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Samir selimi thesis- online grocery shopping

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  • 1. The influence of hurdles and benefits on the diffusion of online grocery shopping Retailing Beyond Borders
  • 2. Foreword Sipko 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 3 Foreword
  • 3. 4 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 4. 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 5
  • 5. 6 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 6. 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 7 Summary
  • 7. Table 1.1 The effect of consumer characteristics on the resistance and adoption 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 8 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 8. 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. 9 Summary
  • 9. 10 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 10. Preface 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 11 Preface
  • 11. 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 12 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 12. 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 13 Table of Contents
  • 13. 14 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 14. 1. Introduction 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 15 Introduction
  • 15. 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). 16 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 16. 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. §1.1 Research questions 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?” 17 Introduction
  • 17. 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. §1.2 Relevance & uniqueness of thesis Considering 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 . 18 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 18. 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. 19 Introduction
  • 19. 20 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 20. 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. §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 21 Diffusion of innovations
  • 21. 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. 22 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 22. 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 innovations Besides 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. 23 Diffusion of innovations
  • 23. 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 Figure 2.1: Stages of innovativeness (Rogers, 1995) and S-shaped diffusion curve (Bass, 1969) 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 24 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 24. 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 25 Diffusion of innovations
  • 25. 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 Figure 2.2: Five stages of the decision path (Rogers, 1995) 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. §2.5 Resistance vs. Adoption As 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 26 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 26. 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. 27 Diffusion of innovations
  • 27. 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). §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. 28 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 28. 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. §3.1 Factors, underlying antecedents and adoption path 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. 29 Factors influencing the resistance and adoption
  • 29. Figure 3.1: Innovation Adoption framework (adapted from e.g. Ram, 1987; Rogers, 1995; Kleijnen et al., 2004) §3.2 Innovation characteristics 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 30 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 30. (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. Table 3.1: Innovation characteristics 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) *(Not mentioned by Rogers (1995), but added based on findings of Ram & Sheth (1989)) 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 31 Factors influencing the resistance and adoption
  • 31. 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. 32 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 32. 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. §3.3 Consumer characteristics (moderator) 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. 33 Factors influencing the resistance and adoption
  • 33. 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. 34 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 34. 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 35 Factors influencing the resistance and adoption
  • 35. 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. §3.4 Adoption path- willingness to retry and degree of resistance 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. §3.5 Conceptual model for conjoint study 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 subchapter 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 36 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 36. 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. Table 3.2: Theory based hurdles and benefits of online grocery shopping 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; WilsonJeanselme & 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; WilsonJeanselme & Reynolds 2006; Alba et al. 1997; Darian 1987 Shopping enjoyment is less possible during online grocery shopping (hedonic motivations). Compatibility Sources (e.g.) Price advantage compared to an offline store. Relative advantage Aspect 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 37 Factors influencing the resistance and adoption
  • 37. Complexity Communicability 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 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 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 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). Perceived Risk Sources (e.g.) The perceived risk of doing business over the internet (Payment, information sharing) Divisibility Aspect Reinders et al. 2008; Shankar et al., 2002 38 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 38. §3.6 Conclusion 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. Figure 3.3: Preliminary conceptual model 39 Factors influencing the resistance and adoption
  • 39. 40 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 40. 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. Method Participants: 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. 41 Methodology
  • 41. 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 Conclusion Conclusion 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). 42 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 42. 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 43 Methodology
  • 43. 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. 44 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 44. Figure 4.1: Final conceptual model conjoint analysis 4.2.1 Method Participants: 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 45 Methodology
  • 45. 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 Table 4.1: Theory based hurdles and benefits of online grocery shopping Rank a long time. This has resulted in the following ranking: 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 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. Conclusion It 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 46 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 46. 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 method Conjoint 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). 47 Methodology
  • 47. 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 development After 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 48 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 48. 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). 49 Methodology
  • 49. Table 4.2 Online Grocery Shop design elements Quality of ordered goods 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 Saves no extra time Benefits Deliver options No delivery fee Hurdles Delivery Fees 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 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. 50 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 50. 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 analysis As 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). 51 Methodology
  • 51. 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. 52 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 52. 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 Sample The 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 behaviour The 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 53 Results
  • 53. 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). 54 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 54. Table 5.1 Socio demographic figures CBS 2011/2012 EFMI & CBL 2011 Gender N/S ** Male Female 50.2% 49.8% 29% 71.% Current study (N=178) 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 51.7% 48.3% 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 14% 5.6% 18.5% 61.8% 0% Household compositionb Single Living together Living together + 1 child Living together + >1 children Single + >1 children Household size 1 person 2 person >3 person Education Primary school Secondary school Vocational Education University (BSc.) University (MSc.) Age <20 years 21 – 30 years 31- 41 years 41 – 50 years 51 – 60 years >60 years ** ** 34.5% 27.0% 9.3% 16.2% 13% N/A N/A N/A N/A N/A 21.3% 10.1% 15.2% 23.0% 30.3% ** ** 34.5% 27.0% 38.5% 36% 33% 31% 22.5% 43.8% 9.6% 20.2% 3.9% ** ** 3.4% 51.3% 22.5% 14.7% 7.9% N/A N/A N/A N/A N/A 22.5% 43.8% 33.7% ** ** 23.5% 12.2% 12.8% 15.6% 13.7% 22.2% N/A N/A N/A N/A N/A N/A 3.9% 15.2% 15.2% 53.9% 27.0% 1.1% 41.0% 26.4% 21.4% 6.7% 3.4% ** p < .01. N/A= not applicable N/S= not significant Note: 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). 55 Results
  • 55. 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. 56 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 56. Table 5.2 Grocery shopping figures EFMI 2011 Responsible for grocery shopping N/A Yourself Partner Together Other N/A N/A N/A N/A Frequency of grocery shopping (week) N/S 1 time a week 2 times a week 3 or more times a week 16% 32% 52% Current study (N=178) Amount of minutes per visit < 15 minutes 16 to 30 minutes > 30 minutes 51.1% 5.6% 40.4% 2.8% 15.7% 24.7% 59.6% ** 31% 51% 18% 11.7% 47.2% 51.1% ** p < .01. N/A= not applicable N/S= not significant Note: One choice option was possible for the item; “responsible for grocery shopping”. §5.2 Measurement purification Eleven 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. 57 Results
  • 57. 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 58 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 58. (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). 59 Results
  • 59. Table 5.3 Overview of the new constructs and number of items Construct Number of items a Technology readiness 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 a Shopping enjoyment 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 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 60 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 60. 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 shopping The 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, 61 Results
  • 61. 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 nonnormality. 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. 62 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 62. 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. 63 Results
  • 63. Table 5.4 Model summary R2 F-value Sig. 0.743 3 items 0.000*** *** p < .001 Note: 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 64 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 64. Table 5.5 Output multiple linear regression analysis for willingness to (re)try (after deleting variables with p > .4). Standardized Coefficients Beta t-value Sig. 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 Construct (Constant) ** 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 65 Results
  • 65. 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 66 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 66. 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. 67 Results
  • 67. 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. 68 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 68. Table 5.6 Model fitting information Model -2Log Likelihood Intercept -2Log Likelihood Final Chi-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) Table 5.7 Psuedo R-Squares model. Model -2Log Likelihood Intercept -2Log Likelihood Final Chi-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. 69 Results
  • 69. Table 5.8 Output Ordered Multinomial Logistic regression Variables Estimate Std. Error Wald DF Sig. 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 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 Treshhold Location 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 N/A N/A N/A 0 N/A Household - Single + >1 child 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 70 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 70. 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. 71 Results
  • 71. 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, sociodemographics 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). 72 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 72. 5.4.1. Model specification In 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 Table 5.9 Model specification (all partworth) way (Malhotra, 2010; Hair et al., 2010). Furthermore, only the main effect is measured in our CBC analysis and no interaction effects are added. Value (levels) Attributes Type Preference function 1 Delivery fees 2 3 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 level First, 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. 73 Results
  • 73. 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 74 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 74. Table 5.10 Output aggregated CBC analysis Attributes % choice division Delivery fee - €0.00 - €4.99 - €9.99 3.21a 52.8% 30.1% 17.1% Delivery options - Afternoon - Afternoon & evening - Free choice N/Aa 13.0% 40.4% 46.6 Quality of goods - No items below - 5% below quality - 10% below quality €0 Max Relative importance 1.1264 23.0% 1.2780 26.0% 0.00 0.8452 17.2% 0.64 0.0464 0.9% 0.00 0.4658 9.5% 0.00 €9.99 Range 0.00 Parameter Sig. 0.00 Min 1.1487 23.4% 0.000 -0.569 -1.140 N/A N/A N/A N/A N/A N/A N/Aa 48.0% 31.4% 20.6% 1 (0%) 3 (10%) Time saving - 0% - 5% - 10% 5.07a 32.6% 33.3% 34.1% 0% Price advantage - 0% - 5% - 10% 5.76a 25.9% 32.7% 41.3% 0% Order procedure - 20 min - 40 min - 1 hour 32.73a 53.2% 29.9% 16.9% 20 min -0.800 0.328 0.472 -0.423 -0.845 -1.268 10% 0.000 0.030 0.059 10% 0.000 0.226 0.452 1 hour -0.570 -1.140 -1.710 Note: Effects coding is used for nominal attributes. N/A not available, the values are nominal. a the mean of the attribute 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. 75 Results
  • 75. 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). 76 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 76. 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 5.11 Output 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 77 Results
  • 77. Table 5.12 Relative advantage models Relative advantage Model 1 Class 1 Class 2 Model 2 Class 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 Note: underlined value is the highest. All figures are percentages. 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 78 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 78. 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 79 Results
  • 79. Table 5.13 Parameters of attributes Model 1 Class 1 Class 2 Model 2 Class 1 Class 2 Class 3 -0.2423 -0.0013 -0.1539 0.000 -0.1313 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 -0.1494 -0.9385 -0.527 0.000 -0.533 0.0464 0.0101 0.0981 0.011 0.0005 0.1056 0.02 -0.0762 0.000 0.0316 -0.0454 0.0005 0.0796 0.000 0.036 Relative advantage Delivery fee Delivery options Quality of goods Time saving Price benefit Order procedure 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. 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. 80 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 80. 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 Table 5.14 Class memberships based on covariates used to further categorise the three segments. Price oriented Covariates Time oriented 33% 30% Class 1 Size Quality and delivery 37% Model for Classes Class 2 Class 3 p-value Gender Male 55.85 46.27 53.41 Female 44.15 53.73 46.59 0.033 Occupation Student 22.55 12.21 2.44 Unemployed 8.58 2.97 4.87 Part-time 17.26 22.31 14.56 Full-time 51.61 62.51 78.12 <1500 29.72 19.62 9.81 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 Single 30.17 18.14 16.36 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 0.078 7.33 Income 0.026 Household composition 0.089 81 Results
  • 81. Price oriented Quality and delivery Time oriented p-value 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 1 9.08 7.32 40.91 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 1.43 0 0 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 Model for Classes Education Frequency of grocery shopping 0.047 Attitude towards online payment (Negative attitude) 1 0,017 Satisfaction with general online shopping (Dissatisfied) 1 to 4 15.71 10.43 4.88 5 21.81 29.87 26.18 6 44.13 40.13 44.45 (Satisfied) 7 to 8 18.36 19.58 0.096 24.49 82 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 82. Price oriented Model for Classes Quality and delivery Time oriented p-value 0.019 Satisfaction with general grocery shopping (Dissatisfied) 1 to 4 11 92 19.34 33.48 5 37.06 20.75 51.62 6 38.68 43.25 11.98 (Satisfied) 7 to 8 12.34 16.65 2.92 (Low readiness) 1 15.84 22.33 26.74 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 (Low motivation) 1 28.22 13.46 15.14 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 (No need for interaction) 1 29.05 10.42 28.47 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 Technology readiness 0.033 Motivation 0.0062 Need for interaction 0.0016 Note: only the significant covariates are used. 83 Results
  • 83. 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. 84 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 84. 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 85 Results
  • 85. 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. 86 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 86. 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. 87 Conclusions & managerial implication
  • 87. Table 6.1. The effect of consumer characteristics on the resistance and adoption 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). 88 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 88. 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 89 Conclusions & managerial implication
  • 89. 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 90 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 90. 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. 91 Conclusions & managerial implication
  • 91. 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)? 92 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 92. 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 93 Conclusions & managerial implication
  • 93. 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, 94 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 94. 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. 95 Conclusions & managerial implication
  • 95. 96 The influence of hurdles and benefits on the diffusion of online grocery shopping
  • 96. 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. 97 Limitations and directions for further research
  • 97. 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. 98 The influence of hurdles and benefits on the diffusion of online grocery shopping
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  • 110. Colofon Author 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 111 Colofon
  • 111. Disclaimer This document is prepared by either Coöperatieve Centrale Raiffeisen-Boerenleenbank B.A., trading as Rabobank International (“RI”) or Coöperatieve Centrale Raiffeisen-Boerenleenbank B.A., acting through its New York Branch and any of its associated or affiliated companies and directors, representatives or employees. Coöperatieve Centrale Raiffeisen-Boerenleenbank B.A. is incorporated in the Netherlands. The liability of its members is limited. Coöperatieve Centrale Raiffeisen-Boerenleenbank B.A. is authorised and regulated by De Nederlandsche Bank N.V. Furthermore, RI in the Netherlands is regulated by the Netherlands Authority for the Financial Markets. RI, London Branch is regulated by the Financial Services Authority for the conduct of UK business. RI, London Branch is registered in England and Wales under no. BR002630. With respect to this document, in the U.S.A., any banking services are provided by Coöperatieve Centrale Raiffeisen-Boerenleenbank B.A., New York Branch and any securities related business is provided by Rabo Securities USA, Inc., a U.S. registered broker dealer. This document is directed exclusively to either Eligible Counterparties and Professional Clients on the one hand and Market Counterparties and Intermediate Customers on the other. It is not directed at Retail Clients respectively Private Customers. This document is for information and discussion purposes only. Neither this document nor any other statement (oral or otherwise) made at any time in connection herewith is and is not, and should not be construed as an offer, invitation or recommendation to acquire or dispose of any securities or a commitment to enter into any transaction. Any transaction would be subject to contract, satisfactory documentation and market conditions. All parties are advised to seek independent professional advice as to the suitability of any products and their tax, accounting, legal or regulatory implications. The information and opinions contained in this document have been compiled or arrived at from public sources believed to be reliable, but no representation or warranty, express or implied, is made as to the accuracy, completeness or warranty, express or implied. This document does not constitute investment advice, nor is it intended to be investment research. All opinions expressed in this document are subject to change without notice. This document does NOT purport to be an impartial assessment of the value or prospects of its subject matter and it must not be relied upon by any recipient as an impartial assessment of the value or prospects of its subject matter. The information contained in this document is not to be relied upon by the recipient as authoritative or taken in substitution for the exercise of judgement by any recipient. To the extent permitted by law, neither RI, nor other legal entities in the group to which it belongs accept any liability whatsoever for any direct or consequential loss howsoever arising from any use of this document or its contents or otherwise arising in connection therewith. Insofar as permitted by applicable laws and regulations, Coöperatieve Centrale Raiffeisen-Boerenleenbank B.A. or other legal entities in the group to which it belongs, their directors, officers and/or employees may have had or have a long or short position or act as a market maker and may have traded or acted as principal in the securities described within this document, or related investments, or may otherwise have conflicting interests, including acting as advisors, brokers, bankers or providing services to companies whose securities, or related investments, are mentioned in this document. Further Coöperatieve Centrale Raiffeisen-Boerenleenbank B.A. may have or have had a relationship with or may provide or have provided corporate finance or other services to companies whose securities (or related investments) are described in this document. This document may not be reproduced, distributed or published, in whole or in part, for any purpose, except with the prior written consent of Coöperatieve Centrale Raiffeisen-Boerenleenbank B.A. By accepting this document you agree to be bound by the foregoing restrictions. © www.rabobank.com © Rabobank International Croeselaan 18, 3521 CB Utrecht, The Netherlands © Rabobank International, London Branch Thames Court, One Queenhithe, London EC4V 3RL, United Kingdom © Coöperatieve Centrale Raiffeisen-Boerenleenbank B.A., New York Branch 245 Park Avenue, New York, New York 10167, United States of America 112 The influence of hurdles and benefits on the diffusion of online grocery shopping
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