’s behavioral beliefs (bi) and subjective

evaluation of the desirability of outcomes (ei).26 Subjective norms represent the perceived social

pressure to perform or not to perform a certain behavior that is formed as the individual's

normative beliefs (nbj) concerning a particular referent weighted by motivation to comply (mcj)

with that referent. Finally, perceived behavioral control may be conceptualized as the perceived

easiness or difficulty of performing a behavior which is equated with individual's control beliefs

(cbk) weighted by the perceived facilitation (pfk) in either inhibiting or facilitating the behavior

(Ajzen, 1991). The present study proposed hypotheses that have been mentioned in the table 4.12:

26 refer to Annexure I

81

Table 4.12: Hypotheses proposed to test the model ‘consumer choice towards durable products’

Hypotheses Relationship

Consumers with a higher attitude towards durable products will have a higher intention to purchase durable products

ATT � PI (+)

Consumers perceiving durable products as more useful will have a positive attitude towards durable products

PU � ATT (+)

Consumers perceiving more social pressure to buy durable products will have a higher intention to purchase durable products

SN � PI (+)

Consumers with higher normative beliefs will perceive higher social pressure to purchase durable products

NB � SN (+)

Consumers perceiving higher control to buy durable products will have a higher intention to purchase durable products

PBC � PI (+)

Consumers perceiving durable products as more easily available will have a higher control over the purchase of durable products

PAVA � PBC (+)

Consumers perceiving durable products as more affordable will have a higher control over the purchase of durable products

PAFF � PBC (+)

Consumers with higher awareness towards durable products will have a higher control over the purchase of durable products

PAWAR � PBC (+)

Note: ‘+’ sign shows a linear positive relationship among variables; ATT, attitude; PI, purchase intention; PU, perceived usefulness; SN, subjective norms; NB, normative beliefs; PBC, perceived behavioral control; PAVA, perceived availability; PAFF, perceived affordability; and PAWAR, perceived awareness

Drawing on these hypotheses, the present study developed a conceptual model (Fig. 4.1)

that was tested by using structural equation modeling in AMOS. In this section, an attempt has

been made to present findings of the aforementioned model. The statistical differences in mean

scores of various constructs with respect to rural and urban consumers were examined by using

independent samples t-test. Measurement model was tested for invariance across rural and urban

consumers; reliability and validity of the model was also examined. Structural model represented

results the proposed hypotheses. The model was also examined for mediation in the paths and

statistical differences in the proposed relationships were diagnosed through a multi-group

moderation analysis in SEM.

82

Fig. 4.1: The conceptual model for examining ‘consumer choice towards durable products’

Descriptive statistics of the model ‘consumer choice towards durable products’

Mean scores of constructs were calculated by averaging the consumers’ response towards

various statements in the respective constructs. Independent samples t-test was used to examine

statistical differences in mean scores of constructs with respect to rural and urban consumers.

Findings (table 4.13) highlight that all inter-group differences were significant at 5 per cent level

of significance. Urban consumers were found to perceive durable products as more useful for

them than rural consumers. Also, urban consumers were found to have a higher attitude towards

durable products and perceived higher social pressure to purchase durable products in comparison

to rural consumers. Rural consumers perceived lesser control over purchasing durable products in

contrast to urban consumers. This may be due to low-income of rural consumers in comparison to

urban consumers. Urban consumers perceived higher availability, affordability and awareness

toward durable products. Further, urban consumers were also found to have a higher intention to

purchase durable products than rural consumers.

Perceived availability

Control variables: Age

Gender Education

Children in household

Moderator: Rural/ Urban

Perceived behavioral control

Attitude

Subjective norms

Perceived affordability

Perceived awareness

Normative beliefs

Perceived usefulness

Purchase intention

83

Table 4.13: Consumers’ perceptions towards various constructs for purchase of durable products

Rural (n = 400)

Urban (n = 200)

Mean comparison

(t value)

Perceived usefulnessa 12.72 (3.69) 13.36 (3.60) 2.02*

Normative beliefsa 14.06 (3.49) 14.94 (3.87) 2.81*

Perceived availabilitya 12.49 (3.58) 13.21 (2.95) 2.59*

Perceived affordabilitya 12.42 (3.78) 13.31 (3.53) 2.83*

Perceived awarenessa 15.22 (2.08) 15.71 (2.16) 2.62*

Attitudeb 3.24 (0.77) 3.40 (0.77) 2.43*

Subjective normsb 3.48 (0.99) 3.73 (0.92) 2.99*

Perceived behavioral controlb 3.59 (1.22) 3.81 (1.13) 2.09*

Purchase intentionb 3.17 (0.80) 3.39 (0.75) 3.21*

Note: aScale values range from 1 to 25; bScale values range from 1 to 5; *significant at p < 0.05; Standard deviations are shown in parentheses; Means and standard deviations represent values of summated scales

Measurement analysis of the model ‘consumer choice towards durable products’

The measurement model was tested for two forms of invariance such as configural and

metric invariance. Configural invariance refers to same pattern of factor loadings of measurement

items across two groups (Steenkamp and Baumgartner 1998). It was diagnosed by conducting

confirmatory factor analysis without constraint in which all factors were set free across two

subgroups viz. rural and urban consumers. Results indicated an acceptable model fit with

X2 = 1513.282, p < 0.001, d.o.f = 796, X2/d.o.f = 1.901 ≤ 3 (Hair et al 2006); GFI = 0.869 ≥ 0.8,

AGFI = 0.836 ≥ 0.8 (Baumgartner and Homburg 1996). However, authors also argue that values

of GFI and AGFI should be greater than 0.9; but recent development of other fit indices like CFI,

TLI and RMSEA has lead to a decline in the use of GFI and AGFI (Hair et al 2006). Other fit

measures of the model were CFI = 0.956 ≥ 0.9 (Hair et al 2006); and RMSEA = 0.039 ≤ 0.08

(Steiger 1990). The results also highlighted all factor loadings to be statistically significant across

two subgroups. The above evidence indicates measurement model to be configural invariant. The

second form of invariance, metric invariance, implies that variables are measured according to the

same scale intervals across two groups (Steenkamp and Baumgartner 1998). The full metric

invariance was diagnosed by comparing model fit with factor loadings constrained to be equal

across two groups, to previously tested configural invariance model. The results indicated an

insignificant change in the fit measures (∆X2 = 26.4, ∆d.o.f = 31, p = 0.702). Therefore,

measurement model was also found to be fully metric invariant. The other fit indices like GFI,

84

AGFI, CFI and RMSEA were found to be within the respective threshold values. Thus,

differences between the proposed relationships, if any, will be due to the causal relationships

themselves and not to the measurement of constructs.

Further, the measurement model was tested for validity and reliability through

confirmatory factor analysis. Findings (table 4.14) indicated values of composite reliabilities

(CR) higher than the minimum threshold value of 0.70 (Nunnally 1978), that highlighted a good

measure of internal consistency. All average variance extracted (AVE) values were found to be

greater than 0.5; and each statement had a factor loading more than 0.5 on the expected constructs

that confirmed convergent validity among constructs (Fornell and Larcker 1981). The correlations

among constructs were lower than the square root of average variance extracted; which confirmed

discriminant validity (Fornell and Larcker 1981). The above estimates of reliability and validity

highlighted that the constructs were robust to potential bias due to measurement error. Further,

results indicated an acceptable fit of the measurement model with X2 = 1094.133, p < 0.001, d.o.f

= 398, X2/d.o.f = 2.749 ≤ 3; GFI = 0.904 ≥ 0.8, AGFI = 0.881 ≥ 0.8; CFI = 0.957 ≥ 0.9; and

RMSEA = 0.054 ≤ 0.08.

Structural analysis of the model ‘consumer choice towards durable products’

The structural model indicated an acceptable fit with X2 = 4174.244, p < 0.001, d.o.f =

1512, X2/d.o.f = 2.761 ≤ 3; GFI = .855 ≥ 0.8; CFI = 0.920 ≥ 0.9; IFI = 0.921 ≥ 0.9; and RMSEA

= 0.038 ≤ 0.08. The results highlighted a marginal influence of control variables on four

endogeneous constructs viz. attitude, subjective norms, perceived behavioral control and purchase

intention. Effect of age on attitude (βstd = -0.13, p ≤ 0.05), subjective norms (βstd = -0.333, p ≤

0.001), perceived behavioral control (βstd = -0.084, p ≤ 0.05), and purchase intention (βstd = -

0.089, p ≤ 0.05) was found to be significant. This result demonstrates that younger consumers

have more positive attitude towards durable products, they perceive higher social pressure, have

greater control, and have higher intention to purchase durable products. Education also emerged

as a significant predictor of attitude (βstd = 0.082, p = 0.041) and subjective norms (βstd = 0.08, p =

0.023). It implies that uneducated consumers have less positive attitude and they also perceive

low social pressure while purchasing durable products. However, two other control variables:

gender and children in household indicated an insignificant effect on attitude, subjective norms,

perceived behavioral control and purchase intention.

85

Table 4.14: Reliability and validity statistics of the model ‘consumer choice towards durable products’

CR AVE ATT NB PU PAVA PAFF PAWAR PI SN PBC

ATT 0.853 0.593 0.770

NB 0.964 0.870 0.590 0.933

PU 0.903 0.757 0.610 0.562 0.870

PAVA 0.852 0.658 0.611 0.443 0.643 0.811

PAFF 0.916 0.731 0.546 0.535 0.716 0.647 0.855

PAWAR 0.877 0.704 0.174 0.063 0.097 0.102 0.067 0.839

PI 0.861 0.673 0.642 0.565 0.695 0.655 0.715 0.116 0.820

SN 0.919 0.743 0.678 0.570 0.767 0.757 0.777 0.084 0.785 0.862

PBC 0.911 0.773 0.648 0.557 0.761 0.702 0.790 0.117 0.794 0.831 0.879

Note: Bold numbers on the diagonal are square root of AVE; and off-diagonal values represent correlation between constructs. CR, composite reliability; AVE, average variance extracted; SN, subjective norms; NB, normative beliefs; PU, perceived usefulness; PAVA, perceived availability; PAFF, perceived affordability; PAWAR, perceived awareness; PBC, perceived behavioral control; ATT, attitude; and PI, purchase intention

86

Structural model results (table 4.15) supported that consumers with a higher attitude

towards durable products have a higher intention to purchase durable products. The study also

establishes that consumers perceiving durable products as more useful revealed a greater attitude

towards such products. The study expected that consumers perceiving higher social pressure to

purchase durable products would have a higher intention to purchase durable products. This

expectation was also confirmed. Findings indicated that consumers with higher normative beliefs

perceived greater social pressure to purchase durable products. Further, consumers perceiving

greater control to buy durable products were found to have a higher intention to purchase these

products. Also, consumers perceiving durable products as more easily available revealed a greater

control over the purchase of durable products. The study anticipated that consumers perceiving

durable products as more affordable would have a greater control over the purchase of durable

products. This prediction was also confirmed. Finally, the study finds that the consumers with

higher awareness towards durable products did not have a higher control over the purchase of

durable products.

Mediation analysis of the model ‘consumer choice towards durable products’

Mediation occurs if the effect of an independent variable (X1) on dependent variable (Y)

is partly or entirely transmitted through another variable (say X2). A mediated path involves a

causal sequence; first, X1 influences X2; and then X2 influences Y. Mediation of attitude,

subjective norms and perceived behavioral control in the paths from their respective antecedents

to purchase intention (Fig. 4.1), was examined through bias-corrected bootstrap confidence

Table 4.15: Structural results of the model ‘consumer choice towards durable products’

Relationship Estimate t-value Relationship

support ATT � PI (+) 0.181 4.373* Yes

PU � ATT (+) 0.577 12.587* Yes

SN � PI (+) 0.400 9.711* Yes

NB � SN (+) 0.467 13.521* Yes

PBC � PI (+) 0.496 11.155* Yes

PAVA � PBC (+) 0.314 7.049* Yes

PAFF � PBC (+) 0.578 13.226* Yes

PAWAR � PBC (+) 0.038 1.350 No

Note: *p < 0.001, ATT, attitude; PI, purchase intention; PU, perceived usefulness; SN, subjective norms; NB, normative beliefs; PBC, perceived behavioral control; PAVA, perceived availability; PAFF, perceived affordability; and PAWAR, perceived awareness

87

intervals for indirect effects at 5 per cent level of significance. According to this method, indirect

effects are significant if bias-corrected bootstrap confidence interval for the estimates does not

contain zero (Shrout and Bolger 2002; MacKinnon et al 2007). Findings in this regard (table

4.16) highlight that nearly all the paths from exogenous constructs to purchase intention were

mediated by their respective mediators, except a path from perceived awareness to purchase

intention through perceived behavioral control. Attitude was found to mediate the path from

perceived usefulness to purchase intention. Similarly, subjective norms was also found to mediate

the relationship between normative beliefs and purchase intention. The findings highlight that

perceived behavioral control mediated the path from perceived availability, perceived

affordability to purchase intention.

Moderation analysis of the model ‘consumer choice towards durable products’

In general terms, a moderator is a variable that influences the direction and/or strength of

the relationship between an independent variable and a dependent variable (Baron and Kenny

1986). For undertaking moderation analysis, a multi-group model was created to test the

differences between proposed relationships across two subgroups: rural and urban consumers. For

comparing statistical differences between estimates of relationships across these subgroups,

unstandardized estimates and standard errors were used to calculate z-score as suggested (Iglesias

Table 4.16: Mediation results of the model ‘consumer choice towards durable products’

Mediated Relationships Bootstrap estimates*

Bias-corrected confidence

interval p-value Mediation

Perceived usefulness towards purchase intention through attitude

0.104 0.062 to 0.178 0.003 Yes

Normative beliefs towards purchase intention through subjective norms

0.187 0.126 to 0.248 0.008 Yes

Perceived availability towards purchase intention through perceived behavioral control

0.156 0.098 to 0.217 0.025 Yes

Perceived affordability towards purchase intention through perceived behavioral control

0.287 0.196 to 0.362 0.015 Yes

Perceived awareness towards purchase intention through perceived behavioral control

0.019 -0.006 to 0.044 0.213 No

Notes: *Standardized indirect effects

88

and Vázquez 2001; Hair et al 2006). It was interesting to note that the strength of relationship

between perceived availability, affordability and perceived behavioral control was found to differ

statistically across two subgroups (table 4.17). For instance, relationship between availability and

perceived behavioral control was found to be stronger for urban consumers as compared to rural

consumers. Furthermore, rural consumers revealed a stronger effect of affordability on perceived

behavioral control in contrast to urban consumers. Rural consumers yielded a greater effect of

awareness on perceived behavioral control than their urban counterparts.

Table 4.17: Multi-group results of the model ‘consumer choice towards durable products’

Relationship Estimates

z-score Moderation Rural p- value Urban p-value

ATT �PI (+) 0.168 0.000 0.139 0.019 0.39 No

PU � ATT (+) 0.134 0.000 0.118 0.000 0.77 No

SN � PI (+) 0.294 0.000 0.188 0.000 1.93 No

NB �SN (+) 0.138 0.000 0.121 0.000 0.85 No

PBC � PI (+) 0.270 0.000 0.350 0.000 1.35 No

PAVA � PBC (+) 0.087 0.000 0.233 0.000 3.20* Yes

PAFF � PBC (+) 0.212 0.000 0.130 0.000 2.47* Yes

PAWAR � PBC (+) 0.047 0.051 -0.042 0.243 2.05* Yes

Notes: *p < 0.05; ATT, attitude; PI, purchase intention; PU, perceived usefulness; SN, subjective norms; NB, normative beliefs; PBC, perceived behavioral control; PAVA, perceived availability; PAFF, perceived affordability; and PAWAR, perceived awareness

Theoretical implications of the model ‘consumer choice towards durable products’

The present study considerably contributed to the literature on purchase behavior of BOP

consumers by clearly highlighting major differences among rural and urban consumers while

purchasing durable products. For example, perceived behavioral control emerged as the strongest

driver of intention to purchase durable products among urban consumers. It means that urban

consumers’ higher perceptions towards resources and control required to purchase durable

products generate higher purchase intentions. Further, once urban consumers perceive the

purchase of durable products as an easy behavior, they are more likely to purchase such products.

For rural consumers, subjective norms were found to be the most important predictor of purchase

intention. This finding indicates that rural consumers perceived higher social pressure to purchase

durable products. Possibly, this happens due to the stronger bonds shared among consumers

living in rural areas of the country. Attitude was found to be the third most important factor

affecting purchase intention for both rural and urban consumers.

89

Findings also helped to extend theory of planned behavior by testing relationships among

original constructs of the theory and their important antecedents. Results found attitude,

subjective norms as mediators between perceived usefulness, normative beliefs and intention to

purchase durable products. Perceived behavioral control was found to mediate the path from

perceived availability, affordability to purchase intention. These findings supported that the

identified antecedents of attitude, subjective norms and perceived behavioral control were

consistent with the given conceptual framework. Therefore, when analyzing the purchase of

durable products by BOP consumers, researchers may include perceived usefulness, normative

beliefs and perceived availability, affordability as antecedents of attitude, subjective norms and

perceived behavioral control in theory of planned behavior.

Managerial implications of the model ‘consumer choice towards durable products’

The study highlighted perceived behavioral control as the strongest predictor of intention

to purchase durable products by BOP consumers. Among antecedents of perceived behavioral

control, affordability emerged as the most important factor. However, findings from the multi-

group model indicated some differences into these relationships. Affordability was found to be

the strongest predictor of perceived behavioral control among rural consumers. In contrast,

availability appeared to be the strongest factor influencing perceived behavioral control among

urban consumers. However, rural consumers were found to have lower perceptions of

affordability towards purchasing durable products in comparison to urban consumers. These

findings suggest companies to enhance affordability perceptions among BOP consumers to

purchase durable products, specifically in rural areas where penetration of such products is still

very low. Due to lower affordability perceptions, these consumers perceive the purchase of

durable products not as an easy behavior. In order to develop affordability perceptions,

companies need to come out with innovative financing schemes that may allow disadvantaged

consumers to purchase durable products through easy installments over a period of time. For

instance, Grameen Shakti, one of the largest companies worldwide selling Solar Home Systems

(SHSs), offers solar lights and lamps to subsistence consumers through such schemes. Consumers

in these markets face erratic power supply and are unable to pay full amount of the solar

appliance in one go. Therefore, paying for a solar appliance through installments offered a

combined solution to both the problems. The company representative collects installments

through technicians’ monthly service visits over up to three years that also includes cost of the

90

equipment, financing charges and maintenance.27 Considering another example, Casas Bahia, a

large retailer of appliances in Brazil, provides credit to consumers with low and unpredictable

incomes. The firm sells appliances to BOP consumers with sophisticated credit rating system

coupled with counseling. The default rate of the firm remains at as low as 8.5 per cent compared

to 15 per cent for competitors (Prahalad 2006).

The study also found subjective norms to be the second most important factor affecting

durables purchase. Results suggested that the identified referent groups such as members of the

social networks and family members exert more social pressure on consumers to buy durable

products. Therefore, managers may promote such products through collaboration with social

networks like self-help groups, non-government organizations and voluntary associations.

Further, the present study established lesser relevance of attitude in predicting the purchase of

durable products. This may be due to the fact that purchase of durable products needs high

involvement where other factors like perceived controllability and subjective norms play a greater

role. Perceived usefulness was also found to influence attitude towards buying durable products.

Companies, therefore, need to undertake efforts to improve usefulness perceptions of durable

products among BOP consumers. This may be achieved by highlighting benefits of the durable

products among residents of subsistence markets. Such benefits should be unique and in line with

the market conditions of these markets. For instance, an Indian company, Indo National Limited,

one of the largest manufactures of dry cell batteries with brand name ‘Nippo’ since 1972, has

expanded its product profile to include products to meet the special needs of BOP consumers.

Keeping in view regular electricity cuts in rural India, company has unveiled a low-cost

rechargeable table fan with a battery backup of six hours. The product has also been equipped

with five pieces of LED night lights to provide light for longer duration. The innovative product

helps to provide relief to its users by giving fresh air and bright light during power cuts. Such

innovation helped the company to get a prestigious award from world’s renowned agency ‘AC

Nielson award for Technology Innovations’.28

27https://static.squarespace.com/static/51bef39fe4b010d205f84a92/t/51f237c4e4b07e4e5ac4e0f6/1374828484103/Full_report_Maketing_for_the_BOP.pdf 28 http://www.nippobatteries.com/html/prod_emrbkp.asp

91

4.4 Willingness to purchase branded products

For examining consumer’s willingness to purchase branded products, specific products

were selected from each selected product category viz. food, FMCG and durable products.

Branded bakery product was selected to examine drivers of branded food choice among BOP

consumers. Further, branded beauty products (face cream, face wash) were selected to examine

consumers’ willingness to purchase branded FMCG; and branded mobile was selected to examine

consumers’ willingness to purchase branded durable products. The criteria of selecting these

products have been presented in the section 3.1.4.2. This section has been mainly divided in to

five sub-sections. The first sub-section presents findings of structural equation modeling that was

used to test the proposed model ‘consumer choice towards branded food’ (Fig. 2.2). Next sub-

section elaborates findings of exploratory factor analysis that was used to explore factors

affecting branded FMCG purchase. Third sub-section presents results of a logistic regression that

was used to examine consumers’ willingness to purchase branded FMCG by taking factor scores

as independent variables. Next sub-section highlights factors affecting the purchase of branded

durable products. Similarly, last sub-section elucidates findings of a logistic regression that was

used to examine consumers’ willingness to purchase branded durable products by taking factor

scores as independent variables.

4.4.1 Consumer choice towards branded food

Previously, several empirical studies have attempted to investigate consumer choice

towards branded products from different perspectives (see for instance, Ni and Wan 2008;

Magnusson et al 2008; Jin and Kang 2011). These studies explored branding issues in emerging

markets like India and China by considering these markets as homogeneous with a specific focus

on high-income consumers. Findings of previous research have limited application in BOP

markets because these consumers face several constraints and have different characteristics than

their high-income counterparts. Despite limited affordability and low-income, BOP consumers

prefer to purchase branded products with price premium and perceive branded products as a

symbol of quality and confidence (D’Andrea 2006). Chikweche and Fletcher (2011) also found

that a large majority of BOP consumers attach high importance to brands while purchasing food

and personal hygiene products. However, many consumers in India prefer to purchase unbranded

and unpackaged food over branded and packaged food products (Ling et al 2004; Mukherjee and

Patel 2005). Studies have observed a major shift from unbranded to branded products buying due

to consumers have started paying more attention to hygiene (Narayanan 2000). Estimates by

Euromonitor International, ranked India as the second fastest growing packaged food market in

92

Asia Pacific in 2007 (Euromonitor 2009 cited in Ali and Kapoor 2009). BOP consumers in India

have food products as a larger share in the shopping basket; for example, share of food products

in total shopping basket remains at 78 per cent for low-income rural consumers in India

(Hammond et al 2007). India provides a suitable low-income population as this country is a home

to more than 700 million poor individuals with a combined income of $378 billion (Anderson and

Billou 2007) that offers an attractive market for branded food.

The above discussion suggested that low-income consumers in India represent a large

part of the population with high expenditure on food where branded food choice is still

understudied. The present study, therefore, aimed to investigate drivers of branded food choice

among BOP consumers with application of theory of planned behavior. On the basis of extensive

literature review, a conceptual model of consumer choice towards branded food was developed

(see Fig. 2.2). The proposed model was tested through structural equation modeling in AMOS.

Statistical differences in the proposed relationships were tested by creating a multi-group model

that compared the structural estimates of the relationships across two groups of consumers: group

1: consumers who have already purchased the branded food (buyers); and group 2: consumers

who have not yet purchased the branded food (non-buyers). Results from the analysis suggested

useful implications for food marketing companies in retaining existing or acquiring potential

consumers of branded food in subsistence markets.

Descriptive statistics of the model ‘consumer choice towards branded food’

Mean scores of constructs were calculated by averaging the consumers’ response towards

various statements in the respective constructs. Independent samples t-test was used to examine

statistical differences in mean scores of constructs with respect to two subgroups: buyers and non-

buyers. Findings (table 4.18) highlight that all inter-group differences were significant at 5 per

cent level of significance. Buyers group was found to have higher mean score for all constructs in

comparison to non-buyers. Results reveal that buyers group perceived branded food as more

useful for health. Also, buyers group was found to have a higher attitude towards branded food

and perceived higher social pressure to purchase branded food. Whereas non-buyers group

perceived lesser control over purchasing branded food. Further, buyers group showed higher

intention to purchase branded food than non-buyers group do.

93

Table 4.18: Consumers’ perceptions towards various constructs for purchase of branded food

Full sample (n = 600)

Buyers (n = 406)

Non-buyers (n = 194)

Mean comparison

(t value) Perceived usefulnessa 12.99 (3.65) 14.58 (2.54) 9.67 (3.36) 18.01*

Normative beliefsa 14.29 (3.61) 15.4 (3.08) 11.97 (3.56) 11.51*

Perceived availabilitya 15.38 (2.12) 15.5 (2.12) 15.14 (2.09) 1.96*

Perceived affordabilitya 12.72 (3.72) 14.4 (2.43) 9.19 (3.50) 18.66*

Perceived awarenessa 12.77 (3.24) 14.16 (2.54) 9.86 (2.54) 19.37*

Attitudeb 3.53 (0.96) 4.02 (0.46) 2.51 (0.93) 21.35*

Subjective normsb 3.29 (0.78) 3.64 (0.57) 2.56 (0.63) 20.98*

Perceived behavioral controlb 3.24 (0.79) 3.61 (0.49) 2.48 (0.76) 18.66*

Purchase intentionb 3.63 (1.14) 4.19 (0.53) 2.46 (1.19) 19.39*

Note: aScale values range from 1 to 25; bScale values range from 1 to 5; *significant at p < 0.05; Standard deviations are shown in parentheses; Means and standard deviations represent values of summated scales

Measurement analysis of the model ‘consumer choice towards branded food’

The study tested measurement model for two forms of invariance such as configural and

metric invariance. Configural invariance refers to same pattern of factor loadings of measurement

items across different groups (Steenkamp and Baumgartner 1998). It was diagnosed by

conducting confirmatory factor analysis without constraint in which all factors are set free across

two subgroups: buyers and non-buyers. Results indicated an acceptable model fit with X2 =

1388.857, p < 0.001, d.o.f = 1048, X2/d.o.f = 1.325 ≤ 3 (Hair et al 2006); GFI = 0.883 ≥ 0.8,

AGFI = 0.859 ≥ 0.8 (Baumgartner and Homburg, 1996); CFI = 0.972 ≥ 0.9 (Hair et al 2006); and

RMSEA = 0.023 ≤ 0.08 (Steiger 1990). The study also found all factor loadings to be statistically

significant across two subgroups. The above evidence specified measurement model to be

configural invariant. Metric invariance implies that variables are measured according to the same

scale intervals across different groups (Steenkamp and Baumgartner 1998). The study tested full

metric invariance by comparing model fit with factor loadings constrained to be equal across

subgroups, to configural invariance model. The results indicated a significant change in the fit

measures (∆X2 = 332.3, ∆d.o.f = 35, p < 0.000), thus full metric invariance was not supported. By

analyzing modification indices, five of the factor loadings were unconstrainted and results

indicated an insignificant change in the fit measures (∆X2 = 18.2; ∆d.o.f = 12; p = 0.11). The

other fit measures such as GFI, AGFI, CFI and RMSEA were also found to be within the

94

respective threshold values. The measurement model, therefore, was supported to be partial

metric invariant across two subgroups. Thus, differences between the proposed relationships will

be due to the causal relationships themselves and not to the measurement of the constructs.

Results of confirmatory factor analysis (table 4.19) revealed values of cronbach alpha (α)

and composite reliabilities higher than the minimum cutoff score of 0.70 (Nunnally 1978), that

highlighted a good measure of internal consistency. All average variance extracted values were

higher than 0.5; each statement had a significant factor loading greater than 0.5 on the expected

constructs that confirmed convergent validity (Fornell and Larcker 1981). The correlations among

constructs were lower than the square root of average variance extracted that supported

discriminant validity (Fornell and Larcker 1981). These estimates of reliability and validity

indicated that the constructs were robust to potential bias due to measurement error. Further,

results indicated an acceptable fit of the measurement model with X2 = 867.72, p < 0.001, d.o.f =

524, X2/d.o.f = 1.65 ≤ 3; GFI = 0.92 ≥ 0.8, AGFI = 0.90 ≥ 0.8; CFI = 0.98 ≥ 0.9; and RMSEA =

0.03 ≤ 0.08.

Structural analysis of the model ‘consumer choice towards branded food’

On the basis of conceptual framework (section 2.2), the present study proposed different

hypotheses (table 4.20) that were used to test the model ‘consumer choice towards branded food’.

The structural model indicated an acceptable fit with X2 = 1404.95, p < 0.001, d.o.f = 639,

X2/d.o.f = 2.19 ≤ 3; GFI = 0.89 ≥ 0.8; CFI = 0.96 ≥ 0.9; IFI = 0.96 ≥ 0.9; and RMSEA = 0.04 ≤

0.08. The study indicated a marginal influence of control variables across majority of the

relationships. The findings revealed a negative effect of age on attitude (βstd = -0.24, p ≤ 0.001)

and subjective norms (βstd = -0.24, p ≤ 0.001). This result highlighted that younger consumers

have a higher attitude and they perceived higher social pressure to purchase branded food.

Education emerged as a significant predictor of attitude (βstd = 0.101, p = 0.01) and subjective

norms (βstd = 0.06, p = 0.03). It implied that uneducated consumers have less positive attitude and

they also perceive low social pressure while buying branded food. However, two other control

variables: gender and children in household showed no effect on attitude, subjective norms and

perceived behavioral control. Further, the results did not support influence of control variables on

intention to purchase branded food.

95

Table 4.19: Reliability and validity statistics of the model ‘consumer choice towards branded food’

α CR AVE SN NB PU AVA AFF AWAR PBC ATT PI SN 0.85 0.85 0.59 0.77

NB 0.97 0.97 0.87 0.57 0.93

PU 0.92 0.92 0.75 0.61 0.54 0.86

AVA 0.87 0.87 0.70 0.17 0.05 0.10 0.84

AFF 0.91 0.91 0.73 0.54 0.53 0.69 0.06 0.85

AWAR 0.90 0.90 0.65 0.61 0.42 0.63 0.10 0.64 0.80

PBC 0.86 0.86 0.67 0.64 0.56 0.69 0.11 0.71 0.65 0.82

ATT 0.94 0.94 0.86 0.67 0.55 0.74 0.08 0.77 0.74 0.75 0.92

PI 0.92 0.92 0.75 0.64 0.55 0.75 0.11 0.78 0.68 0.79 0.82 0.86

Note: Bold numbers on the diagonal are square root of AVE; and off-diagonal values represent correlation between constructs. CR, composite reliability; AVE, average variance extracted; SN, subjective norms; NB, normative beliefs; PU, perceived usefulness; AVA, perceived availability; AFF, perceived affordability; AWAR, perceived awareness; PBC, perceived behavioral control; ATT, attitude; and PI, purchase intention

96

Table 4.20: Hypotheses proposed to test the model ‘consumer choice towards branded food’

Number Hypotheses Relationship

H1 Consumers with a positive attitude towards branded food will have a higher intention to purchase branded food

ATT � PI (+)

H2 Consumers perceiving branded food as more useful will have a positive attitude towards branded food

PU � ATT (+)

H3 Consumers perceiving more social pressure to buy branded food will have a higher intention to purchase branded food

SN � PI (+)

H4 Consumers with higher normative beliefs will perceive higher social pressure to purchase branded food

NB � SN (+)

H5 Consumers perceiving higher control to buy branded food will have a higher intention to purchase branded food

PBC � PI (+)

H6 Consumers perceiving branded food as more easily available will have a higher control over the purchase of branded food

AVA � PBC (+)

H7 Consumers perceiving branded food as more affordable will have a higher control over the purchase of branded food

AFF � PBC (+)

H8 Consumers with higher awareness towards branded food will have a higher control over the purchase of branded food

AWAR � PBC (+)

Note: ‘+’ sign shows a linear positive relationship among variables; ATT, attitude; PI, purchase intention; PU, perceived usefulness; SN, subjective norms; NB, normative beliefs; PBC, perceived behavioral control; AVA, perceived availability; AFF, perceived affordability; and AWAR, perceived awareness

Findings (table 4.21) supported H1 as consumers with a positive attitude towards branded

food were found to have a higher intention to purchase branded food. The study also established

that consumers perceiving branded food as more useful showed a higher attitude towards branded

food, thus supporting H2. From H3, the study expected that consumers perceiving more social

pressure to buy branded food have a higher intention to purchase branded food. The study

confirmed this expectation. Further, H4 was also confirmed, as consumers with higher normative

beliefs perceived higher social pressure to purchase branded food. Next, H5 was also confirmed

because consumers perceiving higher control to buy branded food were found to reveal a higher

intention to purchase branded food. Unlike hypothesized, H6 was not supported as consumers

perceiving branded food as more easily available did not show a higher control over the purchase

of branded food. From H7, it was anticipated that consumers perceiving branded food as more

affordable have a higher control over the purchase of branded food. Findings also supported this

97

hypothesis. Finally, the study also supported H8, as consumers with higher awareness towards

branded food were found to reveal a higher control over the purchase of branded food.

Table 4.21: Structural results of the model ‘consumer choice towards branded food’

Hypotheses Relationship Estimate t-value Hypotheses

support

H1 ATT � PI (+) 0.47 11.54* Yes

H2 PU � ATT (+) 0.64 17.62* Yes

H3 SN � PI (+) 0.06 2.01* Yes

H4 NB � SN (+) 0.42 10.69* Yes

H5 PBC � PI (+) 0.43 11.05* Yes

H6 AVA � PBC (+) 0.03 1.12 No

H7 AFF � PBC (+) 0.56 10.78* Yes

H8 AWAR � PBC (+) 0.23 4.68* Yes

Notes: *p < 0.05, ATT, attitude; PI, purchase intention; PU, perceived usefulness; SN, subjective norms; NB, normative beliefs; PBC, perceived behavioral control; AVA, perceived availability; AFF, perceived affordability; and AWAR, perceived awareness

Mediation analysis of the model ‘consumer choice towards branded food’

Mediation occurs if the effect of an independent variable (X1) on dependent variable (Y)

is partly or entirely transmitted through another variable (say X2). A mediated path involves a

causal sequence; first, X1 influences X2; and then X2 influences Y. Mediation of attitude,

subjective norms and perceived behavioral control in the paths from their respective antecedents

to purchase intention (Fig. 2.2), was examined through bias-corrected bootstrap confidence

intervals for indirect effects at 5 per cent level of significance. According to this method, indirect

effects are significant if bias-corrected bootstrap confidence interval for the estimates does not

contain zero (Shrout and Bolger 2002; MacKinnon et al 2007). Findings from the mediation

analysis (table 4.22) indicate that attitude mediated the relationship between perceived usefulness

and purchase intention. However, study found no mediation between normative beliefs, perceived

availability and purchase intention. Findings highlight that perceived behavioral control mediated

the relationship between perceived affordability, perceived awareness and purchase intention.

98

Table 4.22: Mediation results of the model ‘consumer choice towards branded food’

Mediated Relationships Bootstrap estimates*

Bias-corrected confidence

interval p-value Mediation

Perceived usefulness towards purchase intention through attitude

0.163 0.089 to 0.245 0.015 Yes

Normative beliefs towards purchase intention through subjective norms

0.026 -0.011 to 0.065 0.111 No

Perceived availability towards purchase intention through perceived behavioral control

0.009 -0.009 to 0.032 0.199 No

Perceived affordability towards purchase intention through perceived behavioral control

0.125 0.067 to 0.184 0.013 Yes

Perceived awareness towards purchase intention through perceived behavioral control

0.055 0.018 to 0.1 0.019 Yes

Notes: *Standardized indirect effects; CI: confidence interval

Moderation analysis of the model ‘consumer choice towards branded food’

In general terms, a moderator is a variable that influences the direction and/or strength of

the relationship between an independent variable and a dependent variable (Baron and Kenny

1986). For undertaking moderation analysis, a multi-group model was created to test differences

between proposed relationships across two subgroups: buyers and non-buyers. For comparing

statistical differences between estimates of relationships across two subgroups, unstandardized

estimates and standard errors were used to calculate z-score (Iglesias and Vázquez, 2001; Hair et

al 2006). It was interesting to note that the strength of relationship between attitude and purchase

intention was found to be more intense in non-buyers group as compared to buyers group (table

4.23). Furthermore, the non-buyers group was found to perceive higher social pressure to

purchase branded food. However, no such effect was detected for buyers group. The non-buyers

group yielded higher intensity of relationship between normative beliefs and subjective norms

than for buyers group. Further, perceived affordability significantly influenced control over the

purchase of branded food across both subgroups. This influence was found to be more intense for

non-buyers group than for buyers group.

99

Table 4.23: Multi-group results of the model ‘consumer choice towards branded food’

Hypothesis Relationship

Estimates z-

score Moderation

Buyers p-

value Non-

buyers p-

value H1 ATT �PI (+) 0.29 0.000 0.46 0.000 1.84 No

H2 PU � ATT (+) 0.23 0.000 0.70 0.000 7.41* Yes

H3 SN � PI (+) -0.10 0.12 0.18 0.002 3.39* Yes

H4 NB �SN (+) 0.25 0.00 0.49 0.000 3.28* Yes

H5 PBC � PI (+) 0.30 0.00 0.34 0.000 1.6 No

H6 AVA � PBC (+) 0.02 0.75 0.10 0.10 1.53 No

H7 AFF � PBC (+) 0.16 0.03 0.62 0.000 3.73* Yes

H8 AWAR � PBC (+) 0.16 0.02 0.13 0.11 0.21 No

Notes: * p < 0.05; ATT, attitude; PI, purchase intention; PU, perceived usefulness; SN, subjective norms; NB, normative beliefs; PBC, perceived behavioral control; AVA, perceived availability; AFF, perceived affordability; and AWAR, perceived awareness

Theoretical implications of the model ‘consumer choice towards branded food’

This study contributed to the literature on BOP consumers: firstly, by highlighting the

behavioral variations between buyers and non-buyers with respect to branded food purchase. For

instance, perceived behavioral control emerged as the strongest driver of purchase intention for

buyers group. For non-buyers group, attitude was found to be the most important antecedent of

intention to purchase branded food. It means that once BOP consumers buy branded food, they

acquire more confidence and control over repeat purchases. The higher control reduces the

primary role of attitude in determining purchase intention; thus perceived behavioral control

becomes most important factor affecting buying behavior of low-income consumers. Further,

results established an insignificant influence of subjective norms on intention to purchase branded

food for buyers group. However, this influence was found to be consistent with expectations for

non-buyers group, thus highlighting that BOP consumers perceive social pressure to buy branded

food during initial purchase only; and over subsequent purchases, such consumers do not perceive

any social pressure. This behavioral change may be due to the past purchases undertaken for such

products.

Secondly, previous conceptual literature highlighted availability, affordability and

awareness as key drivers of consumer choice in subsistence markets (Prahalad 2006; Anderson

and Billou 2007; Anderson and Markides 2007). In contrast, the present research found

availability as an insignificant factor. For this, it may be reasoned that such food products are sold

through a number of both traditional and modern retail outlets in India. The availability of such

100

products is unlikely to generate desired purchase intention until disadvantaged consumers are

constrained by low affordability and low awareness that attenuates confidence and control over

buying branded food.

Thirdly, the study helped to extend theory of planned behavior by examining

relationships among original constructs of the theory and its important antecedents. Results found

attitude as a mediator between perceived usefulness and intention to purchase branded food.

Further, perceived behavioral control was found to mediate the influence of perceived

affordability, awareness on purchase intention. These findings reinforce that identified

antecedents of attitude and perceived behavioral control were consistent with the given theoretical

framework. Therefore, when investigating food choice among BOP consumers, constructs like

perceived usefulness and perceived affordability, awareness may be included as antecedents of

attitude and perceived behavioral control respectively in theory of planned behaviour.

Managerial implications of the model ‘consumer choice towards branded food’

The results highlighted attitude as the strongest predictor of intention to purchase branded

food among BOP consumers. The non-buyers group revealed a stronger relationship between

perceived usefulness and attitude as compared to buyers group. However, non-buyers group

perceived branded food as less useful and they were found to have a less positive attitude towards

branded food. Thus, companies need to undertake efforts to improve usefulness perceptions

among non-buyers group that would promote positive attitude and intention to purchase branded

food. This can be achieved by highlighting health benefits of branded food in two ways. First,

such benefits can be communicated through health promoting pictographic on food packaging.

Use of pictographic along with wording seems to be effective since majority of the BOP

consumers are unable to process language information. Second, companies may fortify foods with

necessary vitamins and micronutrients of which BOP population is often deficient. For example,

Britannia Industries Limited,29 one of the top FMCG companies in India, developed fortified

cookies for iron-deficient consumers and grown its share within undernourished population. Such

healthy products helped the company to sell 3.5 billion packets of iron-fortified cookies per

annum in India.30 Similarly, Nestle India Limited, launched an affordable and whole grain based

variant of instant noodles ‘Maggi vegetable atta31 noodles’ with a slogan ‘Taste bhi, health bhi’

(Taste and health too). This product contained vegetables in its garnish and goodness of fiber. A

29 a public listed company 30 http://www.britannia-biscuits.com/bnf/media/britannia-in-health-nutrition.pdf 31 an unrefined variant of wheat flour which contains high fiber

101

small affordable pack of 80g at $0.15 (INR 10) has fiber equal to three chapattis.32 These health

benefits facilitated the company to further strengthen the brand and to make it into top five most

trusted brands of India.33

The study also highlighted perceived behavioral control as the second most influential

antecedent of intention to purchase branded food. Managers, therefore, need to promote branded

food purchase as an easy behavior among low-income consumers. A large majority of the low-

income consumers are unable to understand nutrition information provided in English language

on food packaging. Companies, therefore, may develop food packaging containing nutrition

information in local languages that may strengthen confidence and purchase skills among BOP

consumers. Further, perceived affordability and awareness was found to be significant

determinants of behavioral control to purchase branded food. The results indicated a stronger

influence of perceived affordability on behavioral control for non-buyers group than for buyers

group. This finding reinforced the concept of marketing low-priced packs of branded food to

potential consumers in subsistence markets. For instance, Parle Products Private Limited, a large

FMCG company in India, dropped prices of its premium cookie brands such as ‘Hide and Seek’

and ‘Milano’ to make it more affordable to rural and low-income consumers. The company

developed small packs at $0.08 (INR 5) that are expected to help penetration of branded cookies

deeper into this market. Also, for the first time, dairy major Parag Milk Foods Private Limited

introduced ghee34 in sachets of 18ml and 9ml at $0.3 (INR 20) and $0.15 (INR 10) respectively.

Companies, therefore, should promote small packs of new food product lines, such as breakfast

cereals, for which low-income consumers are likely to begin purchase. Further, significant

influence of perceived awareness on behavioral control suggested managers to enhance brand

awareness through non-traditional communications like radio, live demonstrations and road

shows due to weak presence of traditional media (e.g. T.V) in subsistence markets. Use of

symbolic information like images on food packaging may assist firms to develop and maintain

strong brand awareness among illiterate consumers.

The results revealed low relevance of subjective norms in the prediction of intention to

purchase branded food. One possible reason is that food buying is a low-involvement decision

that largely depends upon past behavior. The non-buyers group showed a higher intensity of

relationship between normative beliefs and subjective norms with respect to buyers group. It can

be implied that identified referent groups exert more social pressure on non-buyers group to

32 a popular variant of Indian bread 33 https://www.nestle.in/Brands/MAGGIVegetableAttaNoodles 34 a type of clarified butter

102

purchase branded food. This finding throws light on the importance of group communications

while promoting branded food among non-buyers group. For this purpose, companies may

collaborate with social networks such as self-help groups, non-government organizations and

voluntary associations. For instance, Nestle India collaborated with Drishtee, a social enterprise

connected to a network of rural retail outlets, to get its fortified ‘Maggi Masala-ae-Magic’35 into

the hands of low-income consumers. This partnership helped the company to achieve an increase

in pan-India sales by 70 per cent from 2010 to 2011.36

4.4.2 Factors affecting the purchase of branded FMCG

In the recent times, consumers in India have started preferring specialized products rather

than basic or functional products. During last few years, Indian markets have been flooded with a

plenty of new brands in FMCG category. FMCG like face creams and face wash have assumed

more importance among both male and female consumers as they have started paying more

attention to physical appearance. The deeper penetration of televisions and other media have

helped the companies to increase consumers’ awareness about branded product. However,

marketing of such products in BOP markets of the country still poses a challenge to multi-

national corporations due to partial penetration of print and broadcasting media in these markets.

Therefore, it seems important to explore factors that influence the purchase of branded FMCG

among BOP consumers. Respondents were requested to rate various parameters on a five-point

scale that influence the purchase of branded FMCG.

Findings (table 4.24) highlight that consumers perceived the strongest influence of

product price on branded FMCG purchase. Due to low-income, such consumers may perceive

branded FMCG as expensive products; thereby they attach more importance to the price of

branded products. It is also important to note that consumers perceived brand name as the second

most important parameter influencing the purchase of branded FMCG. Consumers perceived

product appearance as an important parameter that also influenced purchase decision for branded

FMCG. Further, respondents also perceived that product availability influences their decision to

purchase branded FMCG. Due to poor infrastructure and inadequate distribution channels some

brands of FMCG may not be available in these markets; thereby companies need to make branded

FMCG available near to the place where such consumers live or work. Findings highlight that

consumers also perceived product familiarity as an important parameter that influences their

purchase decision for branded FMCG.

35 a mix of spices that could be used to add flavor to Indian cooking 36 https://www.sharedvalue.org/sites/default/files/resource-files/FSG_SVI_casestudy_r4_051713.pdf

103

Table 4.24: Perceptions towards parameters influencing the purchase of branded FMCG

Parameters Mean score Standard deviation

Product price influences my purchase decision 3.43 1.033

Brand name influences my purchase decision 3.34 0.912

Product appearance influences my purchase decision 3.28 0.955

Product availability influences my purchase decision 3.22 0.921

Product familiarity influences my purchase decision 3.14 0.851

Credit availability influences my purchase decision 2.99 1.096

Product quality influences my purchase decision 2.79 0.831

Products’ ingredients influences my purchase decision 2.79 0.905

Product packaging influences my purchase decision 2.63 0.927

Product fragrance influences my purchase decision 2.59 0.918

In order to examine differences in consumers’ perceptions towards selected parameters

with respect to demographic variables, analysis of variance (ANOVA) and independent samples

t-test was used. ANOVA was applied where demographic variables had more than two categories

(such as education) and t-test was applied where demographic variables had two categories only

(such as age, income, area and gender). The findings in this regard (table 4.25) highlight

significant differences in consumers’ perceptions towards selected parameters influencing the

purchase of branded FMCG. Respondents with age 35 years and below perceived higher

influence of product price on branded FMCG purchase in contrast with respondents with age

above 35 years. Perceptions of younger consumers (age 35 years and below) about the influence

of brand name, product appearance, product availability and product familiarity on branded

FMCG purchase was also found to be significantly higher than the perceptions of older

consumers (age above 35 years). However, consumers’ perceptions towards influence of credit

availability on branded FMCG purchase were not found to differ across two categories of age.

On the basis of income, several differences in the perceptions towards parameters

influencing branded FMCG were found. For example, consumers with higher income

(INR 6,001-8,000) perceived greater influence of product price, brand name, product appearance,

product availability and product familiarity on branded FMCG purchase. In contrast, consumers

with low income (INR 2,000-6,000) perceived higher influence of credit availability on branded

FMCG purchase. This may be due the fact that consumers with lower incomes seek to purchase

branded goods on credit. On the basis of area, urban consumers perceived higher influence of

product availability on the purchase of branded FMCG. Relatively, road and transport

104

infrastructure in urban areas is better that may result in higher availability of branded FMCG in

urban areas. Further, urban consumers were also found to perceive a higher influence of product

familiarity on the purchase of branded FMCG. This may happen due to the reason that there is

deeper penetration of print and broadcasting media in urban areas that make urban consumers to

be more familiar with branded FMCG. Surprisingly, no significant differences in perceptions

towards parameters influencing the purchase of branded FMCG were found for male and female

consumers. On the basis of education, consumers’ perceptions towards product price influencing

the purchase of branded FMCG was found to differ. For examining differences across two

categories of education, Tukey’s post hoc test was applied. Findings highlight that consumers’

perceptions towards influence of product price on branded FMCG purchase was higher in E2 than

E137 (table 4.25). Consumers in E2 and E3 perceived higher influence of brand name and product

appearance on branded FMCG purchase than consumers in E1. Similarly, consumers’ perceived

higher influence of product availability on branded FMCG purchase in E2, E3 and E4 than E1.

Consumers in E1 were found to perceive lesser influence of product familiarity on branded

FMCG purchase than consumers in E2. However, no significant differences in consumers’

perceptions towards the influence of credit availability on branded FMCG purchase were found.

Consumers in E2, E3 and E4 were found to perceive a higher influence of product quality on

branded FMCG purchase than consumers in E1.

In order to reduce the number of parameters into a few meaningful factors that account

for maximum variance in the data, the study applied exploratory factor analysis on selected

parameters. Principal Component Analysis (PCA) was used for extracting the factors through

EFA. The present study used ‘varimax’ method of factor rotation which is the most commonly

used procedure as suggested by Kaiser (1958). The factors with eigen values of 1 or above were

retained in the final analysis (Malhotra 2008). Statements having item-total correlation less than

0.3 were not considered for further analysis. KMO value was found to be 0.831 which is more

than the recommended threshold of 0.6 (Nunnally 1978). Bartlett's test of sphericity was used to

examine the null hypothesis that the variables in the correlation matrix are uncorrelated. P-value

of test statistic was found to be less than 0.001 that rejected the null hypothesis, indicating a

significant correlation among variables; that supported further application of EFA on given

statements (Hair et al 2006).

37

E1: below higher secondary; E2: higher secondary; E3: senior secondary; E4: graduate and above

105

Table 4.25: Differences in perceptions towards parameters influencing branded FMCG purchase with respect to selected demographic variables

Parameters Age Income Area Gender Education

A1 A2 t-

value I1 I2

t- value

R U t-

value M F

t- value

E1 E2 E3 E4 F-

value Product price influences my purchase decision 3.72 3.06 8.11* 3.18 3.64 5.55* 3.38 3.52 1.56 3.44 3.42 0.26 3.21 3.79 3.50 3.71 13.85*

Brand name influences my purchase decision 3.61 2.99 8.74* 3.10 3.54 5.98* 3.29 3.44 1.83 3.32 3.36 0.56 3.17 3.59 3.49 3.41 9.65*

Product appearance influences my purchase decision 3.53 2.96 7.67* 3.01 3.51 6.63* 3.23 3.39 1.94 3.29 3.28 0.14 3.10 3.51 3.50 3.59 9.97*

Product availability influences my purchase decision 3.45 2.94 6.93* 2.97 3.44 6.49* 3.15 3.38 2.92* 3.25 3.20 0.68 3.07 3.41 3.38 3.76 8.52*

Product familiarity influences my purchase decision 3.33 2.89 6.45* 2.98 3.27 4.23* 3.07 3.28 2.86* 3.17 3.11 0.80 2.99 3.40 3.18 3.24 9.46*

Credit availability influences my purchase decision 2.92 3.08 1.77 3.09 2.90 2.15* 3.00 2.97 0.31 2.97 3.01 0.50 3.02 2.92 3.08 2.65 1.06

Product quality influences my purchase decision 3.03 2.48 8.51* 2.59 2.95 5.39* 2.72 2.94 3.15* 2.77 2.82 0.74 2.63 2.93 3.07 3.18 10.12*

Products’ ingredients influences my purchase decision 2.99 2.53 6.45* 2.58 2.97 5.46* 2.71 2.95 3.05* 2.81 2.77 0.44 2.68 2.89 2.93 3.29 4.82*

Product packaging influences my purchase decision 2.90 2.27 8.72* 2.39 2.83 5.97* 2.54 2.80 3.36* 2.66 2.59 0.94 2.44 2.84 2.88 3.00 11.09*

Product fragrance influences my purchase decision 2.82 2.30 7.07* 2.36 2.79 5.83* 2.52 2.74 2.68* 2.56 2.64 1.02 2.45 2.70 2.83 3.12 7.08*

Note: *p< 0.05; A1: age 35 years and below; A2: age above 35 years; I1: INR 2,000-6,000; I2: INR 6,001-8,000; R: rural; U: urban; M: male; F: female; E1: below higher secondary; E2: higher secondary; E3: senior secondary; E4: graduate and above

106

Principal component analysis generated two factors that explained 62.76 per cent of the

total variance in the data. Wherein, first factor explained 33.24 per cent of variance and the

second factor explained 29.52 per cent of the variance. First factor was named as ‘product

appearance, price and brand’ and second factor was named as ‘packaging, quality and

ingredients’. Factor loadings of the statements were found to be more than or equal to 0.5.

Reliability statistics (cronbach alpha; α) for these two factors was found to be more than the

recommended value of 0.70 (Nunnally 1978). Findings in this regard have been presented in the

table 4.26.

Table 4.26: Factor affecting the purchase of branded FMCG: Resultant output

Statements

Factors

Product appearance, price and brand

Packaging, quality and ingredients

Product appearance influences my purchase decision .858 .034

Brand name influences my purchase decision .840 .075

Product price influences my purchase decision .794 .130

Product familiarity influences my purchase decision .791 .078

Product availability influences my purchase decision .500 .363

Product fragrance influences my purchase decision .056 .803

Products’ ingredients influences my purchase decision

.077 .788

Product quality influences my purchase decision .140 .785

Product packaging influences my purchase decision .120 .783

Credit availability influences my purchase decision* --- ---

Variance explained 33.24 29.52

Cumulative variance explained 33.24 62.76

Cronbach alpha (α) 0.83 0.81

Note: *Deleted during analysis; Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalization

107

4.4.3 Willingness to purchase branded FMCG

Based on the findings of the exploratory factor analysis (see section 4.4.2), a binary

logistic model was developed to examine consumers’ willingness to purchase branded FMCG

(see Fig. 4.2). The dependent variable was measured with the help of a dichotomous question by

asking respondents to reveal their willingness to purchase a selected branded FMCG. This

dichotomized variable took a value of ‘1’ for respondents who were willing to purchase selected

branded FMCG and ‘0’ for those who did not wish to purchase. Two factor scores generated

through exploratory factor analysis were included as independent variables; and study also

incorporated four control variables to rule out possibility of confounding relationships in the

model. The findings of the logistic regression have been reported in terms of odds ratio that is an

indicator of the change in odds resulting from a unit change in the independent variable. Odds

ratios less than ‘1’ suggest that increase in a particular predictor is associated with decrease odds

of an event occurring. Whereas odds ratios more than ‘1’ suggest that increases in a particular

predictor is associated with increase odds of an event occurring (Field 2009).

Fig. 4.2: A conceptual model for examining willingness to purchase branded FMCG

Product appearance,

price and brand

Packaging, quality and

ingredients

Willingness to

purchase

branded

FMCG

Control variables:

Area (rural; urban) Gender (female; male)

Age (more than 35; 35 or less) Children in household (No; Yes)

108

The dependent and independent variables included in the logistic regression have been

presented in the form of an equation given below:

�� =IJ +I���K +I�LM +IN���� +IOP�Q + IR�P� + IS�T + �� where Y� = WillingnesstopurchasebrandedFMCGPPB =Productappearance,priceandbrand PQI= Packaging, qualityandingredients GEN = Gender CH = Childreninhousehold

E� = Errorterm

The model statistics (table 4.27) reveal that chi-square coefficient was statistically

significant (Chi-square coefficient = 70.33; df = 6; p< 0.05). This finding indicates that the

relationships proposed in the binary logistic model were effective. Log-likelihood ratio was found

to be acceptable (–2 log likelihood = 397.11), that also indicated the model fit. Logistic model

was able to correctly predict more than 85 per cent of the observed outcomes in data. Cox and

Snell R square was found to as high as 0.11; whereas Nagelkerke R square was found to assume

the value of 0.20 which indicated that about 20 per cent of the variation in willingness to purchase

branded FMCG being explained by the independent variables in the model. Variance inflation

factor was found to be less than three that indicated no potential effect of multi-collinearity

among independent variables (Hair et al 2006).

Table 4.27: Model statistics for examining willingness to purchase branded FMCG

Statistics

Chi-square coefficient 70.33 (df = 6; p< 0.05)

–2 log likelihood 397.11

Cox and Snell R square .11

Nagelkerke R square .20

Correct predictions (%) 86.8

Findings (table 4.28) of the logistic model highlight that out of the four control variables,

only one control variable, age, was found to be statistically significant in the prediction of

willingness to purchase branded FMCG. Younger consumers (age 35 years or less) were more

likely to purchase branded FMCG than older consumers (age more than 35 years). In other words,

odds of younger consumers for purchasing branded FMCG were 11.21 times the odds of older

109

consumers purchasing branded FMCG. Other three control variables viz. area, gender and

children in household failed to influence willingness to purchase branded FMCG. Only one

independent factor ‘product appearance, price and brand’ was able to significantly predict

willingness to purchase branded FMCG. The odds ratio for ‘product appearance, price and brand’

was 1.528, this means that an increase of 1 in this factor increases the odds of buying branded

FMCG over the odds of not buying branded FMCG by 1.528 times. The other factor ‘packaging,

quality and ingredients’ failed to emerge as a significant determinant of willingness to purchase

branded FMCG. This may happen due to the reason that consumers do not attach much

importance to ingredients used in manufacturing FMCG. Consumers attach importance to

variables like appearance, price and brand name that significantly predicted willingness to

purchase branded FMCG. This finding suggests companies to design attractive appearance of

branded FMCG along with developing small packs that low-income consumers are more likely to

afford. For example, in Egypt, P & G downsized the package size from 200 grams to 150 grams

of its flagship washing powder brand ‘Ariel’ that helped the company to boost up its sales

(Kotabe and Helsen 2013).

Table 4.28: Estimates of the model willingness to purchase branded FMCG

Estimates

β p-value Exp(B)

Product appearance, price and brand .424 .013 1.528

Packaging, quality and ingredients .041 .753 1.042

Area (rural; urban) .180 .495 1.198

Gender (female; male) .117 .656 1.124

Age (more than 35; 35 or less) 2.417 .000 11.212

Children in household (No; Yes) -.110 .757 .896

Constant -3.902 .000 .020

Note: Exp(B): Odds ratio

4.4.4 Factors affecting the purchase of branded durable products

Bottom of the pyramid households spend about eight per cent of their income on durable

products. In absolute terms, it generates a market potential of about INR 7,200 crore for these

products (Singhal 2008). A national level survey conducted by National Council of Applied

Economic Research (NCAER) showed that 8 per cent of the poor households own color

television sets, 4 per cent have telephones and 3 per cent have refrigerators (Rao and Shukla

2008). This data shows marginal ownership of consumer durables among bottom tier households

110

in India. Therefore, it is important to explore factors affecting the purchase of branded durable

products. Respondents were requested to rate selected parameters on a five-point scale that

influence the purchase of branded durable products.

Findings (table 4.29) highlight that respondents perceived product price as the most

importance parameter that influence the purchase of branded durable products. Due to

low-income, such consumers may perceive branded durables as expensive products; thereby they

attach more importance to the price of branded durable products. It is also important to note that

consumers perceived product quality as the second most important parameter influencing the

purchase of branded durables. Unlike the purchase of branded FMCG, consumers perceived

credit availability as an important parameter that influence purchase decision for branded

durables. This finding indicates that BOP population seeks credit while purchasing durable

products because such consumers are unable to purchase durable products in cash. Further,

respondents also perceived that after sale services and brand name influences their decision to

purchase branded durables.

Table 4.29: Perceptions towards parameters influencing the purchase of branded durables

Statements Mean score Standard deviation

Product price influences my purchase decision 4.22 0.701

Product quality influences my purchase decision 4.08 0.768

Credit availability influences my purchase decision 3.91 1.01

After sale services influence my purchase decision 3.88 0.980

Brand name influences my purchase decision 3.86 0.775

Product warranty influences my purchase decision 3.74 0.684

Product appearance influences my purchase decision

3.62 0.781

Product familiarity influences my purchase decision 3.16 0.872

Product availability influences my purchase decision

3.12 1.03

In order to examine differences in consumers’ perceptions with respect to demographic

variables, analysis of variance (ANOVA) and independent samples t-test was used. Findings in

this regard (table 4.30) highlight significant differences in consumers’ perceptions towards

parameters influencing the purchase of branded durables. For example, consumers with higher

income (INR 6,001-8,000) perceived greater influence of brand name on the purchase of branded

durables than consumers with low income (INR 2,000-6,000). This finding implies that high

111

income consumers attach more importance to brand names while purchasing durable products.

Similarly, high income consumers were found to perceive a greater influence of product warranty

on branded durables purchase. In contrast, consumers with low income perceived higher

influence of product availability on branded durables purchase. Further, no significant differences

were found in consumers’ perceptions towards the influence of selected parameters on branded

durables purchase with respect to different categories of age, area and gender. Tukey’s post hoc

test highlights that consumers in E3 perceived higher influence of product warranty on branded

durables purchase than E1 and E238. Consumers’ perceptions towards influence of product

appearance on branded durables purchase were found to be higher in E1, E2 and E3 than E4.

For reducing number of parameters into a few meaningful factors that account for

maximum variance in the data, the study applied exploratory factor analysis on selected

parameters. Principal Component Analysis (PCA) was used for extracting the underlying factors

with ‘varimax’ method of factor rotation which is the most commonly used procedure as

suggested by Kaiser (1958). The factors with eigen values of 1 or above were retained in the final

analysis (Malhotra, 2008). Statements having item-total correlation more than 0.3 were retained

for further analysis. KMO value was found to be 0.612 which is more than the recommended

threshold of 0.6 that highlighted sampling adequacy (Nunnally 1978). Bartlett's test of sphericity

was used to examine the null hypothesis that the variables in the correlation matrix are

uncorrelated. P-value of Bartlett's test was found to be less than 0.001; indicating that there exist

significant correlation among variables. The satisfactory values of these tests suggested further

application of EFA on given statements (Hair et al 2006).

38

E1: below higher secondary; E2: higher secondary; E3: senior secondary; E4: graduate and above

112

Table 4.30: Difference in perceptions towards parameters influencing branded durables purchase with respect to selected demographic variables

Parameters Age Income Area Gender Education

A1 A2 t-

value I1 I2

t- value

R U t-

value M F

t- value

E1 E2 E3 E4 F-

value Product price influences my purchase decision 4.22 4.22 0.03 4.26 4.19 1.16 4.21 4.24 0.53 4.19 4.27 1.38 4.24 4.23 4.19 3.94 1.03

Product quality influences my purchase decision 4.11 4.05 0.99 4.05 4.11 0.94 4.08 4.09 0.18 4.07 4.09 0.28 4.07 4.18 3.96 3.88 2.01

Credit availability influences my purchase decision 3.86 3.98 1.42 3.99 3.84 1.71 3.88 3.98 1.19 3.90 3.92 0.13 3.95 3.78 4.07 3.82 1.86

After sale services influence my purchase decision 3.91 3.84 0.06 3.88 3.88 0.023 3.86 3.92 0.79 3.89 3.87 0.26 3.83 3.97 3.86 3.88 0.77

Brand name influences my purchase decision 3.89 3.81 1.33 3.77 3.93 2.47* 3.82 3.93 1.64 3.88 3.83 0.81 3.81 3.92 3.93 3.76 0.99

Product warranty influences my purchase decision 3.78 3.69 0.67 3.66 3.81 2.70* 3.72 3.78 1.05 3.72 3.76 0.80 3.69 3.73 4.00 3.71 4.17*

Product appearance influences my purchase decision 3.63 3.60 0.41 3.65 3.59 0.98 3.60 3.66 0.84 3.63 3.59 0.62 3.65 3.61 3.61 3.06 3.13*

Product familiarity influences my purchase decision 3.17 3.15 0.39 3.15 3.17 0.16 3.16 3.18 0.26 3.18 3.14 0.68 3.16 3.20 3.17 2.82 0.97

Product availability influences my purchase decision 3.11 3.14 0.30 3.22 3.04 2.17* 3.15 3.06 0.98 3.15 3.09 0.62 3.16 3.10 3.11 2.65 1.41

Note: *p< 0.05; A1: age 35 years and below; A2: age above 35 years; I1: INR 2,000-6,000; I2: INR 6,001-8,000; R: rural; U: urban; M: male; F: female; E1: below higher secondary; E2: higher secondary; E3: senior secondary; E4: graduate and above

113

Findings (table 4.31) of EFA highlight that principal component analysis generated three

factors that explained 67.08 per cent of the total variance in the data. Wherein, first factor

explained 29.51 per cent of variance; second factor explained 19.43 per cent of the variance; and

the third factor explained 18.13 per cent variation in the data. First factor was named as

‘familiarity and convenience’; second factor was named as ‘appearance and price’; and the third

factor was named as ‘quality and brand name’. Reliability analysis was also undertaken on the

statements loaded on the factors generated in the final solution. The values of cronbach alpha (α)

for these three factors were found to be more than 0.6 (Nunnally 1978). The statements were

found to have factor loadings greater than the minimum recommended value of 0.5 (Malhotra

2008).

Table 4.31: Factor affecting the purchase of durables products: Resultant output

Statements

Factors

Familiarity and convenience

Appearance and price

Quality and brand

name

Product availability influences my purchase decision .845 .050 .032

Product familiarity influences my purchase decision .816 .029 .069

Credit availability influences my purchase decision .765 -.024 -.067

After sale services influence my purchase decision .599 -.122 -.139

Product appearance influences my purchase decision -.078 .879 -.003

Product price influences my purchase decision .026 .872 -.061

Brand name influences my purchase decision .077 -.041 .850

Product quality influences my purchase decision -.159 -.023 .834

Product warranty influences my purchase decision* --- --- ---

Variance explained 29.51 19.43 18.13

Cumulative variance explained 29.51 48.94 67.08

Cronbach alpha (α) .754 .705 .609

Note: *Deleted during analysis; Extraction Method: Principal Component Analysis; Rotation Method: Varimax with Kaiser Normalization

114

4.4.5 Willingness to purchase branded durable products

Based on the findings of the exploratory factor analysis, a binary logistic model was

developed to examine consumers’ willingness to purchase branded durable products (Fig. 4.3).

Fig. 4.3: A conceptual model for examining willingness to purchase branded durables

The dependent and independent variables included in the logistic regression have been

presented in the form of an equation given below:

�� =IJ +I�w� +I�� +INLKQ + IO���� +IRP�Q + IS�P� + Ix�T + �� where �� = WillingnesstopurchasebrandedFMCG

w� =Familiarity and convenience

AP = �99 212�6 2�,9136

LKQ = L52:3/y2�,I12�,�2z P�Q = P �, 1

�T = �ℎ3:,1 �3�ℎ05- ℎ0:,

�� = �1101/ 1z

Familiarity and convenience

Appearance and price

Willingness to

purchase

branded

durable product

Control variables:

Area (rural; urban) Gender (female; female)

Age (more than 35; 35 or less) Children in household (No; Yes)

Quality and brand name

115

Dependent variable was measured with the help of a dichotomous question by asking

respondents to reveal their willingness to purchase a selected branded durable product. This

dichotomized variable took a value of ‘1’ for respondents who were willing to purchase selected

branded durable product and ‘0’ for those who did not wish to purchase. Three factor scores

generated through exploratory factor analysis were included as independent variables; and study

also incorporated four control variables to rule out possibility of confounding relationships in the

model. The findings of the logistic regression have been reported in terms of odds ratio; an odds

ratios less than ‘1’ suggest that increase in a particular predictor is associated with decrease odds

of an event occurring. Whereas odds ratios more than ‘1’ suggest that increase in a particular

predictor is associated with increase odds of an event occurring (Field 2009).

Findings of the model fit (4.32) revealed chi-square coefficient as statistically significant

in examining willingness to purchase branded durable products (X2 = 153.574; df = 7; p< 0.05).

This finding implies that the logistic model represents effective relationships between dependent

and independent variables included in the study. Log-likelihood ratio (363.968) also indicated the

model fit. The logistic model was able to correctly predict about 90 per cent of the observed

outcomes. Cox and Snell R square was found to as high as 0.226; and value of Nagelkerke R

square was found to be 0.391 that indicates about 40 per cent of variation in willingness to

purchase branded durables being explained by independent variables. Variance inflation factor

was also found to be less than three that indicated no potential effect of multi-collinearity among

independent variables (Hair et al 2006).

Table 4.32: Model statistics for examining willingness to purchase branded durables

Chi-square coefficient 153.574 (df = 7; p< 0.05)

–2 log likelihood 363.968

Cox and Snell R square .226

Nagelkerke R square .391

Correct predictions (%) 89.5

Findings of the logistic model (table 4.33) highlight that none of the four control

variables were statistically significant in examining consumers’ willingness to purchase branded

durables. It means that area, gender, age and children in household have no influence on

willingness to purchase branded durables. All the three independent variables viz. ‘familiarity and

convenience’; ‘appearance and price’; and ‘quality and brand name’ were found to be significant

in the prediction of willingness to purchase branded durables. Of the independent variables, factor

‘familiarity and convenience’ emerged as the strongest predictor, as odds ratio for this factor was

116

found to be the highest. This finding indicated that an increase of one unit in the factor

‘familiarity and convenience’ increases in the odds of buying branded durables by 3.96 times

over the odds of not buying branded durables. Factor ‘appearance and price’ was found to be the

second most important predictor with odds ratio at 1.744, this means that an increase of 1 in the

factor ‘appearance and price’ increases the odds of buying branded durables over the odds for not

buying branded durables by 1.74 times. Similarly, odds ratio for the factor ‘quality and brand

name’ was found to be 1.561. This finding indicated that an increase of 1 in the factor ‘quality

and brand’ increases the odds of buying branded durables by 1.56 times. These findings,

therefore, suggest companies to focus more on making low-income consumers familiar about the

product and making products available near to the place where consumers live or work. In

addition, for enhancing consumers’ willingness to purchase branded durables, companies need to

provide such products on monthly installments and offer better after sale services. For instance,

Grameen Shakti, the largest company worldwide selling Solar Home Systems (SHSs), offered

solar lights and lamps to BOP consumers on monthly installments that were collected through

technicians’ monthly service visits over up to three years including the cost of the equipment,

maintenance and financing.39

Table 4.33: Estimates of willingness to purchase branded durable products

β p-value Exp(B)

Familiarity and convenience 1.378 .000 3.967

Appearance and price .556 .000 1.744

Quality and brand name .446 .001 1.561

Area (rural/urban) -.182 .528 .834

Gender (male/female) .217 .437 1.242

Age (35 or less/more than 35) .128 .641 1.137

Children in household (Yes/No) .471 .176 1.601

Constant 1.893 .000 6.638

Note: Exp(B): Odds ratio

39https://static.squarespace.com/static/51bef39fe4b010d205f84a92/t/51f237c4e4b07e4e5ac4e0f6/1374828484103/Full_report_Maketing_for_the_BOP.pdf

117

4.5 Influence of social networks on purchase behavior

Social institutions dominate in subsistence markets as members of BOP community often

exchange opinions about products and services with members of the social networks. Such

consumers live in hostile circumstances and support each other in day to day works in order to

cope up with poverty constraints. Being unable to make an optimum choice, BOP consumers seek

brand recommendations and purchase related information from members of the social networks

or from whom they have typical face to face interactions. Keeping in view the importance of

social networks in consumer choice at subsistence markets, the present study aimed to explore the

influence of social network on purchase behavior of BOP consumers. This section has been

divided in to two sub-sections: first sub-section reveals information about respondents’ affiliation

to different types of social networks like self-help groups, labor groups, non-government

organizations, trade associations, religious groups and political groups etc. Second sub-section

elucidates findings of the influence of social networks on purchase behavior of bottom of the

pyramid consumers.

4.5.1 Affiliation to different types of social networks

In order to cope up with persistent constraints at subsistence markets, disadvantaged

consumers attempt to get affiliated to various social networks. These networks help subsistence

consumers to gain economic benefits, acquire power from membership, equal rights within the

group, leadership skills, higher self-efficacy and acquaintance with numerical skills. Respondents

were requested to provide information about their affiliation to different types of social networks

like self-help groups, labor groups, non-government organizations, trade associations, religious

groups and political groups.

Findings in this regard (table 4.34) highlight that 49 per cent of the respondents were

affiliated to social networks like self-help groups, labor groups, non-government organizations,

trade associations etc; whereas 51 per cent were not affiliated to social networks. Out of these,

maximum 101 respondents were affiliated to self-help groups, whereas 97 respondents were

affiliated to political groups. A few (about 7 per cent) were affiliated to religious groups, and 5

per cent of the respondents were affiliated to labor groups. Findings also revealed some variations

in affiliation to social networks with respect to rural and urban areas. Only nine per cent of the

urban respondents were found to be affiliated to SHGs; however more than 20 per cent of the

rural consumers were affiliated to SHGs. In contrast, a small proportion (1 per cent) of the rural

respondents was found to be affiliated to NGOs. Overall, majority of the rural respondents

118

(51 per cent) were affiliated to social networks, whereas this figure decreased to 43 per cent for

urban respondents.

Table 4.34: Respondents’ affiliation to different types of social networks

Types of social networks

Frequency

Rural (n=400)

Urban (n=200)

Total (n=600)

Self-help groups 84 (21.0) 17 (8.5) 101 (16.8)

Labor groups 12 (3.0) 18 (9.0) 30 (5.0)

Non-government organizations 4 (1.0) 5 (2.5) 9 (1.5)

Trade associations 5 (1.2) 3 (1.5) 8 (1.3)

Religious groups 31 (7.8) 14 (7.0) 45 (7.5)

Political groups 68 (17.0) 29 (14.5) 97 (16.2)

Not affiliated 196 (49.0) 114 (57.0) 310 (51.7)

Notes: Values in parentheses represent percentage

4.5.2 Influence of social networks on purchase behaviour

By reviewing the pertinent literature, the study identified three characteristics of social

networks namely relationship orientation, similarity and expertise (see section 2.3). Influence of

these characteristics on intention to purchase a product recommended by network members was

examined through two mediators viz. word-of-mouth and trust. The present study adopted

S–O–R (Stimulus–Organism–Response) framework to test the proposed model (see fig. 2.3).

Stimulus was operationalized as characteristics of the social networks (i.e. relationship

orientation, similarity and expertise); organism as word-of-mouth and trust among network

members; and response as the intention to purchase a product recommended by network

members. The study also provided evidence on how the affiliation to social networks causes

differences in the intention to purchase a product recommended by network members. For this

purpose, present study compared the proposed relationships across two groups of consumers:

group 1: consumers affiliated to social networks and group 2: consumers unaffiliated to social

networks. On the basis of literature review, several hypotheses were developed that were tested

by using structural equation modeling. Relationships proposed in the hypotheses have been

mentioned in the table 4.35:

119

Table 4.35: Hypotheses proposed to test the model ‘influence of social network on purchase behavior’

Number Hypotheses Relationship

H9 Consumers perceiving network members as more relationship orientated will have a higher word-of-mouth with network members

RO � WOM(+)

H10 Consumers perceiving network members as more relationship orientated will place higher trust in network members

RO � Trust(+)

H11 Consumers perceiving network members as more similar will have a higher word-of-mouth with network members

Similarity � WOM(+)

H12 Consumers perceiving network members as similar will place higher trust in network members

Similarity � Trust(+)

H13 Consumers perceiving network members as experts will have a higher word-of-mouth with network members

Expertise � WOM(+)

H14 Consumers perceiving network members as experts will place higher trust in network members

Expertise � Trust(+)

H15

Consumers with high word-of-mouth with network members will have a higher intention to purchase a product recommended by network members

WOM � PI(+)

H16 Consumers with a higher trust on network members will have a higher intention to purchase a product recommended by network members

Trust � PI(+)

Note: ‘+’ sign shows a linear positive relationship among variables; RO, relationship orientation; WOM, word-of-mouth; PI, purchase intention

Measurement analysis of the model ‘influence of social network on purchase behavior’

The study tested measurement model for two forms of invariance such as configural and

metric invariance. Configural invariance refers to same pattern of factor loadings of measurement

items across different groups (Steenkamp and Baumgartner 1998). It was diagnosed by

conducting confirmatory factor analysis without constraint in which all factors are set free across

two subgroups viz. group 1: consumers affiliated to social networks and group 2: consumers

unaffiliated to social networks. Results indicated an acceptable model fit with X2 = 629.01, p <

0.001, d.o.f = 346, X2/d.o.f = 1.81 ≤ 3 (Hair et al 2006); GFI = 0.91 ≥ 0.8, AGFI = 0.88 ≥ 0.8

(Baumgartner and Homburg 1996); CFI = 0.97 ≥ 0.9 (Hair et al 2006); and RMSEA = 0.03 ≤

0.08 (Steiger 1990). Findings also highlighted all factor loadings to be statistically significant

across two subgroups. Above evidence specified measurement model to be configural invariant.

Metric invariance implies that variables are measured according to the same scale intervals across

different groups (Steenkamp and Baumgartner 1998). The study tested for full metric invariance

120

by comparing model fit with factor loadings constrained to be equal across subgroups, to

configural invariance model. The results indicated an insignificant change in the fit measures

(∆X2 = 25.73, ∆d.o.f = 21, p < 0.21; ∆GFI = 0.004; ∆AGFI = 0.002; and ∆RMSEA = 0.001). The

measurement model, therefore, was supported to be fully metric invariant across two subgroups.

Thus, differences between the proposed relationships will be due to the causal relationships

themselves and not to the measurement of the constructs.

Results of confirmatory factor analysis (table 4.36) reveal that values of cronbach alpha

(α) and composite reliabilities were higher than the minimum cutoff score of 0.70 (Nunnally

1978), that highlighted a good measure of internal consistency. Average variance extracted values

were higher than 0.5; each statement had a significant factor loading greater than 0.5 on the

expected constructs that confirmed convergent validity (Fornell and Larcker 1981). The

correlations among constructs were lower than the square root of average variance extracted that

supported discriminant validity (Fornell and Larcker 1981). Further, results indicated an

acceptable fit with X2 = 424.49, p < 0.001, d.o.f = 173, X2/d.o.f = 2.45 ≤ 3; GFI = 0.93 ≥ 0.8,

AGFI = 0.91 ≥ 0.8; CFI = 0.97 ≥ 0.9; and RMSEA = 0.04 ≤ 0.08.

Structural analysis of the model ‘influence of social network on purchase behavior’

The structural model indicated an acceptable fit with X2 = 423.13, p < 0.001, d.o.f = 243,

X2/d.o.f = 1.74 ≤ 3; GFI = 0.94, AGFI = 0.92 ≥ 0.8; CFI = 0.98 ≥ 0.9; IFI = 0.98 ≥ 0.9; and

RMSEA = 0.03 ≤ 0.08. The study indicated a marginal influence of control variables across

majority of the relationships. The findings demonstrated a significant influence of education (βstd

= 0.076, p ≤ 0.038) and prior ownership of mobile (βstd = -0.071, p ≤ 0.035) on word-of-mouth. It

implies that educated consumers spread more word-of-mouth within social networks than their

uneducated counterparts. In contrast, consumers owing a mobile phone do not tend to engage in

this type of communication with network members. Further, results did not support influence of

control variables on trust and intention to purchase a product recommended by network members.

121

Table 4.36: Reliability and validity of the model ‘influence of social network on purchase behavior’

α CR AVE Trust

Relationship orientation

Expertise Similarity WOM Purchase intention

Trust 0.91 0.90 0.71 0.84

Relationship orientation 0.85 0.81 0.59 0.63 0.77

Expertise 0.83 0.82 0.54 0.56 0.51 0.73

Similarity 0.94 0.94 0.86 0.72 0.71 0.62 0.92

WOM 0.85 0.85 0.59 0.59 0.69 0.34 0.67 0.77

Purchase intention 0.90 0.91 0.77 0.76 0.71 0.61 0.82 0.64 0.87

Note: Bold numbers on the diagonal are square root of AVE; and off-diagonal values represent correlation between constructs. CR, composite reliability; AVE, average variance extracted; WOM, word-of-mouth

122

Table 4.37: Structural results of the model ‘influence of social network on purchase behavior’

Hypothesis Relationship Std. β t-value Hypotheses

support

H9 Relationship orientation � WOM (+) 0.43 7.01* Yes

H10 Relationship orientation � Trust (+) 0.23 4.77* Yes

H11 Similarity � WOM (+) 0.46 7.30* Yes

H12 Similarity � Trust (+) 0.49 9.01* Yes

H13 Expertise � WOM (+) -0.12 2.45 No

H14 Expertise � Trust (+) 0.21 4.78* Yes

H15 WOM � Purchase intention (+) 0.26 6.44* Yes

H16 Trust � Purchase intention (+) 0.68 14.64* Yes

Note: *p < 0.05, WOM: word-of-mouth

Findings (table 4.37) supported H9 as consumers perceiving network members as more

relationship orientated were found to have a higher word-of-mouth with network members. The

study also established H10, as consumers perceiving network members as more relationship

orientated placed more trust on network members. From H11, the study expected that consumers

perceiving network members as more similar have a higher word-of-mouth with network

members. This expectation also received confirmation. H12 was also confirmed, as consumers

perceiving network members as more similar were found to place more trust on network

members. Unlike hypothesized, H13 was not supported as consumers perceiving network

members as experts were not found to have a higher word-of-mouth with network members.

Next, H14 was also confirmed because consumers perceiving network members as experts

revealed a higher trust on network members. From H15, it was anticipated that consumers with

high word-of-mouth with network members have a higher purchase intention. This prediction

received confirmation. Finally, results also supported H16, as consumers with a higher trust on

network members were found to show a higher intention to purchase a product recommended by

network members.

Mediation analysis of the model ‘influence of social network on purchase behavior’

Mediation occurs if the effect of an independent variable (X1) on dependent variable (Y)

is partly or entirely transmitted through another variable (say X2). A mediated path involves a

causal sequence; first, X1 influences X2; and then X2 influences Y. Mediation by word-of-mouth

and trust in the paths from relationship orientation, similarity and expertise to purchase intention

123

(Fig. 2.3), was examined through bias-corrected bootstrap confidence intervals for indirect effects

at 5 per cent level of significance. According to this method, indirect effects are significant if

bias-corrected bootstrap confidence interval for the estimates does not contain zero (Shrout and

Bolger 2002; MacKinnon et al 2007). Findings from mediation analysis (table 4.38) indicate that

trust mediated the path from relationship orientation to purchase intention. However, word-of-

mouth was not found to mediate the path from relationship orientation to purchase intention.

Results also highlight that both word-of-mouth and trust mediated the path from similarity to

purchase intention. Further, paths from expertise to purchase intention were also found to be

mediated by word-of-mouth and trust.

Table 4.38: Mediation results of the model ‘influence of social network on purchase

behavior’

Mediated Paths Bootstrap estimates*

Bias-corrected confidence

interval p- value Mediation

Relationship orientation towards purchase intention through word-of-mouth

0.036 0.000 to 0.072 0.101 No

Relationship orientation towards purchase intention through trust

0.056 0.023 to 0.086 0.019 Yes

Similarity towards purchase intention through word-of-mouth

0.031 0.003 to 0.070 0.078 Yes

Similarity towards purchase intention through trust

0.117 0.070 to 0.173 0.011 Yes

Expertise towards purchase intention through word-of-mouth

-0.017 -0.034 to -

0.001 0.080 Yes

Expertise towards purchase intention through trust

0.070 0.032 to 0.109 0.012 Yes

Note: *Standardized indirect effects

Moderation analysis of the model ‘influence of social network on purchase behavior’

In general terms, a moderator is a variable that influences the direction and/or strength of

the relationship between an independent variable and a dependent variable (Baron and Kenny

1986). For undertaking moderation analysis, a multi-group model was created to test differences

between proposed relationships across two subgroups: consumers affiliated to social networks

and consumers unaffiliated to social networks. For comparing statistical differences between

estimates of relationships across these subgroups, unstandardized estimates and standard errors

124

were used to calculate z-score (Iglesias and Vázquez, 2001; Hair et al 2006). It was interesting to

note that relationships proposed in H15 and H16 differ statistically for two subgroups (table

4.39). The results found no moderation for other relationships proposed in the study. Consumers

unaffiliated to social networks demonstrated a more intense relationship between word-of-mouth

and intention to purchase a product recommended by network members. On the other hand,

consumers affiliated to social networks yielded a higher strength of relationship between trust and

purchase intention.

Table 4.39: Multi-group results of the model ‘influence of social network on purchase

behavior’

Hypothesis Estimates

z-score Moderation Group 1 p-value Group 2 p-value

H9 0.386 0.000 0.355 0.000 0.29 No

H10 0.202 0.000 0.274 0.000 0.75 No

H11 0.220 0.000 0.314 0.000 1.11 No

H12 0.331 0.000 0.314 0.000 0.22 No

H13 -0.110 0.128 -0.141 0.035 0.31 No

H14 0.272 0.000 0.215 0.002 0.59 No

H15 0.201 0.063 0.530 0.000 2.18* Yes

H16 1.272 0.000 0.904 0.000 2.37* Yes

Note: *p < 0.05; Group1: consumers affiliated to formal social networks; Group2: consumers unaffiliated to formal social networks

Theoretical implications of the model ‘influence of social network on purchase behavior’

This study contributes to the BOP literature in two ways. First, the study identifies

characteristics of social networks such as relationship orientation, similarity and expertise that

potentially influence word-of-mouth and trust among network members. Trust mediated the

influence of these characteristics on intention to purchase a product recommended by network

members. Further, word-of-mouth also mediated the path from similarity, expertise and purchase

intention. These findings indicated that the identified characteristics of social networks and its

influence on BOP purchase behavior are consistent with the given theoretical framework. Second,

the study found significant variations in BOP purchase behavior due to consumer’s affiliation to

social networks. For example, the results found a more intense relationship between word-of-

mouth and purchase intention for consumers unaffiliated to formal social networks. This finding

indicated that word-of-mouth is more effective in adoption of new products among consumers

unaffiliated to networks where they are not restricted by formal rules and regulations. Whilst, for

125

consumers affiliated to formal social networks, the study found a stronger influence of trust on

intention to purchase a product recommended by network members. This may happen due to the

fact that members of a social network maintain stronger social ties that help to develop a high

level of trust among them. Consequently, BOP consumers intend more to buy a product

recommended by members of a social network

Managerial implications of the model ‘influence of social network on purchase behavior’

The results highlighted trust as the strongest predictor of intention to purchase a product

recommended by BOP network members. This finding suggested managers to design promotions

around members who are trusted more within the social networks. Firms may adopt network

marketing approach that relies almost entirely on the use of network relationships and offers

incentives to existing customers for recommending others to purchase a specific product (Stokes

and Lomas 2002). For instance, Godrej and Boyce, an Indian consumer-durable company,

launched a low-cost refrigerator ‘ChotuKool’, potentially for BOP consumers. For distributing

this product in subsistence markets, company partnered with rural consumers and offered a

commission of roughly $3 per unit sold. As a consequence, the company sold about 100,000 units

of ‘ChotuKool’ in its second full year on the market (Tiwari and Herstatt 2012). Also, managers

can provide trial products to trusted members, such as opinion leaders in social networks, and

encourage them to recommend others to adopt the product. These leaders may also facilitate

managers in arranging meetings with network members, where skill-constrained consumers may

learn usage and benefits of products through live demonstrations. This approach proved to be

instrumental for two major consumer-product companies, Procter and Gamble and Hindustan

Unilever Limited, to successfully market low-cost water purifier to economically disadvantaged

consumers in India.

Consumers perceiving network members as more relationship orientated were found to

have a higher word-of-mouth with network members. Such consumers also tend to place higher

trust on network members. Managers, therefore, need to identify individuals having stronger

relationships with network members and encourage them to recommend others to purchase a

specific product. Further, the study found consumers perceiving network members as more

similar have a higher word-of-mouth and trust with network members. This finding reinforced the

fact that BOP consumers interact and place higher trust in network members with similar socio-

economic background. This evidence sheds some insights into the importance for firms to

collaborate with SHGs and NGOs that are conversant with local language and culture. Here,

managers may encourage SHGs and NGOs to recommend products to network members that

126

would potentially generate higher purchase intentions. For instance, Bata Shoe Company

collaborated with CARE Bangladesh, a NGO, to distribute its products through a network of

women sales agents (‘aparjitas’- a woman who does not accept defeat). The firm works with

around 3,000 aparajitas to sell low-cost shoes in BOP markets of the country. NGO members

helped the company to sell over 30 million pairs annually with a turnover of US$70 million

(McKague and Tinsley 2012).

Unlike hypothesized, consumers perceiving network members as experts were not found

to have a higher word-of-mouth with network members. There could be two possible reasons:

first, majority of the BOP consumers are socially excluded where neighborhoods and peer groups

also consist of other excluded consumers (Williams and Windebank 2002; Hamilton 2009). BOP

consumers, therefore, may not perceive network members as experts in terms of providing

reliable information. Second, low-confidence and low-literacy standards may also inhibit

underprivileged consumers to offer an accurate recommendation. Further, results indicated that

consumers perceiving network members as experts place a higher trust on network members.

Managers, therefore, are suggested to navigate product recommendations through local retailers

that are perceived to be experts in offering marketplace information. These retailers offer

products on credit and help disadvantaged consumers to make a good choice.

4.6 Marketing mix strategies of selected companies

In previous sections of this chapter, the study attempted to analyze purchase behavior of

BOP consumers, willingness to purchase branded products and influence of social networks on

purchase behavior for selected products. In this section, an attempt has been made to study

marketing mix strategies of selected companies by collecting data from managers through

structured interviews. In total 50 managers (one from each selected company) were interviewed

including 30 managers from food and FMCG; and 20 managers from durables sector. Managers

were requested to rate various statements measuring their perceptions towards BOP market and

marketing mix strategies of the companies for BOP consumers. In addition, respondents were

asked to rate statements related to customer orientation of the company, top-management’s

commitment towards BOP consumers, performance of the company and effectiveness of selected

promotion mix elements. The statements used to gather the data were developed on a seven-point

likert scale where ‘1’ indicated strongly disagree and ‘7’ indicated strongly agree.

This section has been divided into eight sub-sections. The first sub-section highlights the

profile of the selected companies; second sub-section elucidates scale development and reliability

coefficients. In third sub-section, managers’ perceptions about BOP markets have been examined;

127

fourth sub-section elucidates managerial perceptions about marketing mix strategies of selected

companies. Next, fifth sub-section examines distribution strategy of selected companies. Sixth

sub-section explores managerial perceptions about customer orientation, top-management’s

commitment and company’s performance. In seventh sub-section, an attempt has been made to

examine managerial perceptions towards effectiveness of promotion mix elements in BOP

markets. The last sub-section explores the influence of marketing mix on company’s

performance.

4.6.1 Profile of the selected companies

Respondents were enquired about sector of the company, type of the company (a

domestic company or subsidiary of a foreign company) and number of employees in the

company. The data for annual sales turnover were obtained from the annual reports of the

selected companies. Findings in this regard have been given below:

Table 4.40: Profile of the selected companies

Characteristics Category Frequency (n= 50)

Sector Food and FMCG 30 (60.0)

Consumer durables 20 (40.0)

Company type A domestic company 34 (68.0)

Subsidiary of a foreign company 16 (32.0)

Number of employees 500-1,000 05 (10.0)

Above 1,000 45 (90.0)

Annual sales turnover (Indian rupees)

Less than 5,000 crore 29 (58.0)

5,000-10,000 crore 09 (18.0)

More than 10,000 crore 12 (24.0)

Note: Values in parenthesis represent percentage

Findings (table 4.40) indicated that majority of the companies (68 per cent) were

domestic companies; however about one third (32 per cent) were subsidiaries of foreign

companies. Further, a large majority (90 per cent) of the companies were having more than 1,000

employees, whereas only 10 per cent of the companies were having between 500-1,000

employees. Annual reports of the selected companies highlighted that majority (58 per cent) of

the companies have annual sales turnover of less than INR 5,000 crore; 18 per cent of the

128

companies have annual sales turnover between INR 5,000-10,000 crore; and 24 per cent of the

companies have annual sales turnover more than 10,000 crore.

4.6.2 Reliability of the scales used

Due to lack of empirical research on bottom of the pyramid, an existing validated scale

regarding marketing mix strategies of the companies, customer orientation of the company,

top-management’s commitment towards BOP consumers and performance of the company, could

not be found. Therefore, scales used to operationalize the constructs in the interview schedule

were mainly developed from limited studies available. Respondents were requested to give their

response on a 7-point scale that was used to measure managers’ perceptions towards various

constructs. Distribution strategy of the selected companies was measured by asking respondents

to provide information about company’s collaborations with cross-sector partners in BOP

markets.

Managers’ perceptions about BOP markets were measured with the help of a 5-item

scale, developed from Bang and Joshi (2012). Managers’ perceptions about product strategies for

BOP consumers were measured using a 5-item scale, adapted from Bharti et al (2014); Grawe et

al (2009); Ernst et al (2014); and Viswanathan and Sridharan (2012). Managers’ perceptions

about pricing strategies were measured using a 4-item scale, developed form Kirchgeorg and

Winn (2006); Ernst et al (2014); and Bang and Joshi (2010). The respondents were also asked to

provide information about distribution strategy in the form of company’s collaboration with

several cross-sector partners such as suppliers, logistic service providers, financial institutions,

local retailers, non-profit organizations, non-government organizations, local communities, self-

help groups, centre government institutions and state government institutions (Schuster and

Holtbrügge 2014; Ernst et al 2014; Chikweche and Fletcher 2012). The collaboration of selected

companies with these partners was measured with the help of a dichotomous question. Managers’

perceptions about promotion strategies were measured with the help of a 4-item scale, adapted

from Sengar et al (2014); and Chikweche and Fletcher (2012). Further, managers’ perceptions

about customer orientation of the company were measured by using a 4-item scale from studies

like Grawe et al (2009); and Deshpande and Farley (1998). Managerial perceptions about top-

management’s commitment towards BOP consumers were measured with the help of a 4-item

scale, developed from Brinkhoff et al (2015); and Sengar et al (2014). Managerial perceptions

regarding company’s performance were measured by using a 4-item scales by referring to the

works of Ernst et al (2014); Schuster and Holtbrügge (2014); Bang and Joshi (2012). List of

items along with reliability statistics have been shown in the table 4.41:

129

Table 4.41: List of items and reliability statistics

Construct Items Cronbach alpha (α)

Studies reviewed

Managerial

perceptions about BOP markets

Product development cost is higher for BOP markets* It is easy to distribute products in BOP markets It is easy to promote products in BOP markets BOP offers a future mass market Selling products to BOP consumers is much profitable

.757 Bang and Joshi (2012)

Product strategy

Our company attempts to develop customized products in terms of size, features and design to suit BOP consumers Our company gives special emphasis to co-create products with BOP consumers Our company constantly seeks to develop need-satisfying products for BOP consumers Our company actively engages with BOP consumers to seek their advice on product development Our company seeks to develop products keeping in view the low-literacy of BOP consumers

.950

Bharti et al

(2014); Grawe et

al (2009); Ernst et al (2014); and Viswanathan and Sridharan (2012)

Pricing strategy

Our company offers low-priced products particularly for BOP consumers Our company focuses on low-margin high-volume pricing for BOP consumers Our company tailors pricing mechanisms to suit BOP market conditions Our company attempts to set price keeping in view low-income of BOP consumers

.943

Kirchgeorg and Winn (2006);

Ernst et al (2014); and

Bang and Joshi (2010)

Promotion strategy

Our company provides a separate promotional budget to encourage sales at BOP markets Our company attempts to communicate with BOP consumers in local or regional language Our company attempts to promote products through social networks at BOP markets Our company attempts to promote products through non-traditional/informal media (NGOs, SHGs etc.) at BOP markets

.826

Sengar et al (2014); and

Chikweche and Fletcher (2012)

130

Customer orientation of the company

Our business objectives are driven primarily by customer satisfaction We communicate information about our customer experiences across all business functions Our strategy for gaining a competitive advantage is based on our understanding of customer needs We regularly survey end-customers to assess the quality of our products and service

.875

Grawe et al (2009); and

Deshpande and Farley (1998)

Top-management’s commitment towards

BOP consumers

Top-management assumes its responsibility for offering affordable products to BOP consumers Top-management delegates necessary authority to its employees for marketing products to BOP consumers Top-management allocates a separate budget for marketing products to BOP consumers Top-management allocates a separate team for marketing products to BOP consumers

.937

Brinkhoff et al (2015); and Sengar et al

(2014)

Company

performance

Our company has achieved higher profits than expected Our company has been able to attain growth targets Our company has been able to attract new customers Our company has been able to achieve expected market share

.801

Ernst et al (2014); Schuster and Holtbrügge (2014); Bang

and Joshi (2012) Note: *Reverse coded item

131

4.6.3 Managerial perceptions towards BOP markets

Previous literature on bottom of the pyramid markets indicates that there is no consensus

on profitability and sustainability of companies operating in subsistence markets. Several authors

believe that these markets create mutually rewarding ventures for both consumers and companies,

despite of persisting transactional and operational challenges (Prahalad and Hart 2002; Prahalad

2006; Viswanathan et al 2010). These authors considered BOP market as the world’s largest

market with over four billion customers that are expected to reach six billion by 2045 (Hammond

et al 2007). Studies have also provided empirical evidence on positive associations between

market expansion strategy and sales revenue and profits of the companies in BOP markets (Bang

and Joshi 2010). However, this approach has been criticized by authors who considered BOP

population as manufactures of products rather than consumers only (Karnani 2006; Karnani,

2007; Gold et al 2013). These authors suggest managers to consider BOP population as potential

producers and to involve them in value-generating operations by building their skills and

capabilities. Therefore, it is important to understand manager’s perceptions towards BOP

markets. Managerial perceptions were measured on a 7-point likert scale ranging from strongly

disagree to strongly agree. The mean scores of the statements were calculated and one sample t-

test was used to examine statistical differences between mean scores and mid value ‘4’. The

findings in this regard have been presented in the table 4.42:

Findings (table 4.42) highlight that managers have favorable perceptions about BOP

markets as mean scores of all statements were found to be significantly higher than the mid value

‘4’. For instance, findings highlighted that respondents perceived BOP as a future mass markets.

Managers perceived that selling products to BOP consumers is much profitable. Managers also

Table 4.42: Managerial perceptions towards BOP markets

Statements Mean score t-value Standard deviation

BOP offers a future mass market 5.18 7.994* 1.044

Selling products to BOP consumers is much profitable 5.16 7.137* 1.149

It is easy to promote products in BOP markets 5.02 6.936* 1.040

It is easy to distribute products in BOP markets 4.86 5.320* 1.143

Product development cost is higher for BOP markets** 4.42 2.382* 1.247

Note: *p< 0.05; **reverse coded statement

132

perceived that it is easy to promote and distribute products in BOP markets. Managers were found

to disagree that product development cost is higher for BOP market.

4.6.4 Managerial perceptions towards marketing mix strategies

Managers of the selected companies were requested to rank several statements,

measuring their perceptions about different marketing mix strategies. One sample t-test was used

to examine statistical differences between mean scores and mid value ‘4’. Findings in this regard

have been presented in the table 4.43:

Table 4.43: Managerial perceptions towards marketing mix strategies

Statements Mean score t-value Standard deviation

Product

Our company attempts to develop customized products in terms of size, features and design to suit BOP consumers

4.70 5.081* .974

Our company constantly seeks to develop need-satisfying products for BOP consumers

4.60 3.601* 1.178

Our company gives special emphasis to co-create products with BOP consumers

4.54 3.439* 1.110

Our company seeks to develop products keeping in view low-literacy of BOP consumers

4.50 3.352* 1.055

Our company actively engages with BOP consumers to seek their advice on product development

4.44 2.955* 1.053

Pricing

Our company attempts to set price keeping in view low-income of BOP consumers

5.10 7.375* 1.055

Our company offers low-priced products particularly for BOP consumers

5.08 7.164* 1.066

Our company focuses on low-margin high-volume pricing for BOP consumers

5.02 6.465* 1.116

Our company tailors pricing mechanisms to suit BOP market conditions

4.94 6.800* .978

Promotion

Our company attempts to promote products through non-traditional/informal media (NGOs, SHGs etc.) at BOP

5.36 9.977* .964

Our company attempts to promote products through social networks at BOP

5.36 8.876* 1.083

Our company attempts to communicate with BOP consumers in local or regional language

5.28 9.543* .948

Our company provides a separate promotional budget to encourage sales at BOP market

5.22 7.292* 1.183

Note: *p< 0.05

133

Findings (table 4.43) highlight that mean scores of all the statements were significantly

higher than the mid value ‘4’. Managers perceived that companies attempt to develop customized

products in terms of size, features and design to suit BOP consumers. Managers were also found

to perceive that companies seek to develop need-satisfying products and give special emphasis to

co-create products for BOP consumers. Also, respondents agreed that companies actively engage

with BOP consumers to seek their advice on product development. Further, it was interesting to

find that companies attempt to set price keeping in view the low-income of consumers. Managers

perceived that companies focus on low-margin high-volume pricing; and tailor pricing

mechanisms to suit BOP market conditions. Further, managers agreed that companies promote

products through non-traditional social networks like NGOs and SHGs in subsistence markets.

Also, managers perceived that companies attempt to communicate in local or regional language

and provide a separate promotional budget to encourage sales in BOP markets.

4.6.5 Distribution strategy of selected companies

Distribution of products and services in BOP markets poses a key challenge for

companies due to poor transportation and communication infrastructure. Such constraints become

more severe in far-flung rural areas where cost of reaching per consumer may increase.

Companies operating in these markets attempt to reorganize their formal distribution strategies

and focus on possible alternatives of distribution. In this regard, companies attempt to collaborate

with cross-sector partners like social networks that provide a novel avenue for distributing

products and services in resource-constrained markets. Here, cross-sector collaborations refer to

dependence on the external partners that are spread across different sectors of the economy

(London and Hart 2004; Schuster and Holtbrugge 2014). Through such partnerships, companies

offer technology, expertise, production facilities and methods of distribution whereas social

networks like self-help groups (SHGs) and non-government organizations (NGOs) offer their

knowledge about local economic, social and political systems (Follman 2012). In addition, firms

and partners contribute complementary capabilities, both intangible assets such as reputation, and

brand; and tangible resources, such as human capital, production capabilities and market access.

These partnerships also enable participating companies to create and deliver value in novel ways,

while minimizing costs and risks (Dahan et al 2010).

Keeping in view the importance of such collaborations, respondents were enquired about

company’s collaborations with selected cross-sector partners like suppliers, logistics service

providers, financial institutions, local retailers, non-profit organizations, non-government

134

organizations, local communities, self-help groups, centre government institutions and state

government institutions. The findings in this regard have been presented in the table 4.44:

Table 4.44: Cross-sector collaborations by companies

Cross-sector collaborations

Food and FMCG (n=30)

Consumer durables (n=20)

Total (n=50)

Yes No Yes No Yes No

Suppliers 12 (40.0) 18 (60.0) 07 (35.0) 13 (65.0) 19 (38.0) 31 (62.0)

Logistic service providers 01 (3.3) 29 (96.7) 00 (0.0) 20 (100) 01 (2.0) 49 (98.0)

Financial institutions 04 (13.3) 26 (86.7) 16 (80.0) 04 (20.0) 20 (40.0) 30 (60.0)

Local retailers 19 (63.3) 11 (36.7) 12 (60.0) 08 (40.0) 31 (62.0) 19 (38.0)

Non-profit organizations 11 (36.7) 19 (63.3) 02 (10.0) 18 (90.0) 13 (26.0) 37 (74.0)

Non-government organizations 16 (53.3) 14 (46.7) 08 (40.0) 12 (60.0) 24 (48.0) 26 (52.0)

Local communities 13 (43.3) 17 (56.7) 10 (50.0) 10 (50.0) 23 (46.0) 27 (54.0)

Self-help groups 14 (46.7) 16 (53.3) 08 (40.0) 12 (60.0) 22 (44.0) 28 (56.0)

Centre government institutions 05 (16.7) 25 (83.3) 03 (15.0) 17 (85.0) 08 (16.0) 42 (84.0)

State government institutions 08 (26.7) 22 (73.3) 02 (10.0) 18 (90.0) 10 (20.0) 40 (80.0)

Note: Values in parenthesis represent percentage

According to the responses given by managers, it was found that majority (62 per cent) of

the companies collaborated with local retailers for distributing products among consumers in

subsistence markets. More number of companies in food and FMCG (63.3 per cent) collaborated

with local retailers in comparison to companies in durables sector (60 per cent). About 48 per

cent of the companies collaborated with non-government organizations whereas 44 per cent of

companies collaborated with self-help groups. Major differences in number of companies across

two sectors having collaborations with financial institutions and non-profit organizations were

found. It was interesting to note that a large majority of companies in durable sector (80 per cent)

collaborated with financial institutions as compared to companies in food and FMCG sector

(about 13 per cent). This may be due to the fact that durable products are much expensive in

relation to food and FMCG, thereby BOP consumers need to avail credit facility from financial

institutions that collaborate with companies offering durable products. This seems to be a

rewarding venture where both consumers and companies potentially gain out of the

collaborations. Consumers get an easy access to durable products like television, refrigerators and

two-wheelers etc. that help to enhance productivity of BOP population (Bang and Joshi 2012);

135

whereas companies get an opportunity to offer these products to underserved consumers in BOP

markets. Further, more number of companies in food and FMCG sector (about 37 per cent) were

found to have collaborations with non-profit organizations in relation to their counterparts in

durables sector (10 per cent).

4.6.6 Managerial perceptions towards customer orientation, top-management’s commitment and company performance

Respondents were requested to rank statements measuring their perceptions towards

customer orientation, top-management’s commitment towards BOP consumers and performance

of the company on a 7-point scale where ‘1’ represented strongly disagree and ‘7’ represented

strongly agree. Differences in managers’ perceptions across two sectors were examined by

applying independent samples t-test on mean scores of the statements. Findings in this regard

have been presented in the table 4.40:

Findings (table 4.45) highlighted overall mean scores of the statements in various

constructs more than the mid value ‘5’. This finding indicates that managers’ perceived

companies to be customer-oriented and managers also perceived that top-management of the

companies have commitment towards BOP consumers. Further, findings highlighted several

statistical differences in managers’ perceptions across two sectors. For instance, difference in

manager’s perceptions for the statement ‘we regularly survey end-customers to assess the quality

of our products and service’ was found to be significant. Mean scores of managers’ perceptions

for the statements ‘top-management delegates necessary authority to its employees for marketing

products to BOP consumers’ and ‘top-management allocates a separate budget for marketing

products to BOP consumers’ were found to be higher in durable segment as compared to food and

FMCG segment. There is partial penetration of durable products in these markets (Singhal 2008);

therefore, top-management’s commitment is essential to make such products affordable and

accessible to economically disadvantaged populations. Mangers in durable sector also perceived

that companies have achieved higher profits than expected as compared to food and FMCG

sector.

136

Table 4.45: Differences in managers’ perceptions across different sectors

Statements Overall mean (n=50)

Mean t-

value Food and FMCG

Consumer durables

Customer orientation of the company

We regularly survey end-customers to assess the quality of our products and service

5.58 5.40 5.85 2.05*

Our strategy for gaining a competitive advantage is based on our understanding of customer needs

5.56 5.50 5.65 0.68

Our business objectives are driven primarily by customer satisfaction

5.46 5.33 5.65 1.45

We communicate information about our customer experiences across all business functions

5.44 5.30 5.65 1.31

Top-management’s commitment towards BOP consumers

Top-management delegates necessary authority to its employees for marketing products to BOP consumers

5.60 5.30 6.05 2.3*

Top-management assumes its responsibility for offering affordable products to BOP consumers

5.52 5.33 5.80 1.61

Top-management allocates a separate team for marketing products to BOP consumers

5.50 5.33 5.75 1.28

Top-management allocates a separate budget for marketing products to BOP consumers

5.34 5.03 5.80 2.37*

Company performance

Our company has been able to achieve expected market share

6.40 6.40 6.40 0.00

Our company has been able to attain growth targets 6.26 6.13 6.45 1.42

Our company has been able to attract new customers 6.24 6.30 6.15 0.64

Our company has achieved higher profits than expected 6.18 5.97 6.50 2.13*

Note: *p< 0.05

4.6.7 Managerial perceptions towards effectiveness of promotion mix elements

Respondents were requested to rate effectiveness of selected tools of promotion mix

elements, like advertising, sales promotion and personal selling, that companies use to promote

products in BOP markets. The study used a 7-point scale where ‘1’ represented ‘very ineffective’

and ‘7’ represented ‘very effective’. Advertising included five tools like magazines, newspapers,

wall painting, television, and radio; sales promotion also included five tools like buy one get one

free, free gifts, free samples, price discounts, and bonus pack (e.g. 20% extra); and personal

selling included two tools such as face-to-face interactions and live demonstrations. Independent

samples t-test was used to examine statistical differences in managers’ perceptions towards

137

effectiveness of promotion mix elements across two sectors. Findings in this regard have been

presented in the table 4.46:

Table 4.46: Differences in managers’ perceptions towards effectiveness of promotion mix elements

Promotion mix elements Overall mean

(n=50)

Mean

t-value Food and FMCG

Consumer durables

Advertising

Wall painting 6.12 5.93 6.40 2.19*

Television 5.50 5.60 5.35 1.28

Radio 5.04 4.97 5.15 0.80

Magazine 4.62 4.47 4.85 1.22

Newspaper 4.56 4.63 4.45 0.68

Sales promotion

Price discount 5.62 5.73 5.45 1.09

Bonus pack (e.g. 20% extra) 5.53 5.72 5.25 2.13*

Free gift 5.51 5.77 5.11 2.30*

Buy one get one free 5.32 5.33 5.30 0.11

Free sample 5.31 5.68 4.80 3.47*

Personal selling

Live demonstration 5.50 5.03 6.20 4.31*

Face-to-face interaction 5.44 4.87 6.30 6.31*

Note: *p< 0.05

On the basis of managers’ responses, findings (table 4.46) indicated ‘wall painting’ as the

most effective tool to advertise products in BOP markets. Managers also perceived that ‘wall

painting’ is the more effective tool to promote durable products among BOP consumers than food

and FMCG. Further, ‘price discount’ was found to be the most effective tool among sales

promotion in BOP markets. Within sales promotion, managers perceived ‘bonus pack’ as the

second most effective tool to promote products among subsistence consumers. Effectiveness of

‘bonus pack’ was perceived to be higher for promoting food and FMCG as compared to durable

products. Similarly, effectiveness of ‘free gift’ and ‘free sample’ was perceived to be higher for

promoting food and FMCG than durable products. These findings indicated that ‘bonus pack’;

138

‘free gift’; and ‘free sample’ are more effective for promoting food and FMCG among

subsistence consumers as these tools seem to offer an economic advantage to low-income

consumers. Managers perceived ‘live demonstration’ as a more effective tool for selling products

in these markets in comparison to ‘face-to-face interaction’. Moreover, effectiveness of ‘live

demonstration’ and ‘face-to-face interaction’ was found to be significantly higher for selling

durable products than food and FMCG. Consumers in these markets have limited knowledge and

inadequate information processing skills that may hinder them to make a good choice of durable

products. Here, face-to-face interactions and live demonstrations may provide an opportunity to

acquire necessary information and learn basic functionality of the products. Many multinational

companies have adopted such techniques for promoting products in these markets. For instance,

P&G launched a low-cost water purifier with brand name ‘PuR’ for targeting bottom tier

consumers by training its workforce to give live demonstrations of the product in interiors of the

country that resulted in good response from the consumers.

4.6.8 Influence of marketing mix strategies on company’s performance

The challenge of serving BOP consumers does not lie only in serving a large number of

consumers but also in the revising marketing mix to meet local needs and requirements.

Traditional marketing mix developed in western markets has proved to be inappropriate for BOP

markets that often failed to bring desired results. Marketing mix for BOP consumers require a

highly customized approach keeping in view uncertain and hostile circumstances of the

subsistence markets. Companies need to focus on providing enduring value to BOP consumers by

developing new products, redesigning packaging, revamping distribution systems and offering

innovative promotions. Chikweche and Fletcher (2012) argued that the products offered to BOP

consumers should consider the degree of essentiality and potential value added to it. Essentiality

means if a product having an additional feature, which is not related to the core functionality of

the product, is likely to increase cost of the product and would make it unaffordable for BOP

consumers. Further, traditional distribution channels have limited penetration in these markets,

therefore companies attempt to collaborate with non-traditional partners such as self-help groups,

non-government organizations and community groups, in order to find novel ways to distribute

products among disadvantaged consumers and generate economic and social profits

simultaneously. Previously established promotion elements like TV and internet advertisements

have not proved to be effective for communicating with poor consumers due to low literacy and

limited access to media (Chikweche and Fletcher 2012). Therefore, marketing mix developed as

per unique circumstances at BOP is likely to influence performance of the companies in these

139

markets. Thereby, the present study examined the influence of marketing mix strategies on

company’s performance by using Analysis of Covariance (ANCOVA).

Overall mean scores of the statements in product strategies; pricing strategies; and

promotion strategies were used as independent variables (referred as covariates in ANCOVA)

whereas overall mean score of the statements in ‘performance of the company’ was used as a

dependent variable. Respondents were requested to provide information whether the company has

collaborated with cross-sector partners like suppliers, logistic service providers, financial

institutions, local retailers, non-profit organizations, non-government organizations, local

communities, self-help groups, centre government institutions and state government institutions

for distributing products among BOP consumers. Companies having cross-sector collaborations

with six or more partners were referred to as ‘high-intensity distribution companies’ and

companies having collaborations with five or less partners were referred to as ‘low-intensity

distribution companies’. The dichotomous variable called ‘distribution intensity’ was used as a as

a factor in ANCOVA where ‘1’ represented ‘high-intensity distribution companies’ and ‘0’

represented ‘low-intensity distribution companies’. The relationships among the variables that

were tested through ANCOVA have been presented in the form of an equation given below:

�� =IJ +I�{� +I{ +IN{N +IO{O + �� where �� = � 1401z2�6 04/ℎ 60z92�y

{� = �10,56/-/12/ 83 -

{ = �1363�8-/12/ 83 - {N = �10z0/30�-/12/ 83 - {O = |3-/13I5/30�3�/ �-3/y

�� = �1101/ 1z

Levene’s test was used to check the assumption of homogeneity of error variance.

Levene’s test statistic was found to be insignificant (F= 0.051; p = 0.822), meeting the

assumption that group variances are equal. To diagnose the presence of multi-collinearity among

independent variables (covariates) under study, variance inflation factor was calculated. The

value of variance inflation factor was found to be less than three that rejected the presence of a

serious multi-collinearity problem (Hair et al 2006). The effect size of independent variables in

ANCOVA was measured by using partial eta square (partial η2) that considered the proportion of

variance explained by a variable which is not explained by other variables in the analysis. Effect

sizes between 0.01 to 0.06 were considered as small, between 0.06 to 0.13 were considered as

medium, and greater than 0.13 were considered as large (Harlow 2005). This measure provided a

140

better interpretation of p-value for which very low values can be obtained from large samples.

Findings in this regard have been presented in the table :

Table 4.47: Influence of marketing mix on company’s performance

F-value p-value Partial β2

Product strategies 5.021 .030 .100

Pricing strategies 15.419 .000 .255

Promotion strategies 11.121 .002 .198

Distribution intensity 6.205 .016 .121

Note: Dependent variable: performance of the company; R2= 0.417; Adjusted R2= 0.365

Analysis of covariance results revealed that in terms of prediction power, the model

explained a considerable amount of variation in the dependent variable. For instance, product

strategies, pricing strategies, promotion strategies and distribution intensity explained more than

40 per cent variation in performance of the company. The findings highlight that three covariates

emerged as significant predictors of performance of the company. Therefore, developing product,

price and promotion strategies as per unique circumstances of BOP markets are likely to enhance

performance of the company in these markets. Among these covariates, pricing strategies

emerged as the strongest predictor that explained about 25 per cent of variation in the dependent

variable. The effect size of pricing strategies on performance of the company was found to be

large (Partial η2 = 0.255); suggesting that managers need to offer low-priced products particularly

for BOP consumers with a focus on low-margins and high-volumes. The tailoring of pricing

mechanisms as per BOP market conditions would also help to enhance performance of the

company.

Promotion strategies was found to be the second most powerful determinant of

performance of the company (Partial η2 = 0.198). The effect size of promotion strategies was

found to be more than 0.13 which is considered to be a large effect. Therefore, companies are

suggested to provide a separate promotion budget to encourage sales and communicate with BOP

consumers in local or regional language. The promotions through non-traditional media and

social networks like NGOs, SHGs are also likely to enhance performance of the company in these

markets. Further, the influence of product strategies was also found to be significant (F = 5.021;

p = 0.03) with effect size of 0.10 which is considered to be a medium effect. The companies need

to co-create products with potential BOP consumers that would be more suitable to existing

market conditions. For instance, companies can seek advice on product development and design

products keeping in view low-literacy of BOP consumers. The distribution intensity also reported

141

a significant influence on performance of the company (F = 6.205; p = 0.016) with an effect size

of 0.121 that is considered to be a medium effect. Thereby, company need to make efforts to

collaborate with more number of non-traditional partners to effectively distribute products among

BOP consumers.

4.7 Suggested modifications in marketing mix strategies

On the basis of findings generated from consumer survey and interview of the managers,

the present study has drawn some practical implications that may be helpful for managers to

reconsider existing marketing mix and devise a new set of marketing mix strategies.

Product strategies

Findings suggested that perceived usefulness of branded food products is likely to

develop a favorable attitude of consumers towards branded food. Therefore, companies need to

undertake efforts to improve low-income consumers’ usefulness perceptions towards branded

food. This can be achieved by highlighting health benefits of branded food in two ways. First,

companies may develop food packaging that communicates its health benefits through

pictographic. Traditionally, companies in India have been providing nutrition information on food

packaging in English language which remains beyond the comprehension of BOP consumers.

Use of pictographic along with wording seems to be effective as such consumers recognize

brands from visual appearance of the package. Second, companies may fortify food products with

necessary vitamins and micronutrients of which BOP population is often deficient. BOP

consumers often lack confidence and purchase skills required to make an optimum choice.

Companies, therefore, may develop food packaging containing nutrition information in local

languages that may strengthen confidence and purchase skills among BOP consumers. Findings

also reinforce the concept of marketing low-priced packs of branded food to potential consumers

in subsistence markets. Companies can also develop small packs of premium food brands that are

expected to help in deep penetration of branded food into this market.

Further, ‘product appearance, price and brand’ was found to positively influence

consumers’ willingness to purchase branded FMCG. Companies need to focus more on designing

an attractive appearance of branded FMCG; however it seems that new attractive design may

enhance cost of the product. For this, companies may develop small packs of branded FMCG that

BOP consumers are able to afford. For instance, a few years back HUL launched its flagship

fairness cream brand ‘Fair and Lovely’ in sachets at INR 5. Presently, sachets account for more

than 50 per cent of Fair and Lovely sale in India. Findings highlighted ‘familiarity and

convenience’ as the strongest predictor of consumers’ willingness to purchase branded durable

142

products. This finding suggests managers to make consumers more familiar with brands being

offered in BOP markets. Durable products need to be developed in such a way that it provides

convenience to BOP consumers like in terms of easy to handle and operate.

On the basis of results of managers’ survey, product strategies were found to have a

positive influence on performance of the company. In order to develop products that are more

acceptable among BOP consumers, companies need to undertake several measures. For example,

companies may seek advice of BOP consumers on product design and development so that

product can be developed as per unique circumstances of subsistence marketplaces. Products

developed through co-creation are more likely to satisfying special needs of BOP consumers.

Pricing strategies

Findings of the study highlighted that consumers’ perceptions towards affordability of

branded food positively influences perceived behavioral control. This finding suggests companies

to develop low-priced small packs of branded food that are likely to enhance affordability

perceptions of BOP consumers. When BOP consumers perceive a product to be affordable, they

perceive that it is easy to purchase the underlying product. For enhancing affordability

perceptions among low-income consumers, many companies have started developing small packs

of food and FMCG and dropped per unit price of the products. A major packaged food company

in India, Parle Products Private Limited, developed small packs of its premium biscuit brands

such as ‘Hide and Seek’ and ‘Milano’ with a price of INR 5 to make it more affordable for rural

and low-income consumers. Traditionally, dairy product like ghee was available in Indian

markets in the pack size of 250 gm, 500 gm or 1 kg; that BOP consumers were unable to purchase

due to insufficient funds. In addition, such consumers do not have storage facility to keep it

preserved over a longer time period due to limited ownership of refrigerators. For the first time in

India, dairy major Parag Milk Foods Private Limited introduced ghee in sachets of 18ml and 9ml

at INR 20 and INR 10 respectively. With the launch of such small packs, BOP consumers get an

opportunity to purchase branded food with minimal cash out-lay. Also, there is no need to keep

small packs under preservation because such packs can be consumed before their nutritional

content is lost. Keeping in view the importance of small packs among BOP population,

companies have downsized the price of mosquito repellents to INR 1 per unit.

Findings also suggest managers to enhance consumers’ affordability perceptions towards

durable products, specifically in rural areas where penetration of such products is still very low.

For this, companies need to come out with innovative financing schemes that may allow

disadvantaged consumers to pay price of the product through easy installments over a period of

143

time. Findings highlight that respondents perceived product price as the most importance

parameter influencing the purchase of branded FMCG and branded durable products. Due to

low-income, such consumers may perceive branded FMCG and branded durable products as

expensive products; thereby they attach more importance to the price of branded products.

Therefore, managers have been suggested to offer low-priced products for BOP consumers with a

focus on low-margins and high-volume strategy. The tailoring of pricing mechanisms as per BOP

market conditions is also likely to enhance performance of the company.

Distribution strategies

Distribution of products and services among BOP consumers remains a key challenge for

firms due to lack of transportation and communication means. Traditional distribution channels

are also expected to be ineffective in subsistence markets whereas existing local partners lack

adequate knowledge and capabilities. Thereby, companies need to reorganize their formal

distribution strategy and focus on possible alternatives of distribution. Results highlighted

‘retailer’s recommendations’ as the most preferred source of information among BOP consumers

for purchasing food and FMCG. Also, large majority of the respondents were found to purchase

selected food products and selected FMCG from local nearby retailers. Therefore, local nearby

stores assume a greater role in determining consumer preferences in the purchase of food and

FMCG. Companies, therefore, need to collaborate with local retailers that may be encouraged by

managers to recommend BOP consumers to purchase a specific brand of the product.

On the basis of findings of the study, firms need to adopt network marketing approach

that relies almost entirely on the use of network relationships and offers incentives to existing

customers for recommending others to purchase a specific product. Findings also reinforce the

firms to collaborate with SHGs and NGOs and encourage network members to recommend

products to network members that would potentially generate higher purchase intentions.

Managers also need to identify members in the social networks on whom consumer place higher

trust; and managers can encourage such members to recommend others to adopt the product.

These members may also facilitate managers in arranging meetings with other BOP individuals,

where they may learn usage and benefits of products through live demonstrations.

Companies with higher intensity of distribution were found to show greater performance

in these markets. Companies, therefore, need to make efforts to collaborate with more number of

non-traditional partners like self-help groups, finance institutions, non-government organizations

etc. for effectively distributing products among BOP consumers. A high intensity of distribution

is likely to enhance performance of the company in these markets.

144

Promotion strategies

Results highlighted that female members in BOP households play a greater role in

purchase decisions with respect to food and FMCG; whereas male members in the households

were found to have a greater role in purchase decisions with respect to durable products. These

findings suggest managers to develop advertisements of food and FMCG focusing more on

female consumers; whereas advertisements of durable products need to focus more on male

consumers. Further, while promoting branded food and branded FMCG among subsistence

consumers, companies need to target younger consumers that are more likely to purchase branded

food and branded FMCG.

The present study found a significant influence of subjective norms on intention to

purchase branded food. It can be implied that the identified referent groups like social networks

and family members exert social pressure on BOP consumers to purchase branded food. In

addition, subjective norms were also found to positively predict intention to purchase durable

products. Companies need to promote durable products through important referent groups such as

self-help groups, non-government organizations and community associations that exert social

pressure on consumers to purchase durable products.

The findings suggest managers to design promotions around network members on whom

BOP consumers place a higher trust. For this, managers need to identify opinion leaders in social

networks who may be provided a free sample for trial purpose; and such members should be

further encouraged to recommend BOP consumers to adopt the product. BOP consumers are

more likely to adopt a product recommended by members on whom they place a higher trust.

While promoting food and FMCG in BOP markets, managers need to adopt sales

promotion tools like buy one get other free, free gifts, free samples, price discounts, and bonus

packs to be more effective as these tools are likely to provide an economic benefit to

underprivileged consumers. However, for promoting durables products in BOP markets,

companies may use personal selling tools like face-to-face interactions and live demonstrations.

These elements may provide an opportunity to acquire necessary information and learn basic

functionality of the product in an easy way. Companies are also suggested to provide a separate

promotional budget to communicate and encourage sales in BOP markets. The promotions

through non-traditional media and social networks like NGOs, SHGs are also likely to enhance

performance of the company in these markets.

145

CHAPTER V

SUMMARY

The BOP population in India accounts for 925 million that represents a $1.2 trillion

market, which is 84.8 per cent of the total $1.42 trillion national household market. This

constitutes the largest market of the world’s $5 trillion BOP market after China (Hammond et al

2007), that potentially represents a very large market for multi-national companies offering

products and services to subsistence consumers. In these markets, consumers have limited

literacy, low access to mass media and severe infrastructural constraints that pose serious

challenges to business growth. These hostile circumstances cause such consumers to have distinct

preferences, abilities and choices in contrast with the high-income consumers. For example,

limited literacy standards inhibit these consumers from checking prices and evaluating quality of

the products. In order to cope-up with poverty constraints, majority of the disadvantaged

consumers remain connected with members of the social networks and support each other in

day-to-day works. Companies operating at BOP need to re-examine traditional marketing mix

and develop appropriate marketing mix strategies for serving potential consumers living in hostile

circumstances. Therefore, the present study entitled ‘Understanding purchase behaviour and

analyzing marketing mix strategies: A study of bottom of the pyramid (BOP) consumers’ was

undertaken in order to achieve the following objectives:

1) To investigate purchase behaviour of bottom of the pyramid consumers

2) To examine the willingness of bottom of the pyramid consumers to purchase branded

products

3) To explore the influence of social networks on purchase behaviour of bottom of the

pyramid consumers

4) To study existing marketing mix strategies of companies for bottom of the pyramid

consumers

5) To recommend changes in marketing mix strategies for bottom of the pyramid consumers

First three objectives were achieved by undertaking a consumer survey in which primary

data were collected with the help of a self-designed structured questionnaire. For the present

study, consumers with a monthly family income between INR 2,000-8,000 were considered as

BOP consumers. Using multi-stage sampling, the participants of this cross-sectional study were

drawn from two states of northern India viz. Punjab and Haryana. In total, 600 respondents were

surveyed comprising 300 from each state. These two states have been officially divided into four

146

administrative divisions. For this study, two villages from each selected district were chosen

resulting in selection of total 8 cities and 16 villages from each state. As per the reports of Census

2011, one third of population lives in urban and two-third population lives in rural areas of

Punjab. Correspondingly, 100 participants from urban and 200 participants from rural population

were surveyed. The first participant from a selected city or village was chosen through non-

probability judgment sampling because there is no standard sampling frame of BOP consumers

having the specific income level. Further participants were recruited with the help of snowball

sampling by asking the preceding informant to recommend others. Previously, not many studies

have empirically investigated BOP consumers’ purchase behavior; therefore, no existing

validated scale could be found. Thus, measures used to operationalize the constructs in the

questionnaire were mainly developed from pre-existing conceptual studies. Keeping in mind the

low-literary standards of the target population, questionnaire was translated into two local

languages (i.e. Punjabi and Hindi) of the region by language experts.

For achieving fourth objective, managers of the selected companies were interviewed

with the help of a structured interview schedule. In total 50 managers were interviewed (one from

each selected company) aiming to study marketing mix strategies of companies for BOP

consumers. For the study, companies listed on BSE FMCG index and consumer durables index

were selected on the basis of annual sales turnover. Consequently, 30 companies from FMCG

sector including food companies and 20 companies from consumer durable sector were selected.

The fifth objective was accomplished by drawing implications out of the findings of first four

objectives of the study.

Demographic profile of respondents included slightly more number of males than

females (i.e. about 54 per cent and 46 per cent respectively). About 37 per cent of the respondents

were found to be in the age category of 36-50 years; whereas nearly 35 per cent of the

respondents were in the age category of 26-35 years; and only 6.5 per cent of the respondents in

the age category of more than 50 years. Educational profile of the respondents reveals that

majority (55 per cent) were below matriculation; whereas 30 per cent of the respondents were

found to be matriculate. A few (12 per cent) respondents were having education up to senior

secondary level. Maximum (43 per cent) of the respondents were found to be daily wagers;

whereas 27 per cent of the respondents were house-wives. Income profile of the respondents

highlight that majority (54 per cent) of the households had monthly income between INR 6,001-

8,000; whereas 45 per cent of the households were found to have monthly income between

INR 4,001-6,000.

147

Findings reveal that a large majority (84 per cent) of the households owned mobile

handsets. About 86 per cent of the urban households were found to own television; whereas

nearly 80 per cent of the rural households owned television. About one fourth of the households

were found to own refrigerator; and a few of the households (about eight per cent) owned two-

wheelers. Further, majority of the households (55 per cent) owned LPG stoves. Approximately 44

per cent of the households were not found to own a LPG stove. Study also found that about 62 per

cent of the urban households owned a LPG stove; whereas only 52 of the rural households owned

a LPG stove. The objective wise summary of findings and implications has been presented below:

5.1 Purchase behavior of BOP consumers

An attempt was made to investigate purchase behavior of bottom of the pyramid

consumers towards selected product categories like food, FMCG and durables. Respondents were

enquired about their preferred source of information for purchasing selected products; source to

purchase selected products; source of availing credit to purchase products; frequency of purchase;

and gender differences in purchasing selected products. The study also examined differences

between purchase behavior of rural and urban consumers with respect to the purchase of durables

products.

Findings indicate that ‘retailer’s recommendations’ emerged as the most preferred source

of information for purchasing food and FMCG; however ‘members of the social networks’ were

found to be the most preferred source of information for purchasing durable products. Further,

respondents ranked ‘members of the social networks’ as the second most preferred source of

information for purchasing food and FMCG. For durable products, ‘television advertisements’

were found to be the third most preferred source of information. Based on these findings,

companies offering such products in BOP markets may motivate members of the social networks

and local retailers to provide product related information to potential customers.

Majority (64 per cent) of the respondents preferred to avail credit from local retailers for

purchasing products in food and FMCG category. Findings also highlight that a marginal

proportion of respondents (0.5 per cent) preferred to avail credit from members of the social

networks to purchase products in food and FMCG category. Further, local retailers also emerged

as the most preferred source for availing credit to purchase durable products. More than one

fourth of the respondents preferred to avail credit from members of the social networks for

purchasing consumer durable products. A higher proportion of rural consumers (28 per cent) than

urban consumers (24 per cent) preferred to avail credit from ‘members of the social networks’ for

purchasing consumer durable products. A few (2 per cent) respondents preferred to avail credit

148

from ‘local money lenders’ for purchasing consumer durable products. This may be due to high

rate of interest being charged by local money lenders from illiterate consumers in BOP markets.

Findings in regard to purchase frequency of selected products reveal that a large majority

(85 per cent) of the respondents purchased cereals once a week; this finding indicates that BOP

consumers recoup their requirements more frequently due to scarce availability of wages. About

nine per cent of the respondents purchased cereals fortnightly; whereas only six per cent of

respondents purchased cereals once a month. It is worth to note that majority (61 per cent

approximately) of the respondents purchased dairy products daily; whereas 35 per cent of the

respondents purchased dairy products once a week. Further, a large majority of the respondents

(97 per cent) were found to purchase fruits and vegetables once a week. Approximately, 22 per

cent of the respondents did not purchase bakery products. A large majority of the respondents

(85 per cent) purchased grocery products like sugar, tea and spices once a week.

A large majority (85 per cent) of the respondents purchased soaps once a week; about 85

per cent of the respondents purchased detergents once a week and about 87 per cent of the

respondents purchased shampoos once a week. Surprisingly, about 50 per cent of the respondents

did not purchase beauty products as such consumers may not be left with adequate amount of

money after meeting basic food requirements. However, a sizeable chunk (about 39 per cent)

purchased beauty products once a month; whereas about 10 per cent of the respondents purchased

beauty products fortnightly.

Findings indicate that maximum respondents purchased selected food products from local

nearby shops, except for fruits and vegetables. Such cash-strapped consumers purchase food

products from local shops due to convenience and credit offered by shopkeepers. Majority of the

respondents (589) purchased cereals from local nearby shops; whereas 378 of the respondents

purchased cereals from government depots as low-income consumers in India are provided

cereals for free or at subsidized rates through such depots. Majority (550) of the respondents

purchased fruits and vegetables from street vendors; whereas 388 respondents purchased fruits

and vegetables from local shops. A large majority (576) of the respondents were found to

purchase soaps from local nearby shops; whereas a sizeable number (237) of respondents

purchased soaps from street vendors. Maximum (576) of the respondents purchased detergents

from local nearby shops; whereas 141 respondents purchased detergents from street vendors.

About 50 per cent of respondents purchased a second-hand two-wheeler. Possibly, such

consumers need to commute long distances as their place of work is usually far from their area of

residence where public transportation system remains inaccessible. Due to insufficient funds to

purchase a new two-wheeler, these consumers purchase a second-hand two-wheeler. A large

149

majority (76 per cent) of the respondents was found to purchase mobile handsets from local

nearby shops; whereas a sizeable proportion (about 20 per cent) purchased second-hand mobile

phones. Further, about 48 per cent of the respondents purchased television from local nearby

shops; whereas about one fourth of respondents purchased television from exclusive showrooms.

The study highlighted major differences in role of male and female members in taking

several purchase decisions. Female members in the households were found to play a greater role

in purchase decisions for food and FMCG category. This may happen due to the fact that females

in subsistence markets have fewer opportunities to work and they share greater responsibility of

purchasing low-involvement products. However, male members were found to take purchase

related decisions more than female members with respect to durable products. This finding

indicates that male members in BOP households have more influence on purchasing high-

involvement durable products. The reason may be that females in subsistence markets lack

control over financial resources and are restricted by social norms to travel independently.

The present study also provided empirical evidence regarding differences between

purchase behavior of rural and urban consumers with respect to durable products. For example,

perceived behavioral control emerged as the strongest predictor to purchase durable products for

urban consumers. For rural consumers, subjective norms was found to be the most important

predictor of purchase intention. This finding indicated that rural consumers perceived higher

social pressure to buy durable products. Possibly, this happens due to the stronger bonds shared

among consumers living in rural areas of the country. Further, once urban consumers perceive the

purchase of durable products as an easy behavior, they are more likely to buy such products than

rural consumers. In determining perceived behavioral control, affordability emerged as the

strongest predictor for rural consumers; however for urban consumers availability emerged as the

strongest predictor of perceived behavioral control. It was also important to note that consumers

perceiving durable products as more useful revealed a higher attitude towards such products and

consumers with a higher attitude towards durable products were found to have a higher intention

to purchase durable products. Due to lower affordability of BOP consumers, companies need to

come out with financing schemes that may allow disadvantaged consumers to purchase durable

products through easy installments over a period of time. For instance, Casas Bahia, a large

retailer of appliances in Brazil, provides credit to consumers with low and unpredictable incomes.

The firm sells appliances to BOP consumers with sophisticated credit rating system coupled with

counseling. The default rate of the firm remains at as low as 8.5 per cent compared to 15 per cent

for competitors (Prahalad 2006).

150

5.2 Consumers’ willingness to purchase branded products

For examining consumer’s willingness to purchase branded products, three product

categories namely food, FMCG and durable products were selected. In the food product category,

branded bakery products were selected to examine drivers of branded food choice among BOP

consumers. Further, branded beauty products (face cream and face wash) were selected to

examine consumers’ willingness to purchase branded FMCG; and branded mobile was selected to

examine consumers’ willingness to purchase branded durable products.

For examining drivers of branded food choice among BOP consumers, a conceptual

model was developed that was tested through structural equation modeling. Results highlighted

attitude as the strongest driver of intention to purchase branded food followed by perceived

behavioral control and subjective norms. The statistical differences in proposed relationships

were examined by creating a multi-group model across two groups of consumers: buyers and

non-buyers of branded food. This study considerably contributed to the literature on purchase

behavior by highlighting the behavioral variations in branded food purchase for above mentioned

consumer groups. For instance, perceived behavioral control emerged as the strongest driver of

purchase intention for buyers group. For non-buyers group, attitude was found to be the most

important antecedent of intention to purchase branded food. It means that once BOP consumers

buy branded food, they acquire more confidence and control over repeat purchases. The higher

control reduces the primary role of attitude in determining purchase intention; thus perceived

behavioral control becomes most important factor affecting buying behavior of low-income

consumers. On the basis of findings of the study, companies were suggested to fortify foods with

necessary vitamins and micronutrients of which BOP population is often deficient. The

fortification of food is more likely to develop consumers’ favorable perceptions towards branded

food and they will develop a higher purchase intention.

For examining consumers’ willingness to purchase branded FMCG, several parameters

influencing the purchase of branded FMCG were identified; and these parameters were reduced

into a few meaningful factors by applying exploratory factor analysis. Principal components

analysis (PCA) was used for extracting the factors by using ‘varimax’ method of factor rotation;

and factors with eigen values of ‘1’ or above were retained in the final analysis. Principal

component analysis generated two factors that explained 62.76 per cent of the total variance in

the data. After rotation, first factor explained 33.24 per cent of variance and the second factor

explained 29.52 per cent of the variance. First factor was named as ‘product appearance, price

and brand’ and second factor was named as ‘packaging, quality and ingredients’. Loadings of the

statements on the respective factors were found to be more than or equal to 0.5. Reliability

151

statistics (cronbach alpha) for these two factors was found to be more than the recommended

value of 0.70. Two factors generated through exploratory factor analysis were included as

independent variables in logistic regression that was used to examine consumers’ willingness to

purchase branded FMCG. The logistic model was able to correctly predict more than 85 per cent

of the observed outcomes in data and independent variables explained about 20 per cent of the

total variation in willingness to purchase branded FMCG. First factor ‘product appearance, price

and brand’ was found to be significant in the prediction of willingness to purchase branded

FMCG; whereas second factor ‘packaging, quality and ingredients’ failed to emerge as a

significant determinant of willingness to purchase branded FMCG. These findings suggest

companies to design attractive appearance of branded FMCG along with developing small packs

that low-income consumers are more likely to afford.

Similarly, factors influencing the purchase of branded durable products were explored

through exploratory factor analysis. Resultant output generated three factors that explained 67.08

per cent of the total variance in the data. After rotation, first factor explained 29.51 per cent of

variance; second factor explained 19.43 per cent of the variance; and the third factor explained

18.13 per cent variation in the data. First factor was named as ‘familiarity and convenience’;

second factor was named as ‘appearance and price’; and the third factor was named as ‘quality

and brand name’. The statements were found to have factor loadings greater than the minimum

recommended value of 0.5. Three factor scores generated through exploratory factor analysis

were included as independent variables in logistic regression that was used to examine

consumers’ willingness to purchase branded durable products. Logistic model was able to

correctly predict about 90 per cent of the observed outcomes and independent variables explained

about 40 per cent of total variation in willingness to purchase branded durable products. Of the

independent variables, first factor ‘familiarity and convenience’ emerged as the strongest

predictor as the odds ratio was found to be highest. Factor ‘appearance and price’ was found to be

the second most important predictor and factor ‘quality and brand name’ was found to be the third

most important factor determining consumers’ willingness to purchase branded durable products.

These findings, therefore, suggest companies to focus more on making low-income consumers

familiar about the product and making products available near to the place where consumers live

or work. In addition, for enhancing consumers’ willingness to purchase branded durables,

companies need to provide such products on monthly installments and offer better after sale

services.

152

5.3 Influence of social networks on purchase behavior

Based on the previous literature, a conceptual model was developed for investigating

influence of social networks on purchase behavior. Influence of identified characteristics of social

networks such as relationship orientation, similarity and expertise on intention to purchase a

product recommended by network members was examined through mediators viz. word-of-mouth

and trust. Statistical differences in the proposed relationships were examined by creating a multi-

group model in SEM for two groups: group 1: consumers affiliated to social networks like SHGs,

NGOs and political groups etc; and group 2: consumers unaffiliated to social networks. Findings

suggested that about 48 per cent of the respondents were affiliated to social networks. Out of

these, the maximum 17 per cent of the respondents were affiliated to self-help groups; whereas 16

per cent of the respondents were affiliated to political groups. The results highlighted trust as the

strongest predictor of intention to purchase a product recommended by network members. This

finding suggests managers to design promotions around members on whom BOP consumers

place higher trust. Firms may adopt network marketing approach that relies almost entirely on the

use of network relationships and offers incentives to existing customers for recommending others

to purchase a specific product.

The present study also found significant differences in the proposed relationships due to

consumers’ affiliation to social networks. For example, the results found a more intense

relationship between word-of-mouth and purchase intention for consumers unaffiliated to social

networks than consumers affiliated to social networks. This finding indicate that word-of-mouth

was more effective in adoption of new products among consumers unaffiliated to networks where

they are not restricted by formal rules and regulations. Whilst, for consumers affiliated to formal

social networks, the study found a stronger influence of trust on intention to purchase a product

recommended by network members. This may happen due to the fact that members of a social

network maintain stronger social ties that help to develop a high level of trust among them.

Consequently, BOP consumers intend more to buy a product recommended by members of a

social network.

5.4 Marketing mix strategies of companies

Managers of selected companies from food, FMCG and durable sectors were interviewed

aiming to study their perceptions towards marketing mix strategies for bottom of the pyramid

consumers. Managers were enquired about their perceptions towards BOP markets, marketing

mix strategies, customer orientation of the company, top-management’s commitment towards

BOP markets, company’s performance and effectiveness of selected promotion mix tools.

153

Managers’ responses regarding various statements were obtained on a 7-point scale where ‘1’

represented strongly disagree and ‘7’ represented strongly agree.

Findings highlighted that managers perceived BOP as a future mass market. Managers

also perceived that selling products to BOP consumers is much profitable; and it is easy to

promote and distribute products in BOP markets. Managers perceived that companies attempt to

develop customized products in terms of size, features and design to suit BOP consumers.

Respondents agreed that companies actively engage with BOP consumers to seek their advice on

product development; and set price keeping in view low-income of consumers. Managers

perceived that companies focus on low-margin high-volume pricing; and tailor pricing

mechanisms to suit BOP market conditions. Further, respondents agreed that companies promote

products through non-traditional social networks like NGOs and SHGs in subsistence markets.

According to the responses given by managers, majority (62 per cent) of the companies were

found to collaborate with local retailers for distributing products among consumers in subsistence

markets. About 48 per cent of the companies collaborated with non-government organizations

whereas 44 per cent of companies collaborated with self-help groups. It was important to note

that a large majority of companies in durable sector (80 per cent) collaborated with financial

institutions as compared to companies in food and FMCG sector (about 13 per cent). This may be

due to the fact that durable products are much expensive in relation to food and FMCG, thereby

BOP consumers need to avail credit facility from financial institutions that collaborate with

companies offering durable products on installments to end consumers.

On the basis of managers’ responses, ‘wall painting’ emerged as the most effective tool

to advertise products in BOP markets. Managers also perceived that ‘wall painting’ is a more

effective tool to promote durable products than food and FMCG. Further, ‘price discount’ was

found to be the most effective tool to promote product sales in BOP markets. Managers perceived

‘bonus pack’ as the second most effective tool to promote products among subsistence

consumers. Effectiveness of ‘bonus pack’; ‘free gift’ and ‘free sample’ was perceived to be

higher for promoting food and FMCG than durable products. However, effectiveness of ‘live

demonstration’ and ‘face-to-face interaction’ was found to be significantly higher for selling

durable products.

Of marketing mix strategies of selected companies, pricing strategies emerged as the

strongest predictor of company’s performance followed by promotion strategies, distribution

intensity and product strategies. The effect size of pricing strategies on performance of the

company was found to be the largest; suggesting that managers need to offer low-priced products

particularly for BOP consumers with a focus on low-margins and high-volumes. The tailoring of

154

pricing mechanisms as per BOP market conditions would also help to enhance performance of the

company. The effect size of promotion strategies was also found to be large. Therefore,

promotions through social networks like NGOs, SHGs are likely to enhance performance of the

company in these markets. Distribution intensity and product strategies reported a medium effect

on company’s performance. Thereby, high intensity of distribution is also likely to enhance

performance of the company. Companies also need to co-create products with potential BOP

consumers by seeking their advice on product development and design.

5.5 Limitations and future research

Though the present study provided useful implications for both theory and practice;

however several limitations exist in terms of the generalizability and interpretation of the results.

Since subsistence markets of the country are not homogenous owing to cultural and geographical

variations; a larger sample from diverse areas may help to capture more variations in purchase

behavior of BOP consumers. Future studies may also be undertaken to identify food

characteristics like quality, portion size and sensory attributes that are also expected to influence

food choice among BOP consumers. Results from such studies would improve our understanding

about purchase behavior of consumers in subsistence markets. It may also be useful to consider

the extent to which the influence of social networks on purchase behavior depends upon the type

of product. Future studies may also be undertaken to identify additional characteristics of social

networks influencing purchase behavior of BOP population that would help to improve our

understanding about buying behavior of consumers in subsistence markets.

The sample size of companies was restricted to 50 including 30 from food and FMCG

and 20 from consumer durable sector. Future studies might focus on a larger sample size to

investigate strategies of the companies for bottom of the pyramid consumers. One should be

cautious while generalizing findings of the study as the results may not be valid for other sectors

like banking and services. Future research may also focus on exploring appropriate marketing

mix strategies for services sector. Literature also highlights social parameters that seem to be

important for measuring company’s performance in these markets; future studies may also

examine whether marketing mix strategies influence company’s social performance in BOP

markets.

155

REFERENCES

Akbay C and Jones E (2005) Food consumption behavior of socioeconomic groups for private labels and national brands. Food Qual Prefer 16:621-31.

Ali J and Kapoor S (2009) Understanding consumers’ perspectives on food labeling in India. Int J

Con Studs 33:724–34.

Ali J, Kapoor S and Moorthy J (2010) Buying behaviour of consumers for food products in an emerging economy. Brit Food J 12:109-24.

Alam S and Sayuti N (2011) Applying the Theory of Planned Behavior (TPB) in halal food purchasing. Int J Comm Mgmt 21:8-20.

Alur S and Schoormans J P L (2013) Retailers and new product acceptance in India’s base of the pyramid (BOP) markets. Int J Retail Distri Mgmt 4:189-200.

Ajzen I (1991) The theory of planned behavior. Orgl Behav Human Dec Processes 50:179-211.

Ajzen I (2013) Theory of Planned Behavior. Retrieved on September, 2015 from http://people.umass.edu/aizen/tpb.

Anderson J and Billou N (2007) Serving the world's poor: innovation at the base of the economic pyramid. J Bus Strat 28:14-21.

Anderson J and Markides C (2007) Strategic innovation at the base of the pyramid. MIT Sloan

Mgmt Rev 49:83.

Anonymous (2013) Press Note on Poverty Estimates, 2011-12, Planning Commission, Govt. of India, Retrieved on 4/4/2016 from http:// planningcommission.nic.in/news/pre_pov2307. pdf.

Anselmsson J, Vestman Bondesson N and Johansson U (2014) Brand image and customers' willingness to pay a price premium for food brands. J Pro Brand Mgmt, 23:90-102.

Antin T M and Hunt G (2012) Food choice as a multidimensional experience. A qualitative study with young African American women. Appetite, 58: 856-63.

Antonakis J, Bendahan S, Jacquart P and Lalive R (2010) On making causal claims: A review and recommendations. Leadership Qtrly 21:1086-1120.

Arndt J (1967). Word of mouth advertising and informal communication Risk Taking Info

Handling Con Behav, 188-239.

Arnold D J and John A Q (1998) New Strategies in Emerging Markets. Sloan Mgmt Rev 40:7–20.

Arnott D C (2007) Trust-current thinking and future research. Eur J Mktg 41:981-87.

Astrom A N and Rise J (2001) Young adults' intention to eat healthy food: Extending the theory of planned behaviour. Psychol Health 16:223-37.

Atale N (2012) A Decade of BRICs: Prospects and Challenges for the Next Decade. Ind J Mgmt 5:16-21.

156

Bagozzi R P (1986) Principles of marketing management. Chicago, IL: Science Research Associates.

Bagozzi R P and Yi Y (1988) On the evaluation of structural equation models. J Academy Mktg

Sci 16:74-94.

Bahadir S C, Bharadwaj S G and Srivastava R K (2015) Marketing mix and brand sales in global markets: Examining the contingent role of country-market characteristics. J Intl Bus

Studs, 46:596-619.

Banerjee AV and Duflo E (2007) The economic lives of the poor. J Econ Perspectives 21:141–67.

Bang V V and Joshi S L (2010) Market expansion strategy-performance relationship. J Strat

Mktg 18:57-75.

Bang V V and Joshi S L (2012) Market expansion strategy–reasons for and against: what do managers in India think? J Strat Mktg 20:85-102.

Bansal H S and Voyer P A (2000) Word-of-mouth processes within a services purchase decision context. J Ser Res 3:166-77.

Barki E and Parente J (2010) Consumer behaviour of the base of the Pyramid Market in Brazil. Greener Mgmt Int 1:11-23.

Baron R M and Kenny D A (1986) The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J Person Soc

Psychol 51:1173.

Battor M and Battor M (2010) The impact of customer relationship management capability on innovation and performance advantages: testing a mediated model. J Mktg Mgmt 26:842-57.

Baumgartner H and Homburg C (1996) Applications of structural equation modeling in marketing and consumer research. Int J Res Mktg 13:139-61.

Belch G E and Belch M A (2007) Advertising and Promotion. An Integrated Marketing Communication Perspective. 7th edn. New York: McGraw Hill/Irwin.

Berry W D (1993) Understanding regression assumptions. Sage university paper series on quantitative applications in the social sciences, 07-092. Newbury Park, CA: Sage.

Bharti K, Agrawal R and Sharma V (2014) What drives the customer of world's largest market to participate in value co-creation?. Mktg Intel Plang 32:413-35.

Bhattacharya A K and Michael D C (2008) How local companies keep multinationals at bay. Harv Bus Rev 86:20-33.

Bijapurkar R (2007) We are like that only: Understanding the logic of consumer India. New Delhi: Penguin Portfolio.

Bollen K A and Paxton P (1998) Interactions of latent variables in structural equation models. Struct Equ Modeling 5:267-93.

157

Bonne K, Vermeir I, Bergeaud-Blackler F and Verbeke W (2007) Determinants of halal meat consumption in France. Brit Food J 109:367-86.

Boomsma A (2000) Reporting Analyses of Covariance Structures. Struct Equ Modeling 7:461-84.

Bornkessel S, Bröring S, Omta S O and van Trijp H (2014) What determines ingredient awareness of consumers? A study on ten functional food ingredients. Food Qual Prefer 32:330-39.

Bradley F (2003) International Marketing Strategy 4th Edition, Financial Times, Prentice Hall, UK.

Bredahl L (2004) Cue utilisation and quality perception with regard to branded beef. Food Qual

Prefer 15:65-75.

Brinkhoff A, Ozer O and Sargut G (2015) All you need is trust? An examination of inter-organizational supply chain projects. Prod Opts Mgmt 24:181–200.

Bruschi V, Teuber R and Dolgopolova I (2015) Acceptance and willingness to pay for health-enhancing bakery products–Empirical evidence for young urban Russian consumers. Food Qual Prefer 46:79-91.

Brown J J and Reingen P H (1987) Social ties and word-of-mouth referral behavior. J Con Res 14:350-62

Brown T A (2015) Confirmatory factor analysis for applied research. Guilford Publications.

Briz T and Ward R W (2009) Consumer awareness of organic products in Spain: An application of multinominal logit models. Food Pol 34:295-304.

Byrnes N K and Hayes J E (2015) Gender differences in the influence of personality traits on spicy food liking and intake. Food Qual Prefer 42:12-19.

Cannière M H, De Pelsmacker P and Geuens M (2009) Relationship quality and the theory of planned behavior models of behavioral intentions and purchase behavior. J Bus

Res 62:82-92.

Chakravarthy B and Coughlan S (2012) Emerging market strategy: innovating both products and delivery systems. Strat Leadership 40:27-32.

Chan K and Tsang L (2011) Promote healthy eating among adolescents: a Hong Kong study. J

Con Mktg 28:354-62.

Chan Y Y and Ngai E W T (2011) Conceptualising electronic word of mouth activity: An input-process-output perspective. Mktg Intel Plan 29:488-516.

Changco J A, Pornpitakpan C, Singh R, and Bonilla C M (2011) Managerial insights into sachet marketing strategies and popularity in the Philippines. Asia Pac J Mktg Logistics 23:755-72.

Chaniotakis I E, Lymperopoulos C and Soureli M (2010) Consumers' intentions of buying own-label premium food products. J Pro Brand Mgmt 19:327-34.

158

Chevalier J A and Mayzlin D (2006) The effect of word of mouth on sales: Online book reviews. J Mktg Res 43:345-54.

Chikweche T (2013) Marketing at the bottom of pyramid: market attractiveness and strategic requirements. Mktg Intel Plan 31:764-87.

Chikweche T and Fletcher R (2010) Understanding factors that influence purchases in subsistence markets. J Bus Res 63:643-50.

Chikweche T, Stanton J and Fletcher R (2012) Family purchase decision making at the bottom of the pyramid. J Con Mktg 29:202-13.

Chikweche T and Fletcher R (2011) Franchising at the bottom of the pyramid (BOP): an alternative Distrib approach. Int Rev Retail Distrib Con Res 21:343-60.

Chikweche T and Fletcher R (2012) Revisiting the marketing mix at the bottom of pyramid (BOP): from theoretical considerations to practical realities. J Con Mktg 29:507-20.

Chikweche T and Fletcher R (2013) Customer relationship management at the base of the pyramid: myth or reality?. J Con Mktg 30:295-309.

Chiou J S and Droge C (2006) Service quality, trust, specific asset investment, and expertise: Direct and indirect effects in a satisfaction-loyalty framework. J Acad Mktg Sci 34:613-27.

Choo H, Chung J E and Thorndike Pysarchik D (2004) Antecedents to new food product purchasing behavior among innovator groups in India. Euro J Mktg 38:608-25.

Chung J E, Stoel L, Xu Y and Ren J (2012) Predicting Chinese consumers' purchase intentions for imported soy-based dietary supplements. Brit Food J 114:143-61.

Churchill Jr G A (1979) A paradigm for developing better measures of marketing constructs. J

Mktg Res 64-73.

Crockett D, Downey H, Fırat A F, Ozanne J L and Pettigrew S (2013) Conceptualizing a transformative research agenda. J Bus Res 66:1171-78.

Conley T G and Udry C R (2010) Learning about a new technology: Pineapple in Ghana. Amer

Econ Rev 35-69.

Czepiel J A (1974) Word-of-mouth processes in the diffusion of a major technological innovation. J Mktg Res 172-80.

D’Andrea G (2006) Breaking the paradox of emerging markets: strategies for reaching

consumers at the base of the pyramid. Working paper, Universidad Austral, Argentina.

Dahan N M, Doh J P, Oetzel J and Yaziji M (2010). Corporate-NGO collaboration: Co-creating new business models for developing markets. Long Range Plan 43:326-42.

Davidson K (2009) Ethical concerns at the bottom of the pyramid: where CSR meets BOP. J Int

Bus Ethics 2:22-32.

Davis F D (1989) Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Qtrly 319-40.

159

Dawar N D N and Chattopadhyay A (2002) Rethinking marketing programs for emerging markets. Long Range Plan 35:457-74.

Desai K (2014) A study on consumer buying behaviour of cosmetic products in Kolhapur. Rev Lit 1:10.

Deshpande R and Farley J U (1998) Measuring market orientation: generalization and synthesis. J

Mkt Focusd Mgmt 2:213-32.

Dey B, Binsardi B, Prendergast R and Saren M (2013) A qualitative enquiry into the appropriation of mobile telephony at the bottom of the pyramid. Int Mktg Rev 30:297-322.

DiMaggio P and Louch H (1998) Socially embedded consumer transactions: for what kinds of purchases do people most often use networks? Amer Socio Rev 619-37.

Dolan C Louis M J and Scott L (2012) Shampoo, saris and SIM cards: seeking entrepreneurial futures at the bottom of the pyramid. Gender Dev 20:33-47.

Dubey J and Patel R P (2004). Small wonders of the Indian market. J Con Behav 4:145-51.

Esposito M, Kapoor A and Goyal S (2012) Enabling healthcare services for the rural and semi-urban segments in India: when shared value meets the bottom of the pyramid. Corp Gov 12:514-33.

Fabrigar L R, Wegener D T, MacCallum R C and Strahan E J (1999) Evaluating the use of exploratory factor analysis in psychological research. Psychol Methods 4:272.

Field A (2009) Discovering statistics using SPSS. Sage publications.

Fila S A and Smith C (2006) Applying the theory of planned behavior to healthy eating behaviors in urban Native American youth. Int J Behav Nutri Phy Act 3:11.

Finch F and West S G (1997) The investigation of personality structure: Statistical models. J Res

Person 31:439-85.

Fishbein M and Ajzen I (1975) Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA.

Fletcher R (2005) ‘International marketing at the bottom of the pyramid’, Proc ANZMAC 2005

Conference: Marketing in International and Cross-Cultural Environments, Australia and New Zealand Marketing Academy, 46-51.

Flint E, Cummins S and Matthews S (2013) Do perceptions of the neighbourhood food environment predict fruit and vegetable intake in low-income neighbourhoods?. Health

Place 24:11-15.

Follman J (2012) BoP at ten: evolution and a new lens. South Asian J Glob Bus Res 1:293-310.

Fornell C and Larcker D F (1981) Evaluating structural equation models with unobservable variables and measurement error. J Mktg Res 39-50.

160

Ganguly B, Dash S B, Cyr D and Head M (2010) The effects of website design on purchase intention in online shopping: the mediating role of trust and the moderating role of culture. Int J Elect Bus 8:302-30.

Ghosh P K, Prabeer K R and Bhusnurmath M (2013) Macro-Track: A monthly report by NCAER 15

Gilly M C, Graham J L, Wolfinbarger M F and Yale L J (1998) A dyadic study of interpersonal information search. J Acad Mktg Sci 26:83-100.

Gold S, Hahn R and Seuring S (2013) Sustainable supply chain management in “Base of the Pyramid” food projects—A path to triple bottom line approaches for multinationals?. Int

Bus Rev 22:784–99.

Goldberg L R and Velicer W F (2006) Principles of exploratory factor analysis. In S. Strack (Ed.), Differentiating normal and abnormal personality: Second edition. New York, NY: Springer. (pp. 209-237).

Govindarajan V and Ramamurti R (2011) Reverse innovation, emerging markets, and global strategy. J Glob Stra 1:191-205.

Govindarajan V and Trimble C (2012) Reverse innovation: a global growth strategy that could pre-empt disruption at home. Strat Leadership 40:5-11.

Goyal A and Singh N P (2007) Consumer perception about fast food in India: an exploratory study. Brit Food J 109:182-95.

Grawe S J, Chen H and Daugherty P J (2009) The relationship between strategic orientation, service innovation, and performance. Int J Physical Distri Logist Mgmt 39:282-300.

Grutzmacher S and Gross S (2011) Household food security and fruit and vegetable intake among low-income fourth-graders. J Nutri Edu Behav 43:455-63.

Guesalaga R and Marshall P (2008) Purchasing power at the bottom of the pyramid: differences across geographic regions and income tiers. J Con Mktg 25:413-18.

Hair J F, Black W C, Babin B J, Anderson R E and Tatham R L (2006) Multivariate data

analysis 6. Upper Saddle River, NJ: Pearson Prentice Hall.

Hakansson H and Waluszewski A (2005) Developing a new understanding of markets: reinterpreting the 4Ps. J Bus Industr Mktg 20:110–17.

Hamilton K (2009) Low-income families: experiences and responses to consumer exclusion. Int J

Socio Soc Pol 29:543-57.

Hammond A L, Kramer W J, Katz R S, Tran J T and Walker C (2007) The next 4 billion. Innov 2:147-58.

Hammond A L and Prahalad C K (2004) Selling to the poor. Foreign Pol 30-37.

Hansen T (2008) Consumer values, the theory of planned behaviour and online grocery shopping. Int J Con Studs 32:128-37.

161

Hansen T, Jensen J M and Solgaard H S (2004) Predicting online grocery buying intention: a comparison of the theory of reasoned action and the theory of planned behavior. Int J Info

Mgmt 24:539-50.

Harlow L L (2005) The essence of multivariate thinking: basic themes and methods, Mahwah, New Jersey: Lawrence Erlbaum Associates, Inc., Publishers

Heenam S P, Hamid N, Dufour J P, Harvey W and Delahunty C M (2009) Consumer freshness perceptions of breads, biscuits and cakes. Food Qual Prefer 20:380-90.

Henson S (1992) From high street to hypermarket. Food retailing in the 1990s. National

Consumer Council (Ed.), Your Food: Whose Choice 95-115.

Herbst P G (1952) The measurement of family relationships. Human Relations.

Honkanen P and Frewer L (2009) Russian consumers’ motives for food choice. Appetite 52:363-71.

Huang R and Sarigöllü, E. (2012). How brand awareness relates to market outcome, brand equity, and the marketing mix. J Bus Res 65:92-99.

Iglesias V and Vázquez R (2001) The moderating effects of exclusive dealing agreements on distributor satisfaction. J Strat Mktg 9:215-31.

Ilahiane H and Sherry J W (2011) The Problematics of the “Bottom of the Pyramid” Approach to International Development: The Case of Micro-Entrepreneurs’ Use of Mobile Phones in Morocco. Info Tech Int Dev 8:13–26.

Ingenbleek P T, Tessema W K and van Trijp H C (2013) Conducting field research in subsistence markets, with an application to market orientation in the context of Ethiopian pastoralists. Int J Res Mktg 30:83-97.

Jacobs M K and Goodman G (1989) Psychology and self-help groups: Predictions on a partnership. Amer Psychol 44:536.

Jacoby J (2002) Stimulus-organism-response reconsidered: An evolutionary step in modeling (consumer) behavior. J Con Psychol 12:51-57.

Jalilvand M R and Samiei N (2012) The effect of electronic word of mouth on brand image and purchase intention: An empirical study in the automobile industry in Iran. Mktg Intel Plan 30:460-76.

Jain S (2011) Rural- urban population: Punjab highlights. Retrieved on August, 2015 from http://www.censusindia.gov.in/CensusWebResults.aspx?cx=012918038926302546381%3Ajjz7p38u6maandcof=FORID%3A9andq=rural-urban%20population%20punjabandsa= Search#1200

Jaiswal A K and Gupta S (2015) The influence of marketing on consumption behavior at the bottom of the pyramid. J Con Mktg 32:113-24.

Jin B and Hye Kang J (2011) Purchase intention of Chinese consumers toward a US apparel brand: a test of a composite behavior intention model. J Con Mktg 28:187-99.

Kahn J (2008) Third world first. Boston Globe 20.

162

Kaiser H F (1958) The varimax criterion for analytic rotation in factor analysis. Psychometrika 23:187-200.

Kaiser H F (1960) The application of electronic computers to factor analysis. Edu Psychol

Measure 20:141-51.

Kang J and Maity M (2012) Texting Among the Bottom of the Pyramid: Facilitators and Barriers to SMSs Use Among the Low-Income Mobile Users in Asia. Available at SSRN

2309353.

Kapoor P and Saraiya A (2012) Food Retailing: Backbone of Organized Retail Formats Retrieved on August, 2015 from http://www.technopak.com/Files/food-retailing-backbone-of-organized-retail.pdf

Karakaya F and Ganim Barnes N (2010) Impact of online reviews of customer care experience on brand or company selection. J Con Mktg 27:447-57.

Karnani A (2006) Misfortune at the bottom of the pyramid. Greener Mgmt Int 51: 99-110.

Karnani A (2007) The mirage of marketing to the bottom of the pyramid: How the private sector can help alleviate poverty. Calif Mgmt Rev 49:90-111.

Kathuria L M and Gill P (2013) Purchase of branded commodity food products: empirical evidence from India. Brit Food J 115:1255-80.

Kathuria L M and Singh V (2016) Product Attributes as Purchase Determinants of Imported Fruits in Indian Consumers. J Food Prod Mktg 22:501-20.

Katz E and Lazarsfeld P F (1955) Personal Influence, The Free Press, Glencoe, IL.

Keller K L (1993) Conceptualizing, measuring, and managing customer-based brand equity. J

Mktg, 57:1-23

Khalid R U, Seuring S, Beske P, Land A, Yawar S A and Wagner R (2015) Putting sustainable supply chain management into base of the pyramid research. Sup Chain Mgmt: Int J 20:681-96.

Khanna T and Palepu K G (2006) Emerging giants. Harv Bus Rev, October, 1-10.

Kirchgeorg M and Winn M I (2006) Sustainability marketing for the poorest of the poor. Bus

Strat Envir 15:171-84.

Kitchen P J (1996) Public relations in the promotional mix: a three phase analysis. Mktg Intel

Plan 14:5-12.

Kochhar S K (2014) Putting community first: mainstreaming CSR for community-building in India and China. Asian J Commun 24:421-40.

Kotabe M and Helsen K (2013) Globa Marketing Management. Fifth edition, Wiley India (P.) Ltd.

Kotler P (2009) Marketing management: A south Asian perspective. Pearson Education India.

Kramer M R (2011) Creating shared value. Harv Bus Rev 89:62-77.

163

Ku E C (2012) Beyond price: how does trust encourage online group's buying intention?. Internet

Res 22:569-90.

Kumar V, Sharma A, Shah R and Rajan B (2013) Establishing profitable customer loyalty for multinational companies in the emerging economies: a conceptual framework. J Inl

Mktg 21:57-80.

Kuriyan R and Ray I (2008) Information and communication Tech for development: the bottom of the pyramid model in practice. Info Soc, 24:93-104.

Laroche M, Kim C and Zhou L (1996) Brand familiarity and confidence as determinants of purchase intention: An empirical test in a multiple brand context. J Bus Res 37:115-20.

Lazarsfeld P F and Robert K (1954) Friendship as a social process: A substantive and methodological analysis. Freedom Cont Mod Soc 18:18-66.

Leonard D A (1985) Experts as negative opinion leaders in the diffusion of a technological innovation. J Con Res 11:914-26.

Lin L, Geng X and Whinston A B (2005) A sender-receiver framework for knowledge transfer. MIS Qtrly 197-219.

Lin L Y and Lu C Y (2010) The influence of corporate image, relationship marketing, and trust on purchase intention: the moderating effects of word-of-mouth. Tourism Rev 65:16-34.

Ling S S, Jung Choo H and Thorndike Pysarchik D (2004) Adopters of new food products in India. Mktg Intel Plan 22:371-91.

London T, Anupindi R and Sheth S (2010) Creating mutual value: Lessons learned from ventures serving base of the pyramid producers. J Bus Res 63:582-94.

London T, Esper H, Kaylor A G and Kistruck G M (2014) Connecting poverty to purchase in informal markets. J Strat Entre 8:37–55.

London T and Hart S L (2004) Reinventing strategies for emerging markets: beyond the transnational model. J Int Bus Studs 35:350-70.

Low S P and Tan M C S (1995) A convergence of western marketing mix concepts and oriental strategic thinking. Mktg Intel Plan 13:36-46.

Lymperopoulos C, Chaniotakis I E and Rigopoulou I D (2010) Acceptance of detergent-retail brands: the role of consumer confidence and trust. Int J Retail Distribn Mgmt 38:719-36.

Maccoby E E and Jacklin C N (1974) The Psychology of Sex Differences. Stanford, CA: Stanford University Press.

MacKinnon D P, Fairchild A J and Fritz M S (2007) Mediation analysis. Annual Rev Psychol 58:593-614.

Magnusson M K, Arvola A, Hursti U K K, Åberg L and Sjödén P O (2003) Choice of organic foods is related to perceived consequences for human health and to environmentally friendly behaviour. Appetite 40:109-17.

164

Magnusson P, Hass S M and Zhao H (2008) A branding strategy for emerging market firms entering developed markets. J Int Con Mktg 20:95-103.

Mahon D, Cowan C and McCarthy M (2006) The role of attitudes, subjective norm, perceived control and habit in the consumption of ready meals and takeaways in Great Britain. Food Qual Prefer 17:474-81.

Mair J, Marti I and Ventresca M J (2012) Building inclusive markets in rural Bangladesh: how intermediaries work institutional voids. Acad Mgmt J 55:819–50.

Malhotra N K (2008) Marketing research: An applied orientation, 5/e. Pearson Education India.

Mark N P (2003) Culture and competition: Homophily and distancing explanations for cultural niches. Amer Socio Rev 319-45.

McKague K and Tinsley S (2012) Bangladesh's Rural Sales Program: Towards a scalable rural sales agent model for distributing socially beneficial goods to the poor. Soc Enter J 8:16-30.

McKinsey Global Institute (2007) The ‘Bird of Gold’: The Rise of India’s Consumer Market. Retrieved on 20/4/2015 from http://www.mckinsey.com/insights/asia-pacific/the_bird_of_gold.

McPherson M., Smith-Lovin L and Cook J M (2001) Birds of a feather: Homophily in social networks. Annual Rev Socio 415-44.

Mehrabian A and Russell J A (1974) The basic emotional impact of environments. Perceptual

Motor Skills 38:283-301.

Meiselman H L, King S C and Gillette M (2010) The demographics of neophobia in a large commercial US sample. Food Qual Prefer 21:893-97.

Mohr J J, Sengupta S and Slater S F (2012) Serving base-of-the-pyramid markets: meeting real needs through a customized approach. J Bus Strat 33:4-14.

Montgomery C A and Wernerfelt B (1992) Risk reduction and umbrella branding. J Bus 31-50.

Moorman R H and Blakely G L (1995) Individualism‐collectivism as an individual difference predictor of organizational citizenship behavior. J Orgl Behav 16:127-42.

Morgan R M and Hunt S D (1994) The commitment-trust theory of relationship marketing. J

Mktg 20-38.

Morgeson F V, Sharma P N and Hult G T M (2015) Cross-national differences in consumer satisfaction: mobile services in emerging and developed markets. J Int Mktg 23:1-24.

Mowen J C and Minor M S (2001) Consumer Behavior: A Framework, Prentice Hall, Englewood Cliffs, NJ.

Mukerjee K (2012) Frugal innovation: the key to penetrating emerging markets. Ivey Bus J 76:1-3.

Mukherjee A and Patel N (2005) FDI in Retail Sector India. Academic Foundation, New Delhi.

165

Mukherjee A, Satija D, Goyal T M, Mantrala M K and Zou S (2012) Are Indian consumers brand conscious? Insights for global retailers. Asia Pac J Mktg Logist 24:482-99.

Munro B H (2005) Statistical Methods for Health Care Research. 5th Edition. Lippincott Williams and Wilkins, Philadelphia, PA.

Nachtigall C, Kroehne U, Funke F and Steyer R (2003) (Why) Should We Use SEM? Pros and Cons of Structural Equation Modeling. Methods Psychol Res 8:1-22.

Narayanan P V (2000) Packaging in a developing economy. Packaging India 33:23-30.

Narteh B, Odoom R, Braimah M and Buame S (2012) Key drivers of automobile brand choice in sub-Saharan Africa: the case of Ghana. J Pro Brand Mgmt 21:516-28.

Ni N and Wan F (2008) A configurational perspective of branding capabilities development in emerging economies: the case of Chinese cellular phone industry, Brand Mgmt 15:433-51.

Nitzan I and Libai B (2011) Social effects on customer retention. J Mktg 75:24-38.

Noble S M, Griffith D A and Adjei M T (2006) Drivers of local merchant loyalty: Understanding the influence of gender and shopping motives. J Retail 82:177–88.

Nocella G and Kennedy O (2012) Food health claims–What consumers understand. Food

Pol 37:571-80.

Nunnally J C (1978) Psychometric Theory. 2nd edn.McGraw-Hill, New York.

O’Connor E L and White K M (2010) Willingness to trial functional foods and vitamin supplements: The role of attitudes, subjective norms, and dread of risks. Food Qual

Prefer 21:75-81.

Olsen S O and Tuu H H (2013) The roles of ambivalence, preference conflict and family identity: A study of food choice among Vietnamese consumers. Food Qual Prefer 28:92-100.

Ordabayeva N and Chandon P (2011) Getting ahead of the Joneses: When equality increases conspicuous consumption among bottom-tier consumers. J Con Res 38:27-41.

Ozanne J L, Pettigrew S, Crockett D, Downey H, Firat A F and Pescud M (2011) The practice of transformative consumer research–some issues and suggestions. J Res Cons 19:1-7.

Park M S, Shin J K and Ju Y (2014) The effect of online social network characteristics on consumer purchasing intention of social deals. Global Econ Rev 43:25-41.

Paul J and Rana J (2012). Consumer behavior and purchase intention for organic food. J Con

Mktg 29:412-22.

Pavlou P A and Fygenson M (2006) Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS Qtrly 115-143.

Peterson M, Ekici A and Hunt D M (2010) How the poor in a developing country view business' contribution to quality-of-life 5years after a national economic crisis. J Bus Res 63:548-58.

166

Pitta D and Ireland, J. (2008). Lessons for successful BOP marketing from Caracas' slums. J Con

Mkts 25:430-38.

Podsakoff P M, MacKenzie S B, Lee J Y and Podsakoff N P (2003) Common method biases in behavioral research: a critical review of the literature and recommended remedies. J App

Psychol 88:879.

Porter G (2012) Mobile phones, livelihoods and the poor in Sub‐Saharan Africa: Review and prospect. Geog Compass 6:241-59.

Prahalad C K (2006) The Fortune at the Bottom of the Pyramid. Pearson Education India.

Prahalad C K and Hammond A (2002) Serving the world's poor, profitably. Harv Bus Rev 80:48-59.

Prahalad C K and Ramaswamy V (2000) Co-opting customer competence. Harv Bus Rev 78:79-90.

Prati G, Pietrantoni L and Zani B (2012) The prediction of intention to consume genetically modified food: Test of an integrated psychosocial model. Food Qual Prefer 25:163-70.

Prescott J, Young O, O'neill L, Yau N J N, and Stevens R (2002) Motives for food choice: a comparison of consumers from Japan, Taiwan, Malaysia and New Zealand. Food Qual

Prefer 13:489-95.

Rajagopal (2009) Branding paradigm for the bottom of the pyramid markets. Measuring Bus

Excel 13:58-68.

Rajaobelina L and Bergeron J (2009) Antecedents and consequences of buyer-seller relationship quality in the financial services industry. Int J Bank Mktg 27:359-80.

Rampl V L, Eberhardt T, Schütte R and Kenning P (2012) Consumer trust in food retailers: conceptual framework and empirical evidence. Int J Retail Distrib Mgmt 40: 254-72.

Rangan, V Kashturi, John A Quelch, Gustavo Herrero and Brooke Barton (2007) Business

solutions for the global poor: Creating social and economic value. John Wiley and Sons.

Rao S L and Shukla R (2008) Just how poor is India? Retrieved on 22/9/2013 from http://www.ncaer.org/downloads/MediaClips/Press/RajeshShuklaJust%20how%20poor%20is%20India.pdf.

Ren J, Chung J E, Stoel L and Xu Y (2011) Chinese dietary culture influences consumers' intention to use imported soy‐based dietary supplements: an application of the theory of planned behaviour. Int J Con Studs 35:661-69.

Rich M K (2000) The direction of marketing relationships. J Bus Industrial Mktg, 15:170-91.

Richards R and Smith C (2006) The impact of homeless shelters on food access and choice among homeless families in Minnesota. J Nutri Edu Behav 38:96-105.

Rogers and Everett M (1983) Diffusion of Innovations, New York: Free Press.

Rosa J A and Viswanathan M (Eds.) (2007) Product and market development for subsistence

marketplaces. JAI.

167

Roux C, Le Couedic P, Durand-Gasselin S and Luquet F M (2000) Consumption patterns and food attitudes of a sample of 657 low-income people in France. Food Pol 25:91-103.

Rowley J (1998) Promotion and marketing communications in the information marketplace. Library Rev 47:383-87.

Ruef M, Aldrich H E and Carter N M (2003) The structure of founding teams: Homophily, strong ties, and isolation among US entrepreneurs. Amer Sociol Rev 195-222.

Saiyed K (2011) ‘Thanks to postmen, ‘ChotuKool’ is a rage in rural households’, Retrieved on 8/2/2013 from www.indianexpress.com/news/thanks-to-postmen-chotukool-is-a-rage-in-rural-households/ 890324/0.

Sánchez M, Beriain M J and Carr T R (2012) Socio-economic factors affecting consumer behaviour for United States and Spanish beef under different information scenarios. Food

Qual Prefer 24:30-39.

Saunders, Mark, Lewis, Philip and Thornhill Adrian (2009) Research methods for business

students. 5. ed. Harlow: Financial Times Prentice Hall

Schiffman G L and Kanuk L L (2009) Consumer Behavior, 9th edn. Pearson Prentice Hall, Upper Saddle River, NJ.

Scott N, Batchelor S, Ridley J and Jorgensen B (2004) The impact of mobile phones in Africa, Commission for Africa.

Sengar A, Sharma V, Agrawal R and Bharti K (2014) Prioritisation of barriers to rural markets: integrating fuzzy logic and AHP. Int J Bus Emerging Markets 6:371-94

Shrout P E and Bolger N (2002) Mediation in experimental and nonexperimental studies: new procedures and recommendations. Psychol Methods 7:422.

Schuster T and Holtbrügge D (2014) Resource dependency, innovative strategies, and firm performance in BOP markets. J Prod Innov Mgmt 31:43-59.

Schutte H and Ciarlante D (1998) Consumer behavior in Asia. Macmillan Business Press, London

Sengupta A (2008) Emergence of modern Indian retail: an historical perspective. Int J Retail

Distrib Mgmt 36:689–700.

Shankar V and Balasubramanian S (2009) Mobile marketing: a synthesis and prognosis. J Int

Mktg 23:118-29.

Shahanjarini A K, Rashidian A, Majdzadeh R, Omidvar N, Tabatabai M G and Shojaeezadeh D (2012) Parental Control and Junk‐Food Consumption: A Mediating and Moderating Effect Analysis. J App Soc Psychol 42:1241-65.

Share M and Stewart-Knox B (2012) Determinants of food choice in Irish adolescents. Food

Qual Prefer 25:57-62.

Sharkey J R, Johnson C M and Dean W R (2010) Food access and perceptions of the community and household food environment as correlates of fruit and vegetable intake among rural seniors. BMC Geriatrics 10:32.

168

Shaw R (2011) Developed vs. emerging market returns, yields and valuation multiples. Available at http://seekingalpha.com/article/255837-developed-vs-emergingmarket-returns-yields-and-valuation-multiples

Sheth J N (2011) Impact of emerging markets on marketing: rethinking existing perspectives and practices. J Mktg 75:166-82.

Sichtmann C (2007) An analysis of antecedents and consequences of trust in a corporate brand. Euro J Mktg 41:999-1015.

Siegrist M, Hartmann C and Keller C (2013) Antecedents of food neophobia and its association with eating behavior and food choices. Food Qual Prefer 30:293-98.

Singal A and Jain A R (2012) Outward FDI trends from India: emerging MNCs and strategic issues. Int J Emerg Mkts 7:443-56.

Singhal A (2008) A market at the bottom of the pyramid. Retrieved on 26/9/2013 from http://www.business-standard.com/article/opinion/arvind-singhal-a-market-at-the-bottom-of-the-pyramid-108082801114_1.html.

Sinha D (1994) Origins and development in psychology in India: outgrowing the alien framework. Int J Psychol 29:695-705

Sinkovics N, Sinkovics R R and Yamin M (2014) The role of social value creation in business model formulation at the bottom of the pyramid–Implications for MNEs?. Int Bus Rev

23:692-707.

Sirdeshmukh D, Singh J and Sabol B (2002) Consumer trust, value, and loyalty in relational exchanges. J Mktg 66:15-37.

Smith J B (1998) Buyer–seller relationships: similarity, relationship management, and quality. Psychol Mktg 15:3-21.

Smith R E and Vogt C A (1995) The effects of integrating advertising and negative word-of-mouth communications on message processing and response. J Con Psychol 4:133-51.

Spearman C (1904) General intelligence, objectively determined and measured. Amer J Psychol 15:201-93.

Spence J T (1984) Masculinity, Femininity, and Gender-Related Traits: A Conceptual Analysis and Critique of Current Research. Progress Exp Person Res 13:1–97.

Spence A and Townsend E (2006) Examining consumer behavior toward genetically modified (GM) food in Britain. Risk Anal 26:657-70.

Sprotles G B and Kendall E L (1986) A methodology for profiling consumers' decision‐making styles. J Con Affairs 20:267-79.

Sridharan S and Viswanathan M (2008) Marketing in subsistence marketplaces: consumption and entrepreneurship in a South Indian context. J Con Mktg 25:455-62.

Srinivasan S, Vanhuele M and Pauwels K (2010) Mind-set metrics in market response models: An integrative approach. J Mktg Res 47:672-84.

169

Steenkamp J B E and Baumgartner H (1998) Assessing measurement invariance in cross‐national consumer research. J Con Res 25:78-107.

Steiger J H (1990) Structural model evaluation and modification: An interval estimation approach. Multivariate Behav Res 25:173-80.

Steptoe A, Pollard T and Wardle J (1995) Development of a measure of the motives underlying the selection of food: The Food Choice Questionnaire. Appetite 25:267–84.

Stokes D and Lomax W (2002) Taking control of word of mouth marketing: the case of an entrepreneurial hotelier. J Small Bus Enterprise Dev 9:349-57.

Sun T and Wu G (2004) Consumption patterns of Chinese urban and rural consumers. J Con

Mktg 21:245-53

Sun X and Collins R (2004) A comparison of attitudes among purchasers of imported fruit in Guangzhou and Urumqi, China. Food Qual Prefer 15:229-37.

Sweeney J C, Soutar G N and Mazzarol T (2008) Factors influencing word of mouth effectiveness: receiver perspectives. Euro J Mktg 42:344-64.

Talukdar D (2008) Cost of being poor: retail price and consumer price search differences across inner city and suburban neighborhoods. J Con Res 35:457-71.

Tanusondjaja A, Greenacre L, Banelis M, Truong O and Andrews T (2015) International brands in emerging markets: the myths of segmentation. Int Mktg Rev 32:783-96.

Tarafdar M, Anekal P and Singh R (2012) Market development at bottom of the pyramid: examining the role of information and communication technologies. Info Tech Dev

18:311-31.

Tarkiainen A and Sundqvist S (2005) Subjective norms, attitudes and intentions of Finnish consumers in buying organic food. Brit Food J 107:808-22.

Taylor J P, Evers S and McKenna M (2005) Determinants of healthy eating in children and youth. Canadian J of Public Health/Revue Canadienne de Sante'e Publique, S20-26.

Taylor S and Todd P (1995) Decomposition and crossover effects in the theory of planned behavior: A study of consumer adoption intentions. Int J Res Mktg 12:137-55.

Thakur R (2015) Community marketing: serving the base of the economic pyramid sustainably. J

Bus Strat 36:40-47.

Thurstone L L (1931) Multiple factor analysis. Psychol Rev 38:406-27.

Tiwari R and Herstatt C (2012) Assessing India’s lead market potential for cost-effective innovations. J Ind Bus Res 4:97-115.

Uncles M D, Wang C and Kwok S (2010) A temporal analysis of behavioural brand loyalty among urban Chinese consumers. J Mktg Mgmt 26:921-42.

Vega G and Kidwell R E (2007) Toward a typology of new venture creators: similarities and contrasts between business and social entrepreneurs. New Eng J Entre 10:15-28

170

Viswanathan M (2007) Understanding product and market interactions in subsistence marketplaces: A study in South India. Advan Int Mgmt 20:21-57.

Viswanathan M (2010) A micro-level approach to understanding BoP markets. Next generation business strategies for the base of the pyramid: New approaches for building mutual value 129-164.

Viswanathan M and Rosa J A (2007) Product and market development for subsistence marketplaces: Consumption and entrepreneurship beyond literacy and resource barriers. Advan Int Mgmt 20:1-20.

Viswanathan M and Rosa J A (2010) Understanding subsistence marketplaces: Toward sustainable consumption and commerce for a better world. J Bus Res 63:535-37.

Viswanathan M, Sridharan S, Gau R and Ritchie R (2009) Designing marketplace literacy education in resource-constrained contexts: Implications for public policy and mkts. J

Pub Pol Mktg 28:85-94.

Viswanathan M, Sridharan S and Ritchie R (2010) Understanding consumption and entrepreneurship in subsistence marketplaces. J Bus Res 63:570-81.

Walsh G and Mitchell V W (2010) The effect of consumer confusion proneness on word of mouth, trust, and customer satisfaction. Euro J Mktg 44:838-59.

Wandel M and Bugge A (1997) Environmental concern in consumer evaluation of food quality. Food Qual Prefer 8:19–26.

Weidner K L, Rosa J A and Viswanathan M (2010) Marketing to subsistence consumers: Lessons from practice. J Bus Res 63:559-69.

Wells L E, Farley H and Armstrong G A (2007) The importance of packaging design for own-label food brands. Int J Retail Distrib Mgmt 35:677-90.

Whitney P (2010) ‘Reframing design for the base of the pyramid’, Next Generation Business Strategies for the Base of the Pyramid: New Approaches for Building Mutual Value, FT Press, Upper Saddle River, NJ.

Williams C C and Windebank J (2002) The'excluded consumer': a neglected aspect of social exclusion? Pol Polit 30:501-13.

Woodside A G and Walser M G (2007) Building strong brands in retailing. J Bus Res 60:1–10.

Wooldridge A (2010) The world turned upside down. A special report on innovation in emerging markets. Econ 15.

Wut T and Chou T J (2009) Children's influences on family decision making in Hong Kong. Young Con 10:146-56.

Yorkston E A, Nunes J C and Matta S (2010) The malleable brand: The role of implicit theories in evaluating brand extensions. J Mktg 74:80–93.

Yunus M, Moingeon B and Lehmann-Ortega L (2010) ‘Building Social Business Models: Lessons from the Grameen Experience’, Long Range Plan 43:308-25.

171

Zainudeen A and Ratnadiwakara D (2011) Are the Poor Stuck in Voice? Conditions for adoption of more-than-voice mobile services. Info Tech Int Dev 7:45–59.

Zenk S N, Schulz A J, Hollis-Neely T, Campbell R T, Holmes N, Watkins G and Odoms-Young A (2005) Fruit and vegetable intake in African Americans: income and store characteristics. Amer J Preven Med 29:1-9.

Zeschky M, Widenmayer B and Gassmann O (2011) Frugal innovation in emerging markets. Res-

Tech Mgmt 54:38-45.

Zhou Y, Thøgersen J, Ruan Y and Huang G (2013) The moderating role of human values in planned behavior: the case of Chinese consumers' intention to buy organic food. J Con

Mktg 30:335-44.

172

ANNEXURE I

QUESTIONNAIRE

Dear Respondent, This questionnaire is related to purchase behavior of bottom of the pyramid consumers towards food, FMCG (fast

moving consumer goods such as soaps, detergents, shampoos and beauty products) and durable products (like two-wheeler, mobile, television and refrigerator). The information provided by you, will be kept confidential and used for research purpose only.

1) Name: ________________________ Age (in years): _________

2) Address: _________________________________________________________________________________

3) Total number of family members(including you): _____ Adults _____ Children

4) Gender: Male Female

5) Qualification: Below Higher secondary Higher secondary Senior Secondary

Graduate Post Graduate Any other (please specify)

____________________

6) Occupation: Student Daily Wager House-wife

Business (Small Entrepreneur) Any other (please specify)

_____________________

7) Monthly family income (in Indian rupees):

2,000 – 4,000 4,001 – 6,000 6,001 – 8,000

8) Tick from the following products you own:

Two-wheeler Refrigerator Television

LPG Stove Mobile Air-Cooler

9) I am associated with following group(s):

Self-Help Group (SHG) Labor Group Non-Govt. Organization

Trade Association Religious Group Political Group

Not affiliated

10) From the following, kindly rank the top five sources of information for purchasing products in given categories:

Sources of Awareness Food and

FMCG Durable products

Members of the social networks

Family members

Television advertisements

Radio advertisements

Print media (newspaper, magazine, sign boards etc.)

Point-of-purchase display

Retailer's recommendations

173

11) From where do you most prefer to avail credit/loan while purchasing products in given categories?

Credit/loan source

Products

Local money lenders

Member of the social networks

Local retailers

Do not avail credit

Food and FMCG

Durable Products

12) Kindly rate the following parameters on 5-point scale that influence your purchase decision for branded products in given categories where 1 stands for strongly disagree and 5 stands for strongly agree.

Parameters Branded FMCG Branded durables 1

2 3

4

5

1 2 3 4 5

Product appearance influences my purchase decision

Product price influences my purchase decision

Product quality influences my purchase decision

Brand name influences my purchase decision

Product familiarity influences my purchase decision

Product availability influences my purchase decision

Credit availability influences my purchase decision

Product packaging influences my purchase decision --- --- --- --- ---

Product fragrance influences my purchase decision --- --- --- --- ---

Products’ ingredients influence my purchase decision --- --- --- --- ---

After sale service influences my purchase decision --- --- --- --- ---

Product warranty influences my purchase decision --- --- --- --- ---

13) What is your frequency of purchase for products in the given categories? Please mark-

Products Daily Once a week

Fortnightly Once a month

Do not purchase

Food Products

Cereals (Wheat, Rice etc)

Dairy products (Milk, Curd, Cheese etc)

Vegetables and Fresh Fruits

Bakery Products (biscuits, bread, and packaged salty snacks etc)

ii

174

Meat and related products (Chicken, Mutton, Eggs etc)

Other grocery products (Tea, Sugar, Spices etc)

FMCG

Soaps

Detergents

Shampoos

Beauty products (Face Creams, face wash etc)

14) From where do you purchase the following products? Please tick the appropriate ones-

Products Local Nearby Shops

Street Vendors

Government Depots (PDS)

Local Mandis

Food Products

Cereals (Wheat, Rice etc)

Dairy products (Milk, Curd, Cheese etc)

Vegetables and Fresh Fruits

Bakery Products (biscuits, bread, packaged salty snacks etc)

Meat and related products (Chicken, Mutton, Eggs etc)

Grocery products (Tea, Sugar, Spices etc)

FMCG

Soaps

Detergents

Shampoos

Beauty products (Face Creams, face wash etc)

Consumer Durables Local Nearby Shops

Exclusive Showrooms

Second-hand

Two-wheeler

Mobile

Television

Refrigerator

LPG Stove

Air-Cooler

iii

175

15) What percentage of your monthly family income do you spend on the following product categories?

Products 0-20% 21-40% 41-60% 61-80% More than 80%

Food Products

FMCG

16) Please tick the following purchase decisions on a 5-point scale where 1 – only male decides, 2 – male decides

more than female, 3 – male and female both equally decide, 4 – female decides more than male and 5 – only female decides.

Purchase decisions Food and FMCG Consumer Durables 1 2 3 4 5 1 2 3 4 5

When to buy

From where to buy

How much to buy

How frequently to buy

Which brand to buy

At what price to buy

How to buy (cash or credit)

Budget for buying

Overall decisions related to buying

17) Do you buy or wish to buy the branded products in the following categories? Please tick-

Products Yes No

Food Products

Cereals (Wheat, Rice etc)

Dairy products (Milk, Curd, Cheese etc)

Vegetables and Fresh Fruits

Bakery Products (biscuits, bread, packaged salty snacks etc)

Meat and related products (Chicken, Mutton, Eggs etc)

Other grocery products (Tea, Sugar, Spices etc )

FMCG

Soaps

Detergents

Shampoos

Beauty products (Face Creams, Powder, Perfume etc)

iv

176

Consumer Durables

Two-wheeler

Mobile

Television

Refrigerator

LPG Stove

Air-Cooler

Geyser

18) The following statements pertain to the purchase intention towards branded bakery products (biscuits, breads and packaged salty snacks etc). Please read carefully and rate them on given 5-point scale:

Statements (1) Strongly Disagree

(2) Disagree

(3) Neither Agree

nor Disagree

(4) Agree

(5) Strongly

Agree

Availability of Branded Bakery Products

Most of the branded bakery products are available near to place where I live or work

1 2 3 4 5

Branded bakery products are easily available in the nearby local market 1 2 3 4 5

I need to travel less for buying branded bakery products 1 2 3 4 5

Affordability of Branded Bakery Products

My household income permits me to buy branded bakery products 1 2 3 4 5

I have the money needed to purchase branded bakery products 1 2 3 4 5

It is within my budget to purchase branded bakery products 1 2 3 4 5

I can afford to purchase branded bakery products on credit from nearby retailer

1 2 3 4 5

Awareness about Branded Bakery Products

I have enough information to make a good decision about purchasing branded bakery products

1 2 3 4 5

I have enough information about various brands of bakery products 1 2 3 4 5

I know enough to buy branded bakery products on my own 1 2 3 4 5

I am well informed about prices of branded bakery products 1 2 3 4 5

I am well informed about benefits of branded bakery products

1 2 3 4 5

Perceived Behavioral Control w.r.t. Branded Bakery Products

I have all the resources for purchasing branded bakery products 1 2 3 4 5

I have complete control while purchasing branded bakery products 1 2 3 4 5

v

177

Purchasing branded bakery products is upto me 1 2 3 4 5

Normative Beliefs w.r.t. Branded Bakery Products

Members of my social network think that I should buy branded bakery products

1 2 3 4 5

Members of my social network think that I should consume branded bakery products

1 2 3 4 5

My family members think that I should buy branded bakery products 1 2 3 4 5

My family members think that I should consume branded bakery products 1 2 3 4 5

The local retailers think that I should buy branded bakery products 1 2 3 4 5

The local retailers think that I should consume branded bakery products 1 2 3 4 5

Subjective Norms w.r.t. Branded Bakery Products

Most people who are important to me would think that I should purchase branded bakery products

1 2 3 4 5

Most people who are important to me would think that I should consume branded bakery products

1 2 3 4 5

Most people who influence my decisions would think that I should purchase branded bakery products

1 2 3 4 5

Most people who influence my decisions would think that I should consume branded bakery products

1 2 3 4 5

Perceived Usefulness of Branded Bakery Products

Branded bakery products are nutritious 1 2 3 4 5

Branded bakery products are hygienic 1 2 3 4 5

Branded bakery products are fresh 1 2 3 4 5

Branded bakery products are convenient to eat 1 2 3 4 5

Attitude towards Buying Branded Bakery Products

Buying branded bakery products is healthy 1 2 3 4 5

Buying branded bakery products is beneficial 1 2 3 4 5

Buying branded bakery products is worth 1 2 3 4 5

Buying branded bakery products is good 1 2 3 4 5

Purchase Intention towards Branded Bakery Products

I intend to purchase branded bakery products 1 2 3 4 5

I want to purchase branded bakery products 1 2 3 4 5

I will continue purchasing branded bakery products 1 2 3 4 5

I plan to increase quantity bought of branded bakery products 1 2 3 4 5

vi

178

19) The following statements are related to your purchase intention towards two-wheelers (like scooter/motor-

cycle etc) within next one year. Kindly rate them on given 5-point scale: Statements (1)

Strongly Disagree

(2) Disagree

(3) Neither Agree

nor disagree

(4) Agree

(5) Strongly

Agree

Availability of Two-Wheelers

Most of the two-wheeler brands are available near to place where I live or work

1 2 3 4 5

Two-wheelers are easily available in the nearby local market 1 2 3 4 5

I need to travel less for buying a two-wheeler 1 2 3 4 5

Affordability of Two-Wheelers

My household income permits me to buy a two-wheeler within next one year

1 2 3 4 5

I expect to have the money needed to purchase a two-wheeler within next one year

1 2 3 4 5

It would be within my budget to purchase a two-wheeler within next one year

1 2 3 4 5

I can afford to purchase a two-wheeler on installments/credit within next one year

1 2 3 4 5

Awareness about Two-Wheelers

I have enough information to make a good decision about purchasing a two-wheeler within next one year

1 2 3 4 5

I have enough information about various brands of two-wheelers 1 2 3 4 5

I am well informed about benefits of a two-wheeler 1 2 3 4 5

Perceived Behavioral Control w.r.t. Two-Wheelers

I will have all the resources for purchasing a two-wheeler within next one year

1 2 3 4 5

I will have complete control while purchasing a two-wheeler within next one year

1 2 3 4 5

It is very easy for me to purchase a two-wheeler within next one year 1 2 3 4 5

Normative Beliefs w.r.t. Two-Wheelers

Members of my social network think that I should buy a two-wheeler within

next one year

1 2 3 4 5

Members of my social network think that I should use a two-wheeler within

next one year

1 2 3 4 5

My family members think that I should buy a two-wheeler within next one

year

1 2 3 4 5

My family members think that I should use a two-wheeler within next one

year

1 2 3 4 5

vii

179

Subjective Norms w.r.t. Two-Wheelers

Most people who are important to me would think that I should buy a two-

wheeler within next one year

1 2 3 4 5

Most people who are important to me would think that I should use a two-

wheeler within next one year

1 2 3 4 5

Most people who influence my decisions would think that I should buy a

two-wheeler within next one year

1 2 3 4 5

Most people who influence my decisions would think that I should use a

two-wheeler within next one year

1 2 3 4 5

Perceived Usefulness of Two-Wheelers

Two-wheeler would enable me to accomplish my tasks more quickly 1 2 3 4 5

The advantages of owing a two-wheeler outweigh disadvantages 1 2 3 4 5

Owing a two-wheeler would improve my reputation in the society 1 2 3 4 5

Attitude towards Buying a Two-wheeler

Buying a two-wheeler within next one year is exciting 1 2 3 4 5

Buying a two-wheeler within next one year is important 1 2 3 4 5

Buying a two-wheeler within next one year is good 1 2 3 4 5

Buying a two-wheeler within next one year is pleasant 1 2 3 4 5

Purchase Intention towards Two-wheeler

I intend to purchase a two-wheeler within next one year 1 2 3 4 5

I plan to purchase a two-wheeler within next one year 1 2 3 4 5

I want to purchase a two-wheeler within next one year 1 2 3 4 5

viii

180

20) The following statements refer to your various belief structures. Please rate them on a five point scale where 1 stands for bad idea and 5 stands for good idea.

Statements (1) very bad

idea

(2) bad idea

(3) neither bad nor

good idea

(4) good idea

(5) very good idea

Desirability of Outcome

Accomplishing my tasks more quickly is 1 2 3 4 5

Buying a product with more advantages than disadvantages is 1 2 3 4 5

Buying a product that improves my reputation in the society is 1 2 3 4 5

Buying/consuming a branded nutritious product is 1 2 3 4 5

Buying/consuming a branded hygienic product is 1 2 3 4 5

Buying/consuming a branded fresh product is 1 2 3 4 5

Buying/consuming a branded 'convenient-to-eat' product is 1 2 3 4 5

Motivation to Comply

Generally speaking, I want to do what members of my social network think I

should do

1 2 3 4 5

Generally speaking, I want to do what my family members think I should do 1 2 3 4 5

Generally speaking, I want to do what my friends think I should do 1 2 3 4 5

Generally speaking, I want to do what local retailers think I should do 1 2 3 4 5

Perceived Facilitation

Availability of a product near to place I live or work is 1 2 3 4 5

Availability of a product in the nearby local market is 1 2 3 4 5

Travelling less for buying a product is 1 2 3 4 5

Purchasing a product permitted by household income is 1 2 3 4 5

Having the money needed to purchase a product is 1 2 3 4 5

Purchasing a product within my budget is 1 2 3 4 5

Purchasing a product on installments/credit is 1 2 3 4 5

Having enough information to make a good decision about purchasing a

product is

1 2 3 4 5

Having enough information about various brands of a product is 1 2 3 4 5

Knowing enough to buy a product on my own is 1 2 3 4 5

Being well informed about prices of products is 1 2 3 4 5

Being well informed about benefits of a product is 1 2 3 4 5

ix

181

21) The following statements are related to social networks (like SHG/NGO/social/religious/trade/political/labor group members and your friends etc.) and its influence on your purchase intention towards mobiles. Kindly rate them on given 5-point scale:

Statements (1) Strongly Disagree

(2) Disagree

(3) Neutral

(4) Agree

(5) Strongly

Agree

Relationship orientation with members of social networks

Members of my social network would assist me in buying a mobile 1 2 3 4 5

Members of my social network have old ties with me 1 2 3 4 5

Members of my social network maintain a close relationship with me 1 2 3 4 5

Members of my social network are willing to provide me monetary help 1 2 3 4 5

Similarity with members of social networks

Members of my social network have similar interest with me 1 2 3 4 5

Members of my social network share same culture with me 1 2 3 4 5

Members of my social network have comparable income with me 1 2 3 4 5

Members of my social network have alike standard of living with me 1 2 3 4 5

Expertise of members of social networks

Members of my social network are good in having mobile related information

1 2 3 4 5

Members of my social network usually know more about mobile features 1 2 3 4 5

Members of my social network generally recommend me about the retailers from whom to buy a mobile

1 2 3 4 5

Members of my social network are specialist enough to guide me about which brand of mobile to buy

1 2 3 4 5

Word-of-Mouth with members of social networks

I often seek information about mobile purchase from members of social network to make sure that I buy the right product

1 2 3 4 5

I like to gather information from members of my social network before I buy a mobile

1 2 3 4 5

I consider members of my social network as a good source of information while purchasing a mobile

1 2 3 4 5

Members of my social network like introducing new mobiles with me 1 2 3 4 5

Trust towards members of social networks

Members of my social network are reliable 1 2 3 4 5

Members of my social network are trustworthy 1 2 3 4 5

I trust members of my social network to offer credible information about mobiles

1 2 3 4 5

I trust members of my social network to educate me about mobiles 1 2 3 4 5

Purchase intention towards mobiles

I intend to purchase a mobile as suggested by members of my social network

1 2 3 4 5

I plan to purchase a mobile as suggested by members of my social network 1 2 3 4 5

I want to purchase a mobile as suggested by members of my social network 1 2 3 4 5

THANK YOU FOR YOUR CO-OPERATION

x

182

ANNEXURE II

Interview Schedule

Dear Respondent,

The present research is being undertaken by School of Business Studies, Punjab Agricultural University,

Ludhiana that aims to examine marketing mix strategies for bottom of the pyramid (BOP, low-income) consumers. In this

regard, you are requested to answer the following questions that may help us to achieve objective of the study. The

information, thus, obtained will be kept confidential and used for research purpose only.

1) Name: _____________________________________________________________________________________

2) Designation and company: _____________________________________________________________________

3) Contact No: ______________________________; e-mail: ____________________________________________

4) The company I am associated with is:

A domestic company Subsidiary of a foreign company

5) Number of employees in the company:

< 500 500-1000 > 1000

6) Please indicate your level of agreement with following statements related to product strategies for BOP

consumers where 1 stands for strongly disagree and 7 stands for strongly agree:

Statements 1 2 3 4 5 6 7

Our company attempts to develop customized products in terms of size, features and design to suit BOP consumers

1 2 3 4 5 6 7

Our company gives special emphasis to co-create products with BOP consumers

1 2 3 4 5 6 7

Our company constantly seeks to develop need-satisfying products for BOP consumers

1 2 3 4 5 6 7

Our company actively engages with BOP consumers to seek their advice on product development

1 2 3 4 5 6 7

Our company seeks to develop products keeping in view the low-literacy of BOP consumers

1 2 3 4 5 6 7

Please share any example of your company’s product development/modification particularly for BOP consumers:

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

xi

183

7) Please indicate your level of agreement with following statements related to pricing strategies for BOP consumers

where 1 stands for strongly disagree and 7 stands for strongly agree:

Statements 1 2 3 4 5 6 7

Our company offers low-priced products particularly for BOP consumers

1 2 3 4 5 6 7

Our company focuses on low-margin high-volume pricing for BOP consumers

1 2 3 4 5 6 7

Our company tailors pricing mechanisms to suit BOP market conditions

1 2 3 4 5 6 7

Our company attempts to set price keeping in view low-income of BOP consumers

1 2 3 4 5 6 7

How does your company attempt to lower cost/price to enhance BOP consumers’ capacity to purchase products?

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

8) In order to distribute products among BOP consumers, the company has collaborated with following partners:

Yes No

Business partners

Suppliers

Logistic service providers

Financial institutions

Local retailers

Civil society partners Non-profit organizations

Non-government organizations

Local communities

Self-help groups

Government partners

Centre government institutions

State government institutions

How does your company make products accessible to BOP consumers? Please share one of the examples: __________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

xii

184

9) The following statements are related to promotion strategies for BOP consumers. Please read carefully and rate

them on a 7-point scale given as under:

Statements 1 2 3 4 5 6 7

Our company provides a separate promotional budget to encourage sales at BOP markets

1 2 3 4 5 6 7

Our company attempts to communicate with BOP consumers in local or regional language

1 2 3 4 5 6 7

Our company attempts to promote products through social networks at BOP markets

1 2 3 4 5 6 7

Our company attempts to promote products through non-traditional/informal media (NGOs, SHGs etc.) at BOP markets

1 2 3 4 5 6 7

Do you believe that promoting products among low-income consumers is different from their higher-income

counterparts? If so, please mention, how? _______________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

10) Please indicate your level of agreement with following statements where 1 stands for strongly disagree and 7

stands for strongly agree:

Customer orientation of the company

Our business objectives are driven primarily by customer satisfaction

1 2 3 4 5 6 7

We communicate information about our customer experiences across all business functions

1 2 3 4 5 6 7

Our strategy for gaining a competitive advantage is based on our understanding of customer needs

1 2 3 4 5 6 7

We regularly survey end-customers to assess the quality of our products and service

1 2 3 4 5 6 7

Top-management’s commitment towards BOP consumers

Top-management assumes its responsibility for offering affordable products to BOP consumers

1 2 3 4 5 6 7

Top-management delegates necessary authority to its employees for marketing products to BOP consumers

1 2 3 4 5 6 7

Top-management allocates a separate budget for marketing products to BOP consumers

1 2 3 4 5 6 7

Top-management allocates a separate team for marketing products to BOP consumers

1 2 3 4 5 6 7

Company performance

Our company has achieved higher profits than expected 1 2 3 4 5 6 7

Our company has been able to attain growth targets 1 2 3 4 5 6 7

Our company has been able to attract new customers 1 2 3 4 5 6 7

Our company has been able to achieve expected market share

1 2 3 4 5 6 7

xiii

185

Managerial perceptions about BOP markets

Product development cost is higher for BOP markets 1 2 3 4 5 6 7

It is easy to distribute products in BOP markets 1 2 3 4 5 6 7

It is easy to promote products in BOP markets 1 2 3 4 5 6 7

BOP offers a future mass market 1 2 3 4 5 6 7

Selling products to BOP consumers is much profitable 1 2 3 4 5 6 7

11) Please rate the following promotion mix elements based on their effectiveness to promote products among BOP

consumers:

(1) Very

ineffective

(2) Ineffective

(3) Partially

ineffective

(4) Neither

ineffective nor

effective

(5) Partially effective

(6) Effective

(7) Very

effective

Advertising

Magazine 1 2 3 4 5 6 7

Newspaper 1 2 3 4 5 6 7

Wall painting 1 2 3 4 5 6 7

Television 1 2 3 4 5 6 7

Radio 1 2 3 4 5 6 7

Sales promotion

Buy one get one free

1 2 3 4 5 6 7

Free gift 1 2 3 4 5 6 7

Free sample 1 2 3 4 5 6 7

Price discount 1 2 3 4 5 6 7

Bonus pack (e.g. 20% extra)

1 2 3 4 5 6 7

Personal selling

Face-to-face interaction

1 2 3 4 5 6 7

Live demonstration

1 2 3 4 5 6 7

What do you perceive about opportunities and challenges at BOP markets? Please discuss:

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

__________________________________________________________________________________________________

Thanks for co-operating

xiv

186

ANNEXURE III

List of the Companies Surveyed

Sr. No. Name of the company Sr. No. Name of the company

1 ITC Limited 26 Godrej Consumer Products Limited

2 Venkys India Limited 27 Marico Limited

3 Ruchi Soya Industries Limited 28 Symphony Limited

4 Blue Star Limited 29 Godfrey Phillips India Limited

5 P & G 30 Radico Khaitan Limited

6 Bajaj Corp Limited 31 Vimal Oil & Foods Limited

7 Voltas Limited 32 Emami Limited

8 Bata India Limited 33 Jyothy Laboratories Limited

9 Whirlpool of India Limited 34 Usher Agro Limited

10 Crompton Greaves Limited 35 Kohinoor Foods Limited

11 GlaxoSmithKline Consumer Healthcare

Limited

36 Hitachi Home and Life Solutions India

Limited

12 Bajaj Electricals Limited 37 BPL Limited

13 KRBL 38 Titan company Limited

14 Videocon Industries Limited 39 TTK Prestige Limited

15 Bajaj Hindusthan Sugar Limited 40 Godrej Industries Limited

16 Colgate Palmolive India Limited 41 Havells India Limited

17 Eid Parry (INDIA) Limited 42 Hero MotoCorp Limited

18 Dhampur Sugar Mills Limited 43 Siemens India Limited

19 Heritage Foods Limited 44 Bajaj Auto Limited

20 Eveready Industries India Limited 45 TVS Motor

21 Hindustan Unilever Limited 46 Indo National Limited

22 United Spirits 47 Mirc Electronics Limited

23 United Breweries Limited 48 Britannia Industries Limited

24 Shree Renuka Sugars Limited 49 Kwality Walls

25 Dabur India Limited 50 Nestle India Limited

xv

187

VITA

Name of the student : Amanpreet Singh

Father’s name : S. Harminder Singh

Mother’s name : Smt. Jasbir Kaur

Nationality : Indian

Date of birth : 23-02-1986

Permanent home address : 2468/4, St. No. 7, Jammu Colony, Ludhiana, 141003

EDUCATIONAL QUALIFICATION

Bachelor degree : Bachelor of Arts

University and year of award : Panjab University, Chandigarh (2006)

Marks (%) : 67.4

Master’s degree : Masters of Business Administration

University and year of award : Punjab Technical University, Jalandhar (2008)

Marks (%) : 76.4

Ph. D.

OCPA : 7.81

Title of Ph. D. Thesis : Understanding Purchase Behaviour and Analyzing Marketing Mix Strategies: A Study of Bottom of the Pyramid (BOP) Consumers

Fellowships/Scholarships : Recipient of Junior/ Senior Research Fellowship from University Grants Commission, New Delhi

188