Master Thesis Rens Verweij

Consumers, retailers and the

adoption of innovation. What is the role of retailer brand equity in the consumer’s adoption decision?

Rens Verweij (1839829)

Faculty of economics and business administration

Vrije Universiteit, Amsterdam, the Netherlands

Master thesis

Supervisor: Dr. E.A. Mooi

July 2010

Rens Verweij, 2010 2


Management summary

Despite of extensive previous research, innovation adoption is still a subject that brings up

questions. It frequently happens that a new product isn’t purchased by consumers, despite of the

benefits the new product has to offer (Gourville; 2006). One of the limitations that

manufacturers face in convincing their consumers to adopt the innovation, is that the

manufacturer does not sell the innovation directly to the end-consumer. Most products are sold

indirectly through retailers (Baldauf et al.; 2009). The retailer has an important role in the

quality assessment and the risk reduction of consumers in the purchase decision for common

products. Especially in situations in which consumers perceive a high level of risk or uncertainty,

consumers are inclined to use their retailers’ image as an important decision determinant in

their purchasing decision (Chu & Chu; 1994). A situation with a high degree of uncertainty, and

therefore high risk, is the purchase of an innovation. However, despite of extensive literature

that describes the influence of the retailer on the consumers’ purchase decision for common and

know products, no previous research looked into the influence of the retailer on the consumer’s

adoption of innovation. This research therefore aimed on answering the following research

question:

‘What is the influence of the retailers’ reputation on the consumers’ evaluation of the perceived

attributes of an innovation?’

To answer this question, we conducted an experiment that researched the influence of the

Retailer brand equity level of the retailer that sells the innovation, on the evaluation of the

innovation attributes described by Rogers (1995) and Ostlund (1974). We hypothesized that

consumers perceive the innovation attributes more positive when the perceived Retailer brand

equity was high. This assumption was based on literature about the effects in consumer-retailer

interaction. This showed that consumers use the retailer brand name as a heuristic in quality

assessment and risk reduction. The literature study provided sufficient evidence to hypothesize

that consumers perceive the Compatibility, Relative advantage, Observability and Trialability of

the innovation as higher when the innovation was sold through a retailer with High Retailer

brand equity. In the contrary, we predicted the scores for Complexity and Perceived risk to be

lower when the innovation was sold through a retailer with High Retailer brand equity. We also

hypothesize that this effect implies for the attitude toward the whole innovation by the

evaluation of all the innovation attributes together. In addition, we also found a theoretical basis

for expecting that the perceived product-category fit of the retailer would moderate the previous

mentioned effects. We therefore hypothesized that the innovation attributes will be evaluated

more positive when the retailer’s product-category also is seen as having a good and logic fit

with the product-category fit of the innovation.


To measure the effects we hypothesized, we developed four conditions in which we manipulated

the retailer’s level of Retailer brand equity and perceived product-category fit. We manipulated

these effects by using existing retailers for which we assumed to meet the desirable condition.

We confirmed these assumptions by measuring the levels of Retailer brand equity and perceived

product-category fit of all four existing retailer in the pretest. This pretest confirmed our

assumptions and the four conditions were represented by the four following retailers:

- Retailer with high retail brand equity and a high product category fit: Bol.com

- Retailer with low retail brand equity and a high product category fit: Ereaderstore.nl

- Retailer with high retail brand equity and a low product category fit: Albert.nl

- Retailer with low retail brand equity and a low product category fit: Yourlookforless.nl

The main analysis was conducted via an online questionnaire that surveyed the six perceived

innovation attributes in all four conditions. We primed every condition with cues of the retailer

that we used as example for that condition. The goal of this analysis was to find different

responses in the evaluation of the perceived innovation attributes. By finding significant

differences between the evaluations of the perceived innovation attributes across all four

conditions, we are able to conclude if our assumed hypotheses are valid.

The collected data and analysis showed support for several of the developed hypotheses. We did

not found significant differences between the samples for Relative advantage, Compatibility,

Complexity and Trialability. However, we did find significant differences for Perceived risk and

Observability. We also found significant difference for the perception of all the attributes

together between the high- and low Retailer brand equity conditions. Despite theoretical

foundation, we did not found proof to conclude that the perceived product-category fit of the

retailer with the innovation moderates the effects.

These results led us to conclude that the retailer is capable to affect the consumer’s perception

about the innovation. The degree of retailer brand equity affects certain attributes in the

consumer’s evaluation of the innovation attributes (Perceived risk and Observability). The

higher the consumer perceives the retailer brand equity to be of the retailer that sells the

innovation, the more positive these attributes are evaluated. We also found that previous

interaction with the retailer will stimulate this effect. These results contributes to the current

academic literature by showing that there are ‘mediators’ in the adoption process that can

enhance the likelihood of innovation adoption. It will also enable managers of retailers and

manufacturers to enhance their knowledge about the successful implementation of the

innovation.


Table of content

Management summary ................................................................................................................................................ 3

Chapter 1: Introduction ............................................................................................................................................... 7

1.1 Introduction............................................................................................................................................................. 7

1.2 Problem statement and research question ................................................................................................ 9

1.3 Delimitations ........................................................................................................................................................... 9

1.4 Contribution ..........................................................................................................................................................10

1.5 Purpose and structure .......................................................................................................................................11

Chapter 2: Theoretical framework .....................................................................................................................12

2.1 Consumer-Retailer interaction ......................................................................................................................12

2.2 Adoption of innovation .....................................................................................................................................19

2.3 Product fit and retailer image ........................................................................................................................28

Chapter 3: Methodology ............................................................................................................................................30

3.1 Type of research ..................................................................................................................................................30

3.2 Research design ...................................................................................................................................................31

3.2.1 Research subject: Ebook-reader .............................................................................................................31

3.2.2 Pretest ..............................................................................................................................................................31

3.2.3 Main analysis .................................................................................................................................................32

3.3 Questionnaire .......................................................................................................................................................32

3.3.1 Questionnaire outline .................................................................................................................................32

3.3.2 Manipulations ...............................................................................................................................................34

3.3.3 Dependent variables ...................................................................................................................................35

3.4 Data collection ......................................................................................................................................................36

3.5 Sample......................................................................................................................................................................36

3.6 Data analysis .........................................................................................................................................................37

3.6.1 Pre-analysis ....................................................................................................................................................37

3.6.2 Pre-test ............................................................................................................................................................39

3.6.3 Main analysis .................................................................................................................................................40

Chapter 4: Results .........................................................................................................................................................43

4.1 Pretest ......................................................................................................................................................................43

4.1.1 Factor analysis ..............................................................................................................................................43

4.1.2 Reliability ........................................................................................................................................................43

4.1.3 Data test: Kruskal-Wallis ..........................................................................................................................44


4.1.4 Conclusion ......................................................................................................................................................47

4.2 Main analysis .........................................................................................................................................................48

4.2.1 Sample characteristics ...............................................................................................................................48

4.2.2 Factor analysis ..............................................................................................................................................49

4.2.3 Reliability ........................................................................................................................................................49

4.2.4 Hypothesis ......................................................................................................................................................50

Chapter 5: Conclusion and discussion ...............................................................................................................56

5.1 Conclusions ............................................................................................................................................................56

5.2 Discussion...............................................................................................................................................................60

5.2.1 Academic contribution...............................................................................................................................60

5.2.2 Managerial contribution ...........................................................................................................................61

5.2.3 Limitations & suggestions for further research ................................................................................62

References ........................................................................................................................................................................64

Appendices .......................................................................................................................................................................68


Chapter 1: Introduction

In this chapter, we will explain the ambition, purpose and direction of this research. A brief

introduction of the research will be presented. This will be followed by the problem statement

and the research question. After this, the contributions, academic as well the managerial

relevance and the delimitations of the study will be explained. The chapter is concluded with the

purpose of the research and a brief description of the content of the rest of the study.

1.1 Introduction

“Never before in history has innovation offered promise of so much, to so many, in so short a time.”

(Bill Gates, Founder of Microsoft)

This quote, by one of the most successful entrepreneurs in the last decade, emphasizes the

important role of innovations nowadays. Successful innovations can boost the revenues, sales

and profit-margins of organizations. That is why almost every organization strives to find the

new Windows, Ipod or Color-TV.

Finding the best way of introducing these new products has motivated an extensive amount of

marketing academics to conduct research in the field of adoption of innovations by consumers

(Henard & Szymanski, 2001; Arts, 2008; Boyd & Mason, 1999). Within the adoption literature,

the role of the ‘middlemen’ that sells the product to the consumers received less attention. It is

however known that the retailers’ reputation is used as a signal for product quality (Chu & Chu,

1994; Dawar & Parker, 1994; Chu et al, 2005; Corkindale & Belder; 2009;). Therefore, this

research follows the premise that the retailers’ brand equity affects the consumers’ adoption

decision of an innovation.

The purchase of an innovation is a difficult task for a consumer. The consumer has no prior

experience with the product to base their opinion on and is therefore incapable to make a

judgment about the quality and usefulness of the product before the purchase. In these

situations, consumers are engaged to rely on heuristics. Several marketing- and economic

scholars have found signals that consumers rely on heuristics when: the perceived risk of the

purchase is high (Olson; 1977), the consumer has limited knowledge about the product and

therefore is unable to assess the quality (Rao & Monroe; 1988) and the product is too complex to

make a fair judgment about the quality (Honch & Ha; 1986).

The decision process for innovations is marked by all these three issues for which consumers

rely on heuristics. Interestingly, Dawar & Parker (1994) identified the retailers’ reputation as an

important heuristic that consumers use for the quality assessment of a product. The vital role of

the retailers’ reputation is supported by Purohit & Srivastava (2001) who state that “retailer


reputation also plays an important role in many purchases, as retailers provide the interface

between consumers and manufacturers”. Bhatnagar et al. (2000) found proof that consumers

reduce their perceived risk in purchasing risky product by selecting retailers that minimize their

risk of buying.

The link to the adoption of an innovation is therefore easily made because the reduction of the

perceived risk of an innovation is one of the antecedents that consumers use in evaluating the

innovation. Ostlund (1974) and Bauer (1960) added Perceived risk to the five attributes that

Rogers (1995) described. The other attributes are Relative advantage, Compatibility,

Complexity, Trialability and Observability. These antecedents are called the Perceived

innovation attributes and are developed to describe the criteria that potential adopters use to

evaluate the innovation and have been proved to give a good indication of the consumers’

willingness to adopt the innovation (Holak & Lehmann, 1990; Arts, 2008).

Despite of previous research, innovation adoption is still a subject that brings up questions. It

frequently happens that a new product isn’t purchased by consumers despite of the benefits the

new product has to offer (Gourville; 2006). Sivadas & Dwyer (2000) state that nearly fifty

percent of the innovation-introductions fail to accomplish success. The main reason for this is

that manufactures of innovative products are not able to convince consumers to implement the

new product in their daily routine (Biyalogorsky, Boulding & Staelin; 2006).

One of the limitations that manufacturers face in convincing their consumers is that they often

don’t sell the innovation directly to the consumer. Most products are sold indirectly through

retailers (Baldauf et al.; 2009). As mentioned before, the retailer has a significant role in the

quality assessment and the risk reduction of consumers. Especially in situations in which

consumers perceive a high level of risk or uncertainty, consumers are inclined to use their

retailers’ image as an important decision determinant in their purchasing decision (Chu & Chu;

1994). A situation in which consumers perceive high levels of uncertainty and risk is in online

shopping. In an online retail setting, consumers lack the ability to evaluate the retailer based on

environmental and tangible factors. It is known that the physical environment in a traditional

retail store affects the consumers purchasing behavior and influences behavioral shopping

outcomes (Donovan et al.; 1994) However, despite of research about the effect of online

environmental cues (Eroglu et al.; 2001), there is no research that specifically address the role of

the online retailing environment in the consumers’ willingness of adoption.

Taking the previous into account, it is possible to make the assumption that the sales-

environment of an innovation can enhance/ decrease the willingness of the innovation adoption.


1.2 Problem statement and research question

So to sum up the previous paragraph, despite of extensive literature that described the role of

the retailer on the consumers’ behavior, no previous research investigated the direct influence

of the retailers’ reputation on the consumers’ perceived image of the innovations’ attributes. In

this research, we would like to address this gap in the current literature. The research question

that follows from this all is:

What is the influence of the retailers’ reputation on the consumers’ evaluation of the perceived

attributes of an innovation?

In addition, the attitude toward the products of in the retailer’s assortment is influenced by the

consumer’s perception of the “breadth of different products offered by a retailer under one roof”

(Ailawadi & Keller; 2004). Inman et al. (2004) showed that consumers connect certain types of

products with a belonging type of channel. They state that there are certain product categories

that have ‘signature’ associations with a specific channel. For example, Peanut butter is

associated with supermarkets whereas Cars are associated with special automobile dealers. So,

consumers will not be motivated to buy peanut butter at the automobile dealer and will not be

eager to buy their car in the supermarket. We hypothesize that this also implies for Ebook-

readers. We think that consumers perceive Ebook-readers to belong to bookstores and not to

retailers with other kinds of product-categories. Based on these findings and also exploring the

brand extension literature (Chowdhury; 2007, Aaker & Keller; 1990), we would therefore also

like to research the moderating role of the perceived product-category fit of the retailer with the

product-category of the innovation.

1.3 Delimitations

The adoption literature is too broad to research the whole phenomena. Therefore, this research

reflects a fenced area within the literature. It will specifically investigate the influence of the

retailer on the adoption decision of a consumer. It will therefore not address the adoption

decisions of organizations or the adoption decisions in a B2B environment. It will address the

perspective of the consumer that evaluates the innovation within a B2C environment.

Innovation adoption is a difficult process that consists of several stages (For a review, see

Gatignon & Robertson; 1991). This research will focus on the consumers’ intention to adopt the

innovation. According to Arts (2008), adoption intention refers to “a consumer’s expressed

desire to purchase a new product in the near future. It reflects the consumer’s state of mind

before actual purchase behavior has occurred, and is based on the information and perceptions

that s/he has at that time.” Because this research tries to identify the decision process of the

consumers before the actual purchase, adoption intention is the appropriate focus of this


research. The perceived innovation attributes (Rogers; 1995) are taken as subject for the

measurement of the adoption intention.

The influence of the retailer is measured by its retailer brand equity. It will not explain the effect

of distinct retailer attributes such as location, price, environment, etc. It will limit itself by

explaining the effect of the several constructs in the Retailer brand equity scale developed by

Pappu & Quester (2006).

1.4 Contribution

Our interest to add knowledge and understanding about the adoption of innovations is created

by Vasileiou et al. (2009). The object we will use as example of an innovation is the Ebook-

reader. Vasileiou et al. (2009) described that the successful adoption of the Ebook-reader will

have revolutionary implications for several players in the retail chain, e.g. users, retailers,

libraries, publishers and copyright regulation. That the Ebook-reader is actually adopted by

consumers is not certain at all. Sivadas & Dwyer (2000) state that nearly fifty percent of the

innovation-introductions fail to accomplish success. So, researching and revealing factors that

play a role in the consumer’s adoption of an Ebook-reader and finding evidence that this also

accounts for all other innovations, is therefore interesting. Vasileiou et al. (2009) state that it is

crucial that further research takes existing knowledge and models into account in order to

explain the adoption of innovation as good as possible.

Academic relevance

As we mentioned before, consumers are finding themselves more and more in retailing

situations and most manufacturers of innovations sell their merchandise through retailers

(Baldauf et al; 2009). The academic relevance of researching consumer phenomena within the

retailing context is emphasized by the editorial of Hardesty & Bearden (2009). Their Journal of

Retailing paid special attention to the consumer behavior within the retailing context in 2009,

because they want to promote and encourage marketing scholars to research the way

consumers use the retailer for their purchase decisions. Therefore, we want to explain the role of

the retailer in the adoption process. Our research will add academic knowledge in this field by

researching how the retailer’s brand equity affects the consumer’s adoption decision. In

addition, this research will provide ground for further research by providing evidence that

consumers evaluate products different among retailers with different levels of retailer brand

equity. This way, this research will not only bring up questions in the field of innovation

adoption but also in the field of brand equity research.

Managerial relevance


The managerial relevance for this research is twofold. It can have implications for the

manufacturer of the innovation, as well as for the retailer that sells the innovation. By providing

evidence that consumers evaluate innovations differential when the innovation is sold through a

high brand equity retailer, can encourage manufacturers with an unknown brand to sell their

innovation through high equity retailers. This way the innovation has the biggest chance of being

successful. It will also persuade manufacturers to adapt the retailer as a serious factor in the

introduction-plan of the innovation resulting that the advertising costs of introducing the

innovation can be reduced (Porter; 1974). This research takes a look into online retailing.

Prognoses state that the number of consumers that purchase their goods through online

retailing will grow rapidly in the near future. (Forrester Research; 2010). It will therefore also

provide ground for manufacturers to consider selling their innovation through this new channel

and allows it to benefit from the potential growth.

As this research tries to conduct conclusions that can benefit manufacturers, the findings can

also improve the performance of the retailer that offers the innovation. The results can show the

importance of the retailer’s degree of retailer brand equity. If it shows that consumers evaluate

the products of retailers with high retailer brand equity more positive, it can imply that retailers

should gain more retailer brand equity. Retailers with high retailer brand equity can also decide

to launch the innovation under the brand name of the retailer to enhance the chance of success.

Research has also shown that retailers loose profit when their assortment includes products that

aren’t purchased by their consumers (Simonson; 1999). Cutting out twenty percent of the

assortment will save millions of dollars (Ailawadi & Keller; 2004). In addition, products that fail

to attract the interest of the consumers are causing a decrease of the consumers’ favorable

attitude toward the retailer (Ailawadi & Keller; 2004). The findings of this research will

therefore give retailers directions about their decision whether they will offer certain

innovations to their consumers.

1.5 Purpose and structure

The purpose of this research is to provide a solid theoretical foundation for the influence of

retailer brand equity on the consumers’ adoption decision. The following chapter will describe

why we expect the retailer’s role as a influential on the consumers decision process. We do this

by describing existing literature. The third chapter will describe the methodology of this

research. It will show how we translated the research question and the hypothesis into a

research design that will allow us to draw conclusions. The outcomes and results of this

methodology will be discussed in the fourth chapter. This thesis will end by drawing conclusion

and a discussion of the found results.


Chapter 2: Theoretical framework

In this theoretical framework, we will show how we developed our research question and

describe the current theory that supports this premise. We will also describe the constructs that

will be used in the research and the definition of the variables. We will first explain how

consumers use the retailer as a heuristic for merchandise quality, risk reduction and social/

psychological risk reduction. Thereby, we will explain why we use the retailer’s brand equity

variable for the measurement for the four research conditions. In the second part of this

framework, we will develop our hypothesis combining the adoption of innovation literature with

our findings of the consumer-retailer interaction literature.

2.1 Consumer-Retailer interaction

Retailers spend a lot of money and effort on convincing the consumer of their power, quality and

service. They develop TV-commercials, set up projects to bond with the consumer and a variety

of other marketing mix elements. A very important marketing tool is the store environment

itself. Kotler (1973) state that “the store itself can offer a unique atmosphere and environment

that may influence the consumer’s patronage decision.” In addition, the retailing environment is

very important because of the frequency of the consumers’ interaction with the store

environment. During almost all household purchases, consumers find themselves in a retailer

environment (Sarel; 1981) and therefore most purchase decisions are made within a retailing

context (Keller; 1987).

It is well known that perceptions about an object is affected by what is associated with that

object (Aaker; 2007). Because consumers don’t already have perceptions of an innovation, the

associations that can be formed about the innovation are very important for consumers. Because

innovations are often evaluated in a retailer setting, we predict that the retailer environment has

some effect on the consumers’ adoption decision. However, current literature has lacked to

explore the context of the environment were the adoption decision takes place. We will

therefore first explain how the retailer affects the consumers’ perception of the products quality,

risk and social/ psychological risk.

There are two main streams of research about the role of the retailer in a consumer’s perception

about the retailer’s merchandise. One stream focuses on in-store attributes and the other stream

focuses on the retailer’s reputation. Purohit & Srivastava (2001) labels the two previous

mentioned conceptualizations as High scope versus Low scope cues. They state that these cues

enable consumers to determine the product’s quality. According to them, high scope cues are

cues “that evolve over time such that their valence cannot be changed instantaneously.” The

retailer’s reputation is an example of such a high scope cue. In contrast, low scope cues are


“transient in nature such that their valence can be changed relatively quickly and inexpensively”.

Low scope cues can be things like free trial offers, warranties, etc. Purohit & Srivastava (2001)

states that low scope cues are more powerful in assessing the product’s quality, in situations

where High scope cues are evaluated positive. So, if the retailer’s reputation is perceived as

positive by consumers they should be more inclined to evaluate the low scope cues as positive.

Baker, Grewal & Parasuraman (1994) also provide evidence for the role of Low scope cues on

the consumer’s product evaluation. They highlight the importance of elements in the store

environment. According to several marketing scholars, an important role of the store

environment is providing cues to consumers that inform them about the quality of the products

and the service (Zeithaml; 1988, Olson; 1977, Mazursky & Jacoby; 1986, Kerin et al.; 1992).

Baker (1986) identified three broad groups of environmental cues that affect the product quality

perception of consumer: Ambient-, social- and design factors. Ambient factors include elements

like the store’s music, lightning and smell. Social elements involve the personnel of the retailer,

whereas design factors represent elements like the store’s layout, colors & signs (Baker, Grewal

& Parasuraman; 1994).

In this research we will not focus on the low scope cues, but rather on the high scope cues. We

are aiming to discover the role of the (online) retailer on the consumer’s adoption decision. In an

online retail environment, consumers aren’t confronted with tangible, low scope cues that

enables them to assess the products quality (Eroglu, Machleit & Davis; 2001). So, in online

retailing situations, the retailer’s reputation is important for the consumers to evaluate the

product quality and to reduce the perceived risk (Bhatnagar et al; 2000, Chu et al.; 2005).

Therefore, the theoretical framework will be limit to find previous literature that shows the

influence of ‘high-scope cues’ on the product’s evaluation.

Quality inferences

Manufacturers without any prior established corporate reputation (or brand equity) can offer a

very good product with several advantages over the established product, but still fail in the

marketplace. Corkindale & Belder (2009) state that this is a direct effect caused by the new

organization’s lack of intangible assets such as brand equity or corporate reputation. Reputation

is “an aggregate composite of all previous transactions over the life of the entity, a historical

notion and requires consistency of an entity’s actions over a prolonged time” (Herbig &

Milewicz; 1997). Reputation is important for the success probability of the firms’ actions.

Shapiro (1982) showed that a positive reputation has a direct positive relationship with the

firms’ revenue. This is mainly driven by the effect of reputation on the consumer’s perceived risk

and perceived quality (Shapiro & Moriarty; 1982).


Retailers also hold a reputation among consumers. Consumers use the retailer’s reputation for

assessing the perceived quality and risk of purchasing product of that retailer. Wei Ming Ou et al.

(2006) state that consumers perceive retailers with good reputations as “among other things,

ethical, offer customer good value, communicate honestly and are well managed”. Consumers

that hold a positive retailer reputation are more loyal to that retailer (Nguyen & Leblanc; 2001),

have higher shopping expenditure and are willing to travel further to visit the store (Wei Ming

Ou et al.; 2006)

What differentiates the retailer’s reputation from the manufacturer’s reputation is that

consumers use the retailer’s reputation as a tool to assess the quality of the manufacturer’s

product (Dawar & Parker; 1994). Several studies have reported evidence for the positive

relation between the retailer’s reputation and the increased purchase intentions of products

(Dodds et al.; 1991, Grewal et al.; 1998). The increased purchase intentions are mainly caused by

the increased quality perception of the products. Several scholars have showed that favorable

store reputation increases the consumer’s quality perception about the products that they sell

(Jacoby & Mazursky; 1985, Chu et al; 2005, Biglaiser & Friedmann; 1994).

In light of our research, Chu & Chu (1994) researched the effect of selling a product via a

reputable retailer. They found proof that unknown products are ‘renting’ the retailer’s

reputation to signal product quality. This is caused by the fact that consumer’s believe that

manufacturers of high quality products will sell their products through a retailer with a good

reputation and that the retailer will assess the quality of the product and will only sell products

that deliver high value and high quality. As we see it, the consumer rely on the retailer as a

‘Gatekeeper’ (or as Chu & Chu label it: Quality certifier) to make sure that they only get

acquainted with high quality products in their retailing environment.

In their research, Chu et al. (2005) describe the use of extrinsic cues by consumers to assess the

products quality. They support the previous findings by stating that the retailer’s reputation is

an extrinsic cue. Their findings also show that consumers are more dependent on extrinsic cues

when they have to buy quickly, when they have difficulty assessing the quality of the product its

attributes or when they have no prior experience with or knowledge about the product

(Zeithaml; 1988).

To summarize the previous section, we can conclude that the product that is sold through a

reputable retailer is perceived by consumers as having higher quality. Especially when the

consumers have no prior experience with or knowledge about the product or when they find it

hard to assess the product’s quality.


Risk reduction

With every purchase, consumers perceive some level of risk (Cox; 1967). The perceived risk of

consumer can take several forms. Types of risks that consumer perceive are financial-, product-

and performance risk. In accordance with this research, we reduce the several types to two

categories: product-related risk and psychosocial risk. In this section we will discuss the product

related risk. The psychosocial risk is discussed in a separate section.

Several strategies are applied in marketing to reduce the consumer’s level of perceived risk, such

as brand reputation, warranty and product trial (Tan; 1999). The strategy we like to focus on is

the retailer’s reputation. Several marketing scholars acknowledge the importance for consumers

to use the retailer’s reputation in order to reduce the perceived risk of their purchase with the

retailer (Roselius; 1971; Hawes & Lumpkin; 1986, Mitchell; 1998). Purohit & Svriastava (2001)

stated that selling the product via a retailer with a good reputation, provides the consumer with

more assurance about the product performance. This is mainly driven by the perception that

consumers are able to return the product to a good retailer in case of product failure or

dissatisfaction.

Olson (1977) found that consumers use a heuristic in case they perceive high purchasing risks.

One of these heuristic is the retailer’s reputation. In online retailing, consumers perceive more

risk than in offline purchasing. The absence of a tangible store environment and the

impossibility of trying out the product increases the consumer’s uncertainty about the product’s

value and quality (Chu, Choi & Song; 2005). So, consumers are more inclined to use the retailer’s

reputation in an online retail environment as a patronage tool against possible risks (Tan; 1999).

This is supported by research of Bhatnagar et al. (2000) and Korgaonkar (1982) that say that

consumers “reduce their purchase risk by selecting risky products at stores that minimize their

risk of buying”(Korgaonkar et al.; 2006).

To summarize the previous section, we can say that consumers use the retailer’s reputation as a

heuristic to reduce the perceived risk of their purchase. The better the perceived reputation, the

less the consumer perceive risk.

Social- and psychological risk reduction

As we mentioned before, we explain the role of social- and psychological risk in a separate

section. We pay special attention to this type of risk because of th close relationship with two of

the Perceived Innovation Attributes (compatibility and perceived risk), as we will discuss later.


When consumers are uncertain about the opinion of their social environment (like friends,

family) and how they will judge the purchase, consumers perceive psychological and social risk.

Social- and psychological risk not only applies to the purchased product, but also to the retailer

because “where one shops can be socially judged as much as what one buys” (Mitchell; 1998).

Research has also showed that a consumer’s attitude toward a product is affected by the fit

between the product’s user image and the image the consumer holds about itself (Sirgy; 1982).

The product symbolic cues represent the user’s self-concept. For example, BMW is seen as

bragging whereas Volvo is seen as confined. This is not only limited to products. Also retailers

can represent some congruence with the self-image. For example, consumers that do their

groceries at Albert Heijn perceive themselves to be different than consumers that do their

groceries at Lidl. Sirgy et al. (2000) explain this as “shoppers perceive stores differently in terms

of the store’s typical clientele or patrons.” The greater the match between the consumers self

image and the image they hold about the clientele of the retailer, the more positive the attitude

toward the retailer is. This is of course a very subjective dimension and is different for every

consumer. However, certain factors such as age and culture are less subjective. The improved

compatibility of the retailer with one’s self image will result in an increased purchase intention

of the products, because it reduces the social risk of the purchase (Dodds et al; 1991).

Prasad (1975) showed that consumers perceive ‘socioeconomic product risk’ when making a

purchase decision. They state that when a product is perceived as having a high level of

socioeconomic risk, consumers are inclined to transfer this to the retail store that sells it. In such

situations, consumers are tending to use the retailer image as a risk-handling strategy (Perry &

Hamm; 1969). Consumers are than able to defend their purchase to other members of their

social environment by relying on the reputable image of the retailer. Perry & Hamm (1969) also

found that the perceived social risk was more important than the economic risk. So, when a

consumer perceives a high level of socioeconomic risk, consumers use the retailer to mediate

that risk.

To summarize the previous section, we can conclude that the retailer affects the consumer’s

perceived social- and psychological risk. The more the consumer sees the retailer as ‘matching’

with their self image and the more consumers can defend their purchase to other members of

their social environment, the less they perceive social- and psychological risk. This will then lead

to increased purchase intention of the products.


Research condition: Retailer brand equity

In the previous review of the current literature, we acknowledged the effect of the retailer’s

reputation on the customer’s product perception. In this research, we use the Retailer brand

equity to measure the retailer’s reputation. Erdem & Swait (1998) provide a foundation for this

assumption. In their research they state that brand equity is used as a “signal of the product’s

position that increases the perceived quality, decreases information costs and perceived risk for

consumers”. They also show that brand equity involves the credibility of the brand. When

consumers think the claims that the brand makes are credible, they will perceive more brand

equity. We think that this credibility is very important in the adoption of an innovation because,

as we stated before, consumers are unable to assess the product based on knowledge from their

own experience. Credibility from high-scope cues, like retailer reputation, is therefore more

important. This assumption is supported by Christodoulides et al. (2006). They state that a

credible brand signals customer value by reducing information search costs, reducing perceived

risk and the creation of positive attribute perceptions.

In addition, brand equity is not limited by only reflecting the retailer’s reputation. Although

there is allot of discussion about the antecedents of brand equity, most of the researchers agree

about the multi-dimensional character of brand equity (Yoo & Donthu; 2001).

We chose the model of Pappu & Quester (2006a) to explain the retailer brand equity. This

decision was followed after several considerations. First, Pappu & Quester (2006a) developed a

brand equity model especially for retailer equity. Although there has been allot of research on

brand equity, there is less known about the retailer’s brand equity. Ailawadi & Keller (2004)

already stated that despite of several important branding implications of regular brand equity

theory on retailer brand equity theory, it is distinct in several ways. We therefore think it is

essential to use the Retailer equity variable, so we can increase the validity of this research.

Secondly, research by Rios & Riquelme (2008) showed that there were no significant differences

in measuring online brand equity and offline brand equity. We could therefore also rely on an

offline brand equity model to measure the brand equity of the online retailers in our research.

Several researches on retailer brand equity were how based on low-scope cues like store access,

store atmosphere, pricing and service quality (Ailawadi & Keller; 2004, Arnett et al; 2003). Like

we mentioned before, this research involves online retailer and people rely more on high-scope

cues in the online retail environment. Pappu & Quester (2006a) did focus on high-scope cues

that are applicable for online retailing.


Third, the model of Pappu & Quester (2006a) was closest to the most influential brand equity

models developed by Aaker (1991) and Keller (1993). Pappu & Quester (2006a) define retailer

brand equity the same as Aaker (1991): “the value consumers associate with a retailer, as

reflected in the dimensions of retailer awareness, retailer associations, retailer perceived quality

and retailer loyalty.” Like the definition reveals, the model consists of four dimensions. We used

these four dimensions for measuring the consumer’s retailer brand equity and we will describe

their effect on the retailer’s brand equity shortly.

Retailer awareness involves the consumer’s capability to recognize certain characteristics about

the retailer, like the retailer product-category or the store name. Baldauf et al. (2009) express

the importance of retailer awareness on the retailer’s brand equity by saying that “the trade has

less uncertainty dealing with a proven brand name that has already achieved recognition and

association”. Jinfeng & Zhilong (2009) say that retailer awareness also increases the likelihood

that the retailer will be included in the consideration set of the consumers. So, an increase in

retailer awareness should decrease the perceived risk of the consumers and will increase the ir

purchase intention.

Retailer associations are all the attributes and benefits of the retailer that are linked to the

retailer’s name in the mind of the consumer (Keller; 1993). Retailer associations can contribute

to a higher retailer brand equity because it provides the consumer with more information about

the retailer, like the quality and the commitment of the retailer (Jinfeng & Zhilong; 2009). It is

not just important that consumers have associations about the retailer, but it is more important

that these associations are strong and provide consumer with a clear reason to buy (Baldauf et

al; 2009).

Perceived retailer quality is defined as “a consumer’s judgment about a retailer’s overall

excellence or superiority” (Zeithaml; 1988). As the definition expresses, it isn’t a neutral

assessment of the retailer’s quality but it show the quality perception in the mind of the

consumer. The relationship between a consumer’s perceived retailer quality and the increase in

retailer brand equity is obvious. The higher the perceived quality, the more brand equity the

consumer perceives.

Retailer loyalty is defined as “the tendency to be loyal to a retailer as demonstrated by the

intention to buy from a retailer as a primary choice”(Pappu & Quester: 2006b). Pappu & Quester

(2006a) see retailer loyalty as a dimension of retailer brand equity, whereas others see it as a

consequence of higher retailer equity (Jinfeng & Zhilong; 2009). However, we chose to include

loyalty as a dimension of retailer brand equity because several other brand equity scholars also


found prove for the conclusion of Pappu & Quester (Keller; 1993, Baldauf et al; 2009, Yoo &

Donthu; 2001)

2.2 Adoption of innovation

This research holds implications for the adoption of an innovation, but how is an innovation

conceptualized? As mentioned before, innovation adoption is a popular topic among

researchers. As a result of this popularity, several definitions are developed over time. In

marketing research, it is most common to describe innovations in a consumer perspective. So, it

is not the manufacturer or retailer but the consumer that determines whether a new product is

seen as an innovation. Rogers (1995) also uses this conceptualization and describes an

innovation as “an idea, practice or object that is perceived as new by an individual or other unit

of adoption.”

The major difference between an innovation and a common-purchase-product is showed in the

differential response in their purchase decisions. Whereas the established product is already

known, the utility of the innovation is not yet clear to the consumers what will result in an

increased level of uncertainty about the performance of the innovation. This will lead to a

different approach consumers use to make a decision about buying the innovation (Fisher, Luce

& Jia; 2000). This difference is most significant with discontinuous innovations (Robertson;

1971). Discontinuous innovations represent “the creation of previously unknown products that

usually require a significant amount of new learning” (Saaksjarvi; 2003). Because consumers

lack existing knowledge when evaluating the innovation, they evaluate the innovation by using

existing knowledge that they can relate with the innovation. This knowledge can exist of both

relational and attribute-based knowledge (Roehm & Sternthal; 2001). The relational aspect

supports the previous paragraph where we already explained how consumers use the retailer

(as a relation) to evaluated the product value. We will follow by explaining the effect of the

attribute- based knowledge on the innovation adoption.

In their evaluation of the product, consumers pay attention to the innovation’s attributes

(Rogers; 1995, 2003). The perceived value of the innovation is determined by the consumer’s

assessment of the innovation attributes and several researchers have found that this is a key

driver of the consumer’s adoption decision. Most researchers even state that the perceived

innovation attributes are stronger predictors of innovation adoption, than psychological- or

demographic predictors (Holak & Lehmann; 1990, Agarwal & Prasad; 1997, Mitall et al.; 1999,

Arts; 2008). Especially, the evaluation of the attributes affects the rate of adoption. So, the more

positive the consumer evaluates the innovation attributes, the more he/she is inclined to adopt

the innovation (Rogers; 1995). This evaluation is subjective because every consumer holds its


own perception about the importance of an attribute. This makes the innovation attributes a

fascinating subject to research in marketing. In his book, Rogers (2003) explains this by saying:

“It is the receivers’ perception of the attributes of innovations, not the attributes as classified by

experts or change agents, that affect their rate of adoption.”

That consumers use attributes in their evaluation of an innovation has received significant

support in the marketing literature. The most influential research in this area was performed by

Rogers & Shoemaker (1971). They introduced five dimensions of attributes that consumers use

in evaluating the innovation. The five attributes are Relative advantage, Complexity, Trialability,

Compatibility and Observability. Rogers & Shoemaker (1971) laid the foundation for more

research in this area. Based on conceptualization of Bauer (1960), Ostlund (1974) added an

additional sixth attribute; Perceived risk of the innovation. We chose these six attributes as

subject for our research, because these are the most frequent mentioned attributes to influence

the adoption decision. So, we concluded that the six attributes received sufficient support about

their influence on the adoption decision. Table 1 provides an overview of supporting research

that provides evidence for this statement.

Relative advantage Compatibility Trialability

- Rogers (1995. 2003)

- Rogers & Shoemaker (1971)

- Ostlund (1974)

- Holak & Lehmann (1990)

- Eastlick (1996)

- Gatignon & Robertson (1991)

- Tan Tsu Wee (2003)

- Meuter et al. (2005)

- Rogers (1995. 2003)


- Ostlund (1974)


- Eastlick (1996)




- Rogers (1995. 2003)


- Ostlund (1974)


- Eastlick (1996)




Complexity Observability Perceived risk

- Rogers (1995. 2003)


- Ostlund (1974)


- Eastlick (1996)




- Rogers (1995. 2003)


- Ostlund (1974)


- Eastlick (1996)




- Ostlund (1974)

- Holak & Shoemaker (1990)


- Ellen Bearden & Sharma (1991)

- Arts (2008)

Table 2.1: Support for innovation attributes.

In the following section, we will explain the six innovation attributes and develop the hypothesis

for this research.


The six attributes all have their impact on the perceived value of the innovation in the minds of

the consumers. Increased levels of Relative advantage, compatibility, Trialability and

Observability all have a positive impact on the perceived value, where increased levels of

perceived risk and complexity have a negative impact on the perceived value. Several

researchers concluded that compatibility and relative advantage are the most important

predictors of innovation adoption, but that the other attributes also have a significant impact on

the adoption. In addition, they showed that the six attributes also have interrelations and affect

each other (Holak; 1988, Holak & Lehmann; 1990, Ostlund; 1974). So despite of higher

importance of some attributes, all the attributes have their use in forming the perceived

innovation value (Labay & Kinnear; 1981). All together, the six attributes explain 49% to 87% of

the variance in the adoption rate (Rogers; 1983)

Improving the consumer’s perception of the innovation’s attributes can lead to the increased

chance of success of the adoption of innovation. Holak & Lehmann (1990) showed the direct

relation between the positive evaluation of the innovation attributes and the increased purchase

intentions that consumers hold. The higher the perceived value of the single innovation

attribute, the higher the purchase intention of the total innovation is. We can therefore conclude

that the consumer’s attitude toward the innovation is more positive when they perceive the

innovation as high in Relative advantage, Compatibility, Trialability, Observability and low in

Perceived risk and Complexity.

As we explained before, consumers use the retailer as a gatekeeper for quality assurance and

risk reduction (product-related risk and psychosocial risk). Consumers that perceive their

retailer as having high retailer brand equity, think the products in that stores have high quality,

are trustworthy and they perceive lower social- and psychological risk. In the following section

we will hypothesize the relationships of the retailer’s brand equity on all the innovation

attributes separate. We do this because we think that the perceptions of all attributes are

separately affected by the retailer’s brand equity, but that the sum of all six attributes defines the

overall attitude toward the innovation. So, the main hypothesis is:

H1: Innovations that are sold through a retailer with high retailer brand equity, are evaluated

more positive by consumers than innovations that are sold through a retailer with low brand

equity.

Figure 1 shows the visualization of our conceptual model. The other hypothesis will be explained

next.


Figure 1: Conceptual framework

Relative advantage is one the most important attributes in developing a consumer’s attitude

toward the innovation (Tan Tsu Wee; 2003, Ostlund; 1974). Rogers (1995) describes relative

advantage as “the degree to which an innovation is perceived as being better than the idea it

supersedes”. It is the consumer’s trade off about the benefits and the costs of the innovation

adoption. Consumers are looking for products that will save them time, effort and money. The

perceived relative advantage of an innovation is mainly assessed based on two criteria’s;

economic profitability and social prestige. We use the two criteria to show the influence of the

retailer brand equity on the consumer’s assessment of the relative advantage of the innovation.

The foundation for this assertion is formed by the citation of Rogers (1995): “(..) The diffusion of

an innovation is an uncertainty-reduction process. When individuals pass through the

innovation-decision process, they are motivated to seek information to decrease uncertainty

about the relative advantage of an innovation”.

We already showed that consumers use the retailer as a heuristic for the quality assessment of

products (Chu & Chu; 1994, Chu et al; 2005, Dodds et al; 1991, Grewal et al; 1998). Products that

are seen as high in quality are also seen as products with several benefits to the consumer

(Shapiro; 1983). So, we can conclude that innovations that are sold at retailers with a favorable

image are seen as innovations with higher quality and therefore offer more benefits to the

consumer.


In addition, it is well known that consumer choose brands which they think are prestigious. One

of the most important implications of branding is that consumers can identify themselves with a

brand and are therefore able to enhance their social position (Keller; 2001). This also applies for

retail branding. Mitchel (1998) states that shopping at a retailer with a well-known and

reputable image lead to “more satisfaction of status or prestige needs”. For example, carrying a

shopping bag of Albert Heijn is perceived to be more impressive than carrying a bag of Lidl. We

hypothesize that the social prestige of the retailer will be transferred to the innovation that is

sold at that retailer.

These theoretical findings enable us to formulate the following hypotheses:

H1a: The consumer’s perception of relative advantage of the innovation that is sold through a

retailer with high retailer brand equity is perceived higher than the innovation that is sold through

a retailer with low retailer brand equity.

The perceived complexity of an innovation is one of the attributes that has a negative effect on

the consumer’s attitude toward the innovation. Rogers (1995) describes complexity as “the

degree to which an innovation is perceived as relatively difficult to understand and use”.

Complexity shows the most implications in the adoption of technological innovations, because

technological innovation require more learning and knowledge (Saaksjarvi; 2003). When

consumers think the innovation requires allot of knowledge or cognitive effort to use, their

attitude toward the innovation will be negative.

Upah (1983) looked into the antecedents of a product’s perceived complexity in a retailing

environment. One of the findings was that personal selling and therefore interaction between

the consumer and the retailer (personnel), decreased the consumer’s perceived complexity

about the product. This was mainly caused because consumers try to reduce their uncertainty

about the product. Consumers are more willing to interact with a retailer with high retailer

equity because they are known with the brand and they trust the brand (Ailawadi & Keller;

2004). This lead to our expectation that consumer’s perceived innovation complexity will

decrease when the innovation is sold at a high brand equity retailer.

The improved interaction between high brand equity retailers and consumers is not the only

factor that reduces the perceived complexity of the innovation. As we mentioned before,

consumers see the retailer as a gatekeeper. Consumers expect that the retailer they trust (the

retailer with high brand equity) will only offer products that consumers can handle. For

example, if a consumer purchases a digital camera at Dixon (that is known for selling electronic


products to consumers for amateuristic use), they expect that Dixon made sure the camera was

for an amateur photographer. So, we hypothesis that:

H1b: The consumer perception of the complexity of the innovation that is sold through a retailer

with high retailer brand equity is perceived lower than the innovation that is sold through a

retailer with low retailer brand equity.

The compatibility of the innovation is the “degree to which an innovation is perceived as

consistent with the existing values, past experience and needs of potential adopters” (Rogers;

1995). Eastlick (1996) showed that compatibility also relate to the consumer’s lifestyle.

Consumers will only adopt the innovations that are compatible with their values, past

experiences, needs and lifestyle. Therefore, compatibility has a positive relation with the attitude

toward the innovation.

Products can be compatible, but retailers can also be compatible. Consumers develop stereotypic

images of retailer. They use these images as cues for their compatibility with the retailer (Sirgy

et al; 2000). For example, Lidl is seen as a retailer for the lower-upper class. Lidl is therefore not

compatible with the values, past experience and needs of an upscale shopper. One of the cues

that enable the consumer to determine the compatibility is the merchandise that the retailer

offers. We expect that this effect also works the other way around. We adapt this assumption by

relating that quality is also ‘transferred’ from retailer to their merchandise. So, when consumers

perceive the retailer as compatible, they will perceive the retailer’s merchandise also as more

compatible.

The compatibility of the innovation with the consumer’s existing values will affect their opinion

about the innovation (Rogers; 1995). As we showed previously, Sirgey et al. (2000) explains that

consumers hold a more positive attitude toward a product when the product image is in

accordance with one’s self image. With the purchase of a product, consumers perceive

uncertainty about this congruency. We also showed that the retailer can function as a moderator

for consumers to reduce this perceived uncertainty. We therefore believe that the retailer can

affect the consumers perceived level of compatibility with the innovation.

In addition, our research involves online retailers. Online retailers have a big advantage

compared to offline retailers in the way they can personalize the merchandise they offer to their

consumers. Online retailers can follow the consumer in their consumption behavior which

allows them to offer only the products that matches with the consumer’s needs (Chau & Ho;

2008). Rogers (1995) describes that the innovation’s compatibility with the consumer’s needs is

an important aspect in forming a positive attitude toward the innovation. Consumers with a high


retailer brand equity perception are known with and trust the retailer. We therefore assume

that consumers not only use the retailer as a gatekeeper for the product’s perceived quality and

risk, but also as an reliable partner in offering innovations that are compatible with their needs

and lifestyle. We therefore hypothesize:

H1c: The consumer’s perception of the compatibility of the innovation that is sold through a

retailer with high retailer brand equity is perceived higher than the innovation that is sold through

a retailer with low retailer brand equity.

Trialability is defined as “the degree to which an innovation may be experimented with on a

limited basis” (Rogers; 1995). Innovations that can be tried before the actual purchase are more

rapidly adopted than innovations that consumers can’t try on forehand. The consumer’s lack of

previous experience and familiarity with the innovation encourages them to assess the

innovation personally. Therefore, trialability has a positive relation with the attitude toward the

innovation.

When products aren’t sold directly to the consumers but through a retailer, trialability is more

an issue of the retailer than of the manufacturer (Hawes & Lumpkin; 1986). Therefore,

consumers rely more on the retailer to try the innovation than the manufacturer.

In an offline retail environment, it is easier to try the innovation than in an online retail

environment. Within an online retail environment there is a longer timeframe between making

the purchase and receiving the product. This makes that trialability of an innovation in an online

retailer environment, is more concerned with the retailer’s acceptance of returning the product.

To increase the consumers trust and their ability to try to product, it is helpful for online

retailers to have a product-trial program to increase their retailer brand equity (Chau & Ho;

2008). Also, Purohit & Srivastava (2001) showed that consumers are more positive about the

ability to return the product when they purchase the product at a retailer with high brand

equity.

In addition, Vijayasarathy & Jones (1999) found that the impossibility of trying out a new

product in an online retail environment increased the consumer’s uncertainty about the product.

They reported that in such situations, consumers decrease their uncertainty by turning to well

known manufacturer brand and especially to well know retail brands.

Given this information, we expect that retailers with high brand equity have an advantage over

other retailers in the fact that consumers perceive more trust in the trialability of the product

when they buy the innovation with a high brand equity retailer. So, we hypothesize:


H1d: The Trialability of the innovation that is sold through a retailer with high retailer brand

equity is perceived higher by consumers than the innovation that is sold through a retailer with low

retailer brand equity.

The observability of the innovation is defined by Rogers (1995) as “the degree to which the

results of an innovation are visible to others”. It consists of how easily the innovation is

observed by other members of a social system and how easy it is for the consumer to

communicate the purpose and the advantages to others. The observability of the innovation has

a positive relationship with the attitude toward the innovation. The more the innovation can be

observed by others, the more positive consumers will evaluate the innovation

However, consumers will face a lot of social uncertainty when an innovation is easily observed

by others. This way, observability relates to the consumers perceived social risk of the

innovation. The items that are used in the questionnaire of Meuter et al. (2005) for the

measurement of observability show that observability involves the ability to explain the

purchase to other members of the social environment. We already mentioned before that

consumers use the retailer for reducing their social risk. That is why we believe that the higher

the observability of an innovation, the more perceived social uncertainty the consumer will face.

This uncertainty can be reduced by purchasing the innovation by a well-know, high equity

retailer. This will reduce the consumer’s hesitance to show the innovation to other and will

therefore enlarge the observability.

Based on this we hypothesize that when the innovation is sold through a retailer with high

brand equity, consumers will perceive less social risk and will therefore more inclined to show

the innovation to members in their social environment. So:

H1e: The Observability of the innovation that is sold through a retailer with high retailer brand

equity is perceived higher by consumers than the innovation that is sold through a retailer with low


Ostlund (1974) found that the five attributes we previous mentioned, were not sufficient in

explaining the adoption rate of the innovation. They added a sixth attribute called ‘Perceived

risk’. Perceived risk is defined as the “degree to which risks are perceived as associated with the

innovation” (Ostlund; 1974). According to Cunningham (1967), a consumer’s perceived risk can

hold two dimensions. Those dimensions are defined by Hawes & Lumpkin (1986) as “the

perceived uncertainty of a given event happening, and the consequence involved if the event

should happen”. They categorize the several types of risk as: Financial-, physical-, functional-,

social-, and psychological risk. As we mentioned before, to increase the efficiency of this


research, we reduce the several types of risk to two categories: product-related risk and

psychosocial risk.

First, the product-related risk refers most to the performance of the product. As we mentioned

before, most products are sold through retailers. They form the interface between the product

and the consumers. When the product does not deliver the value that the consumers expect or in

case of product failure, consumers will therefore rely on the retailer to find a suitable and

satisfactory solution. Consequently, the perceived product-related risk becomes an issue of the

retailer. Hawes & Lumpkin (1986) therefore state that the retailer is used as a patronage mode

to reduce the consumer’s perceived product-related risk.

We reported before that consumers that perceive high retailer brand equity, are more certain

that the retailer will return the product and help the consumer with a satisfactory solution

(Purohit & Svriastava; 2001). Furthermore, we also showed that the perceived quality of the

products is also affected by the retailer’s brand equity (Jacoby & Mazursky; 1985, Chu et al;

2005, Biglaiser & Friedmann; 1994). The perceived quality has a significant influence on the

perceived performance of the product (Kerin et al; 1992). The consumer’s increased quality

perception of a product, mediated by the high retailer brand equity, will therefore result in a

decrease in the consumer’s perceived product-related risk.

Secondly, psychosocial risk refers to the degree consumers perceive risk in buying an innovation

that will lead to social embarrassment and a decrease of the social position, as well as the degree

of internal psychological frustration the consumer could perceive after the purchase of the

innovation (Mitchell & Harris; 2005). An important factor in the consumer’s perceived

psychosocial risk is the image the consumer hold about itself. Elliot (1997) explains this by

stating: “consumers do not consume products for their material utilities but consume the

symbolic meaning of those products as portrayed in their images”. The products that consumers

purchase are therefore able to signal symbolic meaning about the consumer itself to other

members of the social environment (Jamal & Goode; 2001). This symbolic meaning is mostly

displayed by the brand of the product. The brand symbolizes an image and consumers reflect

themselves with this image. The more the brand image is congruent with the consumers self

concept, the higher the purchase intention of such brands (Eriksen; 1996).

This also applies for retail brands. Retailers also hold a brand image that consumers use to

evaluate their congruence with their self image (Mitchell & Harris; 2005). In addition, we

showed that consumers use the retailer’s image to reduce their psychosocial risk. When the

congruency between the consumer’s self concept and the retailer’s image increases, consumers


are evaluating the products the retailer sells also as more congruent with their self concept

(Prasad; 1975, Perry & Hamm; 1969).

Based on these findings, we hypothesize that:

H1f: The consumers’ perceived risk of the innovation that is sold through a retailer with high

retailer brand equity is lower than for the innovation that is sold through a retailer with low


2.3 Product fit and retailer image

After explaining all this, we would like to show the assertion that these relations are moderated

by the relation of the consumer’s perception about the product category of the retailer and the

product category of the innovation. As we previously explained, we expect to find proof that

products that are sold through retailers with high brand equity are being evaluated more

positive than otherwise. However, we also expect to find that the product category of the retailer

will increase/ decrease the magnitude of this effect.

The assortment of retailers is becoming more and more deep and broad. This delivers

advantages for the consumer like for example the ease of one-stop-shopping and. It will

therefore also be beneficial for the retailer because consumers are more satisfied and their sales

will improve because of the additional sales. However, when viewed from a branding

perspective, a broad assortment can also negative consequences.

Research has showed that, in the minds of the consumers, certain product-categories have

‘signature’ associations with certain types of retailer (Inman, Shankar & Ferraro; 2004). For

example, groceries are purchased at a supermarket and cars are purchased at a specialized car

dealer. This is also applicable for retailer brands. Once the retailer is related with a certain

product category in the mind of the consumers, they will always see the retailer as the selling

point of that category of products (Ailwadi & Keller; 2004). For example, Albert Heijn is known

to sell groceries. Consumers will therefore not relate Albert Heijn with selling coffee-machines.

That this extension isn’t viable is showed by the PUC that Albert Heijn tried to sell. One of the

reasons why it didn’t become a success was because consumers didn’t see the retailer as the

appropriate selling point for a product from that product-category. So, once a retailer has a

strong signature association with a product category, it is hard for consumers to connect the

retailer with other types of product categories. This is an important reason why assortment

extension often fails. Consumers do not only categorize the retailer on their product category.

Korgaonkar et al. (2006) found that consumers also connect the prestige and allure of the


retailer to certain product categories. They state that high-risk products are sold at retailers

with prestige and allure because this will reduce the consumers perceived risk.

Our assumption is also driven by the brand extension/ alliance literature. In this aspect of

marketing, it is commonly known that the perceived fit between the core product and the

extension is an important factor in the success of the extension. Perceived fit has a positive

relationship with the attitude toward the extension (Chowdhury; 2007). The most important

reason for this relationship is that consumers will doubt the skills of the manufacturer to deliver

an extension of high quality (Aaker & Keller; 1990, Simonin & Ruth; 1998). The brand’s image is

an important aspect in the judging the perceived fit. Just like retailers, brands are also

categorized in the consumer’s mind.

The most important proof for our assumption was delivered by a study of Podnar (2004). They

found that that the success of a new product or brand extension wasn’t only influenced by the

brands high brand equity and their credibility. More important was “whether the product

matched the consumers’ perception of the company’s identity” (Simonin & Ruth; 1998). In

addition, several researchers have also used brand extension literature to explore the

relationship between the attitude toward the retailer and their merchandise (Robertson &

Gatignon; 1986, Corkindale & Belder; 2009)

This evidence motivated us to explore the moderating role of the perceived fit between the

retailer’s product category and the innovation’s product category. We therefore hypothesize:

H2: The innovation attributes are evaluated more positive when the innovation is sold through a

retailer with high retailer brand equity. However, this effect is stronger when the retailer with high

brand equity also is seen as a retailer with high perceived product-category fit with the innovation.


Chapter 3: Methodology

To conduct reliable and valid data, the methodology of the research must be well thought of. In

this chapter we will explain how we designed the research. First, we will describe the type of

research we used. After this, we will give a brief description of the research design and the

motivation why we choose this design. This chapter will than follow in describing how the

questionnaire was formulated. It will also be explained how we collected the necessary data and

what the characteristics of the sample were where we derived the data from. This chapter will

end by explaining the statistical tests we used.

3.1 Type of research

This research is designed in such a way that it fits the goal of the research. The goal of the

research is clear. We would like to determine the causal effect of the retailer’s brand equity on

the consumer’s perceived innovation attributes to predict the influence of the retailer. Our

formal study tries to add knowledge to the current literature and based on previous research

which we used to develop our theoretical framework, we formulated several hypotheses to test

our research question.

Using an experimental setting, we try to manipulate the variables to discover differences in the

respondent’s reactions. Cooper & Schindler (2008) state that using an experiment is the best

way to find causal relationships between variables. The foremost advantage of using an

experiment is ability to manipulate the independent variable. As we will explain later, we

manipulated the retailer’s brand equity in our pre-test. The experiment isn’t done under

laboratory conditions, but under the actual environmental conditions of the respondent.

This research will use quantitative data and research methods. Because prior research allowed

us to formulate hypothesis (see chapter 2), qualitative research was redundant. In addition, this

research tries to predict the consumer’s attitude of the retailer’s brand equity and their

evaluation of the perceived innovation attributes. Alwin & Krosnick (1991) provided evidence

for the reliability of using quantitative data for the measurement of attitudes.

The most suited quantitative measurement process for this research was using a survey.

Although there are many methods for measuring quantitative data, the survey is considered

dominant in the existing literature (Cooper & Schindler; 2008). A survey has the quality of being

effective when the researcher’s aim is to measure differences in among their respondents. It can

be used among a versatile population, can be accessed quickly and is more costs-efficient than

other measurement processes.


3.2 Research design

We used a two-stage experimental research design. The first stage involves a pretest in which

we want to acknowledge the strength of the manipulations. In the second stage, we measured

the attitude toward the innovation in all four manipulations. The disruptive innovation that we

chose to use to measure the perceived innovation attributes is the Ebook-reader. We will first

explain why we chose the Ebook-reader as research subject.

3.2.1 Research subject: Ebook-reader

An Ebook-reader is defined as “the physical, electronic equivalent of a printed book containing a

digital version of the whole text of a book” (Moore; 2009). The Ebook-reader offers several

features that makes the Ebook-reader attractive for consumers in comparison with printed

books, as described by Burk (2001); with Ebook-readers you can adjust the font and size of the

text, the content (ebooks) can be accessed within minutes, the text is better readable because

the higher DPI/ sharpness, pages will not turn yellow or torn and one Ebook-reader is capable to

store more than 100 different ebooks.

Despite of all these benefits, the rate of adoption of Ebook-readers is slow. Burk (2001) states

that the hesitation of the consumer to adopt the Ebook-reader is partly caused by its

categorization as being a disruptive innovation. We mentioned before that when consumers are

confronted with disruptive innovations, they are more inclined to rely on heuristics. In addition,

Vasileiou et al. (2009) confirm the ‘newness’ of the Ebook-reader. They also emphasize the

potential for the Ebook-readers to change the current way of reading.

An additional argument to take the Ebook-reader as the research-subject for this research is that

the Ebook-readers already are being sold through well known online retailers. We will follow

discussing through which retailers we tried to manipulate the retailer’s brand equity.

3.2.2 Pretest

To discover the influence of the retailer on the consumer’s evaluation of the perceived

innovation attributes, we developed four manipulations. First, we want to explore the

differential effect of the consumer toward the innovation, if it is being sold through a high- or

through a low brand equity retailer. Secondly, we also want to discover whether the impact of

this relationship is higher for a retailer with a high- or a low perceived product category fit with

the retailer. To test this, we need conditions in which all these manipulations are found. Based

on face validity and a group interview (N=4), we expected four existing retailers to meet these

conditions. In the pretest we want to confirm the validity and the strength of these four

manipulations we are using for the main analysis.


The goal of the pretest is to confirm four retailers that meet these conditions. Finding four

existing retailers that reflect the appropriate manipulations enabled us to prime the

respondents in the main analysis. We assumed that the following retailers reflect the

appropriate manipulation:

- Retailer with high retail brand equity and a high product category fit: Bol.com

- Retailer with low retail brand equity and a high product category fit: Ereaderstore.nl

- Retailer with high retail brand equity and a low product category fit: Albert.nl

- Retailer with low retail brand equity and a low product category fit: Yourlookforless.nl

We assume that the chosen retailers will match with the right manipulation. The goal of the

pretest is therefore to validate these assumptions. If it seems that our assumptions were wrong,

we will adapt to this by selecting another retailer.

3.2.3 Main analysis

The goal of the main analysis is to measure how consumers evaluate the perceived innovation

attributes in each condition. We wanted to test if there are significant differences in the

evaluation of the innovation attributes, in each of the four conditions. We did this by surveying

respondents with the same items, but exposing them to the primes of one of the four conditions.

3.3 Questionnaire

The questionnaires that were used, tried to measure three variables. As we already described,

the research was performed in two stages; the pretest and the main analysis. This implies that

there are also two questionnaires. Appendix A will show both questionnaires with their items.

We used previous developed measurement scales for all variables. We will discuss why we chose

these scales. After that, we will defend the choice of the items.

3.3.1 Questionnaire outline

The questionnaire in the pretest measured the respondent’s level of retailer brand equity for all

four conditions. We used the same questionnaire for every condition because we wanted

equivalent outcomes for every condition. We only changed the retailer name. The questionnaire

measured four constructs, consisting of eighteen items. Because the order of the questions can

affect the respondent’s answers, the sequence of the items were randomized to avoid any order

bias (Cooper & Schindler; 2008). We used the same questionnaire for every condition. This

because we wanted equivalent surveys for every condition. To disguise the objective of the

questionnaire, information about the survey objective was as limited as possible. Respondents

were asked to read a short description of the retailer, where after they were asked to answer the

items. The introduction was described briefly because respondents could be influenced by this


explanation. One authority has pointed out the importance of a clear and an objective view of the

respondents on the research (Sellitz, Wrightsman & Cook; 1976). By summarizing all the

benefits of the e-book reader, respondents were likely to answer more positively on the given

questions. Sellitz, Wrightsman & Cook (1976) warn that when the property being studied is not

clearly defined, the Halo-effect is difficult to avoid. The Halo-effect is the systematic bias when

the respondent tries to answer uniform to their previous answers.

For the main analysis, we also developed one universal questionnaire with the same items for all

four conditions to ensure equivalence. To prime the respondent in the appropriate condition, we

wrote an introduction with a brief description about the retailer and the innovation, the Ebook-

reader. In this introduction, we emphasized that the retailer was selling the innovation. Next, we

asked the respondents to fill in the questions. The logo of the retailer was also present on the

answering sheet to stimulate the prime. The content of the questions were equal over all four

conditions. Only the retailer name was different per condition. Appendix B shows how the

questionnaires were presented to the respondents for the Bol.com manipulation.

Again, we tried to provide information to the respondent as little as possible to disguise the

objective of the study. Because we used previous developed measurement scales, the validity

and reliability of the questionnaire was granted. In contrast with the pretest, we didn’t

randomize the order of the items. We presented the items per construct, enabling the

respondent

In the questionnaire of the main analysis we asked the respondents to answer twenty-nine

items. These items measured the perceived innovation attributes, the perceived product-

category fit and the respondent’s degree of innovativeness. The other items were used to

measure the sample demographics. Table 3.1 below shows the outline of the questionnaires.

Item number Construct Source: Pretest 1- 4 Retailer awareness Pappu & Quester (2006a)

5-10 Retailer associations Pappu & Quester (2006a)

11-14 Retailer-perceived quality Pappu & Quester (2006a)

15-18 Retailer Loyalty Pappu & Quester (2006a)

19-20 Perceived product-category fit Aaker & Keller (1992)

Main Analysis 1 – 3 Compatibility Meuter et al. (2005)

4-6 Relative advantage Meuter et al. (2005)

7-9 Complexity Meuter et al. (2005)

10-12 Observability Meuter et al. (2005)

13- 15 Trialability Meuter et al. (2005)

16-20 Perceived risk LaRoche (2005)

21-27 Innovativeness Steenkamp & Gielens (2003)

Table 3.1: Outline of the questionnaire.


3.3.2 Manipulations

The manipulated variable in this study is the retailer’s brand equity. In § 2.1, we already

described why we chose the model of Pappu & Quester (2006a) from a theoretical perspective.

In this section, we try to explain why we chose this model from a methodological perspective.

The measurement of retailer brand equity is still an area that needs more research. Although

there are several measurement tools developed for brand equity (Yoo & Donthu; 2001, Arnett et

al; 2003, Keller; 1992), finding the right items to measure the brand equity of a retailer still

seems difficult (Ailawadi & Keller; 2004). This causes the fact that there isn’t allot of literature

available about the measurement of the retailer brand equity. However, Pappu & Quester

(2006a) developed a four-construct variable to measure the retailer brand equity. They based

this variable on prior brand equity research. As we mentioned before, the variable exists of

retailer awareness, retailer associations, retailer perceived quality and retailer loyalty. To

measure all the constructs, they developed a twenty-three item questionnaire. These items all

had a reliability level, measured with Cronbach Alpha, that exceeded 0.7. Pappu & Quester

(2006a) also found proof for predictive-, content- and construct validity of the items. These

results convinced us to use this measurement for our research.

For this research, we reduced the number of items to eighteen. Because the questionnaire was

originally made for offline retailers, some of the items were useless for our research. The items

were measured with a seven-point Licker scale, 1 being totally disagree to 7 being totally agree.

Like we state in the theoretical framework, we also suspect the moderating influence of the fit

between the retailer’s product-category and the product-category of the innovation. . In this

research, the innovation is an Ebook-reader. We therefore also manipulate the fit between the

ebook-reader and the retailer. To measure the strength of the fit, we used the questionnaire

developed by Keller & Aaker (1992). We chose this questionnaire because Keller & Aaker (1992)

state it is applicable for the fit between the retailer and its assortment. The original

questionnaire consisted of four items, but we reduced it to two items. Based on face validity, two

of the four items seems redundant with regard to our research. Keller & Aaker (1992) reported a

reliability of 0.7. This was later acknowledged by Taylor & Bearden (2002). They also found

proof of the validity of the items. The items were measured with a seven-point Licker scale, 1

being totally disagree to 7 being totally agree.


3.3.3 Dependent variables

The dependent variables of this study are the perceived innovation attributes. For the attributes

developed by Rogers (1995), we used the questionnaire developed by Meuter et al. (2003). For

the measurement of the sixth attribute, perceived risk, we used the questionnaire developed by

LaRoche (2005).

Although there has been a great amount of research in the field of innovation adoption, there is

not a great amount of questionnaires developed for measuring the perceived innovation

attributes. Moore & Benbasat (1991) were the first ones to develop a questionnaire for the

perceived innovation attributes. However, these items were more focused on the acceptance of

innovation of organizations, than the adoption of consumers. Meuter et al. (2003) took the

questionnaire of Moore & Benbasat (1991) and adjusted it to measure the attitudes of

consumers. However, the items that Meuter et al. (2003) developed were made for services.

Because our research involves a tangible product (the Ebook-reader), the questionnaire was

adjusted to meet the requirements of our research. Based on face validity, this difference was

most visible with the Perceived Risk attribute. We therefore decided to use a different

measuring scale for this attribute. LaRoche (2005) developed a five-itemed questionnaire that

did show face validity with regard to our research.

As you can see in table 3.2, Meuter et al. (2003) developed questionnaires for the perceived

innovation attributes with high reliability levels. They also state that measurement tool received

enough evidence to report sufficient validity. These two reasons made us believe these items

were good enough to use for our research. The high level of reliability also applied for the

questionnaire for perceived risk developed by LaRoche (2005). The validity was not mentioned

by LaRoche. However, the Handbook of Marketing scales (2005) mentions that it appears that

Attribute Definition Number of items

Cronbach Alpha

Compatibility Innovation is perceived as consistent with existing values,

habits and past experiences of the potential adopter.

3 .95

Rel. advantage Innovation is perceived as superior to ideas it supersedes.

3 .95

Complexity Innovation is perceived as difficult to understand and use.

3 .88

Observability Results of an innovation will be apparent and possible to

communicate to others.

3 .94

Trialability Innovation is perceived as available for trial on a limited

basis, without a large commitment.

3 .85

Perceived risk Risks are perceived as associated with the innovation. 5 .81

Table 3.2: Cronbach Alpha scores


model fits the measurement well. All the variables were measured with a seven-point Licker

scale, 1 being totally disagree to 7 being totally agree.

3.4 Data collection

We used a non-probability sampling method to collect the necessary data. We chose this method,

despite of the fact that non-probability sampling is seen as subjective and the possibility it

increases the chance of sampling error. This research doesn’t have the objective to be

generalized to a population parameter, so there is less concern about the actual representation

of the population in the sample (Cooper & Schindler; 2008). Because the representativeness of

the sample would not affect the results, use a non-probability sampling method was justified.

We used the web-based software Thesistool.nl to collect our data. Using an online survey was

the best option for this research. Our study involves online retailers and using an online survey,

made sure that all our respondents had access to internet, which ensured us that they could

have access to an online retailer. Thereby, online surveys have the advantage of being cost

efficient, can be launched quickly and also have the ability to reach a large and diverse

population (Cooper & Schindler; 2008). Another big advantage of using an online survey was the

ability to assign the surveys randomly to the respondents. Thesistools.nl enabled us to randomly

assign the four conditions to a respondent, with using only one destination link

(www.thesistools.nl/rens). This could guarantee randomization.

3.5 Sample

We used purposive sampling to recruit respondents for our sample. With purposive sampling

the sample doesn’t meet the requirements for representation of the whole population, but the

respondents in the sample do conform to a certain criteria. In our case, we want the respondents

to meet the criteria of being familiar with online retailers. We combined the purposive sampling

with Snowball sampling to gather the necessary data. The questionnaire was spread via several

websites and through online social communities like Hyves & Facebook. We didn’t have any

reason to suspect any differences in the response across demographics of the sample. No

literature showed that age, gender or income would affect our hypothesized relationships. As a

result, we didn’t specify the sample to any demographic characters. We did limit the sample

through geographical constraints. Because the retailers that are used as the research subject of

the four conditions all are Dutch, we only surveyed people from the Netherlands. Respondents

from other countries were excluded to avoid any bias.

This research uses four different conditions and has therefore also four different samples. In

order to compare the outcomes of the four conditions as valid as possible, the four different


samples have to be as equal as possible. As we discussed previously, the randomization option of

Thesistools.nl ensured the random assignment of the four conditions to the respondents. With

this, we tried to avoid any sampling bias. We applied this randomization technique for the

pretest, as well for the main analysis.

The size of the samples is also important, for equivalence and for analysis. To conduct a Factor

analysis, the sample has to meet a minimum of 50 respondents or minimal 5 respondents per

variable.

In order to conduct the factor analysis for the items in the pretest, all four conditions had a

sample size of N=15. All four conditions together made the sample size N=60. Our aim was to

gather enough respondents to perform a factor analysis. We didn’t attempt to meet any sample

size requirements for data analysis. The purpose of the pretest was only to confirm our

assumptions and therefore it didn’t require extensive analysis. For this reasons, we chose a

smaller sample size that allows us to use a non-parametric test.

This is in contrast with the main analysis. The purpose of the main analysis is to provide

evidence on which we can base conclusions. We therefore aimed on reaching a sample size that

enabled us to use a parametric test. A parametric test is seen as more reliable and powerful than

a non-parametric test (Cooper & Schindler; 2008). The minimum number of respondents for

using a parametric test is N=30 (Berenson et al; 2006). We therefore aimed on getting 30

respondents or more per condition for the main analysis. This will allow us to compare all the

four conditions separately.

3.6 Data analysis

In this section of the methodology chapter, we will explain the data analysis we used in this

research. By explaining this, we will provide evidence of the validity and reliability and strength

of the findings of our analysis. First, we will discuss the explorations of the gathered data before

it can be used for analysis. Secondly, the chosen method for the pretest analysis is discussed.

This section will end with the explanation of the chosen method for the main analysis.

3.6.1 Pre-analysis

Factor analysis

We conducted a factor analysis for both the pretest as the main analysis. The goal of the factor

analysis is to reduce the number of items, without reducing the predictive power of the total

variable. Cooper & Schindler (2008) developed a three step approach to conduct a factor

analysis. The first step is to check if the data meets the required assumptions. The second step is


to determine the way the factor analysis is performed. The last step is the interpretation of the

results. The first two steps well be discussed in this chapter. The last step will be described in

the next chapter.

Before a factor analysis can be performed, some assumptions have to be tested. These

assumptions have to be interpreted with regard to the research. When some items doesn’ t met

the requirements of the assumption, the items doesn’t necessarily have to be removed. The

objective of the research have to be considers (Hair et al; 2010). The assumptions in factor

analysis are:

- A sample size bigger than N=50 and a minimum of 5 respondents per item.

- A substantial number of correlations have to be greater than 0.3. This best tested with

the ‘Bartlett test of sphericity’. The test has to be significant (α=.05). This test checks if

there are significant relationship among the items in the factor analysis. H0= there are no

significant correlations among the items. So, when H0 is rejected there are enough

significant relationships to perform the factor analysis.

- The ‘Kaiser-Meyer-Olkin measure of sampling adequacy’ (KMO) also measures the

correlations between the items. It checks if there is at least some significant relationships

among the items. The KMO has to be greater than .60.

- The KMO can also be calculated for every individual item. This is the Measure of

Sampling Adequacy (MSA). The MSA also have to be higher than .60. The items that are

below .60 are considered to be removed.

- The communalities explain the variance in the variable by the number of factors. When

the communalities are low, the variance is badly explained by the factors.

Communalities below .30 are considered to be removed.

The way the factor analysis is performed for the pretest is described by three issues; the way of

extraction, rotation and the number of chosen factors. For the pretest, we will use Principal Axis

Factoring as extraction method. This method is most suitable when it is expected that the

variables will not be normally distributed. We will test the normal distribution of the items with

the Kolmogorov-Smirnov test (Wiersma & Sasovova; 2009). The Kolmogorov tests the H₀: the

variable is normally distributed. When the H₀ rejected (α=.05), the items are not normally

distributen. As the rotation method, we used Direct Oblimin. Direct oblimin is most suitable

when it is expected that the factors will show interdependence. This expectation is supported by

Pappu & Quester (2006a). They show the interdependence between the four constructs of their

retailer brand equity variable. The total number of factors is determined by the Eigenvalue. The


Eigenvalue shows the explained variance in the variables by the factors. A factor shows a

satisfactory level of explained variance when the Eigenvalue is higher than 1(Hair et al; 2010)..

We used the Maximum Likelihood method for the extraction in the main analysis. Maximum

Likelihood is most suitable when the minimum number of respondents (N>50) is met. As we

already described, we attempt to collect information of more than 50 respondents. As the

rotation method of the main analysis, we again used the direct Oblimin method. This method is

most suitable when it is expected that the factors will show interdependence. This expectation is

supported by Holak & Lehmann (1990). They showed that the Perceived Innovation Attributes

also affect each other. The same as with the pretest, factors with a higher Eigenvalue than 1 will

be seen as effective (Hair et al; 2010).

Using these methods in the factor analysis, will result in a table with the factors and the factor

loadings of all the items. The items with factor loadings that exceed .40 will be used for the

explanation of the appropriate variable (Hair et al; 2010). We will assign the items to the

variables it belongs to, based on the found literature.

Reliability

When all items are assigned to the variables they belong to, a test of the reliability of the

measurement of the variable will be performed. We want the items in the variable to measure

equal effects when it is used multiple times. This consistency in their measurement is showed

with the Cronbach Alpha. Constructs that show a Cronbach Alpha value higher than 0.70 are

seen as reliable.

3.6.2 Pre-test

The goal of the pretest is to measure differences in the outcomes of the survey. We want to show

that the Retailer brand equity is higher for Bol.com and Albert.nl than for Ereaderstore.nl and

Yourlookforless.nl. We also want to show that the perceived product-category fit with the

Ebookreader is higher for Bol.com and Ereaderstore.nl than it is for Albert.nl and

Yourlookforless.nl. Thus, we want to compare the answers of more than two independent

samples. As we described before that the sample size of the four conditions in the pretest will be

N=15. This will not allow us to use a parametric test. The collected data is on ratio/ interval

level. We therefore have to rely on a non-parametric test that measures differences between

more than two independent samples.

The Kruskal-Wallis test is the test that meets all these conditions. It is appropriate for testing

more than two independent samples with interval data that not meet the assumptions of the

parametric ANOVA test (Cooper & Schindler; 2008). To confirm that the data in the pretest does


not meet the assumptions of the ANOVA test, we will first test the normal distribution of the

variables with the Kolmogorov-Smirnov test.

The Kruskal-Wallis test measures whether the independent samples have equal medians. The

null hypothesis predicts that the medians (M) of all samples are equal. In our case that will

imply:

H₀: M(bol) = M(Albert) = M(Ereaderstore)=M(Yourlookforless)

H₁: M(bol) ≠ M(Albert) ≠ M(Ereaderstore) ≠ M(Yourlookforless)

We the Kruskal-Wallis test shows a significant relationship (α=0.05), it implies that there is a

significant difference between the samples. However, just showing a difference between the

samples will not suffice the goal of the pretest. We want to conclude that the medians of the

Retailer brand equity variables of Bol.com and Albert.nl are significant higher than the medians

of Ereaderstore.nl and Yourlookforless.nl. For the perceived product-category fit variables, we

also want to establish that the medians of Bol.com and Ereaderstore.nl will significantly differ

from the medians of Albert.nl and Yourlookforless.nl.

We can establish this by relying on the Mean ranks of the variables. The Kruskal-Wallis test not

only tests the difference between the samples, but also the difference between the variables.

This way, we can conclude which variables show higher means than other variables. We do this

for the Retailer brand equity variable and for the perceived product-category fit.

Before the Kruskal-Wallis can be performed, some assumptions must be met (Berenson et al;

2006). All assumptions are important. However, when the last two assumptions aren’t met, the

Kruskal-Wallis test can still be used. The assumptions are:

- All the samples must have been randomly selected from the population.

- The variable under investigation is continuous.

- The gathered data must be big enough to provide at least a set of ranks.

- All the samples have the same variability

- All the samples have the same shape.

3.6.3 Main analysis

The goal of the main analysis is to test the developed hypothesis in the conditions we recognized

in the pretest. Hypothesis H1 and H1a till H1f test if the perceived innovation attributes are

affected by the Retailer’s Brand Equity level. The categories we use to compare the effect are

labeled as ‘High Retailer brand equity’ and ‘Low Retailer brand equity’. Assuming our

assumptions in the pretest are valid, the retailers with High Retailer brand equity will be


Bol.com and Albert.nl. The retailers with Low Retailer brand equity will be Ereaderstore.nl and

Yourlookforless.nl. We will compare the evaluations of the respondents about the perceived

innovation attributes in these two categories, with dividing the four retailers in the two

categories of Retailer brand equity.

The main analysis is designed to measure the differential effect of the respondents evaluation of

the innovation attributes between retailers with High Retailer brand equity and Low Retailer

brand equity. To test this, we need a test that recognizes differences between two independent

samples. The size of the two samples will not exceed N>100. When the N <100, the required

assumptions for using a parametric test cannot automatically be assumed to be met (Hair et al;

2010). We therefore chose to use a non-parametric variant of testing the difference between two

samples. This kind of test can be performed with a Pooled-variance T-test and a Wilcoxon rank

sum test. According to Berenson et al. (2006), the Wilcoxon rank sum test is more suitable for

marketing and consumer behavior research. It is more powerful than the parametric t-test when

certain assumptions can’t be met. We therefore choose the Wilcoxon rank sum test to test

hypotheses H1, H1a till H1f.

The Wilcoxon sum of ranks test if the median is equal in both the samples. When the P-value of

the Wilcoxon W score is below α (0.05), H₀ is rejected which implicates that the medians in the

sample show significant differences. The Wilcoxon sum of ranks test will for this research test if:

H1 H₀: M1 (PIA) = M2 (PIA)

H₁: M1 (PIA) ≠ M2 (PIA)

H1a H₀: M1 (Relative advantage) = M2 (Relative advantage)

H₁: M1 (Relative advantage) ≠ M2 (Relative advantage)

H1b H₀: M1 (Complexity) = M2 (Complexity)

H₁: M1 (Complexity) ≠ M2 (Complexity)

H1c H₀: M1 (Compatibility) = M2 (Compatibility)

H₁: M1 (Compatibility) ≠ M2 (Compatibility)

H1d H₀: M1 (Trialability = M2 (Trialability)

H₁: M1 (Trialability ≠ M2 (Trialability)

H1e H₀: M1 (Observability) = M2 (Observability)

H₁: M1 (Observability) ≠ M2 (Observability)

H1f H₀: M1 (Perceived Risk) = M2 (Perceived Risk)

H₁: M1 (Perceived Risk) ≠ M2 (Perceived Risk)

M1: High Retailer brand equity, M2: Low Retailer brand equity, α= 0.05

Though, to reject or accept our hypothesis it will not suffice to understand that the respondents

in the sample evaluate the perceived innovation attributes different. We hypothesize that the

Retailer brand equity can decrease or increase the strength of the perceived innovation

attributes. By the interpretation of the mean rank scores of the High- versus the Low Retailer


brand equity categories, it is possible to see in which category the effect is the largest or

smallest.

The Wilcoxon sum of ranks test is, as we mentioned before, a non-parametric test. This test is

used when the assumption of the t-test aren’t met. The assumptions of the Wilcoxon test are

therefore less strict than in parametric tests. The most influential assumption is the equality of

the samples. In the next chapter, we will show that the four samples are equal. The next chapter

also involves the results of the tests we performed.

In the theoretical framework, we also expressed the premise of the moderating effect of the

retailer’s perceived product-category fit. We want to measure this effect with H2. Again, we

assume that the retailers match with the condition we think they belong. Confirmation will

follow through the pretest.

Bol.com is the retailer that represents the ‘High Retailer brand equity, High perceived product-

category fit’ condition. Albert.nl represents the condition with High Retailer brand equity but it

differs with Bol.com in the fact that Albert.nl is seen as low in perceived product-category fit.

Comparing these conditions will give us an indication about the effect of the perceived product-

category fit on the perceived innovation attributes. We control one condition (Retailer brand

equity) and we manipulate the other condition (perceived product-category fit). This way, any

differences in the outcomes should be caused by the manipulation of the ‘perceived product-

category fit’ condition.

H2 H₀: Mbol (PIA) = Malbert (PIA)

H₁: Mbol (PIA) ≠ Malbert (PIA)

H2a H₀: Mbol (Relative advantage) = Malbert (Relative advantage)

H₁: Mbol (Relative advantage) ≠ Malbert (Relative advantage)

H2b H₀: Mbol (Complexity) = Malbert (Complexity)

H₁: Mbol (Complexity) ≠ Malbert (Complexity)

H2c H₀: Mbol (Compatibility) = Malbert (Compatibility)

H₁: Mbol (Compatibility) ≠ Malbert (Compatibility)

H2d H₀: Mbol (Trialability = Malbert (Trialability)

H₁: Mbol (Trialability ≠ Malbert (Trialability)

H2e H₀: Mbol (Observability) = Malbert (Observability)

H₁: Mbol (Observability) ≠ Malbert (Observability)

H2f H₀: Mbol (Perceived Risk) = Malbert (Perceived Risk)

H₁: Mbol (Perceived Risk) ≠ Malbert (Perceived Risk)

Mbol: High perceived product-category fit, Malbert: Low perceived product-category fit, α= 0.05

This also involves a two independent sample, non parametric test (N<100). These effects are

therefore also measured with the Wilcoxon sum of ranks test.


Chapter 4: Results

4.1 Pretest

4.1.1 Factor analysis

Before the factor analysis could be performed, we had to check if the items were normally

distributed. Because we assumed the items were not normally distributed, we used the Principal

Axis factoring method for the extraction of the factors. As we mentioned before, we test this with

the Kolmogorov-Smirnov test. In appendix C.1, you can see the results of this test. The test

showed that all the items had all values below α=0.05 and all the H₀ were rejected. So, the items

are not normally distributed and the Principal Axis Factoring method was the appropriate

method for extraction.

Next, we had to check if the data met the assumptions of the factor analysis. The results for these

tests are shown in appendix C.2. The assumptions were all met. The items showed

communalities that exceeded 0.3. The Bartlett test was also significant (P <0.05). This allows us

to assume that there is a sufficient degree of correlation between the items. The value of the

KMO test was also higher than 0.6 (0.916) and a considerable number of items showed MSA

scores higher than 0.6.

The factor analysis confirmed our beliefs. The results were showed in appendix C.3. Despite

three factors showed Eigenvalues higher than 1, we chose to form two factors. The Eigenvalue of

the third factor was considerably small (1.262) and our theoretical framework provided

evidence that explained the two first factors (Pappu & Quester; 2006a, Aaker & Keller; 1992).

The first factor resembled the items that measured the Retailer brand equity. The second factor

showed the two items that measured the perceived product-category fit. For the second factor,

all the factor loadings were higher than 0.4. We had therefore no reason to remove any items.

However, some items showed an insufficient factor loading in the second factor. As we

mentioned before, factor loadings smaller than 0.4 will be removed. Therefore, the total number

of items was reduced by removing three items in the second factor.

4.1.2 Reliability

In the methodology section we already stated

that we use the Cronbach Alpha values to

measure the reliability of the items in the factors.

The Cronbach Alpha values must exceed .70. In

table 4.1, the Cronbach Alpha scores for the

factors in the pretest are displayed. These are the scores for all four conditions. All scores are

Retailer brand equity

product-category fit

Albert 0.95 0.79

Ereaderstore 0.97 0.84

Bol.com 0.97 0.97

YLFL 0.95 0.80

Table 4.1: Cronbach Alpha values


higher than .70. We can therefore conclude that the items we use in the pretest show a sufficient

level of reliability.

4.1.3 Data test: Kruskal-Wallis

In the methodology section, we already mentioned that we didn’t expect the samples to be

parametric. This would result in choosing for a non-parametric test. We confirmed our

assumption by testing the normal distribution of the used variables. We used four conditions in

which two variables were measured. We therefore needed to check for the normal distribution

of eight variables. As appendix C.4 shows, only three of these variables showed insignificance (P

> 0.05). This implies that only three variables were normally distributed. This let us to confirm

our assumption of using a non-parametric test.

As the non-parametric test, we chose the Kruskal-Wallis test. The data shows support for the

first three assumption of the test. The last two assumptions have no effect on the choice for the

test or the outcomes of the test (Berenson et al; 2006).

The results of the Kruskal-Wallis test are shown in table 4.2. The results show that the four

samples significantly differ (P < 0.05). This is for the difference in Retailer brand equity as for

the perceived product-category fit. This allows us to conclude that the retailers we use as

example for this reseach (Bol.com, Ereaderstore.nl, Yourlookforless.nl and Albert.nl), are all

perceived different.

Retailer brand equity Perceived product-category fit

Bol.com 50.20 41.97

Ereaderstore.nl 20.60 45.80

Yourlookforless.nl 17.40 18.30

Albert.nl

33.80 15.90

P value* .000 .000

Chi² 37.981 36.674

N 60 60

Mean 3.28 4.25

Std. deviation 1.778 2.275

* α = 0.05

Table 4.2: the mean rank scores of the four conditions.

The mean rank scores show the level of the variable in each condition. Retailer brand equity give

the highest measurements for Bol.com and Albert.nl. This allows us to conclude that the

respondents see Bol.com and Albert.nl as retailers with a higher level of Retailer brand equity

than the other two retailers.


The perceived product-category fit scores are the highest for Bol.com and Ereaderstore.nl. This

allows us to conclude that the respondents find it more normal, appropriate and legitimate that

Bol.com and Ereaderstore.nl sell Ebookreaders than that Albert.nl and Yourlookforless.nl sell

Ebookreaders.

However, the Kruskal-Wallis test does not enable us to draw conclusions about the significant

differences between all the variables. It says that there are samples in the test that differ from

the other samples, but it not shows which samples significantly differ from other samples. To

look if this, we use the Tukey Post-Hoc test. The Tukey post-hoc test looks if the means of the

samples are significantly different.

Tukey Post hoc test Mean Difference Sig.

FIT Bol Ereaderstore -.267 .945

Yourlookforless 3.400* .000

Albert 3.667* .000

Ereaderstore Bol .267 .945


Albert 3.933* .000

Yourlookforless Bol -3.400* .000

Ereaderstore -3.667* .000

Albert .267 .945

Albert Bol -3.667* .000

Ereaderstore -3.933* .000

Yourlookforless -.267 .945

RBE Bol Ereaderstore 3.187* .000


Albert 1.862* .000

Ereaderstore Bol -3.187* .000

Yourlookforless .276 .913

Albert -1.324* .013

Yourlookforless Bol -3.462* .000

Ereaderstore -.276 .913

Albert -1.600* .002

Albert Bol -1.862* .000

Ereaderstore 1.324* .013


* Mean difference is significant at α=0.05

Table 4.3: Tukey post-hoc results for perceived product-category fit (FIT) & Retailer brand equity (RBE)..

The table shows the Post-Hoc results for the perceived product-category fit and the Retailer

brand equity. The P-values test the (H₀: a= b) with a significance level of α= 0.05. The

samples Bol.com and Ereaderstore differ significantly from Yourlookforless.nl and Albert.nl. This

confirms our predicition. The Retailer brand equity of Bol.com is significantly different from the

other three retailers. However, Albert.nl is significantly different from Ereaderstore.nl and


Yourlookforless. These findings are emphasized by the subset that the Tukey test creates (Table

4.4).

FIT

Tukey HSD

N

Subset (α = 0.05)

1 2

Albert 15 2.20

Yourlookforless 15 2.47

Bol 15 5.87

Ereaderstore 15 6.13

Sig. .945 .945

Table 4.4: Tukey test subsets for FIT/RBE

These results should suffice to draw conclusions. To gain more comprehensive evidence for the

difference of the samples however and to test H1a till H1f, we also tested the difference between

the two highest scoring retailers and the two lowest scoring retailers. This would enable us to

conclude that the retailers in that subgroup had a higher or a lower degree of Retailer brand

equity and Perceived product-category fit. We therefore also used the non-parametric test for

testing the difference between two samples. This is the Wilcoxon sum of ranks test. It tests the

H₀: Medians are equal for both samples. It measures if the medians of the two samples are equal

and it also shows the mean rank scores of the samples.

The results show that there is significance difference in the Retailer brand equity levels of the

two retailers with the highest Mean rank scores for Retailer brand equity (P < 0.05). This effect

is also found for the product-category fit. The Mean rank scores of the two retailers with the

highest product-category fit are significantly higher than the scores of the two retailers with the

smallest product-category fit.

RBE

Tukey HSD

N

Subset (α= 0.05)

1 2 3

Yourlookforless 15 1.95

Ereaderstore 15 2.22

Albert 15 3.55

Bol 15 5.41

Sig. .913 1.000 1.000

Product-category fit

High fit * 43.88

Low fit ** 17.12

Sig .000

Wilcoxon W 513.500

Z score -6.013

* Fit scores of Bol.com and Ereaderstore.nl

** Fit scores of Albert.nl and Yourlookforless.nl

Table 4.5: Wilcoxon sum of ranks test

Retailer brand equity

High RBE * 42.00

Low RBE ** 19.00

Sig .000

Wilcoxon W 570.000

Z score -5.108

* RBE of Bol.com and Albert.nl

** RBE of Ereaderstore.nl and Yourlookforless.nl


4.1.4 Conclusion

The goal of the pretest was to establish that the existing retailers that we chose, actually reflects

the condition we want the retailers to reflect. With the Kruskal-Wallis test, we already proved

that the four samples were significantly different.

We chose Bol.com to reflect the condition in which the retailer is high in Retailer brand equity

and high in perceived product-category fit. The mean rank of Bol.com for the retailer brand

equity was higher than all other three retailers (M= 50.20). Bol.com was also one of the two

retailers with the highest mean rank scores for the perceived product-category fit (M=41.97).

The Wilcoxon W test also showed that the Retailer brand equity of Bol.com combined with

Albert.nl also significantly differed from the other two retailers. This test also provided evidence

for the conclusion that the level of perceived product-category fit of Bol.com combined with

Ereaderstore.nl was significantly higher compared with the other two retailers. Thus, we

conclude that the pretest delivers sufficient evidence to use Bol.com as the condition for high

retailer brand equity and high perceived product-category fit.

We chose Ereaderstore.nl as example for a retailer with a low level of Retailer brand equity, but

with a high level of perceived product-category fit. The Kruskal-Wallis test delivered evidence to

conclude that Ereaderstore.nl has a lower level of Retailer brand equity (M= 20.60) than the two

retailers with the highest Retailer brand equity. However, the test also showed a high level of

perceived product-category fit (M=45.80). So the respondents found it likely for Ereaderstore.nl

to sell Ebookreaders. The Wilcoxon W test emphasized these findings. When combined with

Yourlookforless.nl, Ereaderstore.nl showed to differ significantly from the two retailers with the

highest Retailer brand equity. When combined with Bol.com however, Ereaderstore.nl was

showed as highest in the perceived product category fit. These findings led us to conclude that

Ereaderstore.nl is valid to use as an example for the retailer with low Retailer brand equity, but

with a high perceived product-category fit.

We chose Albert.nl as the retailer that reflects the condition in which the retailer has a high level

of Retailer brand equity, but a low level of perceived product-category fit. The mean rank scores

from the Kruskal-Wallis test showed that Albert.nl was one of the two retailers with the highest

score for Retailer brand equity (M= 33.80). In addition, our expectation was confirmed in the

fact that Albert.nl scored as the lowest on perceived product-category fit (M=15.90). The

Wilcoxon W test acknowledged these findings by showing that Albert.nl when combined with

Bol.com was significantly higher in Retailer brand equity, than the other two retailers. However,

the test also showed that Albert.nl was significantly lower on perceived fit when combined with

Yourlookforless.nl than the other two retailers. This evidence led us to conclude that Albert.nl is


the appropriate existing retailer as example for a retailer with high Retailer brand equity, but

with low level of perceived product-category fit.

The last condition needs a retailer with low Retailer brand equity and low perceived product-

category fit. We chose Yourlookforless.nl to reflect this condition. The Kruskal-Wallis test

showed we were right to pick Yourlookforless.nl as the example for this condition. It had the

lowest level of Retailer brand equity (M=17.40) and almost the lowest level of perceived fit

(M=18.30). The Wilcoxon W test also showed that Yourlookforless.nl belonged to the retailers in

the category of Retailer brand equity and perceived product-category fit that showed to be

significantly smaller than the retailers in the higher category. This evidence convinced us to

conclude that Yourlookforless.nl is the appropriate retailer as the example for the condition in

which the retailer has a low level of Retailer brand equity and a low level of perceived product-

category fit.

4.2 Main analysis

4.2.1 Sample characteristics

We use four different conditions for the main analysis. To assess these conditions, we also use a

different sample for every condition. In the methodology section, we emphasized the importance

of the equality of the four samples, as well as the size (N>30) and the randomization.

Table 4.6 shows that the samples are relatively equal. The samples all have the same size (N=38)

and the most respondents are between the 18 and 30 years old and have completed a middle- to

high education (MBO/HBO). The table also show that the number of respondents of all the

samples exceeds N=30 and the total sample size is higher than N=50. This allows us to perform a

factor analysis to reduce the number of items for analysis.

Bol.com Ereaderstore Albert.nl YLFL.nl Total Basic educ. 0 4 0 1 5

VMBO 5 3 4 3 15

HAVO 3 3 1 3 10

VWO 1 2 0 2 5

MBO 8 10 9 9 36

HBO 13 9 18 11 51

WO-Bach. 6 5 2 6 19

WO-Master 1 2 3 3 9

Else 1 0 1 0 2

Males 20 20 27 20 87

Females 18 18 11 18 65

< 18 1 2 0 0 3

18-30 27 23 29 20 99

31-40 6 4 3 9 22

41-50 4 3 4 7 18

50> 0 6 2 2 10

Total 38 38 38 38 152


4.2.2 Factor analysis

Before we can report the results of the factor analysis, we have to check if the extraction method

we chose was appropriate. We chose Maximum Likelihood because we expected the items to be

distributed normally. However, the Kolmogorov-Sminov test of the items (Appendix D.1)

showed that all the items were significant (P-value <0.05). This implies that the items were not

distributed normally. The extraction method of the factor analysis must be adjusted to Principal

Axis Factoring to gain the best results.

Appendix D.2 reports the assumptions tested for the factor analysis. Almost all the items had

communalities that exceeded .30. Two items had values lower than .30. These items were

removed for further analysis. There was also enough correlation between the items because the

Bartlett’s test showed a P-value (.000) lower than α (0.05). The KMO score (.828) was sufficient

because it was higher than 0.6. The MSA scores of a considerable amount of items also met the

assumption because they showed scores higher than 0.6.

The data of the factor analysis (Appendix D.3) didn’t show conformity with the literature

(Meuter et al; 2005). The Eigenvalue exceeded the minimum value (Minimum Eigenvalue = 1)

for five factors. However, the literature suggests that there are six variables. The factor loadings

in the structure matrix show that the variables ‘Compatibility’ and ‘Relative advantage’ are

combined in factor 1. Despite these results, we ignore this outcome due to the strength of the

previous literature. We did remove two items from the total set. These are the same two items

that showed an insufficient level of communality.

4.2.3 Reliability

As we already stated, the Cronbach Alpha values are used to measure the reliability of the items

in the factors. The Cronbach Alpha values must exceed .70. The results (table 4.7) did not report

values that can cause bias in the reliability of the items.

Table 4.6: Education level, age and gender of the respondents

Albert.nl Bol.com Ereaderstore.nl Yourlookforless.nl Compatibility .88 .81 .89 .84

Relative advantage .86 .82 .95 .86

Complexity .87 .87 .78 .73

Observability .82 .67 .84 .76

Trialibility .87 .76 .95 .83

Perceived Risk .92 .87 .77 .80

Table 4.7: Cronbach Alpha scores for the main analysis


4.2.4 Hypothesis

The developed theoretical framework enables us to assume there are differences in the

perception of the consumer’s toward the perceived innovation attributes, when the innovation is

sold through retailers with different levels of Retailer brand equity. We also suspect that the

perceived innovation attributes are affected by the fit between the product-category of the

innovation with the product-category of the retailer. Before we are going to test the developed

hypotheses, we want to check if the assumptions of our theoretical framework actually hold

ground. We therefore performed a Kruskal-Wallis test to check if the used samples show

differences in the perceived innovation attributes in the four conditions.

Compatibility Rel. advant Trialability Complexity Observability Perc. risk

Bol.com 70.41 72.33 74.30 71.46 76.25 66.47

Ereaderstore 92.12 87.58 80.03 74.82 75.36 72.58

Albert.nl 82.49 88.28 84.18 74.95 90.14 72.25

Yourlookforless 60.99 57.82 67.49 84.78 64.25 94.70

Sig. .012 * .006 * .370 .575 .076 .027 *

N 152 152 152 152 152 152

Mean 3.18 3.05 4.58 3.47 5.58 3.60

Std. deviation 1.468 1.479 1.434 1.429 1.330 1.394

Minimum 1 1 1 1 2 1

Maximum 7 7 7 7 7 7

*Significant at α=05.

Table 4.8: Results of the Kruskal-Wallis test.

As table 4.8 shows, the samples differ significantly for Compatibility, Relative Advantage and

Perceived risk at α=.05. This allows us to assume that the theoretical framework provided in this

research hold valid conclusion and that there is reason to suspect the differential effect of the

retailers Retailer brand equity on the perceived innovation attributes.

Before we test the hypothesis, we first have to acknowledge the correctness of the chosen test.

We assumed that the variables we use for analysis were not distributed normally because of the

unsatisfactory size of the sample (N<100). To recognize this assumption, we tested the

distribution of the variables with the Kolmogorov-Smirnov test (Appendix D.4). The results

reported that only eight of the twenty-four variables showed insignificant P-values (P-value <

α=0.05). This implies that the largest group of variables are not normally distributed, which

confirmed our assumption and validates our choice for the Wilcoxon sum of ranks test.

H1 was developed to test if the global evaluation of the perceived innovation attributes was

affected by the Retailer brand equity level of the retailer which sells the innovation. So, it tested

H₀: M1 (PIA) = M2 (PIA), where M1 is the condition with High Retailer brand equity and M2 is the

condition with low Retailer brand equity. The results are reported in table 4.9.


Mean rank ( ) N Sum of ranks Wilcoxon W Z score Sig.*

High RBE 89.10 76 6771.50 4856.500 -3.528 .000

Low RBE 63.90 76 4856.50

* α= 0.05

Table 4.9: Wilcoxon sum of ranks test results for H1.

These results make clear that there is significant difference between the both samples (P-Value <

α). H₀ is rejected. The mean rank value of the High Retailer brand equity condition outgrows the

mean rank value of the Low Retailer brand equity condition. The total score was calculated by

deducting the scores of the negative attributes (Complexity and Perceived Risk) of the

cumulative scores of the positive attributes (Compatibility, Relative advantage, Trialability and

Observability). The literature review found that the higher the score of the perceived innovation

attributes, the more positive it is viewed (Holak & Lehmann; 1990). This points out that the

respondents evaluated the perceived innovation attributes more positive when it is sold through

a retailer with High Retailer brand equity ( = 89.10) than when it is sold through a retailer with

Low Retailer brand equity ( =63.90). We therefore conclude that there is substantial evidence

to accept H1.

We will further explain all the hypothesized innovation attributes separately. The results of the

Wilcoxon sum of rank test of these hypotheses are found in table 4.10 below.

Mean rank N Sum of ranks Wilcoxon W Z-score Sig Compatibility High RBE 76.45 76 5810.00 5810.000 -0.15 .988

Low RBE 76.55 76 5818.00

Rel. advantage High RBE 80.30 76 6103.00 5525.00 -1.070 .285

Low RBE 72.70 76 5525.00

Complexity High RBE 71.22 76 5450.50 5450.500 -1.354 .176

Low RBE 81.28 76 6177.50

Observability High RBE 83.20 76 6323.00 5305.000 -1.910 .056 *

Low RBE 69.80 76 5305.00

Trialibility High RBE 79.24 76 6022.50 5605.500 -0.775 .438

Low RBE 73.76 76 5605.50

Perceived Risk High RBE 69.36 76 5271.50 5271.50 -2.006 .045**

Low RBE 83.64 76 6356.50

* significant at α=0.10

** significant at α=0.05

Table 4.10: Wilcoxon sum of ranks test for H1a till H1f.

H1a tests if the respondents perceive the Relative advantage of the innovation as more positive

when it is sold through a retailer with High (M1) RBE versus Low (M2) Retailer brand equity.

This is tested with H₀: M1 (Relative advantage) = M2 (Relative advantage).

The mean rank of the high RBE condition ( =80.30) is higher than the mean rank of the Low

RBE condition ( =72.70). Because Relative advantage has a positive effect on the respondents’

attitude toward the innovation, Meuter et al. (2005) suggested that the respondents perceive the


Relative advantage of the innovation more positive when the variables show higher scores. We

can therefore conclude that the respondents evaluated the Relative advantage of the innovation

more positive in the high RBE condition, than in the low RBE condition.

However, despite of a strong theoretical foundation for predicting the influence of Retailer brand

equity on the Relative advantage, we found no significant differences between the respondents

attitude toward the Relative Advantage of the innovation in the two manipulations. The P-value

of the Wilcoxon W test is higher than α=.05 (P-value = .285) and we therefore accept H₀. This

means the two samples did no significantly differ in their attitude toward the Relative advantage

of the innovation. So, regardless of the difference in the mean ranks of the two conditions, we

can’t conclude this difference is significant for the Relative advantage of the innovation. We

therefore conclude that the statistical evidence points out that H1a needs to be rejected.

H1b tests if the respondents perceived the complexity of the innovation as lower when it is sold

through a retailer with High (M1) RBE versus Low (M2) Retailer brand equity. This is tested

with H₀: M1 (Complexity) = M2 (Complexity)

Hypothesis H1b differs from the previous explained hypotheses in the fact that the mean rank

values have to be low in order to be evaluated more positive. As we explained in the theoretical

framework, complexity has a negative effect on the consumer’s attitude toward the innovation.

As we predicted, the mean rank of complexity in the High RBE condition ( =71.22) was lower

than the mean rank of complexity in the low RBE condition ( = 81.28). It can therefore be said

that the respondents considered the complexity of the innovation to be less when it is sold

through a retailer with high Retailer brand equity than through a retailer with low Retailer

brand equity.

Nevertheless, the P-value of the test (P-Value= .176) exceeded the required α (0.05).

Consequently, it is necessary to accept H₀. This implies that there is insufficient evidence to

conclude that there is a significant difference in the respondents’ answers between the samples

in the two conditions. For that reason we can also conclude that the Retailer’s Brand Equity level

has no effect on the consumer’s perception of the complexity of the innovation. Therefore, H1b is

rejected.

H1c tests if the respondents perceive the Compatibility of the innovation as higher when it is

sold through a retailer with High (M1) RBE versus Low (M2) Retailer brand equity. This is

tested with H₀: M1 (Compatibility) = M2 (Compatibility)

The theoretical framework showed that Compatibility has a positive effect on the consumer’s

attitude toward the innovation. Our premise was that the Compatibility of the innovation could


be affect through the Retailer brand equity level of the retailer that sells the innovation. Despite

these conclusions, our research found no significant differences between the High RBE condition

and the low RBE condition. It is even so that the mean rank score of the high RBE condition ( =

76.45) was almost equal to the mean rank score of the low RBE condition ( = 76.55). This

equality was expressed in the P-value. The P-value was almost equal to 1 (P-value= .988) which

implies that the samples are almost identical. The P-value evidently exceeded the required α

(0.05) and therefore H₀ is accepted, meaning the samples are equal. These results provide

sufficient confirmation for stating that the Retailer brand equity level has no influence on the

consumer’s perceived degree of Compatibility of the innovation. As results from these findings,

H1c is rejected.

H1d tests if the respondents perceive the Trialability of the innovation as higher when it is sold

through a retailer with High (M1) RBE versus Low (M2) Retailer brand equity. This is tested

with H₀: M1 (Trialability = M2 (Trialability)

The trialability of the innovation is seen as an important attribute in the consumer’s willingness

to adopt the innovation. The higher the degree of perceived trialability, the more the consumer

is willing to adopt the innovation (Rogers; 1995). In the theoretical framework, we expressed

our premise that the level of perceived trialability could be affected by the degree of Retailer

brand equity of the retailer that sells the innovation. Regardless of this strong theoretical

evidence, we didn’t find significant differences between the samples of the two conditions.

The mean rank score of the high RBE condition ( = 79.24) reported a higher value than the

mean rank score of the low RBE condition ( = 76.76). The interpretation of the difference in the

mean ranks could conclude that respondents think they can try the innovation more easily when

it is sold through a retailer with high Retailer brand equity, than when it is sold through a

retailer with low Retailer brand equity. However, this conclusion is not valid because the

Wilcoxon sum of ranks test showed that there was significant difference between the two

conditions. The P-value (.438) surpassed the α (0.05) considerably, which means that the H₀ is

accepted. These results led us to conclude that the level of Retailer brand equity of the retailer

that sells the innovation has no influence on the consumer’s perceived level of trialability. With

regard to the hypothesis, this means that H1d is rejected.

H1e tests if the respondents perceive the Observability of the innovation as higher when it is


tested with H₀: M1 (Observability) = M2 (Observability).


We predicted that consumers would perceive a higher Observability of the innovation when it is

sold through a retailer with a high level of Retailer brand equity. This prediction is partially

supported through the findings of this research. The high RBE condition reported a mean rank

value of = 83.20, whereas the low RBE condition reported a mean rank value of = 69.80. This

confirms the direction of our prediction; the degree of Observability of the innovation was

reported higher in the High RBE condition than in the Low RBE condition. The pitfall of this

conclusion is the reported P-value (.056). At the required α (.05), the difference is not significant.

However, the difference is significant when we use a significance level of α=.10. This will

increase the chance of a Type 2 error. Though, the P-value exceeds the required α (.05) with

such a small margin, we don’t think it is likely that a Type 2 error occurs. We therefore use the

higher α=.10 for Observability. In this case, H₀ can be rejected.

These findings allowed us to conclude that the samples of the high RBE conditions and the low

RBE condition are significantly different at α=.10. This means that consumers perceive the

Observability of the innovation as more positive when it is sold through a retailer with High

Retailer brand equity, than when it is sold through a retailer with Low Retailer brand equity. The

collected data provided sufficient evidence to conclude that H1e can be accepted.

H1f tests if the respondents perceive the Perceived risk of the innovation as lower when it is


tested with H₀: M1 (Perceived Risk) = M2 (Perceived Risk)

Just as Complexity, the Perceived Risk of the innovation is different from the other perceived

innovation attributes. High levels of Perceived Risk will decrease the consumer’s willingness to

adopt the innovation. In the theoretical framework, we found proof to suspect that innovations

that are sold through retailers with High Retailer brand equity are perceived to deliver less risk

than innovations that are sold through retailers with Low Retailer brand equity.

The results of this research shows proof for this prediction. The P-value (P-value= .045) of the

perceived risk toward the innovation is below the α (.05). This results in the rejection of H₀ and

enables us to conclude that the samples of the High RBE condition and the Low RBE condition

significantly differ. As we mentioned before, the Retailer brand equity level can affect the

consumer’s perceived risk. The mean rank value in the High RBE condition ( = 69.36) was

significantly lower that the mean rank value in the Low RBE condition ( = 83.64). This provides

significant proof to conclude that the respondents perceive the risk of the innovation to be less

when the innovation is sold through a retailer with High Retailer brand equity, than when it is

sold through a retailer with Low Retailer brand equity. This evidence proofs that there is

sufficient evidence to accept H1f.


We explained in the methodology chapter that we will measure the effect of H2, the perceived

product-category fit of the retailer on the perceived innovation attributes, through the

manipulation of two conditions. The first condition is the retailer with high Retailer brand equity

and High perceived product-category fit. The pretest concluded that this condition is

represented by Bol.com. The conditions that is used for comparison is the retailer with high

Retailer brand equity but with Low perceived product-category fit. The pretest concluded that

this condition is represented by Albert.nl. By using these conditions, we are able to measure the

moderating effect of the perceived product-category fit. In these conditions, the Retailer brand

equity level is kept constant. Possible difference in the outcomes will therefore be the result of

the difference in the perceived product-category level of the retailer.

Mean rank N Sum of ranks Wilcoxon W Z-score Sig PIA total Bol.com

Albert.nl

37.16

39.84

38

38

1412.00

1514.00

1412.000 -.530 .596

Compatibility Bol.com 35.62 38 1353.50 1353.500 -1.145 .252

Albert.nl 41.38 38 1572.50

Rel. advantage Bol.com 33.84 38 1286.00 1286.000 -1.846 .065

Albert.nl 43.16 38 1640.00

Complexity Bol.com 37.83 38 1437.50 1437.500 -.269 .788

Albert.nl 39.17 38 1488.50

Observability Bol.com 34.67 38 1315.50 1315.500 -1.577 .115

Albert.nl 42.38 38 1610.50

Trialability Bol.com 35.82 38 1361.00 1361.000 -1.071 .284

Albert.nl 41.18 38 1565.00

Perceived Risk Bol.com 37.18 38 1413.00 1413.000 -.521 .602

Albert.nl 39.82 38 1513.00

α=0.05

Table 4.11: sum of ranks test for H2.

Table 4.11 shows the results of the Wilcoxon sum of ranks test for H2. Despite the theoretical

foundation for assuming the effect of the perceived product-category fit on the perception of the

innovation attributes, none of the perceived innovation attributes reported significant

differences between the two conditions at α=.05. These results provided sufficient proof to

reject H2. No moderating effect of the perceived product-category was found.


Chapter 5: Conclusion and discussion

5.1 Conclusions

Despite of previous research, innovation adoption is still a subject that brings up questions. It

frequently happens that a new product isn’t purchased by consumers despite of the benefits the

new product has to offer (Gourville; 2006). One of the limitations that manufacturers face in

convincing their consumers to adopt the innovation, is that the manufacturer does not sell the

innovation directly to the end-consumer. Most products are sold indirectly through retailers

(Baldauf et al.; 2009). The retailer has an important role in the quality assessment and the risk

reduction of consumers in the purchase decision for common products. Especially in situations

in which consumers perceive a high level of risk or uncertainty, consumers are inclined to use

their retailers’ image as an important decision determinant in their purchasing decision (Chu &

Chu; 1994). A situation with a high degree of uncertainty, and therefore high risk, is the

purchase of an innovation. However, despite of extensive literature that describes the influence

of the retailer on the consumers’ purchase decision for common and know products, no previous

research looked into the influence of the retailer on the consumer’s adoption of innovation. This

research therefore aimed on answering the following research question:

‘What is the influence of the retailers’ reputation on the consumers’ evaluation of the perceived

attributes of an innovation?’

The retailers’ reputation was made operational with the retailer brand equity variable of Pappu

& Quester (2006a). We researched the effect of the retailer brand equity on the six perceived

innovation attributes developed by Rogers (1995). We hypothesized that consumer perceive the

Compatibility, Relative advantage, Observability and Trialability of the innovation as higher

when the innovation was sold through a retailer with high retailer brand equity. In the contrary,

we predicted the scores for Complexity and Perceived risk to be lower when the innovation was

sold through a retailer with high retailer brand equity. We also found sufficient theoretical

foundation for suspecting that the retailer’s perceived product-category fit with the innovation,

moderated the effect of the retailer brand equity.

In contrast to our theoretical findings, we did not found any significant effect of the Retailer

brand equity on the innovation’s Relative advantage, Compatibility, Complexity and Trialability.

Also, the perceived product-category fit of the retailer with the innovation did not show to

moderate the effect of the retailer brand equity on the perceived innovation attributes. Despite

of these findings, the results of our research gave enough evidence to conclude that the retailer’s

reputation do affects the consumer’s evaluation of the perceived innovation attributes. The

overall evaluation of the Perceived Innovation Attributes showed to be significant higher for the


condition in which the retailer has high retailer brand equity. We found specific evidence for the

positive influence the retailer with high retail brand equity has on the evaluation of the

innovation’s Perceived Risk and Observability.

The Compatibility of the innovation was assumed to be affected by the retailer brand equity

because retailers with high retailer brand equity are able to transfer their brand image to the

self-image of the consumer. When a consumer perceive the retailer to have high brand equity, it

is likely that the retailer is compatible with the consumer’s self-image. We therefore predicted

that the innovation that is sold at the retailer with high brand equity will be seen as more

compatible with the consumer’s self-image. As we mentioned before, this research did not found

significant difference in the consumer’s evaluation of the Compatibility for low- versus high

retailer brand equity, despite defensible foundation from previous literature. However, we also

performed a Kruskal-Wallis test to see if the four conditions were equal (table 4.8). The test

showed a significant difference between the four conditions. The condition with High Brand

Equity and High perceived product-category fit (Bol.com) showed the highest mean rank score,

suggesting that this retailer affected the compatibility of the innovation the most. So, we didn’t

find a significant difference for the level of retailer brand equity but there is significant evidence

that the compatibility of the innovation is affected most by the retailer with high RBE and high

product-category fit.

The perceived Relative advantage of the innovation is alleged to be affect by the high retailer

brand equity because consumers use the retailer as a quality heuristic. Consumers perceive the

retailer with high retailer brand equity to sell products with high quality that contain many

advantages. Due to previous interaction or experience with the retailer, consumers trust the

high brand equity retailer more and are therefore more inclined to purchase products of that

retailer. Previous theory also showed that retailers with high Retailer brand equity are seen as

prestigious. This prestige is transferred to the products they sell, what brings an advantage to

the consumer. Nevertheless, the results of our research did not show foundation for these

assumptions. The perceived relative advantage of the innovation was equal across the

conditions. The additional test we performed (table 4.8) did show however, that the Relative

advantage of the innovation is perceived notably lower in the condition where the retailer has

low retailer brand equity and Low product-category fit. These findings are in sync with the

results we derived in table 4.11. Relative advantage showed to be insignificant at the agreed

significance level, but the P-value (0.065) was considerably close to the agreed significance level

(α=.05). The conclusion we can draw from these results is that the perceived Relative advantage

of the innovation is perceived to be very low when the retailer has bad fit with the product-

category of the innovation and is perceived as low in retailer brand equity. This effect can be


caused by the lack of experience with the retailer. Table 4.8 also shows that Albert.nl is

perceived as the retailer at which the innovation offers the highest relative advantage. Albert.nl

is the online sister of Albert Heijn. The high perception of relative advantage at Albert.nl can be

caused because of previous interaction with Albert Heijn. The respondents were maybe more

know with the quality of the products of Albert Heijn than of the other four retailers. Hence, the

results of this research also show that the previous interaction between the retailer and the

consumer can affect the consumer’s perception of the relative advantage of the innovation.

Previous research showed that the complexity of the innovation could be affected by the level of

Retailer brand equity because retailer with high retailer brand equity were more able to grant

the wishes and needs of the consumers. Personal service decreases the perceived complexity of

the innovation and retailers with high retailer brand equity are perceived to be high in personal

service. However, our research did not find any evidence that supports these predictions. The

conclusion we can make is that the perceived Complexity of the innovation is not reduced by the

retailer’s brand equity level.

The perceived Trialability of the innovation was assumed to be moderated by the Retailer brand

equity because consumers perceive retailers with high retailer brand to be more willing to

return the innovation in case of product failure or dissatisfaction. This assumption is not

supported by our results. We didn’t find a differential effect of the Trialability of the innovation

across the four conditions. We can therefore conclude that consumers don’t think the innovation

can be tested more easily at retailers with high retailer brand equity. We did find that the

respondents thought the trialability of the innovation was the highest at Albert.nl (Table 4.8).

This effect can be the result of previous experience of the respondents with returning products

and trying the products at Albert Heijn.

We did found significant differences in the degree of retailer brand equity on the Perceived risk

of the innovation. Innovations that are sold through retailers with high Retailer brand equity are

seen as less risky than innovations that are sold through retailers with low Retailer brand

equity. This supports the findings we reported in the theoretical framework. The influence of the

Retailer brand equity on the innovation’s perceived risk is the result of several effects. The

legitimacy of our claim in supported by a quote of Rogers (1995): “The diffusion of innovation is

an uncertainty-reduction process”. The retailer is seen used as a patronage mode for reducing

the risk of the products. High brand equity retailers are seen as retailers with high quality

products. This reduces the perceived product-related risk. The increased willingness to return

product in case of dissatisfaction or product failure of retailers with high Retailer brand equity

also reduces the perceived product-related risk. We also mentioned before that retailers with


high Retailer brand equity are seen as more congruent with the consumer’s self-image. This

reduces the consumer’s perceived social and psychological risk. This research gives foundation

for assuming that a retailer with high Retailer brand equity reduces the consumer’s perceived

risk of the innovation and these consumers evaluate the innovation therefore more positive.

Additional tests (table 4.8) even shows that this effect is stronger when the retailer with high

Retailer brand equity is also seen as having a high product-category fit with the product-

category of the innovation.

An attribute that connects to the reduced risk is the consumer’s perceived Observability of the

innovation. The theoretical framework showed the Observability of the innovation is important

for the social position of the consumers in their social environment. We postulated that the

Observability involves a high degree of social risk. This social risk is reduced by purchasing

through retailers with high Retailer brand equity. Our findings supported this prediction. The

degree of Observability was significantly higher in the high Retailer brand equity condition, than

in the low Retailer brand equity condition. Additional tests (table 4.8) also found interesting

results. Although the test did not show a significant difference at α=.05, the P-value (.076) did

found significance when the significance level was α=.10. It showed that Albert.nl (High RBE, low

perceived product-category fit) had a substantial higher mean rank score than the other

retailers. This led us to conclude that the effect of the retailer’s image on the Observability of the

innovation is very subjective and varied. It is not only the level of Retailer brand equity, but

there are other retailer characteristics that also play a role in the increased positive attitude

toward the Compatibility of the innovation. As we mentioned before, Albert.nl has a direct link

with Albert Heijn in the memory of the consumers. Consumers are likely to have more previous

experience with Albert Heijn than with the other retailers.

Although our research didn’t paid attention to the phenomena, the results of these findings allow

us to hypothesize that previous experience and interaction with the retailer will also affect the

consumer’s perception of the innovation attributes. The results in table 4.11 also show that

consumers perceive the innovation attributes to be more positive for Albert.nl than for Bol.com.

Even though the differences aren’t significant, it pinpoints the effect of previous interaction with

the retailer. It led us to conclude that the retailer loyalty plays an important role in the

consumers perception of the innovation attributes. This can be the most important factor that

explains why the moderating effect of the perceived product-category wasn’t found. The

respondents didn’t show to be affected by the product-category fit of the retailer with the

innovation. However, they did show higher scores for the retailer with which they had prior

knowledge to rely on.


Thus, the main conclusion of this research is therefore that the overall perception of the

innovation attributes is affected by the Retailer brand equity of the retailer that sells the retailer.

The attributes that aren’t affected by retailers with high Retailer brand equity are Complexity,

Relative advantage, Trialability and Compatibility. The attributes that are affected by high level

Retailer brand equity are Perceived Risk and Observability. We did not find sufficient evidence

to conclude that the perceived product-category fit constantly moderates these relationships.

5.2 Discussion

5.2.1 Academic contribution

The purpose of this research was to explain the role of the retailer in the adoption decision of

consumers. Thereby, we aimed on laying a theoretical foundation for further research about the

consumer’s differential evaluation of the same product across retailers with different levels of

Retailer brand equity. The conclusions that are drawn about these subjects are relevant for three

academic categories of marketing research; consumer behavior, adoption of innovation and

Retailer brand equity.

First, adding knowledge about the consumer behavior phenomena in retailing context is

becoming more important because the most manufacturers sell their products indirectly to their

end-consumers through retailers (Baldauf et al; 2009). Our study contributes to this area of

marketing by displaying the consumer’s deviant evaluation of products across retailers with

different levels of Retailer brand equity. The results of our research show that consumers use

the retailer’s perceived level of Retailer brand equity as a heuristic in their product evaluation.

This implies that consumers not only use tangible retail factors such as ambient-, social- and

design factors (Baker; 1986) in their purchase decision. The perceived Retailer brand equity of

the retailer that sells the product also plays a role in the purchase decision. This is especially

interesting in the consumer behavior in retailing context of online retailers. Because online

retailers lack tangible factors, consumers must rely on other factors to evaluate the products.

Our research shows that the Retailer’s Brand Equity level is used as such a factor.

Secondly, the adoption of innovation literature describes several factors that affect the

consumer’s adoption decision (Rogers; 1995). Allot of research describe several factors that

influences the consumer’s evaluation of the innovation directly. It is for example well known

how the perceived attributes of the innovation affects the adoption decision. However, there is

less known about the role of mediators in the adoption decision. This research laid a foundation

for assuming that there are mediators that play a role in the adoption decision of consumers. We

showed that retailers are able to affect the perceptions of certain innovation attributes and are


therefore moderating the consumer’s adoption process. The results can elicit marketing scholars

to take mediating factors in the adoption process in account in the future.

Third, there is an extensive amount of research known about brand equity. Nevertheless,

Retailer brand equity is an area of brand equity research that did not receive much attention yet

(Ailawadi & Keller; 2004). There is especially little knowledge about the consequences of

different levels of Retailer brand equity on the consumer’s product evaluation. The results of our

research contribute academic knowledge to this area by showing that there is an actual effect of

the retailer’s degree of Retailer brand equity on the consumer’s perception on the products they

sell.

5.2.2 Managerial contribution

The outcomes of this research can also result in practical implications for managers. It

establishes a sufficient basis for assuming that unknown manufacturers of innovations can

benefit by selling their innovations through retailers with high levels of Retail Brand Equity.

Consumers perceive a high degree of risk with purchasing innovation and especially innovations

from unknown brands. This research shows that the retailer with high Retailer brand equity

reduces this perceived risk. Holak & Lehmann (1990) found proof that the reduced level of

perceived risk will increase the consumer’s purchase intention of the innovation. This decreased

resistance toward the innovation will also lead to an increased level of interest in the innovation.

Our research therefore also postulates that the retailer should be adapted as a serious factor in

the introduction and promotion of the innovation.

A more concrete managerial implication of our research involves the possibility to launch the

innovation under the private label of the retailer. Amazon.com is one of the biggest online-book -

retailers of the United States. They introduced an Ebook-reader, the Amazon Kindle. This makes

use of their existing retailer brand equity. Ailawadi & Keller (2004) describe that products that

are launched under the private label of the retailer, have a bigger chance on success if the

retailer brand equity of that retailer is high. This is mainly caused by the transfer of perceived

quality. Our research shows that innovations are also perceived as less in risk when it is

associated with the retailer. It therefore proofs that managers could consider launching

innovations under the name of the retailer, to enhance the possibility of successful adoption.

Maybe the most important managerial contribution of this thesis involves the (online) retailer.

The outcomes showed that consumers evaluate the products more positive when they are sold

through a retailer with a high level of Retailer brand equity. The increased positivity toward the

products will increase the consumer’s purchase intention of the products. These results enable

the conclusion that increasing the Retailer’s Brand Equity level can be very beneficial for the


retailer’s sales. One of the constructs in the Retailer brand equity variable is the Retailer’s

Awareness. It predicts that the increased familiarity with the retailer will enhance the Retailer

brand equity. The retailer’s awareness can be increased through advertising and promotion.

Based on the results of our research, we therefore recommend retailer with a low level of

Retailer brand equity to increase their awareness through promotion and advertising.

5.2.3 Limitations & suggestions for further research

Despite all effort to make this research as complete and rigorous as possible, the research does

have some limitations. We weren’t able to cover every issue in the adoption process. However, a

good research knows its boundaries and limitations. We therefore critically reflect the

limitations of the research and try to formulate relevant suggestions for further research.

The methodology to conduct the research was designed to gather as valid and reliable data as

possible. However, concessions were made for some issues in the methodology due to financial

and time constraints. This research used two groups of separate samples (Pretest and Main

analysis) to measure the strength of the variables. However, there is a chance of bias between

the two samples because the respondent’s degree of perceived Retailer brand equity of the used

retailers can differ across the samples.

We measured the Retailer brand equity and the perceived product-category fit of the example

retailers. Although we measured significant differences in the respondent’s perception toward

these retailers, other not foreseen factors can also cause the differential evaluation of the

perceived innovation attributes. Also, a bigger sample size could make sure that the results can

be tested trough a parametric test. This could increase the power of the test.

The innovation we used as the research-object for this thesis was the Ebook-reader. The data did

show significant difference for the Perceived Innovation Attributes of the Ebook-reader across

the several levels of Retailer brand equity. We used a digital innovation, because research has

showed that this type of innovation is perceived as riskier than for other types of innovations

(Saaksjarvi, 2003). However, we can’t conclude without any hesitation that these significant

differences will also be found for other types of innovations.

As we already put forward in the theoretical framework, we narrowed the research by exploring

the effect of the non-tangible, high scope cues of the retailer. We did not look into the influence

of the low scope cues of the retailer on the perception of the retailer attributes. However, there

is research that connects the low scope cues of the retailer to the quality perception of products

they sell (Purohit & Svrivastava; 2001). We postulate that this could be an interesting field for

further research.


This scope of this research is also limited to a single stage of the adoption process. Rogers

(1995) reported that the consumer goes through several stages before the actual adoption of the

innovation is done. We limited the research by exploring what the influence of the Retailer

brand equity is in the ‘adoption intention’ phase. However, further research can explore the

effect in the other stages of the adoption decision as well.

We researched the effect of Retailer brand equity of online retailers on the perceived innovation

attributes. We suggested that consumers would rely more on the retailer’s reputation in online

retailing because the consumers could not rely on tangible cues. Despite academic foundation

for this assumption (Eroglu, Machleit & Davis; 2001), further research can explore the

differences of online and offline retailing cues more intensive. As we reported earlier, online

retailing is developing rapidly and additional research in this field is becoming more and more

important.

Steenkamp et al. (1999) found proof that innovation is perceived different across consumers of

different cultural backgrounds. Our research reported a significant effect of the Retailer brand

equity on the Perceived Risk of the innovation. The perceived social risk plays a big role in the

perceived risk of the innovation. Our findings supported the notion that high Retailer brand

equity decreases the perceived social risk. This effect can be stronger for consumers in

Collectivistic cultures. These cultures are more aware of their social position and social

embarrassment is more important to be avoided. We therefore propose to conduct further

research to this phenomenon.


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Appendices

Appendices overview

Appendix A: Questionnaires and the items of the pretest and the main analysis.

Appendix B: The outline and presentation of the questionnaire to the respondent.

Appendix C: Data results of the pretest.

Appendix C.1: Normality scores for the items of the pretest

Appendix C.2: Factor analysis assumptions for the pretest

Appendix C.3: Result of the factor analysis of the pretest

Appendix C.4: Normality scores for the variables that are used in the pretest.

Appendix D: Data results of the main analysis.

Appendix D.1: Normality scores for the items of the main analysis

Appendix D.2: Factor analysis assumptions for the main analysis

Appendix D.3: Result of the factor analysis of the main analysis

Appendix D.4: Normality scores for the variables that are used in the main analysis.


Appendix A: The used questionnaires and their items.

Pretest

Retailer awareness: 1. Ik heb wel eens iets gekocht bij (Retailer)

2. Sommige eigenschappen van de (Retailer) website komen snel in mijn hoofd.

3. Ik weet wat (Retailer) voor producten verkoopt.

4. Ik herken (Retailer) tussen anderen online winkels.

Retailer associations 5. Ik vind (Retailer) een goede online winkel.

6. Ik ben trots om producten van (Retailer) te kopen.

7. Ik vertrouw (Retailer) als leverancier van mijn producten.

8. De producten van (Retailer) leveren goede waarde voor hun geld.

9. De website van (Retailer) heeft goede en nuttige extra

10. (Retailer) heeft een goed en breed assortiment.

Retailer perceived quality 11. De producten die (Retailer) verkoopt zijn goede producten

12. De producten die (Retailer) verkoopt zijn elke keer weer van goede kwaliteit.

13. De producten die (Retailer) verkoopt gaan lang mee.

14. De producten die (Retailer) verkoopt vertrouw ik.

Retailer Loyalty 15. Als ik moet kiezen tussen een aantal online winkels, kies ik voor (Retailer).

16. Ik ben loyaal aan (Retailer).

17. Ik zal geen producten van andere online retailers kopen, als ik weet dat (Retailer) ook deze producten

verkoopt.

18. (Retailer) is mijn eerste keus in deze productcategorie.

19. Dat (Retailer) ebook-readers in het assortiment heeft, vind ik logisch.

20. Ebook-readers passen bij het beeld dat ik bij (Retailer) heb gekregen.

Main analysis:

Compatibility 1. Het lezen met een ebook-reader (gekocht bij (Retailer)) komt overeen met mijn behoefte.

2. Het lezen met een ebook-reader komt overeen met mijn normen en waarden.

3. Als ik een ebook-reader ga gebruiken past dit goed binnen de manier waarop ik dingen wil doen.

Relative advantage

4. Het gebruiken van een ebook-reader gaat mijn leesplezier/ leesgemak verbeteren.

5. Over het algemeen genomen, denk ik dat lezen met een ebook-reader in mijn voordeel gaat

uitpakken.

6. Ik denk dat het gebruiken van een ebook-reader op dit moment gewoon de beste manier is van lezen.

Complexity 7. Het is moeilijk om een ebook-reader te gebruiken.

8. Lezen met een ebook-reader is lastig

9. Ik denk dat een ebook-reader gemakkelijk is in gebruik.

Observability 10. Ik zou het niet erg vinden om anderen te vertellen dat ik een ebook-reader gebruik.

11. Ik zou anderen het gebruik van een ebook-reader kunnen aanraden als ik er ook positief over ben.

12. De voor- en nadelen van het lezen met een ebook-reader zijn duidelijk voor me.

Trialability 13. Voordat ik een ebook-reader koop bij (Retailer), kan ik hem eerst gebruiken in een proeftijd.

14. Het is gemakkelijk om een ebook-reader die ik koop bij (Retailer) te proberen voor de aankoop,

zonder dat ik ergens aan vast zit.

15. Voordat ik een ebook-reader koop bij (Retailer) kan ik in de mogelijkheid komen om het product

ergens uit te proberen.

Perceived risk 16. Ik denk dat ik geld verspil als ik een ebook-reader koop bij (Retailer).


17. Ik loop risico als ik een ebook-reader koop bij (Retailer).

18. Ik krijg spijt van het kopen van een ebook-reader bij (Retailer).

19. Ik maak een fout als ik een ebook-reader aankoop bij (Retailer).

20. Een ebook-reader is een riskante aankoop bij (Retailer).

Innovativeness 21. Als ik een nieuw product in de winkel zie liggen, wil ik dit product graag een keer proberen.

22. Ik ben vaak één van de eerste die een nieuw product koopt als het op de markt komt.

23. Ik zal niet zo gauw andere, onbekende producten kopen.

24. Ik ben meestal één van de eerste die een nieuw merk uitprobeert.

25. Ik zal niet zo gauw een product kopen als ik niet zeker weet hoe het werkt.

26. Ik vind het leuke om nieuwe producten te proberen.

27. Ik vind het niet leuk om als eerste een product te kopen, nog voordat iemand anders het gekocht

heeft.


Appendix B: Questionnaire for condition 1; Bol.com

Beste mensen,

Voor mijn Master Marketing aan de Vrije Universiteit ben ik een onderzoek aan het doen naar de adoptie

van innovatie. Ik heb hierbij dringend jullie hulp nodig! Ik wil je vragen om het volgende verhaalt

aandachtig door te lezen, en daarna een aantal stellingen te beantwoorden en een aantal vragen over

jezelf te beantwoorden. Het zijn dus in totaal maar 3 pagina's dus het duurt allemaal bij elkaar misschien 5

minuten.

Je zou me een ontzettend eind op weg helpen naar die velbegeerdetitel!

Alvast ontzettend, Ontzettend, Ontzettend bedankt!

Rens Verweij

Bol.com is marktleider op het gebied van online verkoop van boeken, entertainment en elektronica en tevens

de grootste internetwinkel van Nederland. Bezoekers van de mediawinkel hebben met een klik op de muis

toegang tot ruim twee miljoen artikelen, waaronder nieuwe en tweedehands Nederlands- en Engelstalige

boeken, muziek, DVD’s, notebooks, software, pc-accessoires, games, elektronica, mobiele telefonie, lcd- en

plasma televisies.

In navolging van grote internetboekhandels als Amazon.com en Bruna is ook BOL.COM gestart met de

online verkoop van de nieuwste trend op het gebied van boeken: eBooks. Vooral in Amerika zijn eBooks

zeer populair. Het lijkt erop dat deze digitale boeken ook Nederland gaan veroveren.

De prijs van een eBook is doorgaans enkele euro’s lager dan die van een traditioneel papieren boek. En

bestellen is eenvoudig: u besteld de eBook bij BOL.COM, rekent af en ontvangt per e-mail het boek in een

bestand (vaak pdf) dat u op een eBook reader kunt lezen.

EBook readers

EBook readers, kortweg ook wel eReaders genoemd, zijn er in soorten en maten. De meeste aanbieders

van eBook readers vragen voor een eBook minimaal 199,95 euro. Zo ook BOL.COM. Een hoop geld, maar

daar heeft u dan ook jaren plezier van. Ze hebben een geheugen van 512 MB of 1024 MB (of soms nog

meer), wat neerkomt op een opslagcapaciteit voor respectievelijk duizend en tweeduizend boeken. Dat

scheelt in elk geval veel ruimte in uw vakantiekoffer.


Het lezen met een ebook-reader (gekocht bij BOL.COM) komt overeen met mijn behoefte.

Het lezen met een ebook-reader komt overeen met mijn normen en waarden.

Als ik een ebook-reader ga gebruiken past dit goed binnen de manier waarop ik dingen wil doen.

Het gebruiken van een ebook-reader gaat mijn leesplezier/ leesgemak verbeteren.

Over het algemeen genomen, denk ik dat lezen met een ebook-reader in mijn voordeel gaat uitpakken.

Ik denk dat het gebruiken van een ebook-reader op dit moment gewoon de beste manier is van lezen.

Het is moeilijk om een ebook-reader te gebruiken.

Lezen met een ebook-reader is lastig

Ik denk dat een ebook-reader gemakkelijk is in gebruik.

Ik zou het niet erg vinden om anderen te vertellen dat ik een ebook-reader gebruik.

Ik zou anderen het gebruik van een ebook-reader kunnen aanraden als ik er ook positief over ben.

De voor- en nadelen van het lezen met een ebook-reader zijn duidelijk voor me.

Voordat ik een ebook-reader koop bij BOL.COM, kan ik hem eerst gebruiken in een proeftijd.

Het is gemakkelijk om een ebook-reader die ik koop bij BOL.COM te proberen voor de aankoop, zonder dat

ik ergens aan vast zit.

Voordat ik een ebook-reader koop bij BOL.COM kan ik in de mogelijkheid komen om het product ergens

uit te proberen.

Ik denk dat ik geld verspil als ik een ebook-reader koop bij BOL.COM.

Ik loop risico als ik een ebook-reader koop bij BOL.COM.

Ik krijg spijt van het kopen van een ebook-reader bij BOL.COM.

Ik maak een fout als ik een ebook-reader aankoop bij BOL.COM.

Een ebook-reader is een riskante aankoop bij BOL.COM.

Dat BOL.COM ebook-readers in het assortiment heeft, vind ik logisch.

Ebook-readers passen bij het beeld dat ik bij BOL.COM heb gekregen.

Als ik een nieuw product in de winkel zie liggen, wil ik dit product graag een keer proberen.

Ik ben vaak één van de eerste die een nieuw product koopt als het op de markt komt.

Ik zal niet zo gauw andere, onbekende producten kopen.

Als ik een merk goed vindt, zal ik niet gauw een ander merk kopen om te proberen om het goed is.

Ik ben meestal één van de eerste die een nieuw merk uitprobeert.

Ik zal niet zo gauw een product kopen als ik niet zeker weet hoe het werkt.

Ik vind het leuke om nieuwe producten te proberen.

Ik vind het niet leuk om als eerste een product te kopen, nog voordat iemand anders het gekocht heeft.

Bent u een man of een vrouw? Wat is uw hoogst afgeronde opleiding 0 Basis Onderwijs

0 VMBO

0 HAVO

0 VWO

0 MBO

0 HBO

0 WO-bachelor

0 WO-master

0 Anders

0 Man 0 Vrouw

Wat is uw leeftijd? 0 <18

0 18-30

0 31-40

0 41-50

0 51

Appendix C: Results of the Pretest

Appendix C.1: Measures of the normal distribution of the items in the pretest.

* α=0.05.

Appendix C.2: Assumptions for factor analysis (pretest).

Items (pretest)

Communalities*

Sommige eigenschappen van de (Retailer) website komen snel in mijn hoofd .658

Ik weet wat (Retailer) voor producten verkoopt .499

Ik herken (Retailer) tussen anderen online winkels .761

Ik vind (Retailer) een goede online winke .877

Ik ben trots om producten van (Retailer) te kopen .683

Ik vertrouw (Retailer) als leverancier van mijn producten .909

De producten van (Retailer) leveren goede waarde voor hun geld .945

De website van (Retailer) heeft goede en nuttige extra opties .811

(Retailer) heeft een goed en breed assortiment .902

De producten die (Retailer) verkoopt zijn goede producten .942

De producten die (Retailer) verkoopt zijn elke keer weer van goede kwaliteit .898

De producten die (Retailer) verkoopt gaan lang mee .906

De producten die (Retailer) verkoopt vertrouw ik .900

Als ik moet kiezen tussen een aantal online winkels, kies ik voor (Retailer) .841

Ik ben loyaal aan (Retailer) .760

Ik zal geen producten van andere online retailers kopen, als ik weet dat (Retailer) ook

deze producten verkoopt

.943

(Retailer) is mijn eerste keus in deze productcategorie .773

Ik heb wel eens iets gekocht bij (Retailer) .677

Dat (Retailer) Ebook-readers verkoopt is een goede combinatie tussen winkel en

product

.828

Dat (Retailer) Ebook-readers verkoopt is logisch. .899

* The values of the communalities after extraction. Minimum is 0.3

Items (pretest) Kolmogorov-Smirnov

Values DF Sig.* Sommige eigenschappen van de (Retailer) website komen snel in mijn hoofd .301 62 .000

Ik weet wat (Retailer) voor producten verkoopt .205 62 .000

Ik herken (Retailer) tussen anderen online winkels .235 62 .000

Ik vind (Retailer) een goede online winke .207 62 .000

Ik ben trots om producten van (Retailer) te kopen .214 62 .000

Ik vertrouw (Retailer) als leverancier van mijn producten .169 62 .000

De producten van (Retailer) leveren goede waarde voor hun geld .208 62 .000

De website van (Retailer) heeft goede en nuttige extra opties .185 62 .000

(Retailer) heeft een goed en breed assortiment .196 62 .000

De producten die (Retailer) verkoopt zijn goede producten .236 62 .000

De producten die (Retailer) verkoopt zijn elke keer weer van goede kwaliteit .198 62 .000

De producten die (Retailer) verkoopt gaan lang mee .185 62 .000

De producten die (Retailer) verkoopt vertrouw ik .168 62 .000

Als ik moet kiezen tussen een aantal online winkels, kies ik voor (Retailer) .188 62 .000

Ik ben loyaal aan (Retailer) .264 62 .000

Ik zal geen producten van andere online retailers kopen, als ik weet dat (Retailer)

ook deze producten verkoopt

.289 62 .000

(Retailer) is mijn eerste keus in deze productcategorie .269 62 .000

Ik heb wel eens iets gekocht bij (Retailer) .431 62 .000

Dat (Retailer) Ebook-readers verkoopt is een goede combinatie tussen winkel en

product

.183 62 .000

Dat (Retailer) Ebook-readers verkoopt is logisch. .176 62 .000

74

Appendix C.3: Results of the factor analysis (pretest)

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .917

Bartlett's Test of Sphericity Df. 190

Sig. * .000

* α= 0.05

Factors Eigenvalue % of Variance Cumulative %

1 13,707 68,533 68,533

2 1,917 9,584 78,117

3 1,262 6,311 84,428

4 ,747 3,733 88,161

5 ,428 2,141 90,302

Factor loadings (pretest)

1 2

De producten die (Retailer) verkoopt zijn goede producten .962

De producten van (Retailer) leveren goede waarde voor hun geld .961

(Retailer) heeft een goed en breed assortiment .934

Ik vind (Retailer) een goede online winke .930

Ik vertrouw (Retailer) als leverancier van mijn producten .928

De producten die (Retailer) verkoopt gaan lang mee .906

Ik heb wel eens iets gekocht bij (Retailer) .905

Ik herken (Retailer) tussen anderen online winkels .884

De producten die (Retailer) verkoopt zijn elke keer weer van goede kwaliteit .871

De producten die (Retailer) verkoopt vertrouw ik .863

De website van (Retailer) heeft goede en nuttige extra opties .846

Sommige eigenschappen van de (Retailer) website komen snel in mijn hoofd .816

Ik weet wat (Retailer) voor producten verkoopt .717

Ik ben trots om producten van (Retailer) te kopen .653

Als ik moet kiezen tussen een aantal online winkels, kies ik voor (Retailer) .535

Dat (Retailer) Ebook-readers verkoopt is logisch. .951

Dat (Retailer) Ebook-readers verkoopt is een goede combinatie tussen winkel en product .906

Ik zal geen producten van online retailers kopen, als (Retailer) ook deze producten verkoopt *

Ik ben loyaal aan (Retailer) * .306

(Retailer) is mijn eerste keus in deze productcategorie * .380

Extraction Method: Principal Axis Factoring.

Rotation method: Direct Oblimin

* Items were deleted after factor analysis due to low factor loadings.

75

Appendix C.4: Normality test for the eight variables of the pretest.

Kolmogorov-Smirnov

Statistic df Sig.

RBE Yourlookforless.nl .258 15 .008

FIT Yourlookforless.nl .185 15 .180

RBE Ereaderstore.nl .292 15 .001

FIT Ereaderstore.nl .329 15 .000

RBE Bol.com .150 15 .200*

FIT Bol.com .218 15 .054

RBE Albert.nl .129 15 .200*

FIT Albert.nl .316 15 .000

76

Appendix D: Results of Main analysis

Appendix D.1: Normality test for the items of the main analysis.

Items Kolmogorov-Smirnov

value df Sig.

Het lezen met een ebook-reader (gekocht bij Retailer X) komt overeen met mijn behoefte. .232 151 .000

Het lezen met een ebook-reader komt overeen met mijn normen en waarden. .136 151 .000

Als ik een ebook-reader ga gebruiken past dit binnen de manier waarop ik dingen wil doen. .185 151 .000

Het gebruiken van een ebook-reader gaat mijn leesplezier/ leesgemak verbeteren. .184 151 .000

Over het algemeen, denk ik dat lezen met een ebook-reader in mijn voordeel gaat uitpakken. .180 151 .000

Ik denk dat het gebruiken van een ebook-reader gewoon de beste manier is van lezen. .213 151 .000

Het is moeilijk om een ebook-reader te gebruiken. .162 151 .000

Lezen met een ebook-reader is lastig .133 151 .000

Ik denk dat een ebook-reader gemakkelijk is in gebruik. .155 151 .000

Ik zou het niet erg vinden om anderen te vertellen dat ik een ebook-reader gebruik. .233 151 .000

Ik zou anderen het gebruik van een ebook-reader aanraden als ik er ook positief over ben. .233 151 .000

De voor- en nadelen van het lezen met een ebook-reader zijn duidelijk voor me. .148 151 .000

Voordat ik een ebook-reader koop bij Retailer X, kan ik hem eerst gebruiken in een proeftijd. .199 151 .000

Het is gemakkelijk om een ebook-reader die ik koop bij Retailer X te proberen voor de

aankoop, zonder dat ik ergens aan vast zit.

.179 151 .000

Voordat ik een ebook-reader koop bij Retailer X kan ik in de mogelijkheid komen om het

product ergens uit te proberen.

.154 151 .000

Ik denk dat ik geld verspil als ik een ebook-reader koop bij Retailer X. .130 151 .000

Ik loop risico als ik een ebook-reader koop bij Retailer X. .195 151 .000

Ik krijg spijt van het kopen van een ebook-reader bij Retailer X. .159 151 .000

Ik maak een fout als ik een ebook-reader aankoop bij Retailer X. .174 151 .000

Een ebook-reader is een riskante aankoop bij Retailer X. .189 151 .000

Appendix D.2: Assumptions of the factor analysis (Main analysis)

Communalities (Main analysis)

Values *

Het lezen met een ebook-reader (gekocht bij Retailer X) komt overeen met mijn behoefte. .711

Het lezen met een ebook-reader komt overeen met mijn normen en waarden. .639

Als ik een ebook-reader ga gebruiken past dit goed binnen de manier waarop ik dingen wil doen. .832

Het gebruiken van een ebook-reader gaat mijn leesplezier/ leesgemak verbeteren. .839

Over het algemeen, denk ik dat lezen met een ebook-reader in mijn voordeel gaat uitpakken. .844

Ik denk dat het gebruiken van een ebook-reader gewoon de beste manier is van lezen. .582

Het is moeilijk om een ebook-reader te gebruiken. .947

Lezen met een ebook-reader is lastig .569

Ik denk dat een ebook-reader gemakkelijk is in gebruik. .273

Ik zou het niet erg vinden om anderen te vertellen dat ik een ebook-reader gebruik. .700

Ik zou anderen het gebruik van een ebook-reader kunnen aanraden als ik er ook positief over ben. .586

De voor- en nadelen van het lezen met een ebook-reader zijn duidelijk voor me. .262

Voordat ik een ebook-reader koop bij Retailer X, kan ik hem eerst gebruiken in een proeftijd. .691

77

Het is gemakkelijk om een ebook-reader die ik koop bij Retailer X te proberen voor de aankoop, zonder

dat ik ergens aan vast zit.

.691

Voordat ik een ebook-reader koop bij Retailer X kan ik in de mogelijkheid komen om het product ergens

uit te proberen.

.706

Ik denk dat ik geld verspil als ik een ebook-reader koop bij Retailer X. .517

Ik loop risico als ik een ebook-reader koop bij Retailer X. .564

Ik krijg spijt van het kopen van een ebook-reader bij Retailer X. .739

Ik maak een fout als ik een ebook-reader aankoop bij Retailer X. .709

Een ebook-reader is een riskante aankoop bij Retailer X. .650

* The values of the communalities after extraction. Minimum is 0.3

KMO and Bartlett's Test

Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .828

Bartlett's Test of Sphericity

df 190

Sig. * .000

* α= 0.05.

Appendix D.3: Results of the factor analysis (Main analysis)

Eigenvalues of the main analysis

Factor Eigenvalue % of Variance Cumulative %

1 6.658 33.291 33.291

2 2.361 11.805 45.096

3 2.019 10.095 55.191

4 1.612 8.058 63.249

5 1.450 7.250 70.499

6 .826 4.129 74.628

Items (main analysis)

Factor loading

1 2 3 4 5

Het lezen met een ebook-reader (Retailer X) komt overeen met mijn behoefte. .785

Het lezen met een ebook-reader komt overeen met mijn normen en waarden. .568

Als ik een ebook-reader gebruik past dit binnen de manier waarop ik dingen doe.. .793

Het gebruiken van een ebook-reader gaat mijn leesplezier/ leesgemak verbeteren. .892

Over het algemeen, denk ik dat een ebook-reader in mijn voordeel gaat uitpakken. .869

Ik denk dat het gebruiken van een ebook-reader de beste manier is van lezen. .731

Het is moeilijk om een ebook-reader te gebruiken. -.974

Lezen met een ebook-reader is lastig -.671

Ik denk dat een ebook-reader gemakkelijk is in gebruik. *

Ik zou het niet erg vinden om te vertellen dat ik een ebook-reader gebruik. .959

Ik zou het gebruik van een ebook-reader aanraden als ik er positief over ben. .673

De voor- en nadelen van het lezen met een ebook-reader zijn duidelijk voor me.*

Voordat ik een ebook-reader koop bij Retailer X, kan ik hem eerst gebruiken. .810

Het is gemakkelijk om een ebook-reader die ik koop bij Retailer X te proberen voor

de aankoop, zonder dat ik ergens aan vast zit.

.813

78

Voordat ik een ebook-reader koop bij Retailer X kan ik in de mogelijkheid komen

om het product ergens uit te proberen.

.815

Ik denk dat ik geld verspil als ik een ebook-reader koop bij Retailer X. .517

Ik loop risico als ik een ebook-reader koop bij Retailer X. .668

Ik krijg spijt van het kopen van een ebook-reader bij Retailer X. .710

Ik maak een fout als ik een ebook-reader aankoop bij Retailer X. .749

Een ebook-reader is een riskante aankoop bij Retailer X. .695

Extraction Method: Principal Axis Factoring.

Rotation Method: Direct Oblimin

* Items removed after analysis due to low factor loading (<0.4)

Appendix D.4: Kolmogorov-Smirnov test for the variables of the main analysis.

Variables for the main analysis Kolmogorov-Smirnov

Statistic df Sig.

YLFL_Compatibility .130 37 .117

YLFL_Relative advantage .239 37 .000

YLFL_Complexity .120 37 .194 *

YLFL_Observebility .148 37 .040

YLFL_Trialibility .114 37 .200 *

YLFL_Perceived risk .148 37 .039

AH_Compatibility .125 37 .158 *

AH_Relative Advantage .168 37 .010

AH_Complexity .166 37 .011

AH_Observebility .202 37 .001

AH_Trialibility .141 37 .061 *

AH_Perceived Risk .109 37 .200 *

BOL_Compatibiity .207 37 .000

BOL_Relative Advantage .173 37 .007

BOL_Complexity .171 37 .008

BOL_Observability .168 37 .010

BOL_Trialibility .191 37 .002

BOL_Perceived Risk .126 37 .149 *

ES_Compatibility .110 37 .200 *

ES_Relative Advantage .160 37 .018

ES_Complexity .194 37 .001

ES_Observability .150 37 .035

ES_Trialibility .192 37 .001

ES_Perceived Risk .121 37 .190 *

* insignificant variables (α=0.05). These variables have a normal distribution.

Master Thesis Rens Verweij

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