Samir selimi thesis- online grocery shopping

The influence of hurdles and benefits on the diffusion of online grocery shopping

Retailing Beyond Borders

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ForewordSipko Schat, Member Executive Board Rabobank

The ‘Anton Dreesmann Leerstoel voor Retailmarketing’ Foundation - supported by a group of

leading retailers in the Netherlands - has chosen Rabobank as its partner to host and co-organise its

annual congress. The initial partnership was for a period of three years (2011-2013), but based on the

success of our cooperation we have agreed to extend it for at least three more years (2014-2016). We

appreciate this opportunity to share views on retail with key players in the sector.

The January 2013 congress, ‘Retailing Beyond Borders - Cooperation´ took place in the Duisenberg

Auditorium in Utrecht. During this congress the ´Rabobank Anton Dreesmann Thesis Award´ was

granted to Samir Selimi for his thesis on ´The influence of hurdles and benefits on the diffusion of

online grocery shopping´. Part of this award is the publication of the thesis as a book. The result is

now in front of you.

Capturing and embedding knowledge is important, both for Rabobank as a knowledge driven

financial organisation and for retailers. We therefore support the initiatives of the Foundation to

combine scholarly knowledge with retail practice. The ´Rabobank Anton Dreesmann Thesis Award´ is

one of these initiatives.

The thesis of Samir Selimi discusses an actual, relevant and interesting issue. The online food retail

industry is underdeveloped compared to other online businesses. Various hurdles and benefits from

the perspective of the online consumer are investigated. The thesis concludes that the hurdles are

more important than the benefits, so retailers should focus on taking away the hurdles in order to

drive online shopping. Furthermore the thesis provides some insights on different online market

segments that are driven by different consumer preferences. Although the thesis is focussed on food

retail, we think that the conclusions are also valuable for non-food retailers.

I trust that the thesis will energise and inspire you to go out and grab the opportunities in the

(online) retail market.

Kind regards,

Sipko Schat

Member Executive Board Rabobank

February 2013

Foreword

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The influence of hurdles and benefits on the diffusion of online grocery shopping:

How to improve the adoption rate in the Dutch market?

Samir Selimi1

under supervision of

Prof. dr. L.M. Sloot2

Dr. M.C. Non2

1 Samir Selimi is an MSc student at the Faculty of Economics and Business, University of Groningen, The Netherlands. The research was conducted as a graduation project for the studies Business Administration in Marketing Management and Marketing Research. Address for correspondence: Samir Selimi, Hereweg 104, 9725 AJ Groningen, The Netherlands; Tel. +31 622614931; E-mail: [email protected]; student number: s1912801.2 Laurens Sloot is Professor of Retail Marketing and Mariëlle Non is Assistant Professor of Marketing at the Department of Marketing, Faculty of Economics and Business, University of Groningen, The Netherlands

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Summary

Most retailers in the Netherlands have already been offering multiple channels to their customers

for many years . Although the beneficial effects of a multichannel approach are clear from literature,

not all Dutch industries have managed to apply the approach successfully. An example of this can

be found in the Dutch food industry, which, until recently, did not offer an online channel. However,

take-up has increased in the past few years and many food retailers now offer an online channel for

sales purposes. Nevertheless, it is still not widely used by the Dutch consumer. Therefore, this master

thesis investigates what food retailers can do to encourage the usage of online grocery shops. By

understanding the entire adoption process of innovations food retailers are able to invest in the most

important aspects of the online environment. Not only are the characteristics of the online channel

important, but the characteristics of consumers also play a great role. Even though food retailers are

not able to influence consumer characteristics and how they feel or react to certain things, it can

provide insight into potential target groups. To understand the entire adoption process in this case,

the following problem statement was investigated:

“Which characteristics of online grocery shops cause resistance or increase the rate of adoption towards

online grocery shopping and are different strategies necessary in order to meet the needs of different

consumer (groups)?”

In order to enhance further insights into this question several research questions were formulated,

which served as an outline for finding relevant literature. The findings led to the following conclusion

and recommendations for management:

The decision path of Rogers (1995) showed that the adoption depends on several stages. Therefore,

food retailers should understand each step in order to enhance the adoption and decrease the

resistance to online grocery shopping.

The conclusions of the consumer characteristics, which are also part of the decision path of Rogers

(1995), indicate that not all characteristics affect the resistance of the adoption. Table 1.1 shows the

significant effects of the consumer characteristics on the resistance and adoption.

• Some consumer characteristics have an effect on both the adoption and the resistance,

while others only influence one of the two; for example, shop enjoyment. This indicates that

if people dislike shopping in general they will not per se resist online grocery shopping. But if

Summary

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they do like shopping in general the probability is higher that they

will adopt online grocery shopping faster than consumers who

dislike shopping in general.

• The results in table 1.1 also indicate that the characteristics, which

are related to someone’s beliefs and values, have a higher effect

Table 1.1 The effect of consumer characteristics on the resistance and adoption of online grocery shopping

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on the resistance. Characteristics, which are related to beneficial aspects of online grocery

shopping influence the adoption more. Finally, more general consumer characteristics have an

effect on resistance as well as on adoption.

The second stage of the decision path of Rogers (1995) relates to the characteristics of the online

channel itself. Unlike consumer characteristics, the food retailer can influence these characteristics

directly.

• The results of the aggregated conjoint analysis have shown that the hurdles are indicated as

more important than the benefits.

• The time taken to order online is perceived as the most important attribute, but the quality

of delivered goods, delivery fee and the delivery options are also seen as very important. The

most significant change in usage is when the delivery option for receiving the goods in the

afternoon is also added.

• The segmented CBC analysis indicates that there are three segments:

(1) the price benefit,

(2) the quality and delivery options and

(3) the time benefit.

The first segment comprises mainly lower educated people who are more often unemployed, the

second segment consists mainly of women who are responsible for the grocery shopping. The

final segment includes the most highly educated, who have the highest income and like grocery

shopping the least.

Finally, the insights above have enabled us to answer our initial problem statement. The three

characteristics which create resistance are: 1) delivery options, 2) delivery fee and 3) quality of ordered

goods. The three characteristics, which increase the adoption are: 1) price benefits, 2) time benefit

and 3) the order procedure. Of course the effect of each (utility) differs from each other. Overall the

hurdles have a higher effect (utility) on resistance than the benefits have on the adoption.

However, the effects do differ between the three segments and therefore, one strategy is not

sufficient to meet the needs of all potential segments.

Summary

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Our environment is changing rapidly. We are fully dependent on our smart phones, Internet and

social media. As a consumer I can fully relate to these changes. They influence my daily life and offer

many benefits. However, these changes also have a large effect on my work as a marketer. Consumers

react differently across all the channels and are more demanding. This thesis has helped me to better

understand the effect of the online channel on the consumer’s preference and vice versa.

The input, which I have received from my supervisors, Laurens Sloot and Mariëlle Non, was of great

value and provided me with new insights regarding this topic and academic research in general.

Therefore, I would like to thank them for their support during this research. Moreover, I am also very

grateful for their comments and suggestions, which have added significantly to the value of this

thesis. Finally, I would also like to thank my friends and family for motivating me and providing me

with very useful tips.

Without the support of the above, writing this thesis would not have been as interesting as it was.

Samir Selimi

Preface

Preface

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Table of contents

Foreword 3

Summary 7

Preface 11

Table of contents 12

1. Introduction 15

§1.1 Research questions 17

§1.2 Relevance & uniqueness of thesis 18

§1.3 Outline 19

2. Diffusion of innovations 21

§2.1 Online shopping (e-shopping) 21

§2.2 Innovations 21

§2.3 Diffusion of innovations 23

§2.4 Diffusion path 25

§2.5 Resistance vs. Adoption 26

§2.6 Conclusion 28

3. Factors influencing the resistance and adoption 29

§3.1 Factors, underlying antecedents and adoption path 29

§3.2 Innovation characteristics 30

§3.3 Consumer characteristics (moderator) 33

§3.4 Adoption path- willingness to retry and degree of resistance 36

§3.5 Conceptual model for conjoint study 36

§3.6 Conclusion 39

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Table of Contents

4. Methodology 41

§4.1 Study one – qualitative study 41

§4.2 Study two – top six attributes 44

§4.3 Study three – quantitative study 47

5. Results 53

§5.1 Sample and sample characteristics 53

§5.2 Measurement purification 57

§5.3 Regressions 61

§5.4 Conjoint analysis 72

6. Conclusions & managerial implication 87

§6.1 Conclusion 87

§6.2 Managerial implications 91

§6.3 Implications for Truus.nl and Appie.nl 93

7. Limitations and directions for further research 97

References 99

Colofon 111

Disclaimer 112

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The enhancement of customer value has become very important for retailers (Neslin et al., 2006).

To achieve this it is important that retailers improve their customer acquisition, retention and

development processes (Neslin et al., 2006). Geyskens, Gielens and Dekimpe (2002) state that

enabling consumers to choose from multiple channels can enhance customer value as well.

The channels typically include the store, web, catalogue, sales force, third party agencies and call

centres (Neslin & Shanker, 2009). Besides the increase in customer value, a multichannel strategy

also offers other benefits, for example, to counteract competitor’s actions (Grewal, Comer, & Mehta,

2001), to decrease the costs per transaction (Dutta, Heide, Bergen, & John, 1995) or to increase their

scope within the market (Friednamdn & Furey, 2003). Besides these benefits, other studies argue

that offering multiple channels could also lead to disadvantages, such as less information, search

costs for consumers, lower switching costs and better insight in the price developments within a

market (Wallace, Giese & Johnson, 2004; Verhoef, Neslin & Vroomen, 2007). This can result in higher

competition as well as to force retailers in investing more in acquiring and retaining customers

(Brynjolfsson & Smith, 2000; Tang & Xing, 2001).

Even though negative aspects are present when a multichannel strategy is used, it seems that its

organisational benefits outweigh the disadvantages. Moreover, offering multiple channels also has

beneficial effects on consumer behaviour. For example, it leads to the improvement of the brand

image, the improvement of customer experience and the enhancement of customer loyalty across

all channels (Danaher, Wilson & Davies, 2004; Bailer, 2006; Harvin, 2000; Shanker, Smith & Rangaswamy,

2003; Wallace, Giese & Johnson, 2004). This is mainly caused by the increase in customer convenience

since consumers are able to choose their preferred channel for each purchase and each channel

satisfies different needs. For example, stores enable face-to-face contact, instant gratification and

physical examination, while the web increases the accessibility for consumers and access to product

and price information. Thus, when combined all the different channels enable retailers to meet more

complicated consumer needs (Wallace, Giese & Johnson 2004; Bucklin, Ramaswamy & Majumar, 1996).

Even though the beneficial effects of a multichannel approach are clear from literature, not all Dutch

industries have been able to profit from it. While most Dutch retailers within different industries (e.g.

fashion, travel, electronics and furniture industry) already apply the multichannel strategy, the Dutch

food industry has not paid the same amount of attention towards the multichannel strategy. This is

mainly due to their lack of attention towards the online channel for sales purposes (Twinkle, 2011).

The online channel was mainly used to provide customers with information (e.g., C1000.nl, 2011;

Jumbo.nl, 2011, Plus.nl, 2011) and not as an online channel for sales purposes. Therefore, it does not

1. Introduction

Introduction

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fit the definition of an online grocery shop, which is; “an online grocery shop offers the ability for

consumers to order groceries from home electronically (i.e. Internet) and have them delivered at

their own preferred location” (Burke, 1997; Gillett, 1970; Peterson, Balasubramanian & Bronnenberg,

1997). While most Dutch food retailers did not use a multiple channel approach, only one Dutch

food retailer offered an online channel, which best fits this definition. From 2010 until the beginning

of 2011 Albert Heijn (AH) was the only Dutch food retailer to offer the ability to purchase groceries

online (Ah.nl, 2011; Twinkle, 2011). However, other supermarket chains like Coop, Dekamarkt, Plus and

Boni have adapted their online channel and since 2011 have enabled their customers to purchase

groceries online as well (Twinkel, 2011). Since most Dutch food retailers have only started using

the online channel for sales purposes recently, it can still be characterised as an innovation (Rogers,

1995; Gatignon & Robertson, 1989). A comparison between general and food related online sales

developments confirm this conclusion.

In 2010 the total online spending in the Netherlands, for products, increased by 10% to €4.2 billion

(Thuiswinkel.org, 2011; ING, 2011). This is approximately a share of 5% of the total Dutch retail

market in 2010. However, a comparison with food related figures show that less than 1% of the total

spending on groceries is done online (ING, 2011). Albert Heijn, for example, which had a monopoly

until the beginning of 2011, had an online turnover of ±€150 million in 2010. This was approximately

1.49% of their total turnover (ING, 2011; Ahold.nl, 2011). While in the Netherlands online spending is

quite low, in other countries, for example the UK, the online grocery market already accounts for 3.2%

(€5.55 billion) of their total food sector in 2010 (IGD, 2011) and is expected to grow to €10 billion

by 2015. These figures and the previously mentioned figures regarding the general online market in

the Netherlands, indicate that the online channel can still offer many opportunities for Dutch food

retailers and can still be expected to grow.

Alongside the literature and market related figures, several studies (e.g. Verhoef & Langerak, 2001)

also indicate that consumers have a generally positive attitude towards online grocery shopping.

They indicate that consumers expect shopping via an electronic channel to be more convenient and

time saving. Other studies also state that time pressure (Srinivasan & Ratchford, 1991), the increase

of Internet usage and situational factors (Hand, Riley, Harris, Singh & Rettie, 2009) positively influence

the adoption of online grocery shopping. Interestingly, market research (e.g. GfK, 2010) shows that

only 5% of Dutch Internet users indicated to have purchased groceries online in 2010 and in addition

57% show high resistance and have even indicated to be unwilling to purchase groceries online at

all; which is quite odd as general consumer figures characterise Dutch consumers as the most active

users of the online channel (Twinkle, 2010). In addition 72% of them have, at least once, purchased

goods online, which makes shopping the 4th most important activity online (GfK, 2010).

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This leads to the conclusion that there is a large discrepancy between the intended consumer

behaviour and the actual behaviour regarding online grocery shopping. This conclusion is drawn

from the fact that literature has shown many positive consumer intentions towards online grocery

shopping. However, the actual sales figures and market research indicate the opposite. It seems that

studies which have found positive intentions towards the adoption of the online channel for grocery

shopping have been performed in situations in which the respondents had little or no experience

with online (grocery) shopping (e.g. Verhoef & Langerak, 2001; Wilson & Reynolds, 2006). Also, during

these studies the intention to shop online for groceries was measured based on more general

metrics and combined with the limited experience of the participants this might have led towards

a biased conclusion. Another reason for the discrepancy between the positive consumer intentions

and the actual online grocery sales might be the fact that many factors, which have been found to be

beneficial, for the adoption of online grocery shopping (e.g. time restraints and increase of Internet

usage) are not controllable by food retailers. Therefore, food retailers are not able to control the

situation in order to increase the rate of adoption of the online channel for grocery shopping.

Hence, the aim of this study will be to find out which characteristics of an online grocery shop

have negative and which have positive influences on the intention for consumers to engage in

online grocery shopping. It has been decided to study the effects of the online grocery shop itself,

in order to provide Dutch food retailers with insights, which are more controllable. Contrary to

non-controllable aspects (e.g. time restraints) our findings enable food retailers to assess their own

situation and if needed adapt their strategy and online environment to better meet customer needs.

However, this does not mean that the non-controllable aspects will be omitted, as they are needed

to provide insight as to whether differences exist between consumers and thus, whether there are

different adopter groups. If this is the case, food retailers will have to use different strategies to attract

different adopter groups.

In order to gain more insight into aspects that influence the usage of the online grocery shop,

negatively or positively, the following main research question will be covered in this paper:

“Which characteristics of online grocery shops cause resistance or increase the rate of adoption towards

online grocery shopping and are different strategies necessary in order to meet the needs of different

consumer groups?”

§1.1 Research questions

Introduction

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§1.2 Relevance & uniqueness of thesisConsidering the information and arguments, which have been presented above, this study’s main

contribution to existing literature is to provide insight into the degree of importance of the three

most important hurdles and the three most important benefits of online grocery shops. These

insights enable food retailers to fully benefit from the opportunities of an online grocery shop and to

build the most ‘ideal’ online grocery shop. Moreover, by taking the non-controllable aspects (e.g. time

restraints) into consideration, information can be provided on whether or not these aspects impact

the relative importance of the hurdles and benefits .

An answer to this question will give food retailers better insight as to how to adapt their online

grocery shops in order to diminish hurdles and increase the rate of adoption. Therefore, to answer the

problem statement five research questions have been formulated:

1. What does the adoption process of new innovations look like?

2. According to literature, which consumer characteristics cause resistance and which increase

the rate of adoption of online grocery shopping?

3. According to literature, which characteristics of online grocery shops cause resistance or

increase the rate of adoption of online grocery shopping?

4. What are the three most important characteristics to create resistance and what are the three

most important characteristics to increase the rate of adoption?

5. What is the degree to which the six most important characteristics affect the choice to resist

or adopt online grocery shopping?

6. What is the best strategy per adopter group to diminish the resistance and increase the

adoption of online grocery shopping?

These research questions serve as an outline in to finding relevant literature regarding hurdles and

benefits towards online grocery shopping. Hurdles and benefits of regular online shopping will also

be taken into account, because it is expected that more literature and knowledge is available on

this topic. This will lead to a better understanding of aspects that influence online grocery shopping

either positively or negatively. Next, the six most important hurdles will be determined, which will

be tested by a Conjoint Analysis to enhance the insight in the degree of importance per hurdle and

benefit. Finally, options to diminish hurdles and increase the awareness of benefits regarding online

grocery shopping will be determined by the use of findings from literature and practice. This will lead

to the formation of different strategies in order to meet the needs of different consumers (consumer

groups). All steps will lead to answers to the research questions, which will contribute to the answer

of the problem statement.

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In the past the main interest in marketing literature regarding online grocery shopping, has been on

the socio-demographic profile of home shoppers (Cunningham & Cunningham, 1973, Darian, 1987;

Gillet, 1976; Peters & Ford, 1972; Reynolds, 1974). Only a few studies have investigated the influences

of both personal characteristics and innovation characteristics on the adoption of innovations (e.g.,

Hirschman, 1980; Labay & Kinnear, 1981; Verhoef & Langerak, 2001; Wilson & Reynolds, 2006). However,

these studies were only able to measure the intention to purchase groceries online when online

grocery shopping was not available for consumers (e.g. Verhoef & Langerak, 2001: Wilson & Reynolds,

2006). This might be the reason why there is a large discrepancy between positive consumer

intention and the actual behaviour towards online grocery shopping. Since online shopping and

online grocery shopping is more common and consumers now have better knowledge of it, it

is expected that our study will be able to better capture consumers’ intentions regarding online

shopping for groceries. Also, our main focus will be on the characteristics of the online grocery shop

itself in order to fill the gap in literature, as the consumer characteristics and other non-controllable

aspects have been studied extensively in the past.

For food retailers, our findings will enable them to better control the situation in order to fully

benefit from the positive effects of a multichannel strategy (e.g. Danaher, Wallace, Giese & Johnson,

2004; Bailer, 2006; Harvin, 2000; Shanker, Smith & Rangaswamy, 2003). In order to do so, food retailers

need to better understand which characteristics of the online grocery shop are perceived as most

important and how they influence the adoption process. Moreover, by studying the differences

between adopter groups, food retailers are provided with insights which enable them to choose the

correct strategy for each adopter group. This is needed as different groups might have different needs

regarding the online environment itself.

§1.3 Outline

This paper will offer more information with regard to innovations and the diffusion of innovations.

Also, a more detailed view of resistance and adoption is provided in chapter two. In chapter three

a theoretical framework will be presented. This initial framework is used to form the methodology

in chapter four which will be followed by the results of the three statistical analyses in chapter five.

Finally, the results from chapter five will be used to formulate the conclusion and the managerial

implications in chapter six. In chapter seven the limitations and directions for further research are

provided.

Introduction

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2. Diffusion of innovations

This section will deal with literature regarding the diffusion of innovations. Since online grocery

shopping is relatively new in the Netherlands it can be considered as an innovation (Rogers,

1995; Gatignon & Robertson, 1989). Therefore, by providing more insight into this topic a better

understanding can be formed of how online grocery shops can be diffused throughout the market.

In paragraph 2.1 more information is provided regarding online grocery shops, followed by insights

originating from previous literature regarding innovations and its diffusion in paragraphs 2.2, 2.3 and

2.4. In paragraph 2.5 the difference between resistance and adoption are provided and finally the

conclusion in 2.6. The different insights, which are provided in this chapter will aid in the formation of

a conceptual model.

§2.1 Online shopping (e-shopping)

Online shopping is defined as: “the ability for consumers to order from home electronically (i.e.,

Internet) and have it delivered at their own preferred location” (Burke, 1997; Gillett, 1970; Peterson,

Balasubramanian & Bronnenberg, 1997). Even though this definition also concerns other channels

such as the fax and telephone, in this study the emphasis will only be on the Internet.

Leeflang and van Raaij (1995) state in their study that a reason for food retailers to introduce an

online grocery shop could be the ability of online shops to better anticipate changes in consumers’

shopping behaviour and differences in social demographic profiles, for example, the increased need

for convenience (Burke, 1997). On the other hand, online grocery shopping is also beneficial for

consumers, as it enables them to save time by shopping online from a preferred location (Verhoef &

Langrak, 2001). Despite the benefits on both sides, online grocery shopping is relatively new in the

Netherlands and is not used by many consumers.

Diffusion of innovations

§2.2 Innovations

The introduction of new products and services is necessary for retailers in order to ensure future sales

and growth (Hoyer and MacInnis, 2008). However, many commercial organisations are still faced with

high failure rates, as many innovations are not adopted by consumers (Moore, 2002; Tauber, 1973;

Rogers, 1983). Therefore, in order for innovations to be successful a better understanding is needed of

what an innovation is and how it diffuses throughout the market (Hoyer and MacInnis, 2008). First of

all a definition of innovations provides us with a better view of what an innovation is; ‘an innovation is

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an idea, practice, or object that is perceived as new by an individual’ (Rogers, 1995; Gatignon & Robertson,

1989). It is thus not important whether the idea, practice or object is new, as long as its (potential)

users perceive it as new. Moreover, changes regarding the way an innovation is used or produced

can also be used to characterise innovations (Robertson, 1971; Gatignon & Robertson, 1989). However,

the degree of change can vary between innovations and with the use of the ‘Innovation Continuum’

of Robertson (1971) innovations can be classified according to the degree in which a change in

consumer behaviour is required. Innovations that do not require a dramatic change (e.g. a wireless

mouse instead of a non-wireless one) are characterised as continuous innovations (Robertson, 1971).

On the other hand, a discontinuous innovation requires a drastic change in the consumption pattern

of consumers (Robertson, 1971). Thus, while continuous innovations are often comparable to existing

alternatives, discontinuous innovations are totally new products or services (Moreau, Lehman &

Markman, 2001). The features of discontinuous innovations are often new to the market and cause a

discontinuity in the existing market or technology-base and that causes the need for a radical change

in consumer behaviour (Garcia & Calantone, 2002; Moreau, Lehman & Markman, 2001).

For online grocery shopping a significant change in consumer behaviour and habits is required, as in

the online channel consumers would have to purchase their groceries in a new way when compared

to the current way of grocery shopping. Moreover, they are not able to perform some tasks, which are

possible in the offline channel; e.g. feeling and smelling the products (Darian 1987; Tauber, 1972). This

is in line with findings from Jager (2003) who states that when an action is performed very often a

habit occurs, which is also the case for the traditional way of grocery shopping in the offline channel.

Thus, Dutch consumers who switch to online grocery shopping require a change in their current

habits regarding grocery shopping and even need to use new technologies to perform the same task

(e.g. use of internet and online payment). This leads to the conclusion that online grocery shopping

can be categorised as a discontinuous innovation (Robertson, 1971; Hansen, 2005; Moreau, Markman

& Lehman, 2001; Molesworth & Suortti, 2001).

Besides the degree of required behavioural change, innovations can also be divided into product

and service innovations. According to Alba et al., (1997) product and service innovations differ (e.g.

tangibility (Lovelock & Wirtz, 2011; Lovelock & Gummesson, 2004)) and therefore, should not be

treated equally. However, to the contrary Dolfsma (2004) argues that the differences between service

innovations and product innovations are only present from a managerial perspective. Consumers

may not even perceive any differences at all, because for them the importance lies only in the added

benefit of products or innovations (Drucker, 1974). The findings of Dolfsma (2004) and Drucker

(1974) are in line with the statement of Fagerberg, Mowery, and Nelson (2005), who state that service

innovations do not follow significantly different diffusion paths compared to product innovations.

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Therefore, even though online grocery shopping can be considered a service innovation, based on

the previous arguments no distinction is made between literature focused on service innovations

and literature focussed on product innovations.

§2.3 Diffusion of innovationsBesides the importance of understanding what an innovation is, it is also important to know why

innovations do (not) diffuse, because it is necessary for an innovation to diffuse properly in order for

it to be successful (Hoyer & MacInnis, 2008). Several studies have investigated the successful diffusion

of innovations (e.g. Rogers, 1995; Mahajan, Muller & Bass, 1995) and the challenges that are present

when an innovation diffuses (Moore, 1991; Moreau et al. 2001; Rogers, 1995). While some studies have

been more focused on high-tech innovations and their technological discontinuities (Moore, 1991;

Linton, 2002), others have focused on low-tech innovations (Atkin, Garcia & Lockshin, 2006). High-tech

innovations are often related to technological discontinuities while low-tech innovations are more

often related to discontinuities regarding consumers and their behaviour (Atkin, et al. 2006). Aspects

from both sides influence online grocery shopping as the technological discontinuities arise due

to the necessity to use new technologies; e.g. new distribution systems, Internet and a web shop.

Behavioural discontinuities are present due to consumers’ strong habits in the offline grocery channel.

Therefore, it is necessary for Dutch food retailers to understand how innovations diffuse throughout

the market in order to improve the diffusion of online grocery shopping as well (Hoyer & MacInnis,

2008).

The traditional diffusion theory of Rogers (1995) is widely used to better understand how innovations

diffuse in a market. According to Rogers’ (1995) theory there are four main concepts that influence

the diffusion of innovations, these are: (1) the innovation, (2) the communication channels, (3) time

and (4) the social system. The innovation was already mentioned in the previous paragraph and

therefore, only the other three concepts will be discussed in this section.

The second concept is the ‘communication channel concept’ in which Rogers states that not all

channels are equally effective in the diffusion of innovations. Mass media is, for example, more

effective for simple (continuous) innovation, while more difficult (discontinuous) innovations require

a more personal channel (Rogers, 1995; Robertson, 1971). Therefore, more information is needed to

aid in the diffusion of discontinuous innovations and to counteract resistance, which is also the case

for online grocery shopping.


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Figure 2.1: Stages of innovativeness (Rogers, 1995) and S-shaped diffusion curve (Bass, 1969)

The ‘time’ concept, the third concept of Rogers (1995) is a good method

to understand the diffusion of an innovation by looking at its pattern of

adoption over time (Hoyer & MacInnis, 2008; Bass, 1969). Several diffusion

patterns have been identified in literature (Hoyer & MacInnis, 2008; Bass,

1969). However, one of the most common patterns is the S-shaped diffusion

curve (see figure 2.1) (Bas, 1969), which is often found in cases where

consumers perceive risk (e.g. social, psychological, economic, performance

and physical risk) in using the innovation (Hoyer & McInnis, 2008).

Finally, the diffusion of innovations can also differ between consumers or

consumer groups. The adopter categorisation framework of Rogers (1995),

which is also the final concept; i.e. the social system, provides insight into the

different stages of innovativeness per adopter group (see figure 2.1). The five

different stages are adoption categories and are defined as: ’a classification of

individuals within a social system based on their innovativeness’ (Rogers, 1995).

In this concept the diffusion rate is determined by the match between the

innovation and the norms, values and the degree of interconnection within

the social system. The better the fit the higher the diffusion rate (Hoyer &

McInnis, 2008). An important note regarding the adopter categorisation

framework of Rogers (1995) is the critique that some studies have shown

towards the number of adoption categories. They state that the amount of

categories differs per innovation (e.g. Shih & Venkatesh, 2004; Peterson, 1973;

Darden & Reynolds, 1974; Baumgarten, 1975). Also, the division of consumers

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across the categories is not always bell-shaped. This means that the largest group is not always in the

middle or at the end, in that case only the ‘more’ innovative consumers should be targeted.

Thus, in the case of online grocery shopping, food retailers should understand the norms, values

and the interconnectivity of the different adopter groups within their market. An understanding

of the division of adopter groups is necessary as well as the amount of adopter groups. Moreover,

the channel through which information is provided should be chosen wisely in order to enhance

the diffusion rate. Finally, a comparison of the diffusion over time increases the awareness of an

innovation’s diffusion performance and whether extra action is needed to enhance the adoption rate

or whether it just needs more time.

§2.4 Diffusion path

Insight which is provided by Rogers’ (1995) concepts aid us in better understanding innovations

and what has to be done in order to have a successful introduction and diffusion of an innovation.

However, besides insight into the innovation itself, some insight into consumers and how they adopt

an innovation (adoption path) is needed. According to Rogers (1995), the diffusion of an innovation

follows a specific path and is divided into five stages, i.e. (1) knowledge, (2) persuasion, (3) decision,

(4) implementation and (5) confirmation (see figure 2.1). The knowledge stage refers to the moment

that a consumer becomes aware of the innovation, when no information is gathered yet. During the

persuasion stage an individual is more interested in the innovation and gathers information, which

is used in the third step to form an attitude in order to make a decision as to whether the innovation

will be rejected or adopted. A positive attitude in step three can result in the trial of the innovation in

step four. Eventually, in the fifth and final stage it is decided whether the innovation will become part

of an individual’s routine and thus if the innovation will be used again.

Hoyer & McInnis (2008), on the other hand, state that the diffusion path (route) is influenced by the

consumer’s motivation, ability and opportunity (MAO) and therefore might differ per individual. If the

perceived risk (e.g. physical, social, economic financial or safety) is high then individuals are most likely

to choose the so-called ‘high-effort hierarchy route’ (Hoyer & McInnis, 2008). This is often the case for

discontinuous innovations, as these kinds of innovations are relatively new and different from existing

alternatives (Moreau, Lehman & Markman, 2001). Therefore, individuals require additional information

regarding the innovation (Moreau, Lehman & Markman, 2001). Individuals who follow the ‘high-effort

hierarchy route’ will gather information first, after they have become aware of an innovation, and

then form an attitude towards the innovation. In case of a positive attitude it can result in trial and

finally, this can lead to the adoption of an innovation. However, individuals who do not perceive any


26


risks and follow the ‘low-effort hierarchy route’ will try the innovation first,

then form an attitude to consider the adoption of the innovation. Thus,

by providing consumers with enough information, their perceived risk

could be lowered, which can result in trial. This is important as trial enables

consumers to better evaluate their self-efficacy or ability and this can lead to

a higher chance of adopting the innovation (Davis, Bagozzi & Warshaw, 1989;

Hansen, 2005).

Thus, the diffusion of an innovation depends on many factors and the

consumer’s perception regarding these factors. Most important in the

diffusion process is the attitude of the consumer and whether or not it is

positive towards the innovation, which is formed in the third stage (see

figure 2.2). Therefore, extra insight into resistance and adoption is provided

in the next sub-chapter.

Figure 2.2: Five stages of the decision path (Rogers, 1995)

§2.5 Resistance vs. AdoptionAs figure 2.2 shows, consumers decide at the third step, after evaluating

the gathered information, whether they resist or try an innovation. The

decision at this step is important as it can lead to the adoption of the

innovation. However, an individual is not automatically willing to adopt an

innovation if there is no resistance towards it and therefore also benefits

are needed in order to persuade the consumer to try and adopt the

innovation (e.g. Gatignon & Robertson, 1989; Herbig & Day, 1992; Ram &

Sheth, 1989). Still many studies often do not differentiate adoption from

resistance and consider them as opposites. This statements would leade

to the fales conlsuion that consumers who have no resistance towards

an innovation will automatically adopt it (Nahib, Bleom & Poiesz, 1997).

According to Rogers (1995) the main reason for this assumption has been

27

the ‘pro-innovation bias’ of researchers, who have often assumed that an innovation should diffuse

and therefore, resistant individuals have not been taken into account. Instead, individuals who did

not adopt the innovation or did this in the latest stage of Rogers’ adoption categorisation theory

were seen as ‘laggards’, instead of resistant consumers. However, different studies have concluded

that resistance is not the mirror image of adoption, but a different form of behaviour (e.g. Gatignon

& Robertson, 1989; Herbig & Day, 1992; Ram & Sheth, 1989). Moreover, adoption only occurs if there is

no resistance (e.g. Ram, 1987; Ram & Sheth, 1989; Hoyer & MacInnis, 2008). This leads to the conclusion

that resistance and adoption are influenced in a different manner.

The understanding of resistance is crucial for successful innovation diffusion; therefore insight into

the reasoning of consumers in resisting innovations is necessary (O’Conner, Parsons, Liden & Herold,

1990; Midgley & Dowling, 1993; Szmigin & Foxhall, 1998). According to Moore (2002) the lack of

consumer insights, before the introduction of innovations, leads to resistance from consumers, as

innovations do not meet their needs (Garcia & Atkin, 2002; Molesworth & Sourtti, 2002). Hoyer and

McInnis (2008) even state that innovations need to appeal to every adopter group of Rogers’ (1995)

adoption categorisation framework, in order to diffuse throughout the market. The mismatch that

occurs due to little consumer insight, prior to the launch of an innovation, is the main reason for the

high failure rates of innovations (Moore, 2002). This is because consumers compare the innovation

with existing alternatives and consciously choose to be resistant (Szmigin & Foxall, 1998), which is in

line with the following definition of resistance: ‘the resistance offered by consumers to an innovation,

either because it poses potential changes from a satisfactory status quo or because it conflicts with their

belief structure (i.e. barrier/hurdles)’ (Ram & Sheth, 1989; Hirschheim & Newman, 1988; Ram, 1987). It also

suggests that resistance is based on the consumer’s beliefs, values and their status quo, rather than

the benefits of the innovation in comparison to existing alternatives. The latter, on the other hand, is

needed to attract consumers to adopt the innovation (Mahajan et al, 1995).

Therefore, it can be concluded that adoption of an innovation can only occur if consumers do not

feel resistant towards it. However, as previously stated, adoption only occurs if an innovation offers

more benefits when compared to existing alternatives (Ram, 1987; Ram & Sheth, 1989; Hoyer &

MacInnis, 2008) and is not automatically the result of non-resistance (e.g. Gatignon & Robertson,

1989). Consumers who perceive no resistance may still refuse or postpone the use of an innovation,

for example, due to the lack of added benefits or due to financial reasons (Greenleaf & Lehmann,

1995). This leads to the conclusion that resistance can lead to more than simply not trying the

innovation, which is in line with findings of Ram and Sheth (1989) and Szmigin and Foxall (1998) who

suggest that innovation resistance is not a single form, but it consists of three types of behaviour; i.e.

(1) rejection, (2) postponement and (3) opposition.


28


§2.6 Conclusion

The information, which is provided in this chapter, has shown that not all innovations are the same

and that different approaches are needed in order to increase the adoption rate. Moreover, aspects,

which are not directly related to the innovation, also require the attention of retailers when the

online channel is launched or adapted; aspects such as the channel through which the innovation is

introduced or the information which is necessary to decrease potential resistance. Furthermore, the

final part has shown that resistance is not the opposite of adoption and therefore, should be treated

differently. Therefore in chapter three insights will be provided into the aspects that create resistance

towards online grocery shops and aspects, which can ensure that consumers adopt the online

channel for grocery shopping. Using these insights a model will be built, which will aid in the search

for theory based hurdles and benefits towards online grocery shopping.

In the rejection type consumers have really evaluated the innovation, which has resulted in rejecting

(Rogers, 2003). Thus, the rejection does not simply occur because consumers ignore new innovations

or because they are not aware of them, but they have consciously made the decision. Also, according

to Lee and Clark (1996-1997) consumers who reject an innovation are often suspicious of new

innovations and are not willing to change their status quo (Hirschheim & Newman, 1988). In the

second option consumers might have overcome the resistance, but they still can decide not to

adopt the innovation at that time and simply postpone the use of it (Greenleaf & Lehmann, 1995).

Finally, consumers who choose to oppose the innovation have not only decided not to use it, but

are even trying to sabotage the innovation (e.g. negative WOM) (Davidson & Walley, 1985). All three

behaviours occur for different reasons (Kleijnen, Lee & Wetzels, 2009). The weakest form of resistance is

postponement (Szmigin & Foxall, 1998), followed by the rejection. Both postponement and rejection

mainly occur because of perceived risk, while the strongest form of resistance, opposition, is mainly

driven by an individual’s personal and societal environment (Kleijnen et al., 2009).

In conclusion it can be stated that the approach to decrease resistance is different from the approach

to increase the adoption rate (Gatignon & Robertson, 1989; Herbig & Day, 1992; Ram & Sheth, 1989).

Moreover, the negative aspects (hurdles) have a far stronger impact on resistance than the benefits

have on adoption (Mizerski, 1982). Therefore, the adoption rate cannot be increased by simply adding

other benefits and thus, the resistance should be decreased first in order to increase the adoption

rate (Fortin & Renton, 2003).

29

Factors influencing the resistance and adoption

3. Factors influencing the resistance and adoption

The theory regarding innovations and their diffusion discussed in the previous chapter is used

here to form a better understanding of the decision stage in the decision process model of Rogers

(1995). These insights are used to enhance our understanding of the influential factors in this stage.

Therefore, in sub-chapter 3.1 a model is formed, which indicates the different factors, the underlying

antecedents and the adoption path of innovations. This model is based on several relevant theories

on the diffusion of innovations. Next, further explanation of the different steps and analyses in

the model, which are needed to better understand the entire adoption process of online grocery

shopping, are discussed. Finally, in sub-chapter 3.3, the theory based hurdles and benefits for the

conjoint analysis are provided, resulting in a preliminary conceptual framework, which will be tested

with the use of a qualitative study in chapter four.

We already know that adoption and resistance are influenced in a different manner and that

adoption only occurs if the resistance is overcome, a framework will be built to visualise which

antecedents influence the resistance and the adoption of an innovation (see figure 3.1). The

framework is based on various relevant theories on the diffusion of innovations e.g. the Diffusion

model of Rogers (1995), the (TRA) Theory of Reasoned Action (Ajzen & Fishbein, 1980; Sheppard,

Hartwick and Warshaw, 1988), the (TAM) Technology Acceptance Model (Davis, 1989), the (TPB)

Theory of Planned Behaviour (Ajzen, 1991) and the Innovation resistance theory of Ram (1987).

The theory of Ram (1987) is one of the few who explicitly mentions the difference between

resistance and adoption, even though his model corresponds with most of the before mentioned

models. Based on the previously mentioned theories it has been decided to use the (1) innovation

characteristics and (2) the consumer characteristics as the two main factors in our model (see figure

3.1). The underlying antecedents have also been formed based on several theories. The choice for

each antecedent is further explained in the next parts. Finally, the degree of resistance, the willingness

to (re)try online grocery shopping and the process for both aspects is also mentioned. Insight into the

process of both aspects is needed. Insight into the influence of the consumer characteristics on the

degree of resistance and the willingness to (re)try online grocery shopping can aid in the detection

and selection of potential segments.

§3.1 Factors, underlying antecedents and adoption path

30


Figure 3.1: Innovation Adoption framework (adapted from e.g. Ram, 1987; Rogers, 1995; Kleijnen et al., 2004)

The first and main dimension that influences the resistance and adoption

is the consumer’s perception of innovation characteristics (Mahajan et

al, 1995), which is also the only dimension that is controllable by food

retailers. The traditional diffusion theory of Rogers (1995) mentions five

innovation characteristics, which determine the rate of the adoption; i.e.

(1) relative advantage, (2) compatibility, (3) complexity, (4) divisibility and (5)

communicability. The relevance of these characteristics and their influence

on the diffusion process have been confirmed by different studies (e.g.

Verhoef & Langerak, 2001; Meuter, Bitner, Ostrom & Brown, 2005; Kleijnen

et al., 2004). Moreover, other models like the TAM (Davis, 1989) and TRA

§3.2 Innovation characteristics

31

Characteristics Definition Source

Relative advantage ‘The degree to which an innovation is being perceived as better than the idea it supersedes (added value)’

(Rogers, 1995)

Compatibility ‘The degree to which an innovation is perceived as consistent with the existing values, past experiences and needs of potential adopters’

(Gatignon & Robertson, 1991)

Complexity ‘The degree to which an innovation is perceived as relatively difficult to understand and use’

(Hoyer & MacInnis, 2008)

Divisibility/ trialability

‘The degree to which an innovation can be tried on a limited basis’

(Rogers, 1995)

Communicability/observability

‘The degree to which an innovation is visible and can be shared with other within a social group’

(Hoyer & MacInnis, 2008)

Perceived risk* the consumer’s perceptions of the uncertainty and adverse consequences of buying a product or service

(Dowling and Staelin, 1994)(Ram & Sheth, 1989)

(Ajzen & Fishbein, 1980; Sheppard, Hartwick and Warshaw, 1988) have used

antecedents that are the same or correspond with the ones mentioned

by Rogers (1995). Therefore, these characteristics have been used in our

framework. In table 3.1 a short explanation is given of each characteristic

and what each characteristic stands for.

Where Rogers’s (1995) framework measures the antecedents that influence

the adoption of an innovation, the framework of Ram and Sheth (1989)

measure the opposite, namely the resistance. However, most of the barriers

that are mentioned by Ram and Sheth (1989) show large resemblances to

the framework of Rogers (1995). The differences and resemblances will be

mentioned in the next part.

According to the study of Ram and Sheth (1989) resistance occurs from

two main barriers; i.e. (1) the psychological barrier and (2) the functional

barrier (see figure 3.2). The psychological barrier requires psychological

change, while the functional barrier requires behavioural change (Gatignon

& Robertson, 1989; Herbig & Day, 1992; Martinko, Henry, & Zmud, 1996; Ram &

Sheth, 1989).

The sub-barriers that form the (1) psychological barrier are related to

consumers and their psychological mindset. For example, the traditional

barrier occurs if the usage of an innovation requires a cultural change

for the consumer; e.g. their current norms and values do not allow them

Table 3.1: Innovation characteristics

*(Not mentioned by Rogers (1995), but added based on findings of Ram & Sheth (1989))


32


Figure 3.2: Innovation resistance framework (Ram & Sheth, 1989).

to use the innovation. The image barrier occurs if the innovation does

not fit with the current ‘image’ that an individual might have within their

social environment. Disapproval towards the innovation from the social

environment could lead to uncertainty and resistance. Both the sub-barriers

of the main psychological barriers show resemblance with the compatibility

barrier of Rogers (1995). Even though it is a barrier related to psychological

aspects, these aspects might be related to characteristics of the online

grocery shop itself and therefore, this barrier is also taken into account.

The second main barrier; i.e. (2) the functional barrier is also influenced

by sub-barriers. The first one is the usage sub-barrier, which increases

if the innovation is not compatible with existing habits, patterns or the

way consumers perform the same task. This sub-barrier is in line with

the compatibility characteristic of Rogers (1995). Next, the value barrier

occurs when the use of new innovations requires higher monetary and

non-monetary costs (Aylott and Mitchell, 1998; Cassill et al., 1997), which

shows resemblance with the relative advantage characteristic of Rogers

(1995). Finally, the risk barrier occurs if consumers feel uncertainty towards

trying the innovation (Dowling and Staelin, 1994). A comparison with

Rogers’s framework shows that this characteristic is not yet represented

and therefore, it will be added to our model. According to the Innovation

Resistance theory of Ram and Sheth (1989) the perceived risk is an

important influencer of resistance.

33

While the innovation characteristics are fully controllable by the food retailers, the consumer

characteristics are not at all controllable. This means that food retailers can only use this information

to better understand the formation of an attitude by consumers towards online grocery shopping.

All characteristics will provide separate insights as to their influence on the degree of resistance

and the willingness to (re)try online grocery shopping. Moreover, a better understanding can be

formed on the importance of the innovation characteristics and potential differences between

consumer(groups). With this information food retailers are better able to understand customers and,

if necessary, adapt innovations to meet their needs (Zaltman, Duncan & Holbek, 1973). In our conjoint

analysis the consumer characteristics will be taken into account as a moderating effect, in order to

identify potential segments. This will enable food retailers to better understand which adopter groups

like and which dislike shopping online for groceries. This is necessary in order to make the innovation

appealing to the most important adopter groups (Rogers, 1995), and in order for the innovation to

diffuse throughout the market properly (Hoyer & McInnis, 2008). In appendix A an overview is given

of the consumer characteristics, which will be taken into account in our studies. The consumer

characteristics are selected by comparing different sources regarding the diffusion of innovations (e.g.

Meuters et al., 2005; Dabholkar, 1996). Additionally, a further explanation will be given in this part for

each characteristic.

Technology readiness: The technology readiness depends on a person’s innovativeness, attitude

towards technology and their anxiety to using technology. Thus, what is a person’s attitude towards

new technologies and the usage of it in daily life (Bobbit & Dabholkar, 2001: Parasuraman, 2000)? For

this study it is therefore, important to know whether the consumer’s degree of technology readiness

influences the usage and adoption of online grocery shopping.

§3.3 Consumer characteristics (moderator)

A more detailed look at the risk barrier shows that different sub-risks influence the main risk barrier:

the (a) economic risk, (b) functional risk, (c) social risk and (d) physical risk. The consumer’s trust in the

innovation and the producer is the main influencer of the sub-risks (Verhoef & Langerak, 2001).

Consumers question the ability of the innovation and its producer to deliver an alternative effectively

and reliably (Doney & Cannon, 1995). Additionally, Kleijnen et al. (2009) state that risk is one of the

most important drivers that form resistance towards an innovation. A remedy for risk perception

might be the information that is provided regarding the aspects that are perceived as risky, by

doing so an individual’s perception can be counteracted (Dowling & Staelin, 1994). This is also in

line with the ‘high effort hierarchy’ statement of Hoyer and MacInnis (2008), in which they show that

information affects the chosen route towards adoption and the consumers’ perception.


34


Motivation: A consumer’s motivation for using online grocery shopping depends on the degree

in which they need grocery shopping to be more convenient (extrinsic/utilitarian) (Braczak, Ellen &

Pilling, 1997: Davis, 1989). This is also the case of the usage of an e-commerce environment (Bridges

& Florsheim, 2008: Pagani, 2004). Therefore, in our case, it is important to understand whether the

motivation of a person influences the degree of resistance and adoption of online grocery shopping.

Need for interaction: The personal interaction between consumers and employees is of course

lower in an online environment. Contrary to a regular supermarket, consumers are less able to

interact with employees. The degree to which a consumer needs personal interaction is referred to as

‘need for interaction’ (Dabholkar, 1996). Thus, the resistance towards trying online grocery shopping

increases if a person has a higher need for personal interaction (Meuters et al., 2000). For this study it

means that food retailers should understand the effect of interaction on the resistance and adoption

of online grocery shopping. If this is indeed an important aspect then alternatives should be offered

for the interaction.

Time pressure: Consumers with a higher time pressure are more likely to look for alternatives

(Childers, Carr, Peck and Carson, 2001). This is also acknowledged by the study of Rogers (1995). In

his study he states that consumers with a lower satisfaction are more likely to look for alternatives.

However, shopping for groceries in an online environment also depends on several other hurdles (e.g.

delivery issues and less interaction). Therefore, it is important to understand whether the time aspect

is more, equally or less important than the hurdles.

Attitude towards the online channel: Whether a consumer will use an online channel also

depends on their attitude towards information sharing and online payment (Childers et al., 2001).

A negative attitude towards information sharing and online payment can influence the willingness

of consumers to try and adopt online grocery shopping. Insight into the effect and influence of the

privacy concerns can help food retailers to better shape the online environment and to decrease the

resistance towards trying online grocery shopping.

Current usage/knowledge (online channel): Studies in the innovation diffusion area and

the adoption have shown that consumers with more knowledge of the online environment or

experience react more positively towards the adoption of new technologies and service (Meuters

et al., 2005; Mahajan et al., 1990; Reinders, Dabholkar & Frambach, 2008). If this is the fact for online

grocery shopping, then food retailers could use this information to attract consumers who already

use other online services as well.

35

Need for convenience: Convenience is becoming more and more important for consumers. Verhoef

and Langerak (2001) already stated in their study that the most important factor for a consumer to

shop online is the convenience that the online channel offers. Therefore, it might also be interesting

to know the effect of this variable on the resistance and the adoption for grocery shopping.

Travel costs/time: The Netherlands has a very high density of supermarkets (CBL, 2008). Therefore,

a better understanding is needed of the effect of travel costs and travel time of Dutch grocery

shoppers. Thus, this characteristic measures whether consumers perceive the monetary costs and

time to visit a regular supermarket as high.

Shopping enjoyment: The general shopping enjoyment (hedonic) of a consumer can influence

consumers in a positive way and increase the chance of trying new shopping services (Childers

et al., 2001; Davis, 1989; Arnold & Reynolds, 2003). Therefore, it is expected that consumers who like

shopping in general have a higher chance of trying online grocery shopping, even if it is solely for fun.

General innovativeness: The technology readiness characteristic is based on a person’s general

innovativeness towards technologies (Bobbit & Dabholkar, 2001: Parasuraman, 2000). However, a

consumer’s general innovativeness influences the degree to which they are open to gathering

information and using new products and services (Baumgartner & Steenkmp, 1996). This is not related

to technologies, but it gives an indication of whether someone is open to gathering information

or using alternatives. In combination with the study of Rogers (1995) this can give an indication on

whether a consumer is an early adopter of actually a laggard.

Satisfaction with general online shopping and general grocery shopping: As it was previously

stated, it is expected that consumers who already have experience with shopping in an online

environment are more likely to try other online shopping services (Meuters et al., 2005; Mahajan et

al., 1995; Reinders et al., 2008). However, it is also expected that the degree of trying additional online

services depends on a person’s current satisfaction with the online environment (Lijander et al., 2006).

Therefore, the satisfaction toward general online shopping is measured as well. Moreover, the study

of Rogers (1995) states that consumers who are not satisfied with a specific product or service will,

more likely, look for alternatives. Therefore, an understanding is needed of whether consumers are

unsatisfied with the current way of grocery shopping and whether they are indeed more likely to try

online grocery shopping (Lijander et al., 2006; Mittal, Kumar & Tsiros, 1999).

Demographics and shopping behaviour: Finally, demographics and grocery shopping behaviour

are taken into account. Aspects such as age, gender and household composition might influence the


36


degree of resistance and adoption (e.g. Rogers, 1983; Venkatraman, 1991). Additional aspects such as

the frequency of grocery shopping, time spent on each visit to regular supermarket and the person

who is responsible for the grocery shopping within a household are also important. These aspects

can all provide more information and help food retailers to target segments with the lowest degree

of resistance and the highest chance of adoption online grocery shopping.

Alongside the influencing antecedents and the moderators our framework also shows the adoption

path, which is adapted from Ram (1987) and (Kleijnen et al., 2009), as our framework indicates a too

high degree of resistance might lead to one of the resistance forms (i.e. postponement, opposition

and rejection). However, if an individual decides to resist an innovation and the innovation is

adaptable, then the entire process can start all over again. However, a perquisite is that an individual

should be willing to re-evaluate the innovation. If this is the case then a re-evaluation of the adapted

innovation might lead to not resisting the innovation and maybe even adopting it (Ram, 1987;

Zaltman, Duncan & Holbek, 1973). Nevertheless, if the innovation is not adapted well enough, it can

again lead to one of the resistance forms. While the opposition and rejection lead to not using the

innovation at all, postponement might still lead to the adoption of the innovation at a later stage

(Kleijnen et al., 2009).

Therefore, both the degree of resistance and the willingness to retry online grocery shopping will also

be analysed. This is done in order to provide insight into the influence of the consumer characteristics

on both variables. In chapter two it has been mentioned already that no resistance does not directly

lead to the adoption of a product of service (e.g. Gatignon & Robertson, 1989; Herbig & Day, 1992; Ram

& Sheth, 1989) and adoption only occurs if there is no resistance (e.g. Ram, 1987; Ram & Sheth, 1989;

Hoyer & MacInnis, 2008). Therefore, a better understanding is needed of the influence of consumer

characteristics on the resistance and the adoption.

§3.4 Adoption path- willingness to retry and degree of resistance

As previously mentioned, consumer characteristics are not controllable and therefore, only

used to better understand potential users. Food retailers, however, can influence the innovation

characteristics. Therefore, the antecedents of this dimension are further investigated in this sub-

chapter and are used to form a preliminary conceptual model (see figure 3.3). In table 3.2 theory

based hurdles and benefits of online grocery shopping are provided. The consumer characteristics

will only be used in our conjoint analysis to identify whether different adopter groups are present

§3.5 Conceptual model for conjoint study

37

and if they react differently towards the innovation characteristics. It also

provides information on how food retailers should attract and target

potential adopters.

Theory based hurdles and benefits: The importance of the innovation

characteristics will be studied in our conjoint model. In order to depict the

most important characteristics, first an overview is given of the hurdles

and benefits of online (grocery) shopping, which have been found in prior

studies (e.g. Verhoef & Langerak, 2001; Hand, Riley, Harris, Singh & Rettie,

2008; Kurnia & Chien, 2003). Through the use of these and other studies a

conceptual framework is formed in which the six innovation characteristics,

mentioned in table 3.1, function as a base for the framework. Additionally, a

short explanation is provided for each aspect in table 3.2 and the sources of

each aspect are also provided.


Aspect Sources (e.g.)

Rel

ativ

e ad

van

tag

e

Price advantage compared to an offline store. Park, Perosio, German & McLaughlin, 1998; Wilson-Jeanselme & Reynolds 2006

Convenience due the ability to receive the groceries at home.

Darian, 1987; Grewal et al. 2004; Wilson-Jeanselme & Reynolds 2006

Time saving (e.g. less wait time & planning time). Burke, 1997; Park et al. 1998; Peterson et al. 1997; Verhoef & Langerak, 2001; Darian 1987

Larger assortments compared to bricks-and-mortar grocery shops and easier to compare.

Grewa et al., 2004; Chu et al. 2010; Wilson-Jeanselme & Reynolds 2006; Alba et al. 1997; Darian 1987

Co

mp

atib

ility

Shopping enjoyment is less possible during online grocery shopping (hedonic motivations).

Alba et al. 1997; Verhoef en Langerak, 2001, Bruner & Kumar 2005; Childers et al. 2001; Mathwick et al. 2001

The quality of the online shop (quality of interface, usability and information quality).

Ahn, Ryu & Han, 2004; Wolfinbarger & Gilly, 2003; Wilson-Jeanselme & Reynolds, 2006

The quality of the delivered groceries should not differ from offline purchased groceries.

Baker, 2000; Ernst & Young, 1999; Citrin et al. 2003; Kurnia & Chien, 2003

Consumers are not able to feel, smell, touch and try the groceries (sensory attributes). Chu et al. 2010; Morganosky & Cude, 2000;

A consumer has to be at home when the groceries are delivered (delivery options). Wilson-Jeanselme & Reynolds 2006

Consumers have to pay a delivery fee. Huang & Oppewal, 2006; Småros, Holmström & Kämäräinen, 2000

Table 3.2:Theory based hurdles and benefits of online grocery shopping

38


Aspect Sources (e.g.)

Div

isib

ility

The possibility to try online grocery shopping on a limited base in order to better understand how it works and to enhance the trust towards it.

Verhoef & Langerak, 2001

Co

mp

lexi

ty

Order and fulfilment procedure should be easy (order time).

Verhoef & Langerak, 2001; Wilson-Jeanselme & Reynolds 2006

Shopping online should be done in a setting that matches the offline environment (Virtual reality- 3D shop- Interface).

Freeman et al., 1999

The online shop(ping) should not differ too greatly from current online shops (non grocery products). Reinders et al. 2008

Co

mm

un

icab

ility

Communication with others is less personal in the online environment and also not as easy as in the offline environment.

Verhoef & Langerak, 2001; Chu et al. 2010; Freeman et al. 1999

Perc

eive

d R

isk

The perceived risk of doing business over the internet (Payment, information sharing)

Zeithaml et al. 2002; Wolfinbarger & Gilly, 2003; Gefen & Straub, 2003; Ha & Stoel, 2009; Park et al. 1998

The risk of receiving groceries with a lower quality.Baker, 2000; Ernst & Young, 1999; Citrin et al. 2003; Kurnia & Chien, 2003; Forsynthe & Shi, 2003

The delivery of products takes too long (time slots) Kurnia & Chien, 2003; Wilson-Jeanselme & Reynolds 2006

The online grocery shop is not working/offline (fails to work/ not robust). Curran & Meuter, 2005; Meuter et al., 2000

Not being able to ask questions to employees (no interaction possible). Reinders et al. 2008; Shankar et al., 2002

39

The different aspects, which are presented above, are used to form the

conceptual model in figure 3.3. The conceptual model in 3.3 is, however,

a preliminary model and its completeness will be tested in chapter four.

This will be done through the use of a qualitative study in which the

current aspects will be presented during individual interviews and group

discussions and, if necessary, additional aspects will be added to ensure a

complete conceptual model.

§3.6 Conclusion


Figure 3.3: Preliminary conceptual model

40


41

4. Methodology

In the previous chapter a preliminary conceptual framework was presented (see figure 3.2).

However, this framework is solely based on insights gathered from literature. In order to be sure

that all important hurdles and benefits are taken into account a qualitative study is conducted to

check our findings and if necessary, to enhance our model with new insights. In a second study

the same participants from the first study and ten additional participants are asked to rank the six

most important hurdles and benefits from the final conceptual model. This is the model which is

derived from literature and study one (see figure 4.1). This will lead to the formation of the final six

attributes, which are tested in the third study. These outcomes provide insight into the importance

of each hurdle and benefit. Additionally, the moderating effects will be tested, as well, to see whether

they affect the hurdles and benefits. Finally, possible segments will be identified in order to better

understand potential differences between customers and their needs.

§4.1 Study one – qualitative study

As was mentioned above, the preliminary conceptual framework (see figure 3.2) is a result of a

literature study in chapter three. In order to determine whether or not it is complete a qualitative

study is conducted in this section, which will test whether the 19 attributes of the conceptual model

are in line with hurdles and benefits according to consumers. The qualitative study consists of two

parts. In the first part individuals are interviewed and in the second part we have conducted groups

discussions.

4.1.1. MethodParticipants: For study one we have conducted seven individual and three group discussions

(three individuals per group). During the group discussion both active and non-active (online)

shoppers were interviewed at the same time in order to create a better discussion and to gain

insight into whether there are differences between the two groups. These differences would

also aid in understanding the completeness of our conceptual model in figure 3.2. Differences

between active and non-active (online) shoppers have also been taken into account during the

individual discussions. Moreover, participants were also selected on the following criteria: household

composition, gender, age and innovativeness. This is done in order to ensure that a representative

group is interviewed and that different needs are taken into account.

Methodology

42


Procedure: Participants in the individual discussion were asked questions regarding (online)

shopping and (online) grocery shopping. These questions (e.g. what do you think of grocery shopping

in general or can you explain your first thoughts if I mention online grocery shopping) were mainly used

to get a discussion started and in order to gain insights into whether there are additional hurdles

or benefits regarding online grocery shopping. The discussion was focused on characteristics of

the online grocery shop and its perceived relative advantage, compatibility, complexity, divisibility,

communicability and risks.

During the group discussions a different approach was used. This time the participants received

different quotes (e.g. if online grocery shopping is cheaper than shopping in a regular supermarket,

then I will probably shop online for groceries or the benefits of online grocery shopping are…),

which they had to share with the rest of the group and explain whether or not they agreed with

the quotes and why this was the case. The quotes were used to gain insight into the different

characteristics of online grocery shopping and its perceived relative advantage, compatibility, trial

ability, communicability and perceived risk as well. However, in some cases additional questions were

asked to the group, because the discussion of the quotes did not always lead to sufficient insights.

4.1.2 ConclusionConclusion individual discussions: During the individual discussions most participants indicated

that they did not really think about shopping for groceries in a different way, as their current way of

grocery shopping was part of their life. They were simply used to shopping for groceries in a certain

way. Additionally they stated that grocery shopping in a regular supermarket offers hedonic aspects

as well and is not always only for utilitarian purposes (e.g. I like to just visit the supermarket and I do

not perceive it as only something that is necessary). On the other hand, they also acknowledge that

their satisfaction with shopping in the offline channel for groceries is low (an average of 6.8 on a scale

of 1-10). Moreover, the satisfaction (average of 6) is even lower for participants who work full-time

and/or have children. They even see online grocery shopping during the week as a burden. Overall,

people are willing to try shopping online for groceries, but they would still prefer to visit the offline

channel as well as using the online channel.

Comparing the aspects, which are mentioned in our preliminary conceptual model and the findings

of the different discussions, it can be stated that most of our aspects are confirmed. The participants

state that the basics of the online grocery shop should work and should continue to work properly.

If not, their trust in the online channel would decrease and they most probably will switch back to

the offline channel again. The same holds for the ordered groceries. They should all have the same

quality as in the offline channel and the orders should always be complete (no missing articles).

43

Moreover, the main focus for the advantage should be on the large assortment, delivery convenience

(delivery fee, delivery time & delivery slots), it should be easy to use and comparable to offline grocery

shopping (e.g. 3D environment). It was very interesting to see that most perceived hurdles were

based on the delivery convenience and whether the online shop was reliable (server stability) than

on payment or online information sharing. This was even the case for the participants who did not

shop online at all.

Based on the individual study it can be stated that there are three additional aspects, which can be

added to our preliminary conceptual model. The first one is the ability to shop at all supermarkets

online (e.g. AH, C1000, Lidl, etc.). Participants have indicated that they shop for groceries at multiple

supermarkets. This means that if one of their preferred supermarkets does not offer the ability to

shop online for groceries they would have to go to a regular supermarket for some products. This

will probably create resistance towards online grocery shopping. The second one is the ability

to purchase food and non-food products at the same time at one retailer. This option might be

interesting for consumers as more and more products are purchased online and the necessity for

them to be at home for deliveries is seen as a hurdle for online shopping. Therefore, by delivering all

food and non-food products together this will save time and counteract the hurdle to shop online.

The final one is the ability to receive the ordered groceries at home at the same time with other

non-food products, even if they are not purchased at the same retailer. Both benefits indicate a need

for convenience during the delivery phase.

The main conclusion that can be drawn from the individual discussions is that consumers prefer

the hedonic aspect of offline shopping and the control they have on the quality of the goods they

purchase. However, the time restraint and the decreasing satisfaction in the offline channel (average

satisfaction in our case of 6.8 on a 1-10 scale) offer opportunities for online grocery shopping as well.

By counteracting the main hurdles; i.e. the quality of the received goods should not be lower than

in an offline environment (e.g. lower quality tomatoes), the delivery phase should be convenient (i.e.

delivery fee & delivery options) and an online shop should be easy to use (time to order), the usage of

online grocery shops could be increased. Moreover, to make it even more attractive to use, an online

grocery shop should offer additional benefits compared with a regular supermarket e.g. convenience,

price and a larger assortment.

Conclusion group discussions: The group discussions led to almost the same conclusions as the

conclusions of the individual interviews. However, it is important to note that during the group

discussions the less innovative and less active shoppers were quite easy to convince by the other

participants. Initially some participants showed distrust towards the payment and information sharing

Methodology

44


risks. However, the distrust would diminish if other more experienced and innovative participants

counteracted these arguments with positive examples gained from experience. This might indicate

that positive WOM could increase the rate of adoption as well. Another noticeable observation is the

fact that the participants within the group discussions were less convinced of the price benefits they

would receive from online grocery shopping. They also indicated that online shopping in general

had a lower service level, as it is more difficult to contact employees in case of problems. The effort

to solve the problem will cost additional time, which will overrule the “small” price benefit. Moreover,

they argue that at this moment the prices between offline and online do not differ greatly for general

products as well. This statement is formed by prior experience with online shopping. Finally, no

additional hurdles or benefits, which are not mentioned in the preliminary conceptual model or in

the individual discussions, are found.

General conclusion: Most participants are willing to try the online channel for grocery shopping.

Their main concern is more towards the quality difference of the received goods and convenience

of ordering groceries via the online channel (e.g. delivery and order time) than on online payment or

information sharing. Positive WOM and time restraint might also positively influence the adoption of

the online channel.

§4.2 Study two – top six attributes

The first study has provided insight into whether there are additional hurdles or benefits, which have

not been taken into account in the literature part. Based on these findings we have adapted our

preliminary conceptual model and have added three new hurdles and benefits (see figure 4.1). In

order to determine the three most important hurdles and the three most important benefits we have

conducted a second study in which all of the hurdles of figure 4.1 have been presented.

45

Figure 4.1: Final conceptual model conjoint analysis

4.2.1 MethodParticipants: For the second study we have asked the same participants

from the first study and ten additional participants to choose their top six

hurdles and top six benefits. The same criteria are used for the additional ten

participants as the criteria mentioned in study one (e.g. active/non active

(online) shoppers, household composition, gender, age and innovativeness).

The additional ten respondents are added in order to increase the sample,

as the study is a more quantitative one than the first study.

Procedure: Two main questions were presented to the participants. In order

to find the top six benefits we have stated the first question in a positive

way: i.e. I would certainly shop online for groceries if… After the main

questions all hurdles and benefits are presented in a sentence form: e.g.

if online grocery shopping is cheaper than grocery shopping in a regular

supermarket, or: if the order procedure in the online environment would be

short. Participants were asked to choose and rank (1 to 6) the top six most

important reasons for them to shop online for groceries. The same was done

to find the top six hurdles, however, this time the main question and the

choice were presented in a negative form: e.g. I would certainly not shop

online for groceries if… and again the hurdles and benefits were presented

Methodology

46


in a sentence form: e.g. if online grocery shopping is more expensive than

shopping in a regular supermarket, or: if the procedure to order online takes

a long time. This has resulted in the following ranking:

The ranking in table 4.1 is formed in the following way. If a participant would

rank a benefit or hurdle as the most important one, the hurdle or benefit

would receive 6 points. The second most important hurdle or benefit would

receive 5 points and so on until the sixth most important hurdle or benefit.

If a hurdle or benefit would not receive a ranking at all it would receive 0

points. At the end the sum of all points has lead to the top six as presented

in table 4.1.

4.2.2. ConclusionIt is clearly visible that the entire delivery process of grocery shopping is

really seen as a large hurdle. Not only are the costs of the delivery important,

but also the number of delivery possibilities per day and the time that it

takes to receive the groceries. Moreover, it seems that consumers do not

want the ability to choose their own products, but they do indicate that the

quality of the order goods should be at least as equally high as the offline

channel. This is in line with the most important benefit, namely the fact that

online grocery shopping should really be time saving. If they would have to

choose each product themselves, it would simply cost too much time. This

also indicates that the benefit of online shopping should not only concern

monetary benefits, but also non-monetary benefits, which are perceived

as more important than monetary benefits. Next, the third most important

benefit again indicates that time is very important. Thus, the time it takes

Rank Hurdles Benefits

1 Delivery fees Time saving

2 Delivery options Price

3 Quality of ordered goods Order procedure

4 Delivery time Quality of ordered goods

5 Price Delivery time

6 Convenience Delivery options

Table 4.1:Theory based hurdles and benefits of online grocery shopping

47

for consumers to order and pay their groceries online should be as short as possible. It is however

remarkable that the online payment and information sharing is not seen as an important hurdle. The

same can be concluded for the assortment. It was expected that a larger assortment would be an

important reason for consumers to purchase online.

It can be concluded that consumers need the online grocery shopping process to be as simple and

quick as possible. This is also the case for the order procedure and the entire delivery process. Thus, by

only offering cheaper products the online channel cannot increase the adoption rate. These findings

are in line with the findings of the individual and group discussions in which the respondents have

indicated that the basics of the online grocery shop should work properly in order for them to

consider adoption.

§4.3 Study three – quantitative study

In this study the findings from the first two studies will be used to form a questionnaire (see appendix

A) in order to conduct the final and quantitative study. First, it will be explained why a Choice Base

Conjoint is used, followed by the survey development, the data collection and finally the data analysis.

4.3.1. Research methodConjoint analysis: In this study we intend to explain what the perceived value of online grocery

shopping is for consumers. Hair, Black, Babin, Anderson and Tatham (2010) state that consumers

evaluate the value of an object by combining the separate amounts of value provided by each

attribute in the object, which are in our case the hurdles and benefits. The value in turn determines

whether the service is adopted or resisted by the consumer. Hence, in our study consumers evaluate

the different sets of attributes and form a perceived value based on the separate values of the

attributes. Therefore, for our research a conjoint analysis is most appropriate (e.g. Hair et al, 2010;

Malhotra, 2010), especially if we compare our description with the definition of a conjoint analysis

according to Malhotra (2010): “a conjoint analysis attempts to determine the relative importance

consumers attach to salient attributes and the utilities they attach to the levels of attributes”.

However, there are different forms of conjoint methods e.g. traditional conjoint analysis, adaptive

conjoint analysis and the choice based conjoint analysis and not all are suited for our study (Hair

et al., 2010; Orme, 2009). In our study we have chosen to use the Choice-Based-Conjoint (CBC)

method, because compared to the standard conjoint and an adaptive conjoint analysis the tasks

in a choice based conjoint analysis represent the market behaviour more directly. Furthermore, it is

recommended to use no more than six attributes in a CBC analysis (Hair et al., 2010; Malhotra, 2010).

Methodology

48


Regression: Besides finding the importance per hurdle and benefit and potential segments, we are

also interested in the degree of resistance and the willingness to (re)try online grocery shopping.

Both are measured at an individual level (Leeflang, Witting, Wedel & Naert, 2000). The willingness to

(re)try is measured with the use of a Likert-scale (i.e. 1-very unlikely to 5 -very likely) and the resistance

is measured based on the following choices; (1) whether someone is likely to try online grocery

shopping very soon, (2) in the future, (3) not at all or (4) not at all and will use negative WOM in order

to stop others from using it as well.

The willingness to (re)try is measured with three items all on a five-point Likert scale. Initially this

could be analysed by using an Ordered Multinomial Logistic Regression (Leeflang et al., 2000).

However, as we intend to take the average of the three items to form one construct, an Ordinary Least

Squares (OLS) method will suffice. The reason is because the five categories on which the items are

measured disappear and the output becomes a scale variable (Hair et al., 2010; Leeflang et al., 2000).

The degree of resistance is measured by using four categories. Initially the use of a Multinomial

Logistic Regression would seem a proper way to analyse this. However, the different options contain

a specific order, as the first option contains no resistance and the other three options increase in

resistance ending with the highest in the fourth option. Therefore, the resistance will be analysed by

using an Ordered Multinomial Logistic regression (Hair et al., 2010; Leeflang et al., 2000).

For both regressions the consumer characteristics and the demographics will be used as covariates

to better understand which consumer characteristics lead to resistance and which to adoption. The

data is cross sectional in both cases (Leeflang et al., 2000).

4.3.2 Survey developmentAfter choosing the research design and conjoint method we developed the questionnaire, which

consists of three sections. The first section of the questionnaire concerns the measures with regard

to consumer characteristics. These measurements will provide insight into the moderating effects

of the different consumer characteristics, it will enable segmentation and the respondents are

triggered to think about their (online) grocery shopping behaviour. The latter is necessary in order to

prepare respondents for the stimuli part, as they have to think about it in the first part. Moreover, the

consumer characteristics will also be used as independent variables in order to study whether they

influence the degree of resistance and the current willingness to (re)try online grocery shopping. In

the second section of the questionnaire, the stimuli are presented. Respondents can choose their

most preferred online grocery shop, which resulted from the top three hurdles and top three benefits.

Finally, section three will provide insight in the degree of resistance towards online grocery shopping

49

and the willingness to re(try) an online shop. Each section is further explained below.

Section one- consumer characteristics: Section one contains the measures with regard to

consumer characteristics, which are: technology readiness, motivation, need for interaction, time pressure,

attitude towards online channel (privacy), current usage of online shops, current knowledge of online

shopping, travel costs/time, shopping enjoyment, grocery shopping behaviour and demographics. These

questions have been presented on a 5-point Likert scale (1- totally disagree to 5- totally agree),

as it is a proper scale to measure consumer attitudes (Malhotra, 2008) and is perceived as easier

when compared to a 7-point Likert scale. The questions for the characteristics are formed by using

existing measurements, which have been found in literature. In some cases we have formed our

own questions to enable the measurement of all characteristics. Each measurement and its source is

explained in appendix A.

Section two- stimulus presentation: This section is formed with the use of study two. In table 4.2

the different hurdles and benefits are shown. Each one is further divided into three attribute levels.

The levels are formed by using real life examples and literature (e.g. AH.nl, 2012; Wilson & Reynolds,

2006).

The formation of the questions for the conjoint part is performed with the use of Sawtooth software

(sawtoothsoftware.com, 2012). To limit the amount of questions, Sawtooth calculates which

combination of questions makes sure that the efficiency per level is at least 0.80. This ensures that

each level is properly represented in the calculation of the utility. The combination of questions is

based on the amount of attributes, amount of levels, amount of respondents and the amount of

versions used in the questionnaire. In our case we have six attributes and three levels per attribute.

To limit the amount of questions we have used three versions of the conjoint questions. This means

that we have three different sets of questions in the conjoint part of our questionnaire. This also

allows us to achieve an efficiency of at least 0.80 for each level with approximately 200 respondents

(N=200). Each set comprises seven questions, which in turn consists of two stimuli. The stimuli are

the combination of the different levels mentioned in table 4.2. Each stimulus consists of six levels

(see appendix B2). The efficiency score for each level is, in this case, at least 0,87, thereby fulfilling the

requirement for a conjoint design (Hair et al., 2010). Next to the seven randomly selected questions

with the use of Sawtooth software, we will present one hold-out question as well. In this question we

have formed two stimuli, which allows us to check how accurate the estimated model predicts the

hold-out sets (Hair et al., 2010).

Methodology

50


Delivery Fees Deliver options Quality of ordered goods

Hu

rdle

s

No delivery fee Only in the afternoon No items below quality

€ 4,99 delivery fee Afternoon and evening 1 out of 20 items is below quality

€9,99 delivery fee Free choice 1 out of 10 items is below quality

Time saving Price Order procedure

Ben

efits

Saves no extra time No price difference 20 minutes to place and order

Saves 5% of total shopping time

5% cheaper than regular supermarket

40 minutes to place an order

Saves 10% of total shopping time

10% cheaper than regular supermarket 1 hour to place an order

Table 4.2 Online Grocery Shop design elements

During the questionnaire respondents are thus offered two options each

time. The none-option is left out, because it is expected that consumers

might choose for the none-option too often as they have little experience

with online grocery shopping. The tasks and the efficiency of each level are

provided in appendix B2 and the different conjoint questions are provided

in appendix B1.

Section three- demographics and shopping behaviour: In the final

section the following measurements are used; willingness to (re)use online

grocery shopping (5-point scale from 1-very unlikely to 5-very likely), satisfaction

with current offline grocery shopping and general online shopping (grade from

1-very dissatisfied to 10-very satisfied), degree of resistance (four categories) and

finally the socio demographic characteristics and the current (online) shopping

behaviour. The demographics and shopping behaviour questions range

from gender and age to the frequency of grocery shopping in a general

supermarket. The overview of all questions is provided in appendix A.

51

4.3.3 Data collection Empirical data was gathered from a cross-sectional survey of Dutch consumers. The consumers

were targeted through an online and an offline questionnaire. This was done in order to be sure

that the outcome is not biased based on the fact that only consumers with Internet knowledge and

Internet preference are targeted. In order to increase the efficiency of the levels in the stimuli we have

formed three versions of the questionnaire. However, only the stimuli part was different over all the

versions and the rest stayed the same. The snowball sampling procedure was used through Direct

Mail and Social Media to distribute the three online versions and to make sure that a sufficiently

heterogeneous sample was reached.

The offline questionnaire was distributed in two cities in the North of Holland. Consumers were

approached twice on a Saturday, once on a Tuesday and once on a Thursday (when all shops are

open till 21:00 pm) at different supermarkets. The distribution over different days is done to ensure a

more representative sample.

As mentioned before, a sample size of N=200 is sufficient to ensure a proper efficiency for each

level. To reach the 200 respondents we have targeted approximately 430 respondents, of which 70%

through the online channels and 30% through offline channels. The division of 70/30 was chosen,

as approximately 70% of Dutch consumers purchase goods online and 30% do not (CBS, 2012).. This

ensures an equal representation across all groups.

4.3.4 Data analysisAs argued before we intend to find potential segments that are interested in using online shopping

for groceries and that are interesting for retailers to target. Therefore, the CBC analysis is first

performed on an aggregate level, followed by an analysis on segment level by using a latent class

analysis (Hair et al., 2010). This indicates whether there is heterogeneity in the estimated parameters.

The segmentation is performed based on the moderating variables, in our case the consumer

characteristics and demographics. Both the demographics and the other consumer characteristics

(e.g. technology readiness, motivation, need for interaction & time pressure) will be used together and

separately to form potential segments. The purpose of using three models is to assess which of those

models generates more distinctive customer segmentation. Furthermore, a fourth model will also

be built with only the innovation characteristics and the satisfaction measurements. The intention

is to analyse the amount of adopter categories based on innovation characteristics and the current

satisfaction with regular supermarkets and general online shops, as these indicate whether someone

will use an innovation in the beginning or later (Rogers, 1995).

Methodology

52


By better understanding the different potential segments food-retailers are provided insight into

how to adapt online grocery shops in order to provide the best possible shops to the different

segments. This information can also be used to exclude aspects in the web shop, which are less or not

important for the most valuable consumers of a specific food retailer. Finally, the validity is ensured by

using a hold-out question. The conjoint analysis and Latent class analysis is performed in Latent GOLD

Software (2012).

Next to the conjoint analysis, two regressions are performed (Malhotra, 2010; Malhotra, 1983). The

first one is performed to study which demographics and other consumer characteristics might

influence the willingness to (re)try online grocery shopping. In this case the consumer characteristics

(including the demographics and the grocery shopping behaviour) will be used as covariates

and the willingness to (re)try as the dependent variable. The second study aims to enhance our

understanding of how the different consumer characteristics affect the resistance to trying online

grocery shopping now and in the future. Together both studies aim to increase our understanding

as to which consumer characteristics increase the willingness to (re)try and which increase the

resistance.

53

Results

5. Results

In the research method section of study three it was mentioned that our analysis consists of three

parts; i.e. the ordinary least squares regression, the ordered multinomial logistic regression, and the

conjoint analysis. The results of the analyses will be discussed in this chapter. However, we will first

describe the sample in sub-chapter one, followed by the actions taken for the purification of the

measurements and finally the results of the different analyses.

§5.1 Sample and sample characteristics

5.1.1 SampleThe three versions of the online questionnaire, which were sent to 301 people, were open for two

weeks. However, it was decided to lenghten the response time by one extra week, as we had not

received enough responses during the first two weeks. Finally, after three weeks we had received 219

(partially) filled in questionnaires, which is a response rate of 59.8% (219/301). However, of the 219

(partially) filled in questionnaires only 131 were filled out entirely and could be used for our study.

The offline questionnaires were gathered directly at supermarkets and therefore, the collection

took only two weeks in total. However, again it was quite difficult to attract people who were willing

to spend five to ten minutes to fill in the questionnaire. However, our effort resulted in 47 fully

completed questionnaires.

In total we were able to gather 178 fully completed questionnaires, of which 73.8% were gathered

online and 26.2% were gathered offline. However, initially the efficiency of the different levels

was measured with a sample size of N=200. To ensure that the efficiency had not declined below

.80 a recalculation was performed, in Sawtooth, for each level. In appendix B2 it is visible that the

efficiencies of all levels are above .82 with a sample size of N=178.

5.1.2. Sample characteristics & grocery shopping behaviourThe socio-demographic characteristics of the 178 respondents are provided in table 5.1 together

with figures of the consumer trends (EFMI Business School and CBL, 2011) and the general Dutch

population (CBS, 2012). The figures of the consumer trends study were gathered from Dutch grocery

shoppers in 2011. Therefore, a comparison to these figures seems most appropriate as it provides a

more elaborate insight into the representation of our sample and the “real” Dutch grocery market. The

CBS figures are used to compare our sample with the general Dutch population.

A comparison based on gender indicates that our sample is almost the same as the general Dutch

54


population. There is a slight difference of approximately 1.5%. The Chi-square test also indicates an

insignificant difference (p=0.69). However, based on figures of the EFMI and CBL (2011) we can see

that there is a large difference (p<0.001). According to the Study of EFMI and CBL men account for

only 29% of the total grocery shopping population. However, this is only measured for the offline

channel (regular supermarket) and might be different in the online channel. The comparison with

occupation indicates dissimilarities as well and is significantly different according to the Chi-square

test (p<0.001). A large difference is the amount of students, which is twice that of the CBS statistics.

The reason for this difference might come from the fact that many connections in our social media

channels are students. A further comparison shows that the participants who work part-time or

full-time are overrepresented and no retired participants are represented in our sample. This is in

our case not a real problem as retired people are often not the main target during the first stage

of an innovation introduction. Next, the income groups of our sample show comparable figures

with the general population and differ mainly in the higher income groups. The highest group is

underrepresented and the second highest is overrepresented, but together the highest two groups

are approximately the same as the highest two groups in the general population, even though, the

Chi-square test indicates a significance difference (p<0.001). The differences between our sample

and the general population on household composition indicate a difference in the single household,

which is underrepresented in our sample. The difference can also be seen in the households without

children. This group is overrepresented in our sample. A reason for these differences can be due to

the general population in the cities in which the questionnaires are distributed, as these cities have

many young inhabitants. This might also be reflected in the degree of education. Our sample shows

an overrepresentation in the higher education groups and an underrepresentation in the lower

education groups. Finally, this is also the case for the age groups in which the younger and mid-aged

groups are more represented than the older groups. However, the differences in the last three groups

are not a major problem, as younger, higher educated and smaller households are seen as interesting

potential groups for the launch of new innovations (Rogers, 1995).

55

CBS 2011/2012 EFMI & CBL 2011 Current study (N=178)

Gender N/S **

Male Female

50.2% 49.8%

29% 71.%

51.7% 48.3%

Occupation ** **

Student Not working Working (part-time) Working (full time) Retired

6.5% 4.1%

11.2% 52.0% 26.0%

N/A N/A N/A N/A N/A

14% 5.6%

18.5% 61.8%

0%

Income (monthly) ** **

<€1500,- €1501,- & €2000,- €2001,- & €2500,- €2501,- & €3500,-> €3500,-

20.2% 10.4% 11.3% 16.1% 42%

N/A N/A N/A N/A N/A

21.3% 10.1% 15.2% 23.0% 30.3%

Household compositionb ** **

Single Living together Living together + 1 child Living together + >1 children Single + >1 children

34.5% 27.0% 9.3%

16.2% 13%

N/A N/A N/A N/A N/A

22.5% 43.8% 9.6%

20.2% 3.9%

Household size ** **

1 person 2 person >3 person

34.5% 27.0% 38.5%

36% 33% 31%

22.5% 43.8% 33.7%

Education ** **

Primary school Secondary school Vocational Education University (BSc.) University (MSc.)

3.4% 51.3% 22.5% 14.7% 7.9%

N/A N/A N/A N/A N/A

3.9% 15.2% 15.2% 53.9% 27.0%

Age ** **

<20 years 21 – 30 years 31- 41 years 41 – 50 years 51 – 60 years >60 years

23.5% 12.2% 12.8% 15.6% 13.7% 22.2%

N/A N/A N/A N/A N/A N/A

1.1% 41.0% 26.4% 21.4% 6.7% 3.4%

Table 5.1 Socio demographic figures

** p < .01. N/A= not applicable N/S= not significantNote: Age groups between 15 and 65 years are taken into account (total of 11 million) and for the household composition we have used data from 2010. Chi-squares tests indicate that our sample differs on almost all demographic variables from the CBS statistics and the EFMI statistics. The only variable which is not significantly different is the gender variable compared with CBS statistics (p=.69).

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Apart from the socio demographic characteristics of our sample we have also measured different

grocery shopping behaviours. This can enable us to better understand how potential segments shop

and what their shopping behaviour is with regard to groceries. Again we have used information of

EFMI and CBL study (2011) to compare our sample.

Firstly we have asked respondents to indicate who is responsible for grocery shopping within their

household. The figures indicate that in 51% of all cases the person who filled in the questionnaire is

responsible for grocery shopping. Within this group we can see a division of 60.4% females and 39.6%

males. Furthermore, the second largest group (40%), in the responsibility question, indicated that

they shopped for groceries together with their partner. This group can be divided into 30.6% females

and 60.4% males. These insights show that females are most often responsible for grocery shopping

within our sample, which is in line with findings of EFMI and CBL (2011). Their study shows that the

ratio male/female in grocery shopping is 29%/71%. Unfortunately, a direct comparison was not

possible due to the lack of statistics. Even so, the comparison with the gender statistics does indicate

that the responsibility question is in line with the EFMI and CBL figures. Moreover, in the second

question respondents were asked to indicate how often they shop for groceries in one week. As table

5.2 indicates approximately 55% shop 2 or 3 times a week and the average trip takes between 10 and

30 minutes (60%). The frequency figures are generally in line with the findings of the EFMI and CBL

study and differ mainly in the “2 times a week” option. This is also acknowledged by our Chi-squares

test (p=0.68). However, if we look at the length of time we see greater differences. Within our sample

only 11.7% indicate shopping for less than 15 minutes, while in the study of the EFMI and CBL more

than 30% indicate shopping for less than 15 minutes. The opposite is found in the >30 minutes

group. In total our sample differs significantly from the EFMI and CBL statistics (p<0.001). However,

this might be a positive aspect for us as our sample spends more time on grocery shopping and

online grocery shopping might therefore be a good time saving option for them. It might emphasise

the effect of time saving that an online grocery shop offers. Of course, this difference should be

taken into account in the formation of potential segments as the comparison with the CBS and EFMI

figures indicate that our entire sample differs in most aspects from the general population and the

shopper population of EFMI and CBL. Therefore, in our outcomes we should take the differences into

consideration.

57

EFMI 2011 Current study (N=178)

Responsible for grocery shopping N/A

Yourself Partner Together Other

N/A N/A N/A N/A

51.1% 5.6%

40.4% 2.8%

Frequency of grocery shopping (week) N/S

1 time a week 2 times a week 3 or more times a week

16% 32% 52%

15.7% 24.7% 59.6%

Amount of minutes per visit **

< 15 minutes 16 to 30 minutes > 30 minutes

31% 51% 18%

11.7% 47.2% 51.1%

Table 5.2 Grocery shopping figures

** p < .01. N/A= not applicable N/S= not significantNote: One choice option was possible for the item; “responsible for grocery shopping”.

§5.2 Measurement purificationEleven of our multiple-item constructs were assessed on a five-point Likert

scales and one with a ten-point scale (ranking 1-very dissatisfied to 10-very

satisfied). Of the eleven five-point constructs ten constructs have “strongly

agree” and “strongly disagree” as endpoints and one has the endpoints “very

unlikely” and “very likely”. Furthermore, the demographics, grocery shopping

figures and the degree of resistance are measured as ratio/nominal (Hair

et al., 2010; Malhotra, 2010). The different items within each measure are

derived from an extensive literature study and are previously validated.

However, some items are adapted and/or created for our context. Therefore,

the reliability and the validity of each construct is assessed by the use of the

convergent validity, discriminant validity, and face validity (Hair et al., 2010).

This is necessary in order to enable summated scales in which separate

items are transformed into a composite measure or construct. All tests for

the construct validity will be assessed in the next parts. An overview of the

mean, standard deviation and the correlation between the constructs is

provided in appendix C.

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5.2.1. Convergent validity Items within a specific construct should converge or share a high proportion of variance. This is also

known as the convergent validity. Different methods can be used to estimate the relative amount

of convergent validity among the items within a construct (Hair et al., 2010). In our study we have

used the composite reliability to assess whether the items within our constructs indeed share a high

proportion of variance and can be formed into composite measurements. If the composite reliability

is not sufficient than also a factor analysis will be used to find the item loading within the different

theoretical constructs. Finally the average percentage of variance extracted among the different

multiple item constructs is provided.

Composite reliability: One of the most commonly applied estimates to measure the reliability is

the reliability coefficient, which can be performed through the use of the Cronbach’s alpha, which is

used to measure the internal consistency of items within a construct (Hair et al., 2010). In appendix

A the Cronbach’s alpha scores for each construct are provided. The generally agreed lower limit for

Cronbach’s alpha is 0.60. This score is therefore necessary in order to consider the internal consistency

as reliable (Janssens, Wijnen, De Pelsmacker & Kenhove, 2008; Hair et al., 2010). Unfortunately, the

figures in appendix A indicate that, based on the different Cronbach’s alpha scores, not all constructs

have sufficient internal consistency. The alpha scores range from 0.137 up to 0.790. To solve this we

could decide to split the lower scoring items into separate constructs or to delete the lower scoring

and less important items. An example is the construct Travel costs/time. It appears that, as the title

indicates, both items measure separate constructs. The first item is related to costs in time and the

second item to costs as in monetary costs. Another solution is to combine the items into other

constructs with the use of a factor analysis. This might lead to different constructs than the ones

formed from studying literature (Hair et al., 2010).

Factor analysis for all items: The Cronbach’s alphas in the composite reliability test show that not all

prior formed constructs have a sufficient internal consistency. Therefore, a factor analysis is performed

to test for latent constructs. Of the 32 items mentioned in appendix A only 29 items are used in our

factor analysis, as three items are measures for the dependent variable (i.e. willingness to (re)try online

grocery shopping). All items are measured on a five-point Likert scale except for two satisfaction

items, which are measured on a ten-point scale. However, this will not influence the overall factors in

our analysis, since SPSS (2012) will recode all items based on the z-score method (Hair et al., 2010).

The output of the factor analysis shows that the Kaiser-Meyer-Olkin test score is 0.74, which is

sufficient to conclude that the factor analysis of the variables is allowed. Furthermore, the second

indicator for the factor analysis, the Bartlett’s test of sphericity, is significant as well

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(2/df=1821,178/406, p< .000). Furthermore, the factor analysis indicates that our 29 items have nine

underlying factors. The nine factors are based on the eigenvalues and the variance explained. The

nine factors all had an eigenvalue >1 and the cumulative variance explained is 66.4%, which is higher

than the necessary threshold (Malhotra, 2010). Moreover, to form the nine factors we have used an

orthogonal rotation method. In our case the varimax rotation method, which maximises the sum of

variances of required loadings of the factor matrix (Malhotra, 2010; Hair et al., 2010).

Nevertheless, the factors which have been formed do not seem preferable. Some multiple items

constructs with a high alpha score are now split up over different new factors. Moreover, some factors

consist of items with high and low alpha scores. Finally, this option also leads to new constructs,

which are not built on prior theories. This increases the difficulty in testing the theory on which the

items and the questionnaire are initially based. Hence, another solution for the reliability problem will

be presented in the next part (see appendix D2).

Solution: To solve the reliability problem it was decided to use the item deletion and item split

solution for the constructs with low Cronbach’s alphas. In this case the less important items will

be deleted and the more important ones will be split into new constructs (see table 5.3). However,

splitting existing constructs into new variables can result in the creation of too many new variables,

which in turn can decrease the stability of the analyses (Malhotra, 2010). In our case we will try to

limit the amount of new constructs by assessing the importance of each item. This is done with the

use of separate factor analyses for each construct and its underlying items, which are used in the

questionnaire (see appendix A). Factor loadings and face validity are used to decide the importance

of each item. The composite reliability of each multiple-item construct is also recalculated and used in

the decision for the formation of the constructs (see appendix D2). The factor loadings of each item,

together with the explanation of the adaptation and the Cronbach’s alphas are provided in appendix

D2. Finally, we saved the output of the factor analyses for all multi-item constructs. This ensures that

each item within the construct is represented sufficiently (the factor loading is used as a weigh factor

to calculate the representativeness).

Besides the alpha scores and the factor loadings, the average variance extracted (AVE) of the new

multi-item constructs is also assessed. The AVE assesses the amount of variance explained as the

underlying factor in relation to the amount of variance due to measurement error (Fornell & Larcker,

1981; Hair et al., 2010). The minimum threshold of the estimates is 0.50, as a lower estimate indicates

that the variance explained by the measurement error is larger than the variance explained by the

factor. In our case all AVE measurements are above 0.77 (see appendix D2).

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Construct Number of items

Technology readinessa 3 items

Motivationa 2 items

Need for interactiona 2 items

Time pressurea 4 items

Attitude towards online - payment safety 1 item

Attitude towards online – personal information 1 item

Need for convenience 1 item

Previous experience 1 item

Travel costs (time) 1 item

Travel costs (monetary value) 1 item

Shopping enjoymenta 2 items

General innovativeness – trying 1 item

General innovativeness – information gathering

1 item

Satisfaction with general online shopping 1 item

Satisfaction with regular grocery shopping 1 item

Table 5.3Overview of the new constructs and number of items

Note: other constructs e.g. willingness to (re)try and degree of resistance maintain the same and are therefore not mentioned above. a Factor output is saved and used as new variable.

5.2.2 Discriminant validity Next to the convergent validity we have also assessed the discriminant

validity. The discriminant validity is the extent to which a construct is

truly distinct from other constructs (Hair et al., 2010). High discriminant

validity provides evidence that a construct is unique and captures some

phenomena other measures do not.

A proper method to assess the discriminant validity is to assess the degree

to which a latent construct explains its item measures better than it explains

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another construct. The square root of the AVE should therefore exceed the inter-correlation of a

construct with the other constructs (Hair et al., 2010; Fornell & Larcker, 1981). In our study none of the

inter-correlations exceed the square root of the AVE (see appendix E1).

5.2.3. Conclusion The outcomes of the different tests above indicate that the reliability and the validity of the new and

adapted constructs are sufficient. Moreover, based on face validity it can also be concluded that the

new constructs are still in line with the goal of our study.

§5.3 Regressions

The reliability and the validity of the multi-item constructs were examined in the previous paragraphs.

Based on the outcomes of the reliability and validity analyses it was decided to adapt some

constructs in order to meet the necessary standards. The adaptations are needed to ensure accurate

outcomes in the analyses performed in the following paragraphs. The purpose of the analyses

is to enhance our knowledge with regard to the effect of explanatory variables (e.g. consumer

characteristics, demographics and shopping behaviour) on the willingness to (re)try online grocery

shopping, the degree of resistance towards online grocery shopping and the importance of the

different hurdles and benefits. Each study is further elaborated in the next sub-chapters.

5.3.1. Willingness to (re)try online grocery shoppingThe willingness to (re)try online grocery shopping is measured in order to better understand

whether and which explanatory variables influence consumers’ willingness to (re)try online grocery

shopping. These insights can be used by food retailers to better target potential users of the online

channel for grocery shopping. They are of course not able to influence the consumer characteristics,

demographics and the shopping behaviour. However, our outcomes can help them to target the

right person within their current customer base. The outcomes and the analysis are further explained

in the next parts.

Dependent variable: The construct willingness to (re)try online grocery shopping is measured with

the aid of three questions; i.e. how likely is it that you will try online grocery shopping, how likely will you

stop shopping online for groceries if your previous experience was a negative experience and how likely

will you shop online for groceries again if the online shop is adapted to better meet your needs? All three

questions are measured on a five-point Likert scale.

Based on the factor analysis (KMO= .665, cumulative variance 70.6%) and the alpha value (0.790) it

was decided to use the three separate willingness items as a new and single construct. Moreover,

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each item was proportionally represented in the new willingness variable, as the separate factor

loadings are used as weight factors. Finally, based on the amount of explanatory variables and the fact

that the dependent variable is scale, a multiple linear regression analysis is used (Malhotra, 2010; Hair

et al., 2010).

Independent variable: Alongside the dependent variable, we have several independent variables,

which are in our case the consumer characteristics (see new constructs in table 5.4), the socio

demographics and the (grocery) shopping behaviour. Our analysis comprises two models. In the

first model we have used all independent variables at once in a multiple linear regression and in the

second model we have deleted all the variables with p > .40. The deletion of the highly insignificant

variables is performed to enhance the stability of our model. A comparison between the first and the

second model shows a clear positive effect on the output. The initial model contained 10 constructs

with a significance between p < .1 and p < .001. In the second model the amount of significant

construct has increased to 12 constructs. Before we can further elaborate on the output of the

second model we first have to test for the assumptions of a normal regression analysis.

Assumptions normal regression analysis: There are two types of potential problems in a normal

regression analysis according to Hair et al., (2006). The first concerns the estimate of variance of

parameters, which can lead to a wrong conclusion about the significance of the effects. The second

potential problem concerns the estimate of parameters, which can lead to wrong conclusions about

effects (biased). However, both aspects can be detected with the use of several methods.

Potential violations in the estimate of variance can occur due to; i.e. (1) autocorrelation (whether

the values of the error term are independent of each other), (2) heteroscedasticity (whether there is

a constant variance in the error terms), (3) non-normality (if the error term is normally distributed)

and (4) multicollinearity (whether the independent variables correlate with each other). As our data

is cross-sectional and is not measured over time we only have tested for assumptions, which do

not contain time; i.e. the heteroscedasticity, non-normality and multicollinearity (Hair et al., 2010).

Additionally, the heteroscedasticity is not measured, because there is no real split in our data. The split

might create heteroscedasticity (e.g. data before and after a price war) (Hair et al., 2010).

Non-normality: Non-normality (the distribution of the error term) can occur due to model

misspecifications (Leeflang et al., 2000). The Kolmogorov-Smirnov test is a good predictor of non-

normality. The output of the Kolmogorov-Smirnov test indicates that our data does not contain a

non-normality problem (p-value =.200 p>0.05, H0: the distribution of the residuals is normal). Thus,

our error term is normally distributed.

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Multicollinearity: The independent variables should only correlate with the dependent variable in

an ideal situation. However, often independent variables also correlate with each other, which can

result in unreliable betas for the independent variables. A correlation matrix can provide insight into

whether the independent variables correlate with each other (Hair et al., 2010). A correlation of >0.90

indicates substantial multicollinearity. Another method is to look at the Variance Inflation Factor (VIF).

This is the inverse of the tolerance factor (Hair et al., 2010). The suggested cut-off point for the VIF is 10

and indicates multiple correlation of 0.95. The highest VIF value of our model is 1.946, which indicates

that no severe multicollinearity exists.

Furthermore, wrong estimates of parameters can occur due to endogeneity (whether a relation

between the predictors and the error term exists, which is not allowed) and non-constant parameters

(the parameters are non-constant over cross-sections or over time). In our case we can only test for

endogeneity, because our data is cross-sectional.

Endogeneity: The endogeneity indicates whether the independent variables correlate with the

(non-standardised) residuals. A correlation can lead to biased parameter estimates (Leeflang et

al., 2000). A Pearson correlation between the independent variables and the residuals shows a

correlation of max .641. However, this is still not above the .9, which is an indicator for endogeneity.

Thus, this assumption is not violated in our model.

Output: All tests for the different violations show that all assumptions are met. Therefore, in this

section the output will be interpreted. An overview of the output is provided in tables 5.6 and 5.7.

The coefficient of determination of our final model is .743 (R2= .743), which means that

approximately 74% of the variance is explained (Hair et al., 2010; Leeflang et al., 2000). An R2 of 1.00

indicates that the regression line fits the data perfectly and 0.00 the opposite. In our case the fit is

74%, which is a proper fit. Furthermore, the overall model fit is significant at p < .001.

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R2 F-value Sig.

0.743 3 items 0.000***

Table 5.4Model summary

*** p < .001Note: Dependent variable is willingness to (re)try online grocery shopping (weighted average based on factor loadings).

Additionally, table 5.5 depicts the output related to the independent

variables. The mean, standardised coefficients beta, t-value and the

significance are provided for each construct. However the table only

provides the information of the constructs, which have proven to be

significant. This means that the deleted constructs, with a p > .10, are not

included in the table. Each construct and its output will be further examined

below. Conclusions with regard to expectations from literature are also

assessed. This information is provided to gain a better insight into the

customers and the customer characteristics which have a significant effect

on the willingness to (re)try online grocery shopping. This is necessary to

better understand what the characteristics of adopters and users of online

grocery shopping are. The same is done in the second part to provide

insight into the effect of the consumer characteristics on the resistance. This

will be further elaborated in part 5.3.2.

The output of table 5.5 shows that several constructs have a significant

influence on the willingness to (re)try online grocery shopping. Two

constructs are significant at p < .001. The ‘monetary travel costs’ construct

(whether the monetary costs to visit a supermarket are perceived as high)

has an effect of -0.460 (Std. beta). This means that if a consumer perceives

the costs to visit a regular supermarket as high they are less likely to (re)

try online grocery shopping. This is quite strange, as the opposite would

be expected. However, an explanation could be found in the fact that

consumers dislike additional costs when shopping online or offline. As the

online channel is known for having high additional costs, the participants

of this study might expect the costs to use the online grocery shop to be

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ConstructStandardized

Coefficients Betat-value Sig.

(Constant) ** N/A -3.455 0.001

Gender** -0.183 -3.317 0.001

Occupation** 0.173 3.070 0.003

Education* 0.096 1.749 0.082

Age** 0.199 3.454 0.001

Frequency** 0.145 2.570 0.011

Need for convenience** 0.156 2.809 0.006

Travel costs (monetary value)*** -0.460 -4.497 0.000

Satisfaction with general online shopping***

0.345 6.245 0.000

Satisfaction with general grocery shopping**

-0.122 -2.093 0.038

Shop enjoyment** 0.143 2.250 0.026

Motivation** 0.198 2.983 0.003

Time pressure** 0.209 2.872 0.005

Table 5.5Output multiple linear regression analysis for willingness to (re)try (after deleting variables with p > .4).

Note: Dependent variable is the average of the willingness to (re)try items. N=178. N/A: Not available *** p. < .001 ** p. < .050 * p. < .100

high. Therefore, this figure might reflect the effect of high costs in the online

channel as well. The “satisfaction with general online shopping” construct

is also highly significant (t-value = 6.245, p <0.001) and indicates that

respondents with a higher satisfaction in regular online shopping are more

likely to (re)try online grocery shopping (Std. Beta = 0.345).

Furthermore, ten other constructs are significant at p < .05. The first one is

‘gender’ (t-value= -3.317, p <0.05 and Std. Beta = -.183). The gender construct

has two options; 1=male and 2=female. Based on the Std. beta we can

conclude that male respondents are more likely to (re)try online grocery

shopping. This is in line with expectations as the online channel offers a

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more convenient shopping experience and male shoppers are known for being efficient and fast

shoppers (Lovelock & Wirtz, 2011). The second construct is ‘occupation’ (t-value= 3.070, p <0.05 and

Std. Beta = 0.173). The occupation construct has four options; 1=student, 2=unemployed, 3=part-time

working and 4 = full-time working. The output indicates that people who work full-time are more

likely to use online grocery shopping, than students. Of course people who with a time restraint will

more likely look for an alternative, which can aid in saving time (e.g. Rogers, 1995). The third construct

is ‘age’ (t-value= 3.454, p <0.05 and Std. Beta = 0.199). The Std. beta of this construct is positive, which

indicates that older people have a higher willingness to (re)try online grocery shopping. A cross

tabulation between age and the separate items of the independent variable; i.e. how likely is it that

you will try online grocery shopping, stop after a negative experience and start using again if the

online shop is adapted to counteract the negative experience, indicates that older people are less

likely to stop using the online shop if they have had a negative previous experience. The expectation

would be that younger people are more likely to use and try online grocery shopping. However, an

explanation for this finding could be found in the higher expectations of younger people, as they

use online shops more than older people and therefore have become more demanding. Therefore

the online grocery shop needs to meet more demands in the younger age group. The fourth

construct ‘frequency’ (t-value= 2.570, p <0.05 and Std. Beta = 0.145), suggests that consumers who

shop more often for groceries are expected to have a higher willingness to (re)try online grocery

shopping. Rogers (1995) stated in his study that consumers who use a specific product or service in

a higher frequency will more likely look for alternatives to save more time or to make sure that their

time is spent in an efficient way. The findings related to the frequency construct indeed indicate

that frequency plays a role in the willingness to (re)try online grocery shopping. Furthermore, along

with the efficiency aspect, time saving also influences consumers when they use a specific service

or product. If the service they are currently using can become more time saving then they will

most probably look for alternatives (Rogers, 1995). This is in line with the conclusion related to ‘time

pressure’ (t-value= 2.872, p <0.05 and Std. Beta = 0.209). Apart from the fact that consumers look for

a more efficient way to shop they might also need the experience to become more convenient as

well. The construct, ‘need more convenience’ (t-value= 2.809, p <0.05 and Std. Beta = 0.209) indeed

indicates that consumers with a higher need for convenience are more likely to retry online grocery

shopping. This is the case for consumers with a higher ‘motivation’ (t-value= 2.983, p <0.05 and Std.

Beta = 0.198) to use a more time saving method in which they shop for groceries. Additionally, the

output shows that the degree of satisfaction also affects the willingness to (re)try online grocery

shopping. Respondents who are less satisfied with the current way of grocery shopping have a

higher willingness to (re) try online grocery shopping (t-value= -2.093, p <0.05 and Std. Beta = -0.122).

This is very logical, as a higher dissatisfaction leads to the search for a substitution of a product and/

or service (Rogers, 1995). Rogers already argued that innovations have a higher chance of adoption

67

if consumers are looking for alternatives due to an increase in dissatisfaction. Finally, consumers with

a general high ‘shopping enjoyment’ have a higher willingness to (re)try online grocery shopping

(t-value= 2.250, p <0.05 and Std. Beta = 0.143). This means that respondents who enjoy shopping

in general might be interested in trying online grocery shopping, either just for fun or for general

interest.

Next to the constructs with a significance of p< .05, one construct has a significance of p< .1. The

construct ‘education’ (t-value= 1.79, p <0.1 and Std. Beta = 0.143) is measured with five options;

1=primary school, 2=secondary school, 3=vocational school, 4=University (Bsc.) and 5=University

(Msc.). Again the beta is positive which indicates that more highly educated people have a higher

willingness to (re)try online grocery shopping. This is a logical finding as, according to literature, more

innovative consumers are more highly educated, shop more frequently, have the highest full-time

working rate and have a lower amount of minutes per visit (Hoyer & MacInnis, 2010). Therefore,

it is quite logical that they will most probably be interested and willing to (re) try online grocery

shopping.

Conclusion: The findings above provide different interesting insights with regard to the influencing

factors for the willingness to (re)try online grocery shopping. The conclusions above confirm many of

our expectations, based on the literature study and have helped to achieve a better understanding of

the consumer who is more likely to (re)try online grocery shopping. Of course, food retailers are not

able to influence these aspects, but by having an understanding of the different effects, they are able

to better select and target consumers who are more likely to try and/or adopt online shopping.

5.3.2. Resistance towards online grocery shopping While the previous study concerned the willingness to (re)try, the next study analyses the resistance

towards online grocery shopping. As it was mentioned in the literature section, resistance is not

simply a “yes or no” question. Consumers might be resistant for many reasons and at different levels,

ranging from very low to very high resistance (Kleijnen et al, 2010). Therefore, the dependent variable

is measured with four options; i.e. 1= I will probably try online grocery shopping very soon, 2= I will

probably not shop online for groceries at this moment, but I will probably try it in the future, 3= I will

not shop online for groceries and will not do this in the future as well and 4= I will not shop online

for groceries and I will strongly discourage others who do shop online for groceries. The descriptive

statistics indicate that the division in the answers above is; (1) 39.3%, (2) 41.0%, (3) 18.5% and (4) 1.1%.

Hence, the resistance towards online grocery shopping is very low in The Netherlands, as only 19.6%

of the respondents indicate to be unwilling to shop online for groceries.

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However, besides knowing the general degree of resistance the aim of this study is to understand

whether consumer characteristics such as shopping behaviour and socio-demographics influence

the resistance and to what degree. To answer this question a logistic regression is performed, as our

dependent variable has four nominal options. In the next part a further explanation is given with

regard to our analysis and its outcomes.

Dependent variable: In this study the degree of resistance was measured with four options (nominal)

ranging from no resistance to a high resistance (Shah, 1989; Kleijnen et al., 2010). The multiple options

in the dependent variable and the order of the options lead to the conclusion that an Ordered

Multinomial Logistic regression is the most suited analysis (Hair et al., 2010).

Independent variables: These variables are also used to gain a better insight into which variables

influence the degree of resistance in a positive or negative way. In our case the consumer

characteristics, shopping behaviour and the socio-demographics are tested. All categorical (nominal)

variables are used as factors and the interval/ratio variables are used as covariates, resulting in 6

variables as factors and 17 variables as covariates.

Output: The first output showed that four variables were significant at p< .10, but many were not.

Therefore, it was decided to delete 12 variables with p> .40 and re-run the analysis. This resulted in

three additional significant variables. Finally, in the last run three final variables with p> .10 are deleted.

The model fitting information in table 5.8 shows that the variables add significantly in all three

models compared to a model with only the intercept. However, the decision to delete some

variables is, as was mentioned in the first part, based on the low significance of the deleted variables.

Furthermore, regarding the validation of the models the pseudo R-squares are taken into account

(see table 5.7). The Cox and Snell and the Nagelkerke R-squares are related to each other and range

from 0 to 1. While the Cox and Snell is not able to reach up to 1 the Nagelkerke is. Therefore, this

value is more appropriate to use. A Nagelkerke R-square close to the value 1 indicates a perfect fit.

In our case the Nagelkerke R-square is .434. Hence, our model has a fit of 43%, which is a proper fit.

According to Leeflang et al. (2000) the proper cut-off point for the Nagelkerke R-square is .40.

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Table 5.6Model fitting information

Model-2Log Likelihood

Intercept -2Log Likelihood

FinalChi-square DF Sig.

1 389.976 290.540 99.437 36 0.000

2 389.976 294.554 95.433 20 0.000

3 389.976 302,1032 86,558 15 0.000

Finally, the McFadden R-square is also provided, which is based on the

likelihood of the standard model (null) and the model with the added

variables. A higher McFadden R-square indicates that the model including

the additional variables is more appropriate than the standard model. A

value of .222 indicates a model which is better than the standard (null)

model.Table 5.7Psuedo R-Squares

Model-2Log Likelihood

Intercept -2Log Likelihood

FinalChi-square DF Sig.

1 389.976 290.540 99.437 36 0.000

2 389.976 294.554 95.433 20 0.000

3 389.976 302,1032 86,558 15 0.000

Output: Next to the model summary, this section deals with the

interpretation of the parameter estimates of the final model. In table 5.8 an

overview is provided of the final model.

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Table 5.8Output Ordered Multinomial Logistic regression

Variables Estimate Std. Error Wald DF Sig.

Treshhold

U1 (cut-of-point option 1 and 2) -1.988 1.765 1.268 1 0.260

U2 (cut-of-point option 2 and 3) 0.694 1.76 0.155 1 0.693

U3 (cut-of-point option 3 and 4) ** 4.579 1.854 6.102 1 0.014

Location

Motivation** -0.658 0.2 10.772 1 0.001

Time pressure** -0.407 0.205 3.957 1 0.047

Attitude towards information sharing** -0.393 0.162 5.922 1 0.015

Need for interaction** -0.360 0.18 3.989 1 0.046

Satisfaction with general online shopping** -0.324 0.137 5.585 1 0.018

Travel costs (monetary) ** 0.449 0.188 5.694 1 0.017

Satisfaction with regular grocery shopping* 0.261 0.152 2.946 1 0.086

Income - <€1500,- ** 1.550 0.537 8.335 1 0.004

Income - €1501,- and €2000,- ** 1.789 0.614 8.485 1 0.004

Income - €2001,- and €2500,- ** 1.158 0.549 4.452 1 0.035

Income - €2501,- and €3500,- * 0.882 0.491 3.22 1 0.073

Income - >€3500,- N/A N/A N/A 0 N/A

Household - Single** -2.024 0.904 5.014 1 0.025

Household - Living together** -2.599 0.876 8.796 1 0.003

Household - Living together + 1child** -2.115 0.958 4.874 1 0.027

Household - Living together + >1 child** -2.192 0.919 5.693 1 0.017

Household - Single + >1 child N/A N/A N/A 0 N/A

Note: Dependent variable is the degree of resistance (4 choice options). N=178. N/A: not available (this parameter is redundant)** p. < .050* p. < .100

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The first aspects in the table are the thresholds, which indicate the cut-off-points between the

different choice options (U1=-1.988, U2=0.684 and U3=4.579). U1 is the cut-of-point between the first

two choice options, which are: 1= I will probably try online grocery shopping very soon, 2= I will probably

not shop online for groceries at this moment, but I will probably try it in the future. U2 is the cut-of-point

between options two and three and U3 is the cut-of-point between options three and four. Hence, if

the utility, which is based on the estimates of the different variables in our model, is < -1.988 then a

respondent will most likely choose option 1. If the utility is >-1.988 and <0.684, a respondent falls into

option 2 and so on.

In table 5.10 the variables are all significant, as the insignificant variables are deleted in the first two

models. Furthermore, some variables have a positive and some have a negative estimate. The utility

for the “no resistance” option is U1<U2<U3. This means that the variables with a negative estimate

decrease the resistance towards online grocery shopping, as they influence the utility to become

smaller and to tend more towards a negative summed value. The opposite is the case for the variables

with a positive estimate (Malhotra, 2010).

The variable ‘motivation’ has the highest negative value (-.658) followed by the ‘time pressure’ (-.407),

‘attitude towards online information sharing’ (-.393), ‘need for interaction’ (-.360) and the ‘satisfaction

towards general online shopping’ (-.324). This means that if the motivation increases the utility of the

respondent becomes more negative and thus, tends more towards the first option (i.e. I will probably

try online grocery shopping very soon). The negative estimates are logical for all variables except for the

‘need for interaction’. The estimate of this variable indicates that if the need for interaction increases

the utility of a respondent will tend more towards a negative value and thus, less resistance. However,

it is expected that consumers with a higher need for interaction have a higher resistance towards

online grocery shopping (Hoyer & MacInnis, 2010). This outcome might indicate that respondents do

not consider online shopping as less interactive due to the ability to communicate through social

media.

Finally, one categorical variable is negative as well. This is the ‘household composition’ variable. The

estimates of the categories are all negative. However, there are also differences in the estimation

value. Respondents who live together and have no children have the highest negative value,

meaning that this category has the highest chance in having no resistance. The lowest absolute value

is for the single respondents. However, they all influence the utility in a negative way compared to the

base.

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In addition, our outcomes also show variables with positive estimates, which are the variables ‘travel

costs (monetary)’ (.499) and ‘the satisfaction towards grocery shopping in a regular store’ (.261). It is

strange that respondents who perceive the monetary travel costs to visit a regular supermarket as

high, are more likely to tend towards a positive utility and thus, towards a higher resistance. It is not

clear why this variable has a positive estimate. The second positive estimate indicates that consumers

who have a high satisfaction level with the current way of grocery shopping are more likely to show

resistance towards online grocery shopping. This was also expected (Rogers, 1995).

Finally, one categorical variable also has a positive estimate. The income categories all indicate a

positive effect on the utility, but the estimate decreases if the income increases, which is a very logical

and expected outcome.

Conclusion: From the outcomes above it can be concluded that there are many aspects which

influence respondents in their degree of resistance towards online grocery shopping. Some

outcomes were expected and hypothesised by literature and some variables had outcomes opposite

to our expectations. However, these insights still aid in the understanding of the resistance towards

online grocery shopping. This is important, as the resistance should be diminished before additional

benefits are offered to increase the chance of adoption (Ram & Seth, 1989). Additional benefits only

increase the adoption rate if the resistance is lowered to meet someone’s threshold. If this resistance

is still above the threshold then the online alternative for grocery shopping will not be tried and/or

adopted by consumers (Ram, 1987; Ram & Sheth, 1989; Hoyer & MacInnis, 2008). Hence, food retailers

can better understand their current customer base, with the use of the conclusions in this paragraph.

The consumers with characteristics that fit the less resistant utility group should be targeted fist.

Additionally, the consumer characteristics which increase the willingness to (re)try online grocery

shopping should be used to target the customers who are more likely to (re)try online grocery

shopping.

§5.4 Conjoint analysis

While the first two analyses are used to understand which consumer characteristics, socio-

demographics and shopping behaviour influence the willingness to (re)try and the resistance

towards online grocery shopping, this analysis focuses more on the characteristics of the online shop

itself. Therefore a Choice Based Conjoint method is used to find the importance (utility) of the six

hurdles and benefits (Malhorta, 2010). Moreover, segments in the utilities will be sought based on

the consumer characteristics, socio-demographics and shopping behaviour. The CBC analyses are

performed with Latent GOLD Choice software (statisticalinnovations.com, 2012).

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5.4.1. Model specificationIn our CBC analysis we have used six attributes, which are based on

our literature and qualitative study. All six attributes have three levels in

which they differ. To specify the utility functions for each attribute firstly

all attributes are set as partworth and an aggregate model is performed.

Based on the output of the information criteria and the value of each level a

choice is made whether the utility function is vector, quadratic of partworth.

In table 5.11 the preference function for each attribute is shown. This was

based on the fact that the information criteria did not change a lot (e.g.

AICall partworth and AIConly delivery option partworth = 1234.81 and

1227) and the values of the different levels increased or decreased in a linear

way (Malhotra, 2010; Hair et al., 2010). Furthermore, only the main effect is

measured in our CBC analysis and no interaction effects are added.

Table 5.9Model specification (all partworth)

Attributes Type

Value (levels)

Preference function

1 2 3

Delivery fees Metric 0.494 0.166 -0.661 Vector (linear)

Delivery options Nominal -0.777 0.344 0.433 Partworth

Quality of ordered goods Nominal 0.435 0.028 0.407 Vector (linear)

Time saving Metric -0.050 0.013 0.064 Vector (linear)

Price Metric -0.198 0.381 0.237 Vector (linear)

Order procedure Metric 0.581 0.019 0.601 Vector (linear)

Note: In Latent GOLD Software the partworth utilities were indicated as nominal and the vector (linear) utilities as numeric.

5.4.2. CBC analysis at an aggregate levelFirst, an aggregated model is estimated to determine the importance of the

attribute levels and provide an indication which attribute levels are preferred

by the entire sample. The aggregated model is performed with the use of

LatentGOLD choice software.

Variables: To find the aggregated preferences a one-segment CBC choice

analysis is performed (Hair et al., 2010). In our questionnaire respondents have

received seven conjoint questions with two stimuli in each question. Each

stimulus was different in the levels of the six underlying attributes.

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The respondents are asked to choose the stimulus, which suits their preference the most, resulting in

a 0 if a stimulus is not chosen and a 1 if it is chosen. This variable is used as the dependent variable in

our analysis.

Next to the dependent variable we have also added the attributes and indicated whether it

concerned a numeric or a nominal attribute. No covariates are added, as it concerns the aggregated

model. Finally, the coding for the nominal variables was set to effects coding. This method takes out

the multicollinearity as it is centred around 0 (LatentGOLDUsersguide, 2012).

Output: The output of our CBC analysis shows an R2 of 0.251 for the entire aggregated model, which

indicates that the prediction error is 25%. This is in line with the hit rate (75%) of our model (# of

correctly predicted values / # of total observed values; 935/ 1246 = 0.748). These figures indicate a

sufficient internal validity (Hair et al., 2010). To ensure a stable model the model was rerun three times

in which only the number of iteration were increased from the standard of 250 up to 500. This means

that Latent GOLD is allowed to look further than the standard iteration to find the global maximum.

In our case the figures did not change after the increase in iteration. Thus, the initial model can be

assumed to be a stable model.

Besides the internal validity the output also provides an overview of the relative importance of

the different attributes. In table 5.10 we have provided an overview of the minimum, maximum,

parameter value, significance, range and the relative importance of each attribute.

The attribute with the highest relative importance is the ‘delivery option’ attribute, followed by the

‘order procedure’ and the ‘delivery fee’. It is strange to see that ‘time saving’ has the lowest relative

importance, as it would be expected that consumers are looking for an alternative to grocery

shopping which saves them time. A reason might be that the time saving levels are presented, in

the questionnaire, as a percentage and this might be difficult for the respondents to relate to. The

attributes related to the order procedure (i.e. ‘delivery fee’ and ‘delivery options’) are indicated as very

important as well. Therefore, it can be concluded that the entire sample generally prefers a smooth

order process more than having additional benefits. This also leads to the conclusion that the hurdles

should indeed be diminished first before a customer is willing to look at the benefits. Thus, hurdles are

perceived as more important than the additional benefits offered by the service (importance hurdles

– 66.2% vs. importance benefits – 33.8%).

Furthermore, the parameters show that the higher negative effect in the utility is offered by the

option where the order procedure takes one hour. By shortening the order procedure the utility

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decreases to -1.140. However, this is still too high and will cause too much

resistance. Therefore, the order procedure should be around 20 minutes.

Next, the 10% below quality also causes a high resistance with a utility

of -1.268. This also decreases if the quality difference is made smaller.

However, the utility does not equal zero at the same quality as in a regular

supermarket. This can be an indication that consumers might still doubt

whether the quality is indeed equal to the products they choose for

themselves in a regular supermarket. The delivery fee of €9.99 has the

third highest utility and will decrease to a zero if the delivery fee is €0.00.

However, it does not create a positive utility, which would be expected.

Table 5.10Output aggregated CBC analysis

Attributes% choice division

Min Max Parameter Sig. RangeRelative importance

Delivery fee - €0.00 - €4.99 - €9.99

3.21a 52.8% 30.1% 17.1%

€0 €9.99 0.000 -0.569 -1.140

0.00 1.1264 23.0%

Delivery options - Afternoon - Afternoon & evening - Free choice

N/Aa 13.0% 40.4% 46.6

N/A N/A N/A

N/A N/A N/A

-0.800 0.328 0.472

0.00 1.2780 26.0%

Quality of goods - No items below - 5% below quality - 10% below quality

N/Aa 48.0% 31.4% 20.6%

1 (0%) 3 (10%) -0.423 -0.845 -1.268

0.00 0.8452 17.2%

Time saving - 0% - 5% - 10%

5.07a 32.6% 33.3% 34.1%

0% 10% 0.000 0.030 0.059

0.64 0.0464 0.9%

Price advantage - 0% - 5% - 10%

5.76a 25.9% 32.7% 41.3%

0% 10% 0.000 0.226 0.452

0.00 0.4658 9.5%

Order procedure - 20 min - 40 min - 1 hour

32.73a 53.2% 29.9% 16.9%

20 min 1 hour -0.570 -1.140 -1.710

0.00 1.1487 23.4%

Note: Effects coding is used for nominal attributes.N/A not available, the values are nominal. a the mean of the attribute

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Next to these figures it is interesting to note that the utility of the option to receive the goods only in

the afternoon is negative -0.800. However, the increase is the highest of all when the option to also

receive the goods in the evening is added. The utility becomes positive at 0.328, which is an increase

of 1.1282. This is the only attribute that is not linearly increasing or decreasing.

In general terms, it can be concluded that the hurdles have a higher effect on the total utility than the

benefits. This is again in line with the literature expectations.

5.4.3. CBC analysis at segment level The aggregated model has provided information with regard to the general importance and utility

of each attribute. However, differences in the utility may exist based on the consumer’s characteristics,

demographics and grocery shopping behaviour (explanatory variables). Therefore, a CBC analysis on

segment level will be performed.

The explanatory variables are used as covariates to find potential segments (class membership) with

homogeneous utilities (Malhotra, 2010; Hair et al., 2010). All the explanatory variables are used to find

the class memberships. However, having many explanatory variables also means that you need a high

amount of respondents. If this ratio is off then it can result in negative Degrees of Freedom (df ) (Hair

et al., 2010). Therefore, only the models with a positive df can be chosen.

Variables: Again a CBC choice analysis is performed with the use of Latent GOLD software (2012).

The dependent variable is the ‘choice of the stimuli’. The attributes added in this model are equal to

the aggregated model (five vector and one partworth). However, contrary to the aggregated model,

this model also contains explanatory variables (covariates). The segmented CBC analysis contains 23

covariates (6 nominal and 17 numeric).

Output (all covariates): Firstly, the model fit likelihood ratio chi-squared statistic (L2), which is an

indication of how well the model fits the data, is significant for all models at p < .001 (Hair et al.,

2010; statisticalinnovations.com, 2010). Thus, the estimated frequencies are significantly similar to the

observed frequencies for all models. Moreover, the number of segments needs to be determined

as well, which is possible by using the information criteria and face validity. Only the information

criteria for models one to four are provided in table 5.13. The df of the other models is not positive

and a positive df is a pre-requisite for the formation of the model. The lowest point in the information

criteria values indicates the best model fit. Hence, the model with the lowest points in the information

criteria is preferred (Hair et al., 2010).

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Unfortunately the output in table 5.11 does not provide a clear fit. The

information criteria indicate different numbers of segments. The lowest

value of the BIC(LL) is at a one segment model, for the AIC(LL) at a three

segment model and for the AIC3(LL) at a two segment model. All three

criteria weight the fit and parsimony by adjusting the Log Likelihood (LL) to

account for the number of parameters in the model (Hair et al., 2010; Latent

GOLDManual, 2012). The most restrictive criterion is the BIC followed by the

AIC and the AIC3.

Table 5.11Output segmentation CBC analysis

Model LL BIC(LL) AIC(LL) AIC3(LL) DF P-value

1 class -611.31 1259.10 1236.83 1243.83 171 0.000

2 class -522.95 1310.17 1147.90 1198.90 127 0.000

3 class -471.52 1435.3 1133.03 1228.03 83 0.000

4 class -429.21 1578.68 1136.42 1275.42 39 0.000

Note: The underlined information criteria are the lowest.

A one segment model does not provide insight into the output of the

covariates. Therefore only a comparison will be made on the three and

two segment models. A comparison in the relative importance for the two

and three segment models leads to the conclusion that a three segment

model is the most logical solution (see table 5.12). The segments, based on

the relative importance, are divided into three groups; (1) price, (2) product

quality and delivery options and (3) time benefit. In the two-segment option

the division is less clear than in the three-segment option. For example,

the price related aspects are in the same segment as the order procedure.

This is not the case in the three-segment solution. In general there would

not be a clear division on which aspects to focus, as the division in relative

importance does not give a clear segment on which to focus. This is also the

case if the parameters are compared (see appendix F1 & F2).

Output (model 2): The classification statistics of the three-segment model

show an R2 of 0.93, which indicates a very good model fit. This is in line with

our hit rate of 99.28% (176.73/178). The degrees of freedom are also positive

(83) and thus, the model is overall stable. Furthermore, to test whether the

model is solid the analysis was rerun multiple times in which the number

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Table 5.12Relative advantage models

Relativeadvantage

Model 1Class 1

Class 2

Model 2Class 1 Class 2 Class 3

Delivery fee 30.67 3.19 35.79 0.24 15.08

Delivery options 15.1 47.93 10.48 58.47 26.3

Quality of goods 11.85 33 4.42 35.28 10.33

Time saving 2.36 8.27 6.87 1.89 9.61

Price benefit 8.63 6.98 15.61 3.76 7.47

Order procedure 31.39 0.64 26.83 0.35 31.21

of iteration is increased from the standard 250 up to 1500. By increasing

the number of iterations the analysis continues to search beyond the initial

bounds for a global maximum. A lower number of iterations might restrict

the analysis, which can result in a conclusion in which a local maximum is

seen as the global maximum (Hair et al., 2010). The output did not change

by much after increasing the number of iterations, which is an indication

of a stable model. Moreover, the class sizes do not differ greatly from each

other, which means that they are well separated (Vermunt, 2003). Class 1 is

the largest with 39.39 percent of the respondents followed by the second

class with 37.59 percent and finally the third class with 23.02 percent.

Hold-out: Next to the seven question, which are generated by Sawtooth

one question is also added as a hold-out question. This question is used to

calculate the hit rate of the final mode. By using the initial seven questions

the individual parameters are calculated, which in turn are used to calculate

the utility based on the hold-out choices. This is then used to form a

prediction, which is compared with the actual choice in the hold-out

question.

For our hold-out question two stimuli are formed. The first stimulus is more

focused around the price benefit and the second around the time benefit.

Based on the predicted choices and the actual choice of the hold-out

Note: underlined value is the highest. All figures are percentages.

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questions, the hit rate of our model is calculated. The hit rate figure indicates that our model is able to

predict 41% of the actual choice in all cases. However, the reason for this low hit-rate can be found in

the fact that we have three segments and only two stimuli. The second segment in our data is more

focused on the quality and the delivery options, while our two stimuli only focus on the price and

time aspect. Therefore, the respondents belonging to segment two are removed and a recalculation

indicates a hit-rate of 62.28%, which is an increase of 21.28% and a proper hit-rate for a conjoint

model (Malhotra, 2008)

Description clusters (parameters): In the previous part it was already depicted that the first

segment was focused on the price aspect, the second on quality and delivery options and the final

on the time benefit. In table 5.13 it is indeed visible that the effect of these attributes is higher within

the specific segments. Each attribute is further elaborated below.

An increase in the delivery fee has the highest negative effect in segment one, while it has almost

no negative effect in the second segment. This is not strange as the second segment is focused on

the quality of the delivered goods and the delivery options: for a higher quality and more delivery

options they are clearly willing to pay more. The option to receive the groceries only in the afternoon

has a negative effect in all segments. This is clearly not a great option for consumers. However, when

the option to receive groceries in the evening as well is added, all parameters become positive and

the highest in segment three. This indicates that this segment wants to be able to receive the goods

in the evening. On the other hand, the second segment wants to have a free choice and would like to

be able to receive the goods in the morning as well.

Along with the delivery options the second segment also values the quality of the delivered goods.

If the quality decreases, the negative effect is almost twice as high in the second segment when

compared to the third segment and more than six times higher than the first segment. The time

saving attribute has the highest effect in the third segment. However, this effect is very small. The

price benefit has, as mentioned before, the highest positive effect in the first segment. It is strange to

see that the effect is negative in the third segment. A negative effect in the second segment would

be explainable, as this segment is focused on the quality aspect and a lower price might have been

seen as an indication for lower quality products. However, an explanation for the negative value in

the third segment might be that the respondents in this segment perceive online shops, which are

focused on price, to be of less quality. Often online shops that are more focused on the price benefit

are less innovative and are therefore, less able to offer a smooth and time saving procedure. Moreover,

this segment indicates to be willing to pay more to save time. Finally, the order procedure has the

highest effect in the third segment. Again the value is negative in this segment. The reason for the

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negative value in the first segment could be the opposite mentioned for

the price benefit attribute. Online shops, which are easier to use and thus,

more sophisticated might be perceived as more expensive.

In conclusion, based on the attributes and the parameter values, three

segments can be formed. To better understand the three segments the

covariates are also used in the next part. The covariates will aid in better

understanding the formation of the three segments, which is necessary to

target them better.

Description clusters (covariates): In this part the significant covariates

are used to categorise the potential segments. As noted previously, the first

segment is more focused on the price benefit, the second on the delivery

options and quality of delivered products, and the third on the time benefit.

Table 5.13Parameters of attributes

Relative advantageModel 1Class 1

Class 2

Model 2Class 1 Class 2 Class 3

Delivery fee

-0.2423 -0.0013 -0.1539 0.000 -0.1313

Delivery options

Only afternoon -0.3909 -1.9241 -1.4794 0.000 -1.2178

Afternoon and evening 0.0728 0.7379 1.2029 0.583

Free choice 0.3181 1.1862 0.2765 0.6348

Quality of goods

-0.1494 -0.9385 -0.527 0.000 -0.533

Time saving

0.0464 0.0101 0.0981 0.011 0.0005

Price benefit

0.1056 0.02 -0.0762 0.000 0.0316

Order procedure

-0.0454 0.0005 0.0796 0.000 0.036

Note: the underlined figures are based on the relative importance (see table 5.14). The significance of the Wald (=) is shown in this table. The difference with the Wald significance can be found in the time saving attribute.

81

Furthermore, of the 23 covariates which are used only 13 are significant at

p < .10 or higher (see table 5.14). Therefore, only these 13 covariates will be

used to further categorise the three segments. Table 5.14 Class memberships based on covariates

Model for ClassesPrice

orientedQuality and

delivery Time

oriented p-value

Size 37% 33% 30%

Covariates Class 1 Class 2 Class 3

Gender

Male 55.85 46.27 53.41 0.033

Female 44.15 53.73 46.59

Occupation

Student 22.55 12.21 2.44 0.078

Unemployed 8.58 2.97 4.87

Part-time 17.26 22.31 14.56

Full-time 51.61 62.51 78.12

Income

<1500 29.72 19.62 9.81 0.026

1500 to 2000 12.9 11.87 2.45

2000 to 2500 7.14 23.76 14.89

2500 to 3500 35.84 13.42 16.81

>3500 14.41 31.34 56.04

Household composition

Single 30.17 18.14 16.36 0.089

Living together 48.27 35.67 49.53

Together +1 0.02 20.89 7.34

Together +>1 15.84 25.29 19.45

Single +>1 5.71 0 7.33

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orientedQuality and

delivery Time

oriented p-value

Education

Secondary 5.7 1.49 4.89 0.064

Vocational 10.48 17.87 18.79

BSc. 63.83 43.48 54.06

Msc. 19.99 37.15 22.26

Frequency of grocery shopping

1 9.08 7.32 40.91 0.047

2 22.64 17.88 39.48

3 38.56 34.27 17.12

4 14.02 24.14 2.46

>5 15.69 16.4 0.03

Attitude towards online payment

(Negative attitude) 1 1.43 0 0 0,017

2 5.71 1.49 0

3 14.32 5.98 12.1

4 55.97 65.42 56.1

(Positive attitude) 5 22.58 27.11 31.8

Satisfaction with general online shopping

(Dissatisfied) 1 to 4 15.71 10.43 4.88 0.096

5 21.81 29.87 26.18

6 44.13 40.13 44.45

(Satisfied) 7 to 8 18.36 19.58 24.49

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orientedQuality and

delivery Time

oriented p-value

Satisfaction with general grocery shopping

(Dissatisfied) 1 to 4 11 92 19.34 33.48 0.019

5 37.06 20.75 51.62

6 38.68 43.25 11.98

(Satisfied) 7 to 8 12.34 16.65 2.92

Technology readiness

(Low readiness) 1 15.84 22.33 26.74 0.033

2 29.66 16.42 17.6

3 18.5 14.79 19.86

4 18.64 23.85 17.01

(High readiness) 5 17.36 22.61 18.78

Motivation

(Low motivation) 1 28.22 13.46 15.14 0.0062

2 17.09 19.42 14.71

3 29.09 26.65 31.17

4 12.72 15.13 16.99

(High motivation) 5 12.89 25.34 21.99

Need for interaction

(No need for interaction) 1 29.05 10.42 28.47 0.0016

2 27.17 11.92 9.66

3 15.4 17.91 32.3

4 22.72 38.79 24.68

(Need interaction) 5 5.66 20.96 4.88

Note: only the significant covariates are used.

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Segment 1 – Price benefit: The first segment has 10% more male than female members.

Furthermore, it is clear that this segment has the lowest income. Approximately 30% is in the lowest

income group. This is also in line with their occupation. The largest peaks are in the student and

unemployed groups. The full-time group is the smallest when compared to the other segments.

The low income might also be due to the fact that most members of this segment live alone (30%).

The largest part of this group is highly educated (BSc. and MSc. 83%). However, it also contains 5% of

members who left school after secondary school, which is very low for a country like the Netherlands.

Finally, the median of the frequency on which the members shop for groceries is approximately three

times a week, in the first segment. This is an average compared to the other two segments. Next to

the demographics the other consumer characteristics also show interesting figures on which the

segments differ. The first one is the attitude towards online payment. All segments indicate to be

very positive towards online payment. However, where the other two segments have 0% in the most

negative figures, 7% of the members in this segment are very negative or negative towards online

payment. The satisfaction towards general online shopping is the lowest in this segment. More than

15% are very dissatisfied to dissatisfied. However, still more than 60% indicates to be slightly satisfied

or satisfied. Satisfaction towards online grocery shopping is around the 5 and 6 (70%). This shows

that they are not very satisfied or dissatisfied. The technology readiness, motivation and the need for

interaction are all very low for this segment. While the first two variables are negative for food retailers

the low need for interaction is positive, as the online channel offers less interaction than the offline

channel.

All the above-mentioned figures indicate that this segment comprises members who do not

have the means (yet) to shop for expensive products. This segment is mostly focused on low-cost

shopping. Most of the other consumer characteristics also indicate that this group is not really

interested in the benefits of convenience and time saving. An option could be to offer online grocery

shopping without some of the additional service (e.g. no delivery at home).

Segment 2 – Delivery options and quality of products: Based on the demographics this segment

can be categorised as a group in which females have a higher representation. Moreover, the largest

group in this segment works full-time, has an average income and is highly educated. Also, more than

50% in this group has one or more children. Finally, this segment has the highest grocery shopping

frequency. Altogether it can be concluded that this segment mainly contains consumers who are

normally responsible for the groceries at home, which are more often the females.

Besides the demographics. the other consumer characteristics also indicate some differences. A

small difference between the other segments can be found in the attitude towards online payment.

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All segments have a generally positive attitude, however, the attitude of this segment is the highest

towards online payment. Next, the satisfaction towards general online shopping, shows that this

segment has the fewest members in the higher satisfaction categories. The opposite is the case for

general grocery shopping. Finally, this segment has the highest technology readiness, motivation

and need for interaction. The technology readiness indicates a positive attitude towards using new

technologies and thus, also online shopping for groceries. The same is the case for the motivation.

This segment shows a high motivation for shopping in a more effective way for groceries. Finally, the

need for interaction is the highest in this group, which is something in which the online channel

might lack.

Thus, this segment is usually responsible for the grocery shopping within their household. They are

not used to shopping online for other products. The other factors such as the attitude towards online

payment, the technology readiness and the motivation shows that this might be an interesting

group. Moreover, if an alternative is offered for general grocery shopping the delivery and the quality

of the goods should not create extra hassle compared to their current way of grocery shopping. Thus,

a food retailer should make sure that the basics work to attract this group.

Segment 3 – Time benefit: This segment has, like the first segment, 10% more male members

than female. Moreover, there is a clear division in occupation. More than 78% of this segment works

full-time and has the highest income of all segments. The household composition indicates that

approximately 80 live together, but only 27% have children. Just like the other segments, this segment

is also highly educated. Finally, the members of segment three have the lowest grocery shopping

frequency of all.

Next to the demographical aspects the other consumer characteristics also provide interesting

insights. As mentioned before, all groups have a positive attitude towards online payment. However,

the attitude is the most positive in this segment. This is in line with the satisfaction towards regular

online shopping, in which this segment has the highest score. This means that they are the most

satisfied group with regard to general online shopping. On the other hand, this segment shows

a large dissatisfaction towards regular grocery shopping. Approximately 85% indicate to be very

dissatisfied to dissatisfied. Finally, the technology readiness of this group is the lowest of all, the

motivation and need for interaction are both high. This shows that they are looking for alternatives.

However, the alternative should be simple in order to counteract the low technology readiness.

This leads to the conclusion that the members of this group are mainly working people, who have

very little time and are always in a hurry. The general finding and conclusion, which we can derive

Results

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from this group is the fact that this group has a lot of time restraints. This is also in line with the need

for interaction, which is low to very low; again indicating that shopping has to be done as quickly

and as easily as possible. Hence, to attract this segment, food retailers need to make the order and

payment procedures as simple and quick as possible. The price benefit even has a negative effect

on the utility indicating that this segment is willing to pay more for a quick and easy way of grocery

shopping.

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6. Conclusions & managerial implication

The conclusion of this study is addressed in this chapter. The different questions, which are formed in

the first chapter, are discussed with the use of the results of chapter five, followed by the implications

for food retailers. Finally, the limitations and the directions for further research are presented.

§6.1 Conclusion

The introduction of new products and services is necessary to ensure future sales and growth (Hoyer

& MacInnis, 2008). However, many new introductions still fail and are not adopted by consumers (e.g.

Moore, 2002). Therefore, firstly we sought a better understanding as to how the adoption process of

new innovations looks. This is necessary to ensure a better diffusion of online grocery shopping.

The decision path of Rogers (1995) showed that the adoption depends on several stages. In the first

stage an individual becomes aware of the innovations, in the second stage the individual shows

more interest and gathers information with regard to the innovation and compares the innovation’s

characteristics with existing alternatives. This information is then used in the third stage to either

resist the innovation or to adopt it.

Consumer characteristics: These insights have led to the following question; “which consumer

characteristics cause resistance and which increase the rate of adoption of online grocery shopping

according to literature?” The decision path of Rogers (1995) shows that the first stage is influenced by

the consumer characteristics. This means that depending on someone’s characteristics he or she will

gather information in the second step, which will lead to the adoption or resistance of online grocery

shopping. The effect of the different consumer characteristics on the resistance and the adoption are

shown in table 6.1. The table only consists of the consumer characteristics, which have a significant

effect on one or both sides. The other characteristics which have shown no effect in both of them are

left out (see appendix F1 for output of all characteristics)

It is visible that some characteristics have an effect on both the resistance and the adoption.

However, this contradicts with the findings and conclusions of e.g. Ram & Sheth (1989) and Gatigon

and Robertson (1989). They state that resistance is not the opposite of adoption and vice versa. This

would mean that the consumer characteristics should not show a significant effect on the resistance

towards online shopping if there is also a significant effect on the adoption.

Conclusions & managerial implication

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Thus, our results show that consumer characteristics can influence the

resistance and the adoption of online grocery shopping, in contrary to the

statements of other studies (e.g. Ram & Sheth, 1989; Kleijnen et al., 2010).

Even though, our finding contradicts previous studies this only concerns

the consumer characteristics and not the innovation characteristics. The

findings with regards to the innovation characteristics have indeed shown

that hurdles (resistance) are more important than the benefits (adoption).

Table 6.1.The effect of consumer characteristics on the resistance and adoption of online grocery shopping

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In addition table 6.1 also shows that some characteristics have an effect on one dependent variable

while they have no effect on the other. This might be due to the reason that, for example, resistance

is formed mainly by an individual’s belief, barriers and hurdles (Ram & Sheth, 1989). In our case a lower

income and the attitude towards information sharing have an effect on the resistance, but not on

the adoption. Respondents experience these characteristics as hurdles and beliefs and thus it has

only an effect on the resistance. On the other hand ‘shop enjoyment’ and ‘need for convenience’ have

an effect on the adoption, but not on the resistance. This in turn is caused by their beneficial effect

(creates more convenience), which is necessary for an individual to consider adopting an alternative

product or service (Mahajan et al., 1995). Hence, the characteristics, which are related to someone’s

beliefs and values, have a higher effect on the resistance and characteristics, which are more related

to beneficial aspects are more related to the adoption. However, the more general consumer

characteristics have an effect on both sides.

Innovation characteristics: In the second stage of the decision path consumers gather and

evaluate information about the innovation itself. These are aspects like, the relative advantage, the

compatibility, the complexity, trialability and the observability. If the innovation is perceived as

positive in these aspects then an individual might decide to try the innovation in the third stage. The

opposite will occur if the aspects are seen as negative. In our study a conjoint analysis is performed

with six characteristics of the online channel. Three characteristics are selected as they are indicated

as the most important hurdles for consumers to shop online for groceries and three are selected as

benefits because respondents have indicated they are most important for online grocery shopping.

The results of the aggregated conjoint analysis have shown that the hurdles are indicated as more

important than the benefits (see table 5.12). The time to order online is perceived as the most

important attribute, but the quality of delivered goods, delivery fee and the delivery options are

also seen as very important. The highest change in utility is when the delivery option to receive

the groceries in the evening is added. Basically, these results lead to the conclusion that the online

channel needs to be user friendly, offer goods which are at least as good as the goods in a regular

supermarket, deliver the goods at a €0.00 delivery fee and offer at least the option to receive the

goods in the evening.

The above-mentioned conclusions are based on the aggregated and overall scores of the conjoint

study. However, there might be some latent classes based on heterogeneous preferences between

the respondents. Therefore, the consumer characteristics are used as covariate to find potential

segments. The findings show that the largest segment is focused around the price aspect. This means

that the innovation characteristics; delivery fee and price benefit are seen as most important. This is


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in line with statements from Rogers (1995) who concluded that the largest adopter group is often

focused around the price aspect. Therefore, this group will only use the innovation if it offers them a

price related advantage. The second segment indicates willing willingness to use the online channel

if the products offered have at least the same and not a lower quality than the products in a regular

supermarket. Also, the online channel should offer many delivery options. It seems that this segment

focuses on the general aspects with regard to grocery shopping, because in the offline channel

consumers also look for good quality products and supermarkets with a good location. Moreover,

it also seems that this segment is not really looking for an alternative way of grocery shopping.

However, they are also not resistant to it. Therefore, by offering an alternative, which is at least as good

as regular grocery shopping with some additional benefits, this segment might be willing to use the

online channel instead. The third and final segment wants the entire process to be time saving during

the order procedure and in general. Moreover, they are willing to pay more for the additional benefit.

Thus, these consumers are really looking for a better alternative to regular grocery shopping.

Therefore, the third segment should receive the most attention. Even though this is the smallest

segment it does have the highest potential for using the online channel for grocery shopping.

Moreover, the covariates indicate that this segment is highly educated and has the highest income.

According to Hoyer and MacInnis (2008) these consumer characteristics are indicators for consumers

with a higher social influence on others and are often seen as opinion leaders (Lyons & Henderson,

2005).

Finally, the insights above have aided us to answer our initial problem statement: “Which

characteristics of online grocery shops cause resistance or increase adoption of online grocery shopping

and which strategy(ies) are necessary to meet the needs of consumers?”

The three characteristics, which create resistance are delivery options, delivery fees and quality of

ordered goods and the three characteristics which increase the adoption are price benefits, time

benefits and the order procedure. The effect of each (utility) of course differs from the other. Overall

the hurdles have a higher effect (utility) and the benefits have a lower effect.

However, the effects do differ between the three segments and therefore, separate strategies are

needed in order to attract all segments. Thus, one strategy is not sufficient to meet the needs of

all potential segments. Therefore, a differentiation should be made based on the three segments,

which are mentioned above. The second segment should receive the most attention at the start.

even though it seems that the third segment should receive the most attention and has the most

potential. The attributes which are central in the second segment, are basic for all segments and thus

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necessary for a good service. The second segment might not be the most interested in an alternative

way of grocery shopping, but they are also not resistant towards it. If the additional benefits of the

third segment (time related) are added, than the second segment might be stimulated to try it as

well. Moreover, this will lead to an attractive service for the third segment as well. The final segment

is more focused on the price benefit and therefore, a large scale is necessary to be able to offer lower

price. In that case the first two segments can be used to grow as an organisation in order to offer the

scale benefits to customers of the first segment in a later stage.

§6.2 Managerial implications

This study provides insights into the effects of consumer and innovation characteristics on the

resistance and the adoption of online grocery shopping. The findings show that consumer

characteristics can give an indication on whether or not someone is resistant to online grocery

shopping. Therefore, it is necessary for food retailers to investigate which of their current consumers

are probably resistant and which are open to adopting the online channel. Of course consumer

characteristics are not controllable by food retailers. However, a better understanding will aid the

targeting of potential customers in a more efficient way. Furthermore, some consumer characteristics

are also an indication in which direction the development of the online channel should be. For

example ‘time pressure’ indicates that consumers are looking for an alternative for grocery shopping

as they have less time and want to spend this differently. Hence, before targeting potential customers

food retailers should investigate who to target with their marketing campaign and what to

communicate in order to increase the chance of adoption.

Generally speaking the most important characteristics of the online environment are the basic

aspects. The participants indicate they want a good delivery system and products, which have at least

the same quality as the product in a regular supermarket.

The delivery options are addressed as the most important aspect. A food retailer should therefore

invest in a good delivery system. The options to receive the goods in the evening and in the

afternoon seem sufficient as this option has a positive utility. Furthermore, the delivered products

should be of at least the same quality as the products in a regular supermarket. This has been noted

as a concern in the qualitative study as consumers have to hand over the control to the retailer

during the product picking. On the other hand they also indicated not to be interested in the option

to choose the products by themselves. Thus, they simply want products of good quality, but this

should not cost them additional time.


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The additional aspects, which are mostly the benefits, except for the delivery fee, have lower utilities

and thus a lower effect. These aspects can be used by food retailers to position the online channel

differently from a regular supermarket and other online channels. Food retailers such as Albert Heijn

in The Netherlands already position themselves as a high quality food retailer with many brands

at affordable prices. The products (easy to prepare meals) and additional services (self-scanning in

supermarket), which are offered, are focussed around the time saving aspect. With the addition of the

online channel, this image could be enhanced.

The additional benefits are especially important due to the different interests of customers. In this

study we have identified three segments. However, the amount of segments might differ between

retailers. However, we do believe that the general preferences of the online grocery market are

indeed divided into the price benefit, general preferences (quality and delivery options) and the time

benefit.

The first group is focused on the price benefit, the second on quality and delivery and the final

on the time benefit. The second segment more interested in the basic aspects. It is necessary for

this segment to maintain the quality level and ensure that the delivery options are also available.

These aspects are also above all the most important. Therefore, it is expected that it will also attract

consumers from the other two segments; in particular from the third segment, which has a higher

need in time benefit. This segment is really looking for a more time saving alternative to regular

grocery shopping. It means that the online grocery shop should be easy to use and the order

procedure should be as easy to use and as quick as possible. A potential option is to offer the ability

to form a shopping basket based on the consumer’s standard grocery purchases. This will allow

them to shop faster. Furthermore, the steps should be simple, including the payment phase. Higher

investments are of course necessary for these aspects. However this segment has shown to be willing

to pay more for additional benefits such as more delivery options and a better and faster ordering

system. Finally, the third segment is focused on the price benefit. This means that by offering groceries

at lower prices in the online channel more consumers will be attracted. This step seems difficult,

as the delivery of the products is more expensive than having a regular supermarket. Therefore, it

is advised to pay less attention to the price benefit and more attention to the fact that the online

channel works as well as a regular supermarket (good quality products and proper delivery options).

Furthermore, it saves time and is more convenient due to the easy to use system and the ability to

shop from wherever you are and whenever you want. Of course, at a later stage, if the online food

retailer has the ability to lower the prices, they should consider this. This will have a positive effect

on the first segment as well. However, additional research is needed into the costs of the different

attributes. For example what does each delivery option cost (morning, afternoon and evening)?

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If the costs are known then a real comparison can be made into the costs per attribute and level and

its effect on the attraction of new consumers. This will lead to the formation of the most attractive

online grocery shop for every retailer.

§6.3 Implications for Truus.nl and Appie.nl

In the first chapter it was mentioned that some retailers, in the Netherlands, already offer an online

service for ordering groceries. Two examples are Truus.nl and Appie.nl. The first is an online shop

which offers household cleaning products and care products. Appie.nl also offers some household

cleaning products and care products, but is mainly focused on offering food products. In this part the

implications of the previous sub-chapter will be reflected on these two online shops.

Truus.nl: Truus.nl is an online shop which offers consumers the option to order household cleaning

products and care products and have them delivered. Based on our findings on the consumer

characteristics there are several conclusions which can be drawn for Truus.nl. Firstly, our findings

indicate that several aspects create resistance and increase the rate of adoption. For Truus.nl it means

that they need to better understand who their current customers are and how they relate to the

different consumer characteristics. By doing so a better understanding can be formed of which needs

aid in the adoption process and which characteristics create resistance. Motivation for example,

whether a consumer needs a more efficient way to shop for groceries, has an effect on the resistance

and the adoption as well. This means that consumers with a certain lifestyle will be more willing to

adopt the online service than others. Therefore, the communications of Truus.nl should be formed

around this feeling and benefit. It might help other non-users to relate to this feeling and start using

the online channel as well. This is also relevant for time pressure, travel costs for visiting a regular

grocery shop, aspects of a regular grocery shop which are regularly seen as dissatisfactory and the

current satisfaction of Truus.nl customers with the online channel. All these aspects have shown to

increase the adoption or decrease the resistance. It means that these “feelings” should be targeted

and addressed during a marketing campaign. For example, by addressing that ordering online saves

time, money (travel costs) and it saves stress, because you do not have to wait in line. Also by using

the satisfaction rate of current customers at Truus.nl, the retailer could show new customers how

well their service works. In conclusion our understanding of the consumer characteristics can help to

target the correct feelings and needs of consumers in a more efficient way.

Next to the consumer characteristics the innovation characteristics also offer opportunities to

increase the number of users. A review of Truus.nl shows that the delivery costs are €4.95 if an order

of <€45.00 is placed and €0.00 if an order of >€45.00 is placed. Our study has shown that delivery


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costs of €4.99 decrease the utility with -0.569. In the case of Truus.nl the utility will decrease with

-0.564. Of course a negative utility is something a retailer would not want to have. However, the

average product on Truus.nl can be bought in batches and saved for a longer period. Therefore,

the minimum order quantity of €45.00 is a very good solution to the negative utility. Moreover,

the delivery costs are not the most important aspect. The delivery options, which is one of the

most important attributes, are quite good at Truus.nl. Consumers are able to receive the goods

from Monday to Saturday from 8:00 until 17:00and with evening deliveries offered from Tuesday to

Friday. This means that in most cases the utility is positive (between 0.328 and 0.472). Furthermore,

there is also no problem with the quality of the goods as it concerns goods which do not spoil. The

other attributes, such as time saving have a positive utility as well. However, a negative aspect of the

website is the fact that they only offer a solution for some products. Consumers therefore still need

to go to a regular grocery shop for the rest of their groceries. The price benefit is also very low on the

website and it seems that consumers can not benefit from the promotions offered by many regular

retailers. It is known that consumers purchase household cleaning products and care products

mostly when they are on promotion.

Overall, Truus.nl offers a good alternative to regular grocery shopping for household and care

products. However, they only focus on some products of the entire grocery list. On the plus side,

these are often very large and heavy products and thus having them delivered at home might be

seen as a large advantage. The website itself offers many advantages which altogether can lead to a

positive total utility. Still, the main concern remains the price difference, as these products are often

purchased when they are on promotion. More attention should be focused on this aspect and in

better understanding, reaching and targeting the best-suited customer.

Appie.nl: Like Truus.nl, also an online grocery service, Albert Heijn’s Appie.nl is reviewed. The products

which are offered online on Appie.nl are mostly food products, but also additional products such as;

care products, household cleaning products and fresh products. For Appie.nl the same conclusions

can be drawn for the consumer characteristics. Firstly they should better understand who their

current consumer is and how they relate to the consumer characteristics. Moreover, they should

increase attention for the motivation, time pressure, the satisfaction of regular grocery shopping

and the other characteristics as well to increase adoption. On the other hand, aspects such as the

attitude towards online information sharing should also receive some attention. Our study has shown

that consumers, who are less open to sharing information online, are more resistant towards online

grocery shopping. This means that the safety of online information storage should receive attention.

Non-consumers need to be educated and shown how well their information is saved. Appie.nl could

also decide to decrease the amount of information that is saved online. The outcomes, in our study,

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related to demographics also provide information with regard to the consumer profile, which is the

most interesting target group for Appie.nl. Based on information from the customer database of

Albert Heijn, Appie.nl could target the most potential consumers with a marketing campaign. For

example, higher educated people who have a higher shopping frequency, work full-time and have

children.

Next to the consumer characteristics the characteristics of the website show some interesting

findings as well. While Truus.nl offered free delivery for order >€45.00, Appie.nl offers no free delivery.

They do have some promotions based on the delivery costs. However, the delivery costs are still

quite high. For orders >€100.00 the delivery costs are between €2.50 and €4.99 (depending on

promotions) and for orders <€100.00 the delivery costs are above €4.99. This means that in all cases

the utility of the delivery is negative and thus, causes resistance. However, the delivery options are

very extensive. Consumers have the ability to receive the groceries between 08:00 and 21:00 (except

for Sundays). On the other hand, this is only the case in some parts of the Netherlands (mainly west

and central). Other regions are not able to purchase groceries online at Appie.nl. The additional

attributes influence the utility in a positive way. For example, consumers are able to form a grocery list

by typing it in, scanning the barcode with the app on a smart phone and also via voice. This all saves a

lot of time for consumers and is easy to use. Moreover, the grocery list is saved online and thus offers

the ability to shop faster and smarter each time, as it becomes a checklist. This has a positive effect on

the order procedure and the time saving. Finally, the price benefit is very small compared to a regular

grocery shop and therefore, this attribute has a little to no effect on the total utility.

In conclusion, Appie.nl offers a very handy and fast solution to ordering groceries online. However,

the hurdles (delivery costs and not delivering in all areas) are still very high. Therefore, these aspects

should receive more attention. By using the findings of our study with regard to the consumer

characteristics and general population figures in various regions of the Netherlands, Appie.nl could

study whether they should add their service to a specific area or not. Moreover, the demographics

can be used to better target and understand current and potential customers.


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7. Limitations and directions for further research

Several limitations will be discussed in this part, which should be taken into consideration in further

research.

First of all the sample of this study consists of 178 respondents. Even though the efficiency of all levels

is sufficient with 178 respondents it is advised to increase the amount. The increase is necessary as

many covariates are added to the conjoint analysis.

Furthermore, the respondents are gathered from only in two cities in the north of the Netherlands.

Respondents from other parts of the Netherlands are necessary, as they might have different needs.

Also, in the north the density of supermarkets is less high than, for example, in the west of the

Netherlands. Therefore, further research could focus on generalising the findings of this study in

different regions in the Netherlands.

Finally, the comparison with the general Dutch population and the EFMI and CJB shopper population

has indicated that our sample differs significantly with almost all demographics. Therefore, in further

research a more representative sample should be targeted.

Other additional limitations are the choice for the six attributes. These are based on the choice of a

small sample size. This might be different if the sample size is larger or if other regions are also taken

into account.

In general the additional attributes provide different interesting questions. For example, what should

the online grocery shop look like? Consumers are used to shopping for groceries in a certain way and

studies have shown that consumers also use layout of supermarkets as a “grocery list” (Levy & Weitz,

2009). This means that a 3D grocery shop, which looks like a regular grocery shop might be preferable.

However, this might also be very difficult , as consumers are not able to walk around freely. Therefore,

additional research is needed on the visual side of the online grocery shop.

Except for insight into the “front” side of the online grocery shop, retailers also need to better

understand how the entire technical aspects should be formed:, what is necessary to enable a

smooth online service for grocery shopping and additionally, what are the costs to achieve this? If

the costs are known then a comparison could be made with the utilities presented in this study. This

would enable food retailers to form the best and most profitable online grocery shop.

Limitations and directions for further research

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Finally, as there are different needs within the market, different retailers are active with different

concepts. The additional attributes could aid food retailers to differentiate themselves from others.

Supplementary research, in which the current food retail formats are compared and translated to

online formats, could provide a better view of what the basic and additional needs are of consumers.

In conclusion, the entire online channel for grocery shopping offers many potential and good

areas for further research. As the general online market has shown, the online channel for grocery

shopping can offer many advantages and has a large potential for food retailers. It is therefore,

necessary to better understand the needs and wants of the market.

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111

ColofonAuthor

Samir Selimi (student University of Groningen)

E-mail: [email protected]

Supervisor

Prof. dr. L.M. Sloot

Art direction and production

Gerben van Eijk, Q&A Research & Consultancy

Bergdrukkerij, Amersfoort

Contact details

Rabobank International

Industry Knowledge Team


Senior Relationship Banking



Websites

www.rabobank.com

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Colofon

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