0 Manuscript of Gupta & Shukla-Store Choice Behavior for Consumer Durables in NCT_Delhi-Version 1

Running Head: STORE CHOICE BEHAVIOR FOR CONSUMER DURABLES 1

The final, definitive version of this paper has been published in Paradigm, December 2015 by

SAGE Publications India Pvt Ltd., All rights reserved. Copyright © (2015) Institute of

Management Technology.

Link for final article

http://par.sagepub.com/content/19/2/152.abstract

Reference for final published article

Gupta, A. K., & Shukla, A. V. (2015). Store Choice Behaviour for Consumer Durables in NCT-

Delhi: Effect of Shopper’s Demographics. Paradigm, 19(2), 152-169. doi:

10.1177/0971890715609847

Store Choice Behavior for Consumer Durables in NCT-Delhi: Effect of Shopper’s

Demographics

Author 1: Anoop Kumar Gupta

Email: [email protected]

Mobile Number: +91 9868997448

Affiliation: PhD Research Scholar at Birla Institute of Management Technology, Greater Noida,

U.P., India and Assistant Professor of Marketing with Department of MBA in Maharaja Agrasen

Institute of Technology, Sector 22, Rohini, New Delhi, India.

Author 2: A.V. Shukla

Email: [email protected]

Mobile Number: +91 9717705560

Affiliation: Professor of Marketing at Birla Institute of Management Technology, Greater

Noida, Uttar Pradesh, India.

Correspondence concerning this article should be addressed to Anoop Kumar Gupta,

Department of MBA, Maharaja Agrasen Institute of Technology, Sector 22, Rohini, New

Delhi-110086.

Emails: [email protected]

[email protected]

http://par.sagepub.com/content/19/2/152.abstract

STORE CHOICE BEHAVIOR FOR CONSUMER DURABLES 2

ABSTRACT

Retail shopping behavior is an important area for market researchers. Most of the research has

focused on studying the effect of store characteristics on store choice behavior, that too in the

context of grocery and apparel retailing; whereas studies for understanding the effect of

consumer characteristics on store choice behavior particularly for consumer durables have been

comparatively less. The purpose of this study is to explore if demographic variables like gender,

occupation, and age affect store choice behavior for consumer durable goods. Data was collected

by a structured questionnaire from 177 respondents in National Capital Territory of Delhi (NCT-

Delhi). Discriminant analysis of the collected data was done to understand if customers could be

grouped on the basis of their characteristics of gender, occupation, and age for store choice

behavior. Results indicated that gender and occupation do not make significant differences

whereas age has significant effect on the store choice behavior for consumer durable goods.

Possible implication for retailer’s strategy is that market can be segmented on the basis of age.

Keywords: Store choice behavior, consumer durables, demographics, discriminant analysis

JEL Classification: D12


Store Choice Behavior for Consumer Durables in NCT-Delhi: Effect of Shopper’s

Demographics

Retailing has been defined as business activities involved in selling goods and services to

consumers for their personal, family or household use (Berman and Evans, 2010). The retail

sector in India is growing at a good pace. KPMG (2014) estimates the Indian retail market at

USD 534 billion, and is largely unorganized and fragmented. But the sector is growing, evolving

and shifting from unorganized to organized with BCG (2015) estimating size of retail sector to

reach USD 1 trillion by the year 2020 and size of organized retail to reach USD 180 billion from

current size of USD 60 billion. With the growth in organized retail, the Indian consumer

especially in urban areas, is exposed to modern1 format retail stores. Example of modern formats

include department stores like Marks & Spencer’, supermarkets like Big Bazzar, Hypermarkets

like MORE, franchise stores like Van Heusen, discount stores like KB’s Fair Price and consumer

durables stores like TATA Croma, Reliance Digital and many others. Organization of Indian

retail sector is estimated to be 8% by KPMG (2014) and 10% by BCG (2015). Going by the

estimates the sector is still in infancy stage. The organization of consumer durables2 retail is

comparatively higher and stands at 12% (IBEF, 2014). This is largely because consumer

durables require minimal investments in backend infrastructure (Deloitte, 2013); have good

market growth and promising potential, the sector is witnessing a surge in entry of organized

retailers, both offline and online. Electronic retailers (e-tailers) like Flipkart, Snapdeal and

Amazon are getting large contributions of their revenues through consumer durables sales. PwC

(2014) estimates share of consumer durables sales to be 34% of the e-tailing sector. Some

consumer durable manufacturers are now adding online sales channel to their channel mix, like


Panasonic is planning to start their online store and Godrej Appliances is tying-up with online

marketplaces like Flipkart and Amazon (Mitra and Pinto, 2014).

The retailing space is getting crowded with addition of whole new range of retail formats.

Probably retailers might need information regarding consumer buying behaviour for consumer

durables. But currently, there is limited information and literature availability in the public

domain for shopping behavior of Indian consumers particularly for consumer durables. Whereas

extant literature indicates strong relationship between shopper characteristics and store choice

behavior (see, for example: Aaker and Jones, 1971; Leszczyc, Sinha and Timmermans, 2000),

this relationship has not been explored much in the context of store choice behavior for

consumer durables. This study attempts to understand the effect of consumer characteristics like

gender, occupation, and age on store choice behavior for consumer durable goods in NCT-Delhi.

Though this study is confined to NCT-Delhi, reveals some interesting facts about the

relationships between consumer characteristics and shopping behavior.

LITERATURE REVIEW

Store Choice

Retail shopping behaviour is an important area of research globally (Sinha and Banerjee,

2004). Store choice behavior across various purchasing situations has been area of interest for

marketing researchers for many years (Carpenter and Moore, 2006; Sinha, Banerjee and Uniyal,

2002). The shopper behaviour differs according to: the context of shopping (Sinha, Banerjee and

Uniyal, 2002); place of shopping and level of involvement in shopping act (Berman and Evans,

2005). This implies differences in apparel, grocery and consumer durable shopping behavior, as


they are different types of goods (Copeland, 1923). Furthermore need has been felt to understand

shopping behaviour in the Indian retailing context (Sinha and Banerjee, 2004).

Store choice is largely considered to be a cognitive process (Sinha and Banerjee, 2004),

and has been studied in variety of contexts like: location influence (Fotheringham, 1988; Kim

and Jin, 2001; Meyer and Eagle, 1982); pre-purchase information of brand (Dash, Schiffman

and Berenson, 1976); consumer perceived risk inherent in the product purchase decision

(Hisrich, Dornoff, and Kernan, 1972); shopping costs and derived utility (Bell and Lattin, 1998;

Tang, Bell and Ho, 2001); threshold shopping value (Malhotra, 1983); hedonic utility of

shopping (Lumpkin, Greenberg and Goldstrucker, 1985); store ambiance (Kotler, 1973);

monetary and non-monetary costs (Zeithaml, 1988); type of shopping trips (Kahn and

Schmittlein, 1989); travel time (Fox, Montgomery and Lodish, 2004); nature of decision and

decision process (Leszczyc, Sinha and Timmermans, 2000). Store choice has also been studied in

the context of store image and argued to be influenced by consumer demographics (Martineau,

1958). Another area researched amply is of store patronage, which comprises key concepts of

store choice and frequency of store visits (Pan and Zinkhan, 2006), and has contributed to the

knowledge stream of store choice behavior.

Leszczyc et al. (2000) conceptualized the store choice decision as a problem situation

requiring solution, and having two stages. The two stages pertain to information processing for

store location, and shopping trip incidence timings. Kahn and Schmittlein (1989) also found

these two stages to be correlated. These decisions were found to be influenced by shopper

characteristics (Leszczyc and Timmermans, 1997). In an exploratory study Sinha, Banerjee and

Uniyal (2002) found that convenience plays a major role in store choice behavior for grocery

items and merchandise plays important role for consumer durables. Literature review indicates


that consumer buying behavior, for consumer durables, has not been researched much (Mitchell,

1999). A lot of research has been done in the domain of store choice behavior, but availability of

literature on store choice behavior for consumer durables is limited.

Store Choice and Shopper Demographics

Extant literature for effect of shopper demographic characteristics on store choice

behavior is available and studies have largely been done for grocery and apparel shopping in the

context of store patronage and shopping orientation (Carpenter and Balija, 2010). Store choice is

considered to be a hierarchal process wherein it is preceded by store format choice (González-

Benito, Bustos-Reyes and Muñoz-Gallego, 2007), which in turn is an element of store patronage

behavior (Lumpkin and Burnett, 1992), implying that store patronage is a wider concept which

encompasses store format choice followed by store choice. Though ‘store choice’ and ‘store

format choice’ are distinct concepts, the factors predicting them have been studied in an

overlapping manner.

Thenmozhi and Dhanapal (2012) in their study found that demographic profile of the

consumer affects and explains store choice behavior. Theodoridis and Priporas (2009) in their

study on store choice behavior of Greek consumers for computers found that demographics have

strong influence on the choice behavior and can predict it. They also found that location

convenience is not an important determinant in store choice behavior for a durable like

computer. Similar findings were reported by Gupta (2012), like for national brands of consumer

durable goods, location convenience did not matter much and customers were ready to travel to

city outskirts if public transport was available. Fotheringham and Trew (1993) found significant

effect of income on store choice behavior for major grocery shopping. Their study demonstrated

that, contrary to the common belief, low income customers were ready to travel to farther places


for taking advantage of low prices; and higher income customers were ready to forgo savings for

convenience. Gender influence on store choice behavior has been area of interest for marketing

researchers, for quite some time.

Gender and Store choice behavior

Gender differences have been studied in variety of retailer choice context like: store patronage

behavior (Fox, Montgomery and Lodish, 2004); store choice behavior (Baltas and

Papastathopoulou, 2003); retail format choice behavior (Garbarino and Strahilevitz, 2004).

Though in a recent review Meyers-Levy and Loken (2014), for consumer goods buying,

summarized that males emphasized functional attributes (‘hard side’) like information

acquisition, economics, and efficiency of the process; females were concerned for the ‘soft side’

of the buying process mainly the emotional, social, and experiential aspects of shopping process

and found greater identity related issues, whereas Raajpoot, Sharma and Chebat (2008) argued

about ‘soft side’ of male customers and ‘hard side’ of female customers. Exemplars of research

for gender effect on store choice are presented in Table 1. This stream of research has established

gender based difference in store choice behavior in the manner they employ different store

choice criteria, patronize stores, and adopt retail formats. Though some studies were found for

influence of gender on store choice, mostly pertained to grocery and apparel sector, negligible

could be found for consumer durables sector.

Table 1

Exemplars of gender research

Context Reference Gender differences

Relationship between Baltas and Store choice behavior of males and


consumer

characteristics, brand

choice and store choice

criteria

Papastathopoulou

(2003)

females were different with women

paying more attention to economic

criteria

Consumer expenditure

across different retail

formats

Fox, Montgomery

and Lodish (2004)

Working women households have

different store patronage behavior

and the households patronized fewer

stores

Gender effect on risk

perceptions in online

retailing

Garbarino and

Strahilevitz (2004)

Gender differences existed for online

buying with women perceiving

higher risk in online purchasing

Role of gender and

work status in shopping

center patronage

Raajpoot, Sharma and

Chebat (2008)

Few gender based significant

differences existed in shopping mall

patronage behavior, namely: men

consider employee behavior to be

more important and better assortment

makes shopping to be more exciting

for women

Relationship of age and store choice behavior has also been explored to some extent by

the marketing researchers.

Age and Store choice behavior


Moschis (1994) argued that age has an influence on customer buying behavior. Age influence on

consumer behavior has been studied in various contexts like: preference for an information

source (e.g. Kinley, Conrad and Brown, 1999); store patronage (e.g. Lumpkin et al., 1985); store

format choice (e.g. Carpenter and Balija, 2010; Nilsson, Garling, Marell and Nordvall, 2014;

Sousa, Yeung and Cheng, 2008); shopping trip behavior (e.g. Kahn and Schmittlein, 1989).

Exemplars of research for age effect on store choice are presented in Table 2. This stream of

research has established the difference of store choice behavior for different age-groups in the

manner they acquire information from their preferred sources, patronize different stores, choose

retail formats, and their shopping trip behavior.

Table 2

Exemplars of age research

Context Reference Age differences

Store patronage for

apparels

Lumpkin,

Greenberg and

Goldstrucker

(1985)

Young and elderly customers have

different likings for store attributes of

physical environment, location and price

Shopping trip

behavior

Kahn and

Schmittlein (1989)

Day-of-the-week phenomenon

identified, and dependent on age and

income.

Preference for

information source

Kinley, Conrad

and Brown (1999)

Younger shoppers preferred personal

sources of information than older

shoppers


Preference for retail

formats

Sousa, Yeung and

Cheng (2008)

Young customers preferred new retail

formats like online retailers

Preference for online

retailers

Carpenter and

Balija (2010)

Young educated U.S. consumers

preferred online retailers

Shopping behavior of

age cohorts of Baby

Boomers and

Generation Y

Parment (2013)

Baby Boomers valued in-store service

and experience, and their shopping

process started with trusted retail store

whereas for Generation Y it started with

the product

Retail format choice

in grocery buying for

major and fill-in trips

Nilsson, Garling,

Marell and

Nordvall (2014)

Market segments of grocery shoppers

were identified which differed in terms

of age

Various studies have found demographic factors to influence the store format choice

behavior (e.g. Nilsson, Garling, Marell and Nordvall, 2014; Prasad and Aryasari, 2011). Tripathi

and Dave (2013) contend that demographics can predict retail format choice. In the context of

shopping mall, occupation has also been found to influence patronage behavior, wherein

qualified professionals or businessmen with larger families are heavier spenders (Kuruvilla and

Joshi, 2010). Sinha, Banerjee and Uniyal (2002) suggested that age and gender have an influence

on store choice behavior, but their study was overarching and had covered a wide variety of

stores, from Paan/Beedi store to consumer durables store.

Taken together, these research studies provide a linkage between consumer demographic

factors of gender, occupation and age, and store choice behavior, thereby indicating presence of


relationship between them. But most of the studies found in literature were related to food and

grocery, and apparels but studies for retail of consumer durables were not found much. This

study intends to contribute in filling of the gap and explores the linkage between consumer

demographic characteristics of gender, occupation and age and store choice for consumer

durable goods. On the basis of literature review, following research hypothesis were formulated:

Hypothesis 1 (H1) - There is not a significant difference between the store choice behavior of

male and female for consumer durables

Hypothesis 2 (H2) - There is not a significant effect of occupation on the store choice

behavior of customers for consumer durables

Hypothesis 3 (H3) - There is not a significant effect of age on the store choice behavior of

customers for consumer durables

METHODOLOGY

Sampling and Data Collection

The data was collected on thirty one attitude statements pertaining to store choice

behavior and details of gender, occupation and age were also captured. The structured

questionnaire, constructed on five point Likert scale, was administered through personal contact.

The data was collected from 200 individual customers in NCT-Delhi, out of which 177

questionnaires were complete and useable. The respondents were selected through convenience

sampling, due to which, the scope for generalizability of the study may be limited. Nevertheless,

according to Calder, Philips and Tybout (1981) any respondent group can be utilized for theory

application.

Measurement


Variables affecting store choice behavior were identified through literature survey and

are based on previous studies. The variables such as information search for retail store, product,

price, consumer promotion offers, sources of information (Engel, Blackwell and Miniard, 2008);

retail store types (exclusive or multi-brand), brand preference, store attributes (Kotler, 1973;

Baker, Grewal and Levy, 1992; Arnold, Oum and Tigert, 1983; Sinha, Banerjee and Uniyal,

2002); purchase occasion, shopping convenience, facility of credit, location of store, shopping in

neighborhood markets, reach-ability of store (Briesch, Chintagunta and Fox, 2009; Craig, Ghosh,

and McLafferty 1984; Fotheringham, 1988; Lumpkin and Burnett, 1992; Runyan and Droge,

2008; Sinha and Banerjee, 2004; Singh and Sahay, 2012), parking facility (Lumpkin and Burnett,

1992; Pan and Zinkhan, 2006; Waerden, Borgers and Timmermans, 1998); treatment by in-store

salesperson (Hawes, Rao and Baker, 1993; O’Cass and Grace, 2008), were identified and

questionnaire was constructed on their basis. Table 3 lists the variables used for building

questionnaire.

Table 3: Variables used for building questionnaire

VAR1 Product information

VAR2 Retail store information

VAR3 Price information

VAR4 Information for consumer promotion

VAR5 Electronics and appliance magazine as a source of information

VAR6 Newspaper as source of information

VAR7 World Wide Web as source of information

VAR8 Social circle as source of information

VAR9 Preference for exclusive brand stores

VAR10 Market survey as source of information (shopping)

VAR12 Preference for international brands

VAR13 Preference for brands


VAR14 Parking facility

VAR15 Proximity of retail store to home/work place

VAR16 Location of retail store in the market of similar goods

VAR17 Drivability to the store in terms of traffic congestion on the way

VAR18 Availability of public transport for reaching the store

VAR19 Item availability in the store

VAR20 Online purchase option

VAR21 Store ambiance

VAR22 Salesperson mannerisms

VAR23 Level of store service in terms of assistance in choosing a good

VAR24 Item demonstration

VAR25 Salesperson knowledge regarding the product and alternatives

VAR26 List price

VAR27 Discount on list price

VAR28 Exchange price for item trade-in by the consumer

VAR29 Store credit facility

VAR30 Festive occasion driven purchases

VAR31 Need driven purchases

Pilot test and validity

The content validity of the instrument was checked through screening exercise (Sekaran,

2002). The questionnaire was finalized after it was proofread by three marketing academics and

three retail sector professionals. The questionnaire was pre-tested and on the basis of the

debriefing of the pre-test respondents; minor changes were made to improve the clarity and

visual layout of the questionnaire. The reliability of the questionnaire was tested through

Cronbach Alpha (α) and was found to be 0.746, which exceeds conventional benchmark of 0.70

(Baggozi and Yi, 1988; Nunnaly and Bernstein, 1994).

ANALYSIS AND FINDINGS


Data was edited, coded and analyzed through SPSS software. Discriminant analysis was

done to explore if the collected data, for store choice behavior of consumer durables, can be

discriminated on the basis of gender, occupation and age.

1. Testing for Wilks’ Lambda (λ) – When data was subjected to discriminant analysis with

grouping variable as gender, the discriminant function’s Wilks’ λ was not significant at 0.05

level (Table 4); therefore the sample data could not be segregated on the basis of gender (Field,

2009). On the basis of available evidence, hypothesis H1 was not rejected.

Table 4: Wilks’ Lambda for Gender

Test of

Function(s)

Wilks’

Lambda Chi-square df Sig.

1 .830 29.796 31 .528

Then the data was grouped on occupation, Wilks’ λ for all the three discriminant

functions (four groups of occupation yielded three discriminant functions) was not significant at

0.05 level (Table 5); therefore again the sample data could not be segregated on the basis of

occupation. On the basis of available evidence, hypothesis H2 was not rejected.

Table 5: Wilks’ Lambda for Occupation

Test of

Function(s)

Wilks’


1 through 3 .493 112.083 93 .087

2 through 3 .692 58.422 60 .534

3 .853 25.281 29 .664


Lastly, the data was grouped on age and following are the findings of the analysis:

i. The three functions together have Wilks’ λ as 0.475 (Table 6), which is significant at 0.05

level; the functions together significantly discriminate among the age groups (Field, 2009).

ii. When the first function is removed, the Wilks’ λ associated with the second function &

third function is 0.673, which is not significant at 0.05 level. Therefore, the second function does

not contribute significantly to group differences. Similarly when first and second functions are

removed, third function has Wilks’ λ as 0.856, which is not significant at 0.05 level. On the basis

of available evidence, hypothesis H3 stands rejected, which implies that data can be grouped on

the basis of age.

Table 6: Wilks’ Lambda for Age-Groups

Test of

Function(s)

Wilks’


1 through 3 .475 118.127 93 .040

2 through 3 .673 62.806 60 .377

3 .856 24.671 29 .695

2. Pooled within-groups correlation matrix

Because the matrix has values less than 0.8; therefore problem of multicollinearity is unlikely

between predictors (Malhotra and Birks, 2007; Churchill, Iacobucci and Israel, 2011).

3. Univariate F-ratios

Significance of the univariate F-ratios indicates that, when the predictors were considered

individually only ‘preference to online purchase for low risk purchases’, ‘visiting World Wide


Web (internet) for information’ and ‘information gathering about a retail store’, significantly

differentiate between store choice behaviors of different age-groups (Table 7). Complete matrix

can be viewed in Table 11 (Appendix A).

Table 7: Tests of Equality of Group Means

Wilks' Lambda F df1 df2 Sig.

Information gathering about a

retail store

.953 2.833 3 173 .040

Visiting World Wide Web

(internet) for information

.872 8.440 3 173 .000

Preference to online purchase for

low risk purchases

.945 3.363 3 173 .020

4. Percentage of variance explained for Age-Groups (Table 8):

a. First discriminant function explains 48.7% of the variance in the data, and is

significant as well.

b. Second function explains 31.7% of the variance but is not significant.

c. Third function explains 19.6% of the variance and is not significant.

Table 8: Eigenvalues for Age-Groups

Function Eigenvalue

% of

Variance

Cumulative

%

Canonical

Correlation


1 .418a 48.7 48.7 .543

2 .272a 31.7 80.4 .462

3 .168a 19.6 100.0 .380

a First 3 canonical discriminant functions were used in the analysis

5. The standardized canonical discriminant function coefficients

The results for standardized canonical discriminant function coefficients produced in Table 12

(Appendix B); for Function 1, coefficients of the variables like ‘Visiting World Wide Web

(internet) for information’ is 0.499; ‘Importance of store service level for choosing an item’ is

0.450; and ‘Purchase Occasion’ is 0.438; indicating that these variables have greater

discriminating ability.

6. Hit Ratio (Age-wise)

Classification results (Table 9) indicate that 45.2% in ‘less than 25 years’, 83.7% in 25-34 years,

52.3% in 35-50 years, and 50% in ‘more than 50 years’ were correctly classified. On overall

basis, correct classification of 67.2% percent cases is achieved, which is more than minimum

classification criteria of 62.5%, considered to be thumb rule for acceptance (Kuruvilla and Joshi,

2010).


Table 9: Classification Resultsa,c

7. Effect size measure for discriminant analysis:

Discriminant analysis on age-group revealed three discriminant functions. The first explained

48.7% of the variance (Table 10), canonical 𝑅2 = .295; the second explained 31.7%, canonical

𝑅2 = .213; and the third explained 19.6% of the variance, canonical 𝑅2 = .144.

Table 10: Canonical 𝑹𝟐

Function % of Variance

Canonical

Correlation 𝑅2

1 48.7 .543 .295

Less than

25 Years

25 to 34

Years

35 to 50

Years

More than

50 Years

Less than 25 Years 14 16 1 0 31

25 to 34 Years 6 77 8 1 92

35 to 50 Years 2 18 23 1 44

More than 50 Years 1 2 2 5 10

Less than 25 Years 45.2 51.6 3.2 0.0 100.0

25 to 34 Years 6.5 83.7 8.7 1.1 100.0

35 to 50 Years 4.5 40.9 52.3 2.3 100.0

More than 50 Years 10.0 20.0 20.0 50.0 100.0

Less than 25 Years 6 21 4 0 31

25 to 34 Years 12 64 12 4 92

35 to 50 Years 3 25 12 4 44

More than 50 Years 2 3 4 1 10

Less than 25 Years 19.4 67.7 12.9 0.0 100.0

25 to 34 Years 13.0 69.6 13.0 4.3 100.0

35 to 50 Years 6.8 56.8 27.3 9.1 100.0

More than 50 Years 20.0 30.0 40.0 10.0 100.0

c. 46.9% of cross-validated grouped cases correctly classified.

AGE-GROUP

Predicted Group Membership

Total

Original

Count

%

Cross-

validatedb

Count

%

a. 67.2% of original grouped cases correctly classified.

b. Cross validation is done only for those cases in the analysis. In cross validation, each case is

classified by the functions derived from all cases other than that case.


2 31.7 .462 .213

3 19.6 .380 .144

In combination these discriminant functions significantly differentiated the age-groups,

λ=0.475, χ2(93) = 118.127, p < .05, but removing the first function indicated that second

function did not significantly differentiate the age-groups, λ=0.673, χ2(60) = 62.806, p > .05, also

removal of first and second function did not significantly differentiate the age-groups, λ=0.856,

χ2(29) = 24.671, p > .05 (Table 6).

DISCUSSION, SUMMARY AND IMPLICATION

This study was undertaken with the purpose to explore whether store choice behavior, for

consumer durables in NCT-Delhi, differs on the basis of gender, occupation and age.

Discriminant analysis on the collected data revealed that the shoppers do-not differ on the basis

of gender and occupation; but differed in terms of age significantly. This implied that the store

choice behavior, for consumer durables, is different among the age groups of ‘less than 25’, 25 to

34, 35 to 50 and ‘more than 50’ years. The findings also indicated that the shoppers of different

age-groups have different preferences for newer forms of retail formats like online retail, and

usage of internet for information gathering to accomplish shopping task. The outcome of the

research confirms some of the earlier findings, though conducted in other countries, like studies

by Sousa, Yeung and Cheng (2008); Carpenter and Balija (2010), wherein they have reported the

age effect on choice of online retail format. This study also differentiates the behavior of young

and older shoppers in terms of choice of online retail stores and information search through

internet. Another important finding was that while choosing a retail store, shoppers of different

age-groups collected varying levels of information regarding the store. This effectively means


that shoppers of different age-groups have different information needs, while making a store

choice. This finding is in line with conclusions of meta-analysis done by Maity, Dass and

Malhotra (2014) on information search behavior, wherein they found age effect on information

search behavior to be significant and had argued that young customers search for more

information in comparison to older customers. The findings of the study indicate that a consumer

durable retailer might consider market segmentation on the basis of age-groups. Subsequent to

this the marketing strategy implications may include offering of online retail channel option, and

creating website to offer information regarding the retailer and its offerings.


REFERENCES

Aaker, D.A., & Jones, J.M. (1971). Modeling store choice behavior. Journal of Marketing

Research, VIII, 38-42.

Arnold, S.J., Oum, T.H., & Tigert, D.J. (1983). Determinant attributes in retail patronage:

Seasonal, temporal, regional, and international comparisons. Journal of Marketing

Research, 20(2), 149-157.

Bagozzi, R., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the

Academy of Marketing Science, 16(1), 74-94. doi: 10.1007/BF02723327

Baker, J., Levy, M., & Grewal, D. (1992). An experimental approach to making retail store

environmental decisions. Journal of Retailing, 68(4), 445-460.

Baltas, G., & Papastathopoulou, P. (2003). Shopper characteristics, product and store choice

criteria: A survey in the Greek grocery sector. International Journal of Retail and

Distribution Management, 31(10), 498-507.

BCG. (2015). Retail 2020: Retrospect, reinvent, rewrite. Retrieved 15 February 2015, from

Retailers Association of India http://www.bcgindia.com/documents/file181823.pdf

Bell, D.R., & Lattin, J.M. (1998). Shopping behavior and consumer preference for store price

format: Why “large basket” shoppers prefer EDLP. Marketing Science, 17(1), 66–88.

Berman, B., & Evans, J.R. (2010). Retail management-a strategic approach (11 ed.). Upper

Saddle River, New Jersey: Prentice Hall.

Briesch, R.A., Chintagunta, P.K., & Fox, E.J. (2009). How does assortment affect grocery store

choice? Journal of Marketing Research, XLVI, 176–189.

Calder, B.J., Phillips, L.W., & Tybout, A.M. (1981). Designing research for application. Journal

of Consumer Research, 8(2), 197-207.


Carpenter, J.M., & Balija, V. (2010). Retail format choice in the US consumer electronics

market. International Journal of Retail & Distribution Management, 38(4), 258-274.

Carpenter, J.M., & Moore, M. (2006). Consumer demographics, store attributes, and retail

format choice in the US grocery market. International Journal of Retail & Distribution

Management, 34(6), 434-452.

Churchill, G.A., Iacobucci, D., & Israel, D. (2011). Marketing research: A South Asian

perspective. India: Cengage Learning.

Copeland, M.T. (1923). Relation of consumer buying habits to marketing methods. Harvard

Business Review, 1(April), 282-289.

Craig, C.S., Ghosh, A., & McLafferty, S. (1984). Models of retail location process: A review.

Journal of Retailing, 60(1), 5-36.

Dash, J.F., Schiffman, L.G., & Berenson, C. (1976). Risk- and personality-related dimensions of

store choice. Journal of Marketing, 40(1), 32-39.

Deloitte. (2013). Indian retail market-opening more doors. Retrieved 6 June 2014, from Deloitte

http://www.deloitte.com/assets/Dcom-

India/Local%20Assets/Documents/Thoughtware/Indian_Retail_Report_Opening_more_

doors.pdf

Engel, J.F., Blackwell, R.D., & Miniard, P.W. (2008). Consumer behaviour (1 ed.). New Delhi,

India: South Western.

Field, A. (2009). Discovering statistics using SPSS (3 ed.). California: SAGE Publications.

Fotheringham, A.S. (1988). Consumer store choice and choice set definition. Marketing Science,

7(3), 299-310.

Fotheringham, A.S., & Trew, R. (1993). Chain image and store-choice modeling: The effects of


income and race. Environmeant Planning, 25, 179-196.

Fox, E.J., Montgomery, A.L., & Lodish, L.M. (2004). Consumer shopping and spending across

retail formats. Journal ofBusiness, 77(S2), S25-S60.

Garbarino, E., & Strahilevitz, M. (2004). Gender differences in the perceived risk of buying

online and the effects of receiving a site recommendation. Journal of Business Research,

57, 768– 775.

González-Benito, O., Bustos-Reyes, C.A., & Muñoz-Gallego, P.A. (2007). Isolating the

geodemographic characterisation of retail format choice from the effects of spatial

convenience. Marketing Letters, 18(1/2), 45-59.

Gupta, A.K. (2012). Retail store selection criteria used by customers in Delhi-NCR: Implications

for the retailers. International Journal of Research in Commerce, IT & Management,

2(5), 147-151.

Hawes, J.M., Rao, C.P., & Baker, T.L. (1993). Retail salesperson attributes and the role of

dependability in the selection of durable goods. Journal of Personal Selling & Sales

Management, 13(4), 61-71. doi: 10.1080/08853134.1993.10753967

Hisrich, R.D., Dornoff, R.J., & Kernan, J.B. (1972). Perceived risk in store selection. Journal of

Marketing Research, 9(4), 435-439.

IBEF. (2013). Consumer durables. Retrieved 25 April 2014, from India Brand Equity

Foundation http://www.ibef.org/download/consumer-durable-august-2013.pdf

IBEF. (2014). Consumer durables. Retrieved 28 Dec 2014, from India Brand Equity Foundation

http://www.ibef.org/download/Consumer-Durables-March-2014.pdf

Kahn, B.E., & Schmittlein, D.C. (1989). Shopping trip behaviour: An empirical study. Marketing

Letters, 1(1), 55-69.


Kim, J.O., & Jin, B. (2001). Korean consumers’ patronage of discount stores: Domestic vs

multinational discount store shoppers’ profiles. Journal of Consumer Marketing, 18(3),

236-255. doi: 10.1108/07363760110393092

Kinley, T.L., Conrad, R.A., & Brown, G. (2000). Personal vs. Non-personal sources of

information used in the purchase of men’s apparel. Journal of Consumer Studies &

Home Economics, 24(1), 67-73. doi: 10.1046/j.1365-2737.2000.00126.x

Kotler, P. (1973). Atmospherics as a marketing tool. Journal of Retailing, 49(4), 48-64.

KPMG. (2014). Indian retail: The next growth story. Retrieved 28 Dec 2014, from KPMG

https://www.kpmg.com/IN/en/IssuesAndInsights/ArticlesPublications/Documents/BBG-

Retail.pdf

Kuruvilla, S.J., & Joshi, N. (2010). Influence of demographics, psychographics, shopping

orientation, mall shopping attitude and purchase patterns on mall patronage in India.

Journal of Retailing and Consumer Services, 17(4), 259-269. doi:

http://dx.doi.org/10.1016/j.jretconser.2010.02.003

Leszczyc, P.T.L.P., Sinha, A., & Timmermans, H.J.P. (1997). Store-switching behavior.

Marketing Letters, 8(2), 193–204.

Leszczyc, P.T.L.P., Sinha, A., & Timmermans, H.J.P. (2000). Consumer store choice dynamics:

An analysis of the competitive market structure for grocery stores. Journal of Retailing,

76(3), 323–345.

Lumpkin, J.R., & Burnett, J.J. (1992). Identifying store choice determinants of mature

customers. Journal of Applied Business Research, 8(1), 89-102.

Lumpkin, J.R., Greenberg, B.A., & Goldstucker, J.L. (1985). Marketplace needs of the elderly:

Determinant attributes and store choice. Journal of Retailing, 61(2), 75-105.


Maity, M., Dass, M., & Malhotra, N.K. (2014). The antecedents and moderators of offline

information search: A meta-analysis. Journal of Retailing, 90(2), 233-254. doi:

http://dx.doi.org/10.1016/j.jretai.2014.03.001

Malhotra, N.K. (1983). A threshold model of store choice. Journal of Retailing, 59(2), 3-21.

Malhotra, N.K., & Birks, D.F. (2007). Marketing research (3 ed.). England: Pearson Education.

Martineau, P. (1958). The personality of the retail store. Harvard Business Review, 36, 47-55.

Meyer, R.J., & Eagle, T.C. (1982). Context-induced parameter instability in a disaggregate-

stochastic model of store choice. Journal of Marketing Research, 19(1), 62-71.

Meyers-Levy, J., & Loken, B. (2014). Revisiting gender differences: What we know and what

lies ahead. Journal of Consumer Psychology. doi:

http://dx.doi.org/10.1016/j.jcps.2014.06.003

Mitchell, V.W. (1999). Consumer perceived risk: Conceptualisations and models. European

Journal of Marketing, 33(1/2), 163-195.

Mitra, S., & Pinto, V.S. (2014, September 3). E-tail bug bites consumer durables firms, Business

Standard. Retrieved from http://www.business-standard.com/article/companies/e-

commerce-catches-the-eye-of-consumer-durables-companies-this-festive-

114090101119_1.html

Moschis, G. (1994). Consumer behavior in later life: Multidisciplinary contributions and

implications for research. Journal of the Academy of Marketing Science, 22(3), 195-204.

doi: 10.1177/0092070394223001

Nilsson, E., Garling, T., Marell, A., & Nordvall, A.-C. (2014). Who shops groceries where and

how? – the relationship between choice of store format and type of grocery shopping.

The International Review of Retail, Distribution and Consumer Research, 25(1), 1-19.


doi: 10.1080/09593969.2014.940996

Nunnaly, J.D., & Bernstein, I.H. (1994). Psychometric theory. New York: McGraw Hill.

Pan, Y., & Zinkhan, G.M. (2006). Determinants of retail patronage: A meta-analytical

perspective. Journal of Retailing, 82(3), 229–243.

Parment, A. (2013). Generation Y vs. baby boomers: Shopping behavior, buyer involvement and

implications for retailing. Journal of Retailing and Consumer Services, 20, 189–199.

Prasad, C.J., & Aryasri, A.R. (2011). Effect of shopper attributes on retail format choice

behaviour for food and grocery retailing in India. International Journal of Retail &

Distribution Management, 39(1), 68-86. doi: 10.1108/09590551111104486

PwC. (2014). Evolution of e-commerce in India: Creating the bricks behind the clicks. Retrieved

09 Dec, 2014, from ASSOCHAM

http://www.pwc.in/assets/pdfs/publications/2014/evolution-of-e-commerce-in-india.pdf

Raajpoot, N.A., Sharma, A., & Chebat, J.-C. (2008). The role of gender and work status in

shopping center patronage. Journal of Business Research, 61(8), 825-833. doi:

http://dx.doi.org/10.1016/j.jbusres.2007.09.009

Sekaran, U. (2002). Research methods for business: A skill building approach (4 ed.): Wiley.

Sengupta, A. (2008). Emergence of modern Indian retail: An historical perspective. International

Journal of Retail & Distribution Management, 36(9), 689-700. doi:

10.1108/09590550810890939

Singh, H., & Sahay, V. (2012). Determinants of shopping experience: Exploring the mall

shoppers of National Capital Region (NCR) of India. International Journal of Retail &

Distribution Management, 40(3), 235-248. doi: doi:10.1108/09590551211207184

Sinha, P.K., & Banerjee, A. (2004). Store choice behaviour in an evolving market. International


Journal of Retail & Distribution Management, 32(10), 482-494. doi:

10.1108/09590550410558626

Sinha, P.K., Banerjee, A., & Uniyal, D.P. (2002). Deciding where to buy: Store choice behaviour

of Indian shoppers. Vikalpa, 27(2), 13-28.

Sousa, R., Yeung, A.C.L., & Cheng, T.C.E. (2008). Customer heterogeneity in operational e‐

service design attributes. International Journal of Operations & Production

Management, 28(7), 592-614. doi: doi:10.1108/01443570810881776

Tang, C.S., Bell, D.R., & Teck-Hua, H. (2001). Store choice and shopping behavior: How price

format works. California Management Review, 43(2), 56-74.

Thenmozhi, S.P., & Dhanapal, D. (2012). Store choice behaviour in retail outlets. Global

Management Review, 6(2), 10 - 23.

Theodoridis, C.D., & Priporas, C.-V. (2009). Store choice in computer retailing: The case of

home users in Greece. EuroMed Journal of Business, 4(1), 58-68. doi:

10.1108/14502190910956693

Tripathi, G., & Dave, K. (2013). Store format choice and relationship quality in apparel retail: A

study of young and early-middle aged shoppers in New Delhi region. Journal of

Retailing and Consumer Services, 20, 479–487.

Waerden, P.v.d., Borgers, A., & Timmermans, H. (1998). The impact of the parking situation in

shopping centres on store choice behaviour. GeoJournal, 45, 309-315.

Zeithaml, V.A. (1988). Consumer perceptions of price, quality, and value: A means-end model

and synthesis of evidence. Journal of Marketing, 52(3), 2-22.


FOOTNOTES

1The modern retail stores are different from traditional retail stores on aesthetics, service

(largely are self-service), cleanliness, price and promotional schemes (Sengupta, 2008).

2According to IBEF (2013) consumer durables comprise of consumer electronics (brown

goods) and Consumer appliances (white goods).


APPENDIX A

Table 11: Tests of Equality of Group Means

Wilks'

Lambda F df1 df2 Sig.

Information gathering about

features and benefits of the

item and available

alternatives

.984 .920 3 173 .432

Information gathering about a

retail store

.953 2.833 3 173 .040


the prices of the item

.989 .626 3 173 .599


the sales promotions schemes

on the item

.968 1.925 3 173 .127

Use of electronics and

appliance magazines as

information source

.971 1.713 3 173 .166

Use of newspaper as

information source

.983 .979 3 173 .404

Visiting World Wide Web

(internet) for information

.872 8.440 3 173 .000


Reliance on social circle for

gathering information

.966 2.049 3 173 .109

Preference for exclusive

brand stores

.986 .800 3 173 .496

Market survey for gathering

information regarding the

item

.995 .288 3 173 .834

Preference for International

Brands

.975 1.469 3 173 .225

Preference for National

Brands

.992 .457 3 173 .713

Preference for branded items .992 .467 3 173 .706

Convenience in terms of

parking facility

.997 .175 3 173 .913

Convenience in terms of

retail store proximity to home

or office

.993 .434 3 173 .729

Importance of retail store

location in the market of

similar items

.987 .779 3 173 .507

Importance of drivability to

the store (Traffic condition

on way to the store)

.984 .957 3 173 .415


Importance of availability of

Public transport for the retail

store

.995 .285 3 173 .836

Importance of item

availability in the store

(Touch & Feel)

.986 .793 3 173 .499

Preference to online purchase

for low risk purchases

.945 3.363 3 173 .020

Importance of store ambiance .972 1.689 3 173 .171

Importance of salesperson

mannerisms

.959 2.489 3 173 .062

Importance of store service

level for choosing an item

.981 1.100 3 173 .351

Importance of item

demonstration

.979 1.236 3 173 .298


knowledge

.987 .785 3 173 .504

Importance of list price of the

item

.999 .076 3 173 .973

Importance of discount

quantity

.993 .395 3 173 .757

Exchange Price importance

of the item

.968 1.927 3 173 .127


Importance of retailer credit

facility

.964 2.173 3 173 .093

Festival Importance for

timing of purchase

.996 .250 3 173 .861

Purchase Occasion .965 2.112 3 173 .101


APPENDIX B

Table 12: Standardized Canonical Discriminant Function Coefficients

Function

1 2 3

Information gathering about features

and benefits of the item and available

alternatives

.086 .192 -.115

Information gathering about a retail

store

.095 .532 -.071

Information gathering about the prices

of the item

-.076 .022 .107

Information gathering about the sales

promotions schemes on the item

.303 .062 -.367

Use of electronics and appliance

magazines as information source

.042 -.350 .181

Use of newspaper as information

source

-.369 .060 -.012

Visiting World Wide Web (internet)

for information

.499 -.191 -.559

Reliance on social circle for gathering

information

.190 .377 .087

Preference for exclusive brand stores .148 -.050 -.074


Market survey for gathering

information regarding the item

-.280 .023 -.128

Preference for International Brands .171 -.232 .492

Preference for National Brands -.198 .246 -.271

Preference for branded items -.147 -.036 .139

Convenience in terms of parking

facility

.048 .088 .102

Convenience in terms of retail store

proximity to home or office

-.268 .032 -.504

Importance of retail store location in

the market of similar items

.053 -.244 .038

Importance of drivability to the store

(Traffic condition on way to the store)

-.259 .133 .277

Importance of availability of Public

transport for the retail store

.120 -.141 .126

Importance of item availability in the

store (Touch & Feel)

.111 -.077 .187

Preference to online purchase for low

risk purchases

.172 .336 .085

Importance of store ambiance .131 -.152 -.124


mannerisms

.296 -.045 .143


Importance of store service level for

choosing an item

-.450 .016 -.275

Importance of item demonstration -.205 -.314 -.237

Importance of salesperson knowledge .045 .436 .055

Importance of list price of the item -.123 .140 -.237

Importance of discount quantity -.049 -.269 .284

Exchange Price importance of the

item

.263 -.422 .045

Importance of retailer credit facility .091 .399 .524

Festival Importance for timing of

purchase

.217 -.198 .076

Purchase Occasion .438 -.266 .124