Farhat Nawab

8/3/2019 Farhat Nawab

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1 The Effect of Products New Features on Brand

Choice

CHAPTER ONE

INTRODUCTION

New product features are used commonly by companies to differentiate their products

from the competitors products but for that it is important to know that what kind of feature

should be added that can enhance the choice share of brand. It has been observed that adding

common or similar features to the product dont affect too much the brand choice so firms

trying to develop those ideas of product features which are unique. Firms also have to analyze

the characteristics of the features added to its product according to competing environment,

and demographics characteristics of the competing environment by keeping in mind the price,

quality and performance of the product that also affect on choice of that brand.

1.1 Background of the Study

Technology has made life easy. Companies are producing different electronic devices

with advance technologies which are making life of people easy and comfortable. In a rapid

changing technological environment, it is a dire need for electronic appliance companies to

revise their design and range of products to create competitiveness and differentiation. While

strong market competition demands more creativity and innovation Nowlis and Simonson,

(1996). The product design department plays a vital role in product development especially

for user driven electronic devices. As far as electronic devices are concern, features which are

accompanied with advance technology have great influence over the costumer.

Technological research and development plays pivotal role in improvement of technology. So

companies use Research and Development, (R&D) to introduce superior features and develop

innovative products which result in successful brand. Brands create a perception in the mind

of the consumer that there is no other product or service in the market that is quite like yours.

A brand promises to deliver value upon which consumers and prospective purchasers can rely

to be consistent over long periods of time. Therefore it is strategically very important for


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organizations to make brand stronger so that their brands become more competitive as

compare to rival brands.

Features are the attributes that describe the product or service in detail. Product descriptions

include size, weight, color, Technical details, Product specifications etc. Feature and

functionality are very important elements for better product management that can result in

satisfying the preferences of target market as compare to the offerings of competitors.

Most of the prior studies by Nisbett, Zukier, and Lemley, (1981) have concluded that

common features have less impact on brand choice, the reason behind they dont make

differentiation by diluting the extremity of consumers' preferences, but the unique features

provide differentiation and add more value and have more impact on brand choice and also

exerts a greater influence on preferences. Study conducted at Marymount University, (1999)

also showed that the products features that are needed and have distinctive characteristics are

preferred by the consumers because the consumers are paying for that features.

1.2 Contextual Framework

The research in product features started from around 1970s.in a research, (Tversky)

suggested that common features have less effect on brand choice as compare to unique

features. Irrelevant common feature effects are cancelled when choosing alternatives.

Research tried to evaluate how adding new product features effects different aspects of

chosen brands while analyzing its features. Later research evaluated that uniqueness in

product features make it differentiated from its competitors product.

Then the companies started to differentiate their products from competitors. In order to get

more market share firms started to make their products more innovative and competitive by

adding new product features. The research by Gatignon and Robertson McCardle ; Meyer and

Sathi proved that two factors are very important to evaluate the impact of new feature on

brand choice. First, the value that the new feature or innovation contributes to the enhanced


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product and the other is the uncertainty associated with the performance of the feature when

it is added to that product. Competitiveness added to product by adding new features also

depend on other factors like the competing environment. Research by Gatignon and

Robertson suggested that a particular feature itself is not the sole determinant of its sales

impact, the same feature might has different effects depend on the environment in which it

competes.

Uniqueness, similarity, and attractiveness count a lot in selecting alternatives while adding

new feature to a product. In a research Meyer and Eagle Tversky suggested that unique

features can be rule for choosing between two alternatives but the absolute or the overall

value of an alternative depends on both the shared and the unique features of the item and

possible tendency to choose or reject the same item also depends on which of its features

were emphasized.

Later it was suggested that when a person is comparing two alternatives, the features that are

unique to the focal option exert a greater influence on preferences.

Another research about unique product and its competitiveness by, (Howard) suggested that

the unique features of alternatives, the mere fact that an alternative has the focus of attention,

can often enhance its perceived attractiveness because most alternatives that consumers

consider have more positive than negative features and the alternative that is the focus of

attention is likely to appear more attractive, particularly if the features of the alternatives

retrieved from memory and are not externally available.

Recent research suggested that the evaluation of alternative brands vary depending on the

valence of its unique features. Another research by Houston, Sherman, and Baker suggested

that the products pairs that have unique good features are preferred at the time of product

selection and shares bad features are underweighted in the choice process.


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Research suggested that firms, by using R&D, always try to develop their products with

multiple innovative features which can bring about better performance and higher quality.

R&D is an important factor to add product features and to make the product more

competitive. It enables firms to develop and make products more innovative that have

multiple product features, good quality, better product performance and reliability. Evolution

of technology and change in demographics allow firms to use product features as a source for

being competitive.

Now firms use multiple product features in a single package to make their products more

competitive. More over, firms use unique competitive features rather than only common

features which dont attract consumers attention.

1.3 Research Problem

To determine those factors which affect on brand choice through adding new product

features that can create differentiation and competitiveness.

1.4 Purpose of the Study

The goal of this study is:

To evaluate the impact of new product features on consumer behavior and their

preference of brand.

To know how the price difference influences consumer brand choice when

introducing new product features.

To analyze the effect of products new features on brand choice in Pakistans

scenario.

1.5 Research Question

What are those factors which affect on brand choice through adding new product

features that can create differentiation and competitiveness?


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1.6 Hypothesis

H1

All else being equal, new features added to high priced brand has more choice share as

compare to low priced brand.

H2

All else being equal, new features contribute more to the perceived value of a product with

relatively inferior existing features than to a product with superior existing features.

1.7 Scope of this Study

The research on this topic can be beneficial for the companies who manufacture or

assemble the electronic devices such as mobile phones and laptops. It will help manufacturers

at the time of product development to understand what features attract and instigate consumer

to choose one brand over other brand and enable them to incorporate those features which

can increase their sales and market share.

This research could have practical implications for analyzing and forecasting the sales impact

of new product features and developing marketing-mix strategies. It is helpful to identify

those features which can add value to the product and enable companies to achieve the desire

goal. This research will also help marketing manager to incorporate and emphasize those

unique features which add more value to the product as compared to those common features

which have lower value addition and lower impact on brand choice.

1.8 Significance of this Topic

In this research common, unique and enhanced features of products and their effects

on consumers preference were analyzed and it was also analyzed that how consumers choose

brands. The products like laptops and mobile phone were selected in this research but not

pdas and hand helds. The area of the scope of this research was to analyze empirically how

the products features of mobile phones and laptops product categories can have impact on


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consumers when they are choosing brands and what are the characteristics of product features

that can enhance the product quality. It was also analyzed that whether, product price is also a

determinant in buying behavior or not while adding new features.

In such a highly advance and competitive environment it is very difficult for companies to

increase the market share and sales therefore now a days the main problem which is

confronted by the companies is to increase sales and market shares. For this purpose different

strategies are made and very attractive features are added for creating differentiation and

competitiveness.

1.9 Limitation of the Research

The first limitation of this research was that the area of research was very narrow; the

research was not conducted among industry professionals and marketers but only among

students of Business Universities of Karachi. Second, factors which moderate the impact of

new features on brand choice were not discussed in this research and only two brands were

selected for it. More over, in-depth study of product features were not concerned but only

general information of product features were used to collect data. The sample size was

restricted to 100 samples.


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CHAPTER TWO

LITERATURE REVIEW

In this chapter researcher discussed the literature related to the effect of products new

feature on brand choice. In this chapter it was analyzed from previous findings that how

products new features affect brand preference of consumers. It included all the materials

obtain from different research journals, relevant books and internet sources.

2.1 Introduction

This topic is all about the effect of products new features on brand choice of

consumer. The reason for selecting this topic was for developing any product especially for

electronic products, features and attributes are very important factors. It was studied what are

the features which create differentiation and competitiveness whether they are common

features, unique features, new features, enhance features etc.

2.2 Types of Features and Brand Choice

In this study researcher focused the new features effect of product which can be

unique, common or enhanced. Features are characteristics of the product which provide

benefits to the consumers. It is important to be able to fully describe the features of the

product so that customers understand precisely what is being offered. To sell each product

company needs to describe every feature because consumers buying preferences are mostly

depend on them. It was tried to explore that unique features also have great impact on

consumers choice preferences and it create differentiation which is not possessed by its

competitors. Therefore companies use unique features in their products to differentiate.

Product features which are possessed by all competitors called common features they are also

very important because they are necessary for fulfilling consumers prior and basic

preferences. Enhanced product features are those features which enable a new product to


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claim superiority over competitors on the basis of a common ground as these features dont

have any uniqueness but they are the modified version of existing feature.

Brand is a name, symbol term, sign, or a design or a combination of them, intended to

identify the goods and services of one seller or group of sellers and to distinguish them from

those of competition. Whenever marketer creates a new name logo, or symbol for a new

product, brand is created.

It was concluded that consumer buying behavior or preference of any brand is affected by

product features that have been offered and they preferred those products which not only

provide benefits but also satisfy their needs because they are paying for them.

2.3 Other Factors Influencing Brand Choice

It has been described that product features influences the brand choice but there are

also some other variables such as price, quality and perceived value that describes when by

adding product new features can make the product more competitive.

2.3.1 Price

For instance price is important factor when adding new features to the product. If the

product price is high and not offering competitive features so consumers will not tend to buy

them because consumers pay for value. Adding new features is beneficial when product

features are not available and charging high price so at that time adding new features can

make that product more worthy.

2.3.2 Perceived Value

Perceived quality or value also has effect on consumers buying behavior. It is

costumers perception of the over all quality or superiority of a product or services compared

to alternatives and with respect to its intended purpose. Achieving a satisfactory level of

perceived quality has become more difficult as continual product improvements over the

years have led to heightened consumer expectations.


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Consumers are often faced with the need to choose one alternative from set of alternatives.

Features always affect the choice decision process of consumer. The question arose what

features are the beneficial and have the leading affect on consumers choice decision.

2.4 Literature Review all

2.4.1 New Product Features and Brand Choice

J. Sherman and Dhar, (1996) suggested that there are four factors which have great

impact on brand selection at the time of introducing new features. Those factors add more

value to brand first when the brand has relatively inferior existing features. Second, when

brand is associated with lower (perceived) quality. Third, when it has a higher price, and

fourth, when brand is both high-priced and high-quality.

In the same study researcher suggested that the impact of the newly added features depend

also on the characteristics of the product to which features are added and the other factors

which determine the role of new features in the selection process of consumer.

Gatignon and Robertson, (1993); McCardle, (1985); Meyer and Sathi, (1985) suggested that

two factors should be consider at the time of evaluating the impact of new features. First, the

value added by those new features or innovation to the enhanced product. Second, the

performance uncertainty associated with the feature to the product which it is added to.

Ward, (1995) suggested that the innovation is a result of changing the product by adding or

eliminating product attributes or features within an existing category. When a new product is

designed they are derived from existing product categories because the consumer use

structured imagination and therefore developer uses these imaginations to develop new ideas

which replicate the structure and properties of existing categories

Aaker, (1991); Boush and Loken, (1991); Keller and Aaker, (1992) argued that similarity in

the features of core brand and of new product is of vital importance.


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Park, Milberg & Lawson, (1991) studied those factors which decide that which features of the

prior product should be added to the new product the results showed that the new product

features should be decided on the basis of perceived similarity of the features of the brand.

Tversky, (1972) concluded that values of the product features and their declarative

information and the skill component are very important in the selection of different brands.

Zhou and Nakamoto, (2007) analyzed that in the brand choice selection both congruent and

incongruent features are significant. Congruent features are those which are identical and

incongruent features are unique.

Dhar and Simonson, (1992) suggested that choice share and attractiveness can be enhanced

by focusing on those features which are the focal option of its competitor. If the focal option

of features are not considered then the consumer chose the product on the bases of

information of retrieve memory.

Howard, (1977) described that positive features are more considered by consumer as compare

to negative features and if focus of attention is more attractive and can retrieve memory and

not externally available, are more preferred by him.

Keller, (1993, 1998) examined the relation of brand association with three categories

attributes, benefits and attitudes the results showed that all those three factors are involved in

the selection of alternative. Attributes are descriptive features, benefits are personal value of

consumers on its purchase or consumption and brand attitudes are the over all evaluations of

the brand.

Johnson, (1988) analyzed that features are attributes that could be on conceptual or physical

levels. Brand concepts can be high status or brand equity that typically originates from a

particular configuration of product features like high price or good design or functions.

Moreover, since these readily accessible, prestige brand concepts, (e.g., luxury, status) are

more abstract than functional concepts, (e.g., reliability, durability) they may be able to


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accommodate a more diverse set of objects that share fewer features Johnson (1984); Sujan

and Dekleva, (1987).

Wright, (1974) suggested that few features receive attention in the evaluation and choices of

alternatives.

Haugtvedt, (1992) suggested that some people are cognitive, more effortful and analytical.

Those people pay attention to the main features of the products.

S., Warlop, Mick, and Seeger, (1997) suggested that consumer should pay attention only to

the prominent benefits and ignore those features which dont have salient benefits.

Murthi and Srinivasan, (1999) analyzed the purchase evaluation factors such as price display,

and features for key brands on the basis of occasional purchase. The result suggested that the

consumer doesnt evaluate the brand on those factors on each occasion other than select the

brand on the basis of household demographics.

John Wiley and Sons, Inc, (1997) suggested that the consumers that elaborate on prior

experiences are less influenced by the external context they just focus on some features and

not the others in brand evaluation.

Kisielius and Sternthal, (1984) suggested that the consumers who elaborates on the

Past experiences they recall those experiences them to mind with the use of imagery or

simulation to try to surmise which features of the product were responsible for the past

negative or positive experiences.

Alba and Chattopadhyay, (1985); Bettman and Sujan, (1987); Sanbonmatsu,(1992)

suggested that beginners are more susceptible to external information as compare to expert

consumers. In other words a person who has more expertise in a product category has less

effect of external information.


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Coupey and Nakamoto, (1988) indicated that not only costumer characteristics but alsoproducts are responsible to elaborate the reason of retention of customers because like all

consumers all products are not equal.

Hoyer, (1984) suggested that the consumers perceptions dont based on detailed

consideration of the exact physical features or ingredients, but on limited information

processing.

Batra and Sinha, (2000) explored that the consumers found the experience products features

in their purchase decision is more risky than the search products features.

Greifeneder, Bless, & Kuschmann, (2007) explored the effect of mood and favorability on the

evaluation of brand extensions. It was concluded that the evaluation of new product is depend

on the over all inference of brand rather than on the feature of product.

Consumers are often faced with the need to choose one alternative from set of alternatives.

Features always affect the choice decision process of consumer. The question arose what

features are the beneficial and have the leading affect on consumers choice decision.

2.5 Literature Review on Unique Features of Brand Choice

Unique features are often introduced by companies to create differentiation and to

compete with the dominant brands in the market. With unique features, a product offers

something that other brands lack but it is important to understand how unique features affect

consumer preference of new product. When consumers are experienced, they perceive a

product with unique features more favorably than an enhanced one.

Meyer and Eagle Tversky, (1989) suggested that unique features can be a rule for choosing

between two alternatives and the absolute or the overall value of an alternative depends on

both the shared and the unique features of the item and possible tendency to choose or reject

the same item also depends on which of its features were emphasized.


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Kardes and Kalyanaram, (1992) suggested that in various ways unique features are more

differentiated for instance new and expected features support new product and stimulate

attention especially in today information-overloaded market.

Kardes et al.,(1992) suggested that the unique features exert influence on consumer

preferences and also increase weight in selection of product.

Meyers-Levy and Tybout, (1989) focused that unique features are mostly unpredicted by

consumers so they are more likely to attract consumers and instigate to buy product. The

author has concluded that unique features are very uncommon and they create more

attractiveness for consumers in selecting alternatives.

Heath and Tversky, (1991) concluded that the consumers are less uncertain and more

competent when gain more experience with the product category with new features. The

author showed that when a product with new features is introduced the consumers have little

prior product experience, their product learning may be a very difficult and complex task but

once the experience is gained consumers may view enhanced products as too similar and

unique products as moderately similar.

It is concluded that inexperienced consumers are more uncertain the performance of unique

features because they are not well aware of those features and feel less competent as contrast

of those consumers who are experienced are more likely to recall unique features and prefer a

product with unique features.

Huber and Pluto, (1983); Tveesky, (1972) suggested that the central notion by many

theoretical models is that unique features or attributes should dominate larger and more

exclusive shares of consumers choice.

Tversky's research and work by Agostinelli, (1986) explained that unique features to the focal

option should also exert a more influence on choice priorities when consumer is comparing


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alternatives the relative preferences for the central option depends on whether the focal

option has unique bad features or unique good features.

Recent research suggested that the evaluation of alternative brands vary depending on the

valence of its unique features. Another research by Houston, Sherman, and Baker suggested

that the products pairs that have unique good features are preferred at the time of product

selection and shares bad features are underweighted in the choice process.

Dhar et al., (1996) suggested the attractiveness of alternatives can be increased or decreased

by changing good or bad unique features.

J. Sherman et al., (1996) focused that unique features are given more importance as

compare to shared features at the time of selecting alternatives and these shared features are

cancel out the authors suggest that the attractiveness of the choice set can be enhanced or

reduced by changing which features appear unique.

Houston and Sherman, (1995) suggested that the relative importance for an alternative not

based on whether its features are shared or unique in the selection. The evaluation of

alternatives is depending upon how many numbers of unique features it has.

Houston, (1989) reflected that if pair of alternative are presented with unique good features

but share their bad features as indicated by feature matching model by Houston ,(1989) the

shared, (bad) features will be underweighted in the choice process of alternatives. The

alternative with, (good) feature of the two alternatives will receive greater attention.

Houston, (1991) suggested that the consumers are satisfied if the initial choice is among

alternatives having unique good pairs because the bad features of chosen options are

underweighted.

2.6 Shared and Common Features and Brand Choice

Relevant shared features are very important in choice selection between alternatives

and they are cancelled out if they are irrelevant to the unique features.


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Li, Zheng, and Li, (2007) suggested that shared features by alternatives only matter when

they relevant to the unique features and they may be cancelled when they are irrelevant.

Houston and Roskos-Ewoldsen, (1998); Houston et al., (1995); Houston, (1991) suggested

that cancellation of features shared by alternatives during preference judgment should only

depend on the basis of unique features.

Houston et al., (1995) suggested if the alternatives have to be selected having unique good,

(bad) features of the alternatives, all the shared features are limited to bad, (good).

Ones the alternative with shared bad and unique good will is preferred and the shared bad

features will be cancelled out in the selection because the unique features of both alternatives

are primarily focused and the selection is to be made according to the positive unique feature.

CF. Lee, 1971; von Winterfeldt and Edwards, (1986) suggested that if each alternative shared

its good but not its bad features with one other and on the other side, while sharing its bad but

not its good features with another alternative. Weak dominance states that if there are two

alternatives A and B both shared good attributes and if A has at least one good

attribute over B so A will have dominancy over B

2.7 Common

Common features are also very important just like unique features because

consumers prior preferences are linked with common features. Chou, (2008) suggested that

some times common features are also deleted by the marketers which are deleted by its

competitors. Research proved that deletion of these features only motivate consumers to

disregard the overall value of the prior preferred option instead of strengthen a previous

choice. Therefore by deleting common features of promotional packages marketers risk

altering consumers prior preferences and may not gain advantage over competitors.

Chou, (2008) also suggested that unappealing common features cannot improve the overall

attractiveness of the initially favored alternative.


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Chernev, (2001) demonstrated that the impact of the attractiveness of the promotional

features and participants prior preferences play important role in increasing or decreasing the

affect of adding of common features

Janis and Mann, (1977); Klayman, (1995); Lord, Lepper and Ross,, (1979), Chernev, (2001)

suggested that the importance of these common features are deemphasized by those

consumers who has initial preferences and because they think those features are commonly

shared by others.

Chernev A., (1997) explored that consumer preferences are enhanced by common features

when the brand attributes have differences in their significance. In contrast it was also

suggested that if brand attributes are not significant common features have equalizing affect

on brand choice shares.

Tversky, (1972) suggested that because common features are non investigative they will

simply be overlooked by consumers. This prediction is reliable with most of the sequential-

elimination models of choice. The sequential-model elimination model describes those

redundant aspects of decision problems are edited out prior to choice consideration. Hence,

one can predict that common features will have no effect on selection.

Tversky, (1977) explored that common features make all brand similar for choice share of

consumers because common features enhance the perceived similarity of brands.

Gaeth, (1991); Troutman and Shanteau, (1976) explained that by not adding unique features

and just adding common features can decrease the brand differentiation and may dilute the

consumers preferences.

Carpenter and Nakamoto, (1989) suggested that the advantages of dominant brand are

emphasized by the similarity between two brands, (me-too and a pioneer) thus it was

concluded that common features enhance the existing preferences for given brand, leading to

a divergence of their choice shares.


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Park et al., (1991) suggested that Product feature similarity and brand concept consistency are

two factors which differentiate between successful and unsuccessful brand extension.

The results explored that the brand that have high concept consistency and that have higher

product feature have successful brand extension in other words When a brand's concept is

consistent with those of its extension products, the reputation brand seems to have better

extendibility to products with low feature similarity than the functional brand does.

2.8 Enhance Features

Dhar et al., (1992) explored that to differentiate products and services and to increase

sales; the most common method is to introduce new and enhanced features.

Zhou et al., (2007) suggested that product with enhanced features are selected by the

consumers over unique features when they are unfamiliar with the product. Zhou and

Nakamoto, (2007) also suggested that in contrast the experienced consumer preferred and

perceived unique features over enhance features more positively.

Zhang and Markman, (1998) identified that it is good to introduce enhanced features because

consumers can easily recognize their benefits through the contrast along a common

dimension.

Ziamou and Ratneshwar, (2003) suggested that when the product is a typical with regard to

its enhanced features direct comparison can receive good consumers evaluations

It is quite general that consumers have more uncertainty about unique features as compared to

enhanced features. In the research by Nowlis et al., (1996) concluded that when a feature is

newly introduced, perceived linked with its utility arises

Hsee, (1996) reflected that for the evaluation of features consumers must have some

experience or previous knowledge of its utility on a complete scale for enhance feature that

kind of knowledge is required not for new features.


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Kardes and Kalyanaram, (1992) analyzed that Enhanced features, which fall within their

expectations, tend to be perceived as too similar or unneeded.

Zhang et al., (1998) suggested that enhanced features become the focus of learning and get

particular interest because they provide a direct comparison to the alternatives on the attribute

about which consumer taking decision.

2.9 Price and New Product Features

M. Degeratu, Rangaswamy, & Wu, (2000) suggested that price is more significant

than features in traditional stores.

Nowlis et al., (1996) analyzed that the product with higher price have disadvantage will gain

more advantage by adding new features over low price product with few features because

after adding the features the product become more worthy.

Huber, (1986) suggested that a brand with high price and high quality by adding a new

features will bring weaker effect on price sensitivity because the price already high before the

features were added

2.10 Perceived Value and New Product Features

Nowlis et al., (1996) suggested that adding new features will more increase perceived

value to the products that have substandard existing features or substandard perceived quality

than to products with superior features or brand names.

2.11 Conclusion

Prior studies showed that new product features constantly affect the choice decision

process of consumer and revealed that there are also some other characteristics of features

that affect the choice process like unique, common and enhanced features. Studies revealed

that unique and new features are very significant but common features are also important and

these should not be deleted because of consumer prior preferences. Many researches have

been conducted on this topic. However none of these studies has been carried out in Pakistan


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therefore in this research researcher tried to find out how new features affect brand choice of

consumer in Pakistans scenario. Further research might also explore additional task and

product characteristics that enhance the effect of new features on brand choice in Pakistan.


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CHAPTER THREE

RESEARCH METHODOLOGY

This chapter described the methodology that has been used to conduct this research. It

included the research approaches, research design, population and sampling, data collection

techniques, research instrument, data analysis, validity and reliability test, pilot study, and the

ethics related to this research.

3.1 Research Approaches

There are three types of approaches to conduct research; which are

Qualitative

Quantitative

Mixed

In this research quantitative approach has been used because the researcher had determined

the relationship between variables. Quantitative research approach is used when it is

important to establish out the relationship between one thing, (an independent variable) and

another, (a dependent or outcome variable) in a population. Hopkins, (2000) had defined

quantitative research that precisely measure variables and that aim to determine the

relationship between dependent and independent variables to determine an association.

Quantitative research design is an outstanding way of finalizing results and proving or

disproving a hypothesis. The structure has not changed for centuries, so is standard across

many scientific fields and disciplines.


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3.2 Research Design

There are four types of research design:

Descriptive

Correlational

Causal-comparative

Experimental

Experimental research design is used in this research to test the hypothesis on the basis of

analyzing the relationship between dependent and independent variables by experiment.

Another reason for using experimental research design is it independent variable manipulates

the dependent variable and it also identifies cause and effect relationship and predicts the

outcome. In this research it has been predicted that how much is the impact of products new

feature on brand choice.

3.3 Population and Sampling

Statisticians define a population as the entire collection of items that is the focus of

concern. Sampling is indispensable technique of behavioral research; the research work

cannot be undertaken without use of sampling. , (David S.Fox) suggested that In the social

sciences, it is not possible to collect data from fractional part of the respondents to our study

but only from some fractional part of the respondents. The process of selecting the fractional

part is called sampling.

In this research Karachi was taken as the population and Business University students of

IQRA University and University of Karachi were selected as sample. The sample size was

100. In this research Non-Probability sampling technique has been used.


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There are three types of non-probability sampling.

1- Quota Sample

2- Purposive Sample

3- Convenience Sample

In this research purposive sample has been taken. In this study the reason for taken purposive

sample is that it is used for very specific purpose or need and to select very specific group or

respondents. In this research respondents have been selected those were well aware of

Laptops and Mobile phones prices and specifications.

3.4 Data Collection

For conducting any research it is very important to collect relevant data that suits the

methodology and research design. Mark Saunders, (2006) explained that in primary data

collection different methods are used such as survey, observation, focus group. There are two

types of data collection techniques Primary and Secondary Data Collection. In this research

primary data was used.

Data that never gathered before is called primary data. The reason for selecting primary data

source was that through primary data the needed information could be gathered that suited the

purpose of research as it was not published.

3.5 Research Instrument

Research instrument is a testing device for measuring a given phenomenon. In this

research, questionnaires were used to gather information which is the most widely used

survey data collection techniques.

Through questionnaire each person, (respondent) is asked to respond to the same set of

questions, it provides efficient way of collecting responses from a large sample prior to

quantitative analysis.


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This research involved survey with structured questionnaire to gather complete details.

Although people generally use the term survey and questionnaire interchangeably, the term

survey is used as a general category with questionnaire and interviews as specific

methodologies to conduct survey research, Gay, (1992). The reason for using structured

questionnaire was the questions asked were precisely decided in advance. Close-ended

questions are particularly useful where the study topic concerns to factual

issues with a limited range of responses. The overall reason for using structured

questionnaire and close ended questions was that it supported the research technique that

have been used in this research for the testing.

3.6 Data Analysis

Data analysis is a set of procedures which is used to collect, analyze, present and

interpret the data.

3.7 Validity and Reliability

Reliability is used to measure the consistency of the data which is used to analyze the

instrument whether it provides the same results if it is used in the same way and same

conditions with the same objects repeatedly. One thing that is very important to remember

that reliability is not measured, it is estimated.

Shuttleworth, Martyn, (2008) suggested that before analyzing any data, however, and even

before testing any subjects, the issues of variable selection and control, reliability, and

validity must be addressed. The simplest definition of validity is that degree to which a test

measures what it is supposed to measure Gay, (1992).

Validity is the accuracy of the results and the strength of the conclusions. Cook and

Campbell, (1979) defined it as the best available approximation to the truth or falsity of a


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given inference, proposition or conclusion. Each type of validity would highlight a different

aspect of the relationship between treatment and observed outcome.

There are four types of validity Conclusion, Internal, Construct and External validity. In this

research internal validity was used as it analyzed the relationship between the program and

the outcome and causal relationship. Validity is more important than reliability because if an

instrument does not accurately measure there is no reason to use it even if it measures

consistently.

The objective of analyzing the reliability and the validity of the data is to check how much

accurate relationship between the measure and underlying trait it is trying to measure.

3.8 Pilot Study

Pilot study is the small scale rehearsal of the larger research design. This technique

refers to a smaller scale version of the experiment and equipment tests are the important part

of sub-group of experiments.

Pilot studies can be based on quantitative and/or qualitative methods and large-scale studies

might employ a number of pilot studies before the main survey is conducted. Thus

researchers may start with "qualitative data collection and analysis on a relatively unexplored

topic, using the results to design a subsequent quantitative phase of the study", Tashakkori

and Teddlie, (1998)

In this study the reasons for using pilot testing was to develop and test adequacy of research

instruments. It was also used for assessing the feasibility of full scale study, designing

research protocol, analyzing the sample frame and research technique and collecting

preliminary data. In this study 30 out of 100 respondents were used for the purpose of pilot

study.


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3.9 Ethical Consideration

Ethics refers to the appropriateness of researcher behavior in relation to the right of

subject of work which is affected by research. Wells, (1994) defines ethics in terms of a

code of behavior appropriate to academics and the code of conduct of research .

In this research by following rules of ethics, all details of participants were remained

confidential and they were completely informed related to the nature of the study, they were

given fair consideration and personal biasness didnt hamper the way of research, and the

results were accurately represented as they were told and observed.


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CHAPTER FOUR

DATA ANALYSIS

4.1 Introduction

In this chapter the researcher analyzed the collected data through SPSS software by applying

different types of tests accordingly and interpreted the data to read. In this research there are

two types of studies based on two different hypotheses. Both of the studies used different

types of tests. For study one logit analysis (Binary regression) was used whereas for study

two a one way analysis of variance was used.

4.2 Case 1

In study 1 it has been tested whether adding new feature to high price brand create more

value for buyers or by adding new feature to low price brand create more value for buyers

and for which one they have more buying preference. Two types of different products have

been taken for the testing purpose; mobile phone and laptop.

4.2.1 Variables

For the testing purpose we have taken brand type (whether high or low price) as dependent

variable which is dummy coded or categorical variable. Independent variable was preference

(preference of buying high price brand or low price brand after adding new feature).

Independent variable brand type is an ordinal variable. The value of buying preference is in

the range of 1 to 10 (1 is for the lowest buying preference of brand with new feature and 10 is

for highest).

4.2.2 Reliability Test

Table 4.1 Reliability Statistics

Cronbach's Alpha N of Items

0.712 3


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The value of Cronbachs Alpha is 0.712 (>.70) which shows high internal consistency of the

scale. The higher Alpha value shows that instrument is very consistent and had better

reliability.

4.2.3 Statistical Test

Logit analysis or binary logistic regression has been used in this study. The reason for using

binary logistic regression in this study is the dependent variable is dummy coded categorical

variable having only two levels that ishigh price or low price and independent variable isordinal. Binary logistic regression tests the relationship between independent variable buying

preference and a function of probability of the occurrence that function is called logg odds or

logarithm of odds of occurrence the dummy coding for dependent variable is 0 is for low

price brand and 1 is for high price brand for both mobile and laptop.

The equation of regression model is

4.2.4 Results

Table 4.2 (for mobile phone)

Omnibus Tests of ModelCoefficients

Chi-Square Sig

101.753 0.000

Table 4.3 (for laptop)

Omnibus Tests of Model

CoefficientsChi-Square Sig

120.599 0.000

Table 4.4 Model Summary (mobile phone)Cox & Snell R

SquareNagelkerke R

Square

0.399 0.532Estimation terminated at iteration

number 5 because parameterestimates changed by less than .001.


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Table 4.5 Model Summary (laptop)Cox & Snell R

SquareNagelkerke R

Square

0.453 0.604a Estimation terminated at iterationnumber 5 because parameterestimates changed by less than .001.

Table 4.6 (for mobile)

Variables in the equation

B Sig Exp(B)

prefrence_mobile 1.050 0.000 2.858Constant -6.453 0.000 0.002

Dependent variable isbrand_typeIndependent variable isprefrence_mobile


Variables in the equation

B Sig Exp(B)

prefrence_laptop .979 0.000 2.662

Constant -5.834 0.000 0.003

Dependent variable isbrand_type

Independent variable is prefrence_laptop

The Table 4.2 and 4.3 Omnibus Tests of Model Coefficients table of mobile and laptop

provides a test the over all efficiency of the model and test the predictive ability of the model

by covariates. The results showed that (p


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approximation that how much variation in the brand type (dependent variable) is due to

rating. In this case rating explains 39.9% to 53.2% variation in the brand type. And for laptop

table 4.5 which explains 45.3% to 60.4% variation in the brand type of laptop.

For mobile phone in Table 4.6 the sig value p=0.000 (p


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The probability for high preference there for replace x=10 probability for high preference for

each brand type has been calculated to determine that which brand type has more chances of

getting high preference of buying therefore first the independent variable value is set to 10.

The probability of selecting the brand with high price is 0.9828 therefore the hypothesis is

accepted and it is suggested that for the mobile phone by adding new feature to high price

brand has more impact on buying preference which have probability near to 1 high

probability. The same calculation is x=1 probability for low preference for each brand type

has been calculated to determine that which brand type has more chances of getting low

preference of buying therefore first the independent variable value is set to 1.

Probability of selecting low price with high preference is 0.0044 which is very low therefore

it is suggested that for the mobile phone by adding new feature to low price brand has less

impact on buying preference have probability far to 1 low probability.

For laptop in Table 4.7 the sig value p=0.000 (p


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anti log or Exp and it should be converted to represent probability because the prediction in

logistic regression based on probability of occurrence.

Probability of occurrence of high price brand has higher preference.

The probability for high preference there for replace x=10 probability for high preference for

each brand type has been calculated to determine that which brand type has more chances of

getting high preference of buying therefore first the independent variable value is set to 10.

.

The probability of selecting the brand with high price is 0.98122 therefore the hypothesis is

accepted and it is suggested that for the laptop by adding new feature to high price brand has

more impact on buying preference which have probability near to 1 high probability. The

same calculation is x=1 probability for low preference for each brand type has been

calculated to determine that which brand type has more chances of getting low preference of

buying therefore first the independent variable value is set to 1.

Probability of selecting low price with high preference is 0.0077 which is very low therefore

it is suggested that for the laptop adding new feature to low price brand has less impact on

buying preference have probability far to 1 low probability.


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4.3 Case 2

In study it has been tested whether adding new feature to advance model or superior product

create more value for consumer or adding new feature to basic model or inferior product

create more value. Two types of different products have been taken for the testing purpose;

mobile phone and laptop.

4.3.1 Variables

Dependent variable was price differential (advance model price basic model price) which is

scale variable and Independent variable was product category which is a categorical variable.

This variable was categorized in three categories advanced model with feature, basic with

feature, and neither models with feature so to test the price differential with these categories

planned contrast (a planned contrast is a type of test in which the comparisons are pre-

decided) was used for every three product separately.

For the testing purpose first we have taken price differential between advanced model and

basic model without adding new feature. After that the price differential is taken by adding

new feature only in advance model and compares it with neither models with feature and then

price differential is taken by adding new feature only in basic model and compare it with

neither model with feature. If the price differential in advance model is more increased by

adding new feature, the more the value given to that model and if price differential is more

decreased by adding new feature in basic model, the more value given to that model. To

analyze which product model is more significant or more valuable by adding new feature the

price differential is compared in both cases. If the price differential in advance model with

feature is increased by higher ratio than the price differential is decreased in basic model with


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feature so it is suggested that by adding new feature in advance model is more significant or

valuable for consumer and If the price differential in basic model with feature is decreased

by higher ratio than price differential increase by adding new feature in advance model so it

is suggested that by adding new feature in basic model is more valuable for consumer.

4.3.2 Statistical Test

In this study one way ANOVA was used to test whether there is difference in the means of

price difference in these three groups advanced with feature basic with feature and neither

with feature. The reason for using one way ANOVA was that there is one dependent variable

which is scale and one independent variable which is categorical with more than two groups.

This is the basic requirement of using one way ANOVA and another reason for using one

way ANOVA planned contrast was used in this case. In planned contrast different groups are

tested with price differential as planned contrast.

4.3.3 Results

Table 4.8 (for mobile)Test of Homogeneity of Variances

price_diff_mobile

Levene Statisticdf1 df2 Sig

131.832 2297 0.000


Test of Homogeneity of Variancesprice_diff_laptop

Levene Statisticdf1 df2 Sig

83.570 2297 0.000

Table 4.8 shows Test of homogeneity of variances (for mobile phone) using LEVENE

statistics shows the p


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Table 4.9 shows Test of homogeneity of variances (for Laptop) using LEVENE statistics

shows the p


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feature is added to either advanced model or basic model in the mobile product category there

is a significant change in price differential.

Table 4.12 (Contrast construction for mobile phone)

Contrast Coefficients

Contrast

product_category

Advancedmodel with

featureBasic with feature

Niethermodelswith

feature

1 1 0 -1

2 0 1 -1

Table 4.13 (Contrast construction for laptop)

Contrast Coefficients

Contrast

product_category

Advancedmodel with

feature

Basic model with

feature

Niethermodels

withfeature

1 1 0 -1

2 0 1 -1

Table 4.12 for mobile phone and table 4.13 for laptop show contrast development for the

planned comparison. Such comparisons can be done by using the contrast analysis which is

based on theoretical considerations. A most common and most frequently used technique is to

use a linear contrast combination. The assignment of weight is done through assigning

contrast coefficient in this case for contrast 1 advanced model with feature is compared with

neither models with feature and the price difference between advance model and basic model

has been analyzed therefore advanced model with feature has been assign the contrast

coefficient value (1) and neither models with feature has been assign the contrast coefficient

value (-1) for both mobile and laptop. This assignment compares advanced model with

feature with neither models with feature. The sum of the contrast coefficients should be equal


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to zero. for contrast 2 basic model with feature is compared with neither models with feature

and the price difference between advance model and basic model has been analyzed therefore

basic with feature has been given (1) and neither models with feature has been given (-1) for

both mobile and laptop. This assignment compares basic model with feature with neither

models with feature the sum of the contrast coefficients should be equal to zero.

Table 4.14 Contrast test for mobile

Contrast tSig. (2-tailed)

price_difference_mobile

Assumeequal

variances

1 3.388 0.001

2-15.212 0.000Does not

assumeequalvariances

1 3.072 0.003

2-28.572 0.000

Independent variable = product_category

Dependent variable = price_difference_mobile

Table 4.15 Contrast test for laptop

Contrast tSig. (2-tailed)

price_difference_laptop

Assumeequalvariances

1 21.842 0.000

2 -29.289 0.000Does notassume equalvariances

1 26.457 0.000

2 -32.375 0.000

Independent variable = product_category

Dependent variable = price_difference_mobile

Table 4.14 is the main test for planned comparison for mobile phone. Contrast 1 shows

comparison of advanced model with new feature and neither models with feature and contrast

2 shows the comparison of basic model with feature and neither models with feature and the

price difference between advance and basic model has been analyzed in the contrast test.

First of all the unequal variance condition is assumed. The results for contrast 1 show the

contrast between advanced model with feature and neither model with feature. The price

difference has been statistically significant which means that there is a significant difference


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in the means of that group. The value of p=0.000 in this case it is two tailed test therefore

p=0.003/2=0.0015 which is less than 0.05 therefore result is significant which means that by

adding new feature to advance model it creates a significant difference or in other words it

can be said that the difference between before adding new feature and after adding new

feature is significantly effective for advance model further more by Eta Square it can be

analyzed that which contrast is more predictive. For contrast 2 which is between basic with

new feature and neither models with feature p=0.000/2=0.0000 which is highly significant

which means that there are significant differences in the means of that group and also means

that by adding feature to basic model creates a significant difference (the difference between

before adding new feature and after adding new feature is much significant) therefore the

difference between advance and basic model is significantly changed concluded that adding

feature to basic model is also significantly effective.

Further Eta Square was used to measures strength of association or strength of difference

between groups or the influence of independent variable (product_category). In this case Eta.

Square has been used to analyze that how much variation is occurred in price difference by

adding new feature in basic model which was statistically significant.

For contrast 1 Eta. Square is

= .0.0454 or 4.54%

For contrast 2 Eta. Square is 0.8048 which is 80.48% which clearly shows that by adding new

feature in basic model cause significant variation in the price before adding new feature in

basic model and price after adding the new feature in basic model which creates a bigger

impact in the value creation of basic model. The negative t value shows that the difference

between advance model and basic model is reducing. The reason of this negative t value is by


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adding the new feature in basic model the price was increased for basic model therefore the

difference between advance model and basic model tends to reduced. By the comparison of

both Eta square for advance model and basic the Eta square for basic model is 75% higher

than advance however both of them are significant but adding new product feature in basic

model cause much high impact as Eta square is 80.48% as compare to advance model the Eta

square is 4.54%.

Table 4.15 is the main test for planned comparison for laptop. Contrast 1 shows comparison

of advanced model with new feature and neither models with feature and contrast 2 shows the

comparison of basic model with feature and neither models with feature and the price

difference between advance and basic model has been analyzed in the contrast test.

First of all the unequal variance condition is assumed. The results for contrast 1 show the

contrast between advanced model with feature and neither model with feature. The price

difference has been statistically significant which means that there is a significant difference

in the means of that group. The value of p=0.000 in this case it is two tailed test therefore

p=0.000/2=0.000 which is less than 0.05 therefore result is significant which means that by

adding new feature to advance model it creates a significant difference or in other words it

can be said that the difference between before adding new feature and after adding new

feature significantly effective further more by Eta Square it can be analyzed that which

contrast is more predictive. For contrast 2 which is between basic with new feature and

neither models with feature p=0.000/2=0.0000 which is highly significant which means that

there are significant differences in the means of that group and also means that by adding

feature to basic model creates a significant difference (the difference between before adding

new feature and after adding new feature is much significant) therefore the difference

between advance and basic model is significantly changed concluded that adding feature to

basic model is significantly effective.


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For contrast 1 Eta. Square is 0.779 which is 77.9% which shows that by adding new feature

in advance model also cause significant variation in the price before adding new feature in

advance model and price after adding the new feature in advance model which creates a

sensible impact in the value creation of advance model. The positive t value shows that the

difference between advance model and basic model is increasing. The reason of this positive t

value is by adding the new feature in advance model the price was increased for advance

model therefore the difference between advance model and basic model tends to increase

more.

For contrast 2 Eta. Square is 0.8411 which is 84.11% which clearly shows that by adding new

feature in basic model cause significant variation in the price before adding new feature in

basic model and price after adding the new feature in basic model which creates a bigger

impact in the value creation of basic model. The negative t value shows that the difference

between advance model and basic model is reducing. The reason of this negative t value is by

adding the new feature in basic model the price was increased for basic model therefore the

difference between advance model and basic model tends to reduced. By the comparison of

both Eta square for advance model and basic the Eta square for basic model is 6.21% higher

than advance however both of them are significant but adding new product feature in basic

model cause high impact as Eta square is 84.11% as compare to advance model the Eta

square is 77.9%.

4.4 Discussion

This research is carried out to measure the effect of products new feature on brand choice.

The findings of the research supported the first hypothesis developed for the study. In

analyzing the first hypothesis binary logistics regression test was used. According to the

results it is found that after addition of new features most of the respondents have given high

rating to high price brand as compare to low price brand and also high price brand was


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statistically significant for having high rating. The probability of getting high rating to high

price brand was much higher than the probability of getting high rating to low price brand.

The second hypothesis was also supported by the findings. In analyzing the second

hypothesis one way analysis of variance was used with planned comparisons. It is found that

after adding the new features most of the respondents suggested higher price increase to basic

model as compare to advance model. In this test price increase in both models was used to

measure the value increase in both models. The advance model for mobile was statistically

insignificant by adding new feature and basic model was statistically significant but in laptop

both advance and basic models were statistically significant but the impact of adding new

feature/s was higher in basic model as compare to advance model.


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CHAPTER FIVE

CONCLUSION

5.1 Conclusion

The objective of the research was to find out that how product new features can be used in a

way so that they can impact on brand choice in a competitive way. In this research it was

analyzed that how new product features affects brand choice and which type of products or

brands are more effected. The first hypothesis developed was All else being equal, new

features added to high priced brand has more choice share as compare to low priced brand

the second hypothesis was All else being equal, new features contribute more to the

perceived value of a product with relatively inferior existing features than to a product with

superior existing features in this research quantitative research approach has been used and

the research design was experimental. Primary data was collected through questionnaires.

Non probability purposive sampling method was used and the selected respondents were

students of business universities. To analyze the first hypothesis binary logistics regression

was used and to analyze the second hypothesis analysis of variance was used. The results of

the first study supported the hypothesis and showed that the product which are inferior or

lacking in features are benefited by adding feature more than the products which already have

more features because adding new features could not bring such differentiable impact as it

brings on the inferior product with few existing features. The second hypothesis was also


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supported by results and suggested that the brand with high prices can be more benefited

from new feature/s as compare to brands with low prices. The underlying reason of this

phenomena is that the brands with high prices which introduced newly in the market new

product feature/s can help them to penetrate in the market and develop awareness in the

consumers as compare to brands with prices already dissipated and reduced in the way of

competition and competed enough to not get such big benefit from new product feature/s

therefore results concluded that new product features can impacts brand choice but it depends

on competing environment, feature/s itself, performance of the feature and also the brand to

which it is applied.

5.2 Policy Recommendation

A firm should introduce products new features according to the needs and behavior

of the consumers. Firms should analyze the characteristics of the brand to which the new

feature is added. To introduce the new features firms should use marketing mix strategies

such as changing brand price, altering distribution, and offering price promotions by

increasing the advertising to develop the awareness about the brand and also about the new

feature to which the consumers are not much aware. In addition, it is recommended that the

firm should add new feature/s to its high price brand instead of low price brand, especially to

the brands which are relatively lesser known in the market place because the high priced,

import brands that enters newly in the markets and it creates awareness to the consumers

about that brand because the consumers are taking that brand as a very high price brand but

the features are not matching with the price therefore adding feature to that brand could

equalize the value provided by the brand with its features. Companies could use the unique

feature/s to differentiate their brands from the competitors but the performance of that unique

feature/s is very important because if the unique feature/s fails then the brand could also be

failed to differentiate.


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Companies could use new products features to improve the performance of their brands. The

companies can use new product features for its basic product model more than it advanced

products the reason behind this inferior or basic product have few features and advanced

model has already more features so adding new feature to basic model could bring major

impact on inferior or basic model and can make it more attractive as compare to advance

model which already has more feature/s so that feature/s could not bring a major impact. To

add common features is winning strategy. Companies can use common features as new

feature/s to retain its competency in the market the reason behind this is adding attractive

common features supports the consumer preference for the already differentiated brand.

Common feature played a important role for staying competitive and retaining its market

share for its brands but these common features should be relevant. if a firm introduce a

unique and attractive feature and it is copied by its competitors so it can loss its market

shares but if it has common features as well so that common features can help the firm to

retain its market share for that brand.


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