samir selimi thesis- online grocery shopping
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The influence of hurdles and benefits on the diffusion of online grocery shopping
Retailing Beyond Borders
3
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
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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: samir@selimi.nl; 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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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 influence of hurdles and benefits on the diffusion of online grocery shopping
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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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
§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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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.
Diffusion of innovations
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The influence of hurdles and benefits on the diffusion of online grocery shopping
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
25
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
Diffusion of innovations
26
The influence of hurdles and benefits on the diffusion of online grocery shopping
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.
Diffusion of innovations
28
The influence of hurdles and benefits on the diffusion of online grocery shopping
§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
The influence of hurdles and benefits on the diffusion of online grocery shopping
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))
Factors influencing the resistance and adoption
32
The influence of hurdles and benefits on the diffusion of online grocery shopping
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.
Factors influencing the resistance and adoption
34
The influence of hurdles and benefits on the diffusion of online grocery shopping
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
Factors influencing the resistance and adoption
36
The influence of hurdles and benefits on the diffusion of online grocery shopping
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.
Factors influencing the resistance and adoption
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
The influence of hurdles and benefits on the diffusion of online grocery shopping
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
Factors influencing the resistance and adoption
Figure 3.3: Preliminary conceptual model
40
The influence of hurdles and benefits on the diffusion of online grocery shopping
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
The influence of hurdles and benefits on the diffusion of online grocery shopping
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
The influence of hurdles and benefits on the diffusion of online grocery shopping
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
The influence of hurdles and benefits on the diffusion of online grocery shopping
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
The influence of hurdles and benefits on the diffusion of online grocery shopping
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
The influence of hurdles and benefits on the diffusion of online grocery shopping
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
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The influence of hurdles and benefits on the diffusion of online grocery shopping
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
The influence of hurdles and benefits on the diffusion of online grocery shopping
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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
59
(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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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
61
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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.
63
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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
65
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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
71
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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 influence of hurdles and benefits on the diffusion of online grocery shopping
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
75
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
Model for ClassesPrice
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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Model for ClassesPrice
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.
Results
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The influence of hurdles and benefits on the diffusion of online grocery shopping
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.
85
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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
88
The influence of hurdles and benefits on the diffusion of online grocery shopping
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
89
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
Conclusions & managerial implication
90
The influence of hurdles and benefits on the diffusion of online grocery shopping
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.
Conclusions & managerial implication
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The influence of hurdles and benefits on the diffusion of online grocery shopping
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)?
93
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
Conclusions & managerial implication
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The influence of hurdles and benefits on the diffusion of online grocery shopping
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,
95
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.
Conclusions & managerial implication
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The influence of hurdles and benefits on the diffusion of online grocery shopping
97
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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The influence of hurdles and benefits on the diffusion of online grocery shopping
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: samir@selimi.nl
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
E-mail: jos.voss@rabobank.com
Senior Relationship Banking
E-mail: dereck.van.hovell@rabobank.com
E-mail: jeroen.wortman@rabobank.com
Websites
www.rabobank.com
www.rabobank.nl/retail
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