A RETAIL SETTING. Bert Weijters, Devarajan Rangarajan, Tomas Falk, Niels Schillewaert

Transcription

1 1 DETERMINANTS AND OUTCOMES OF CUSTOMERS USE OF SELF-SERVICE TECHNOLOGY IN A RETAIL SETTING Bert Weijters, Devarajan Rangarajan, Tomas Falk, Niels Schillewaert Journal of Service Research The online version of this article can be found at: DOI: / Journal of Service Research : 3 Keywords: Self-Service Technology; Retailing; Consumer attitudes and behavior; Technology Adoption Bert Weijters, Assistant Professor of Marketing, Vlerick Leuven Gent Management School, Reep 1, B-9000 Ghent, Belgium, Phone: , Fax: , E- mail: bert.weijters@vlerick.be. Devarajan Rangarajan, Assistant Professor of Marketing, Vlerick Leuven Gent Management School, Reep 1, B-9000 Ghent, Belgium, Phone: , deva.rangarajan@vlerick.be. Tomas Falk, Post Doctoral Research Fellow, Fraunhofer Institute for Systems and Innovation Research, Breslauer Strasse 48, D Karlsruhe, Germany, Phone: , tomas.falk@isi.fraunhofer.de. Niels Schillewaert, Associate Professor of Marketing, Vlerick Leuven Gent Management School, Reep 1, B-9000 Ghent, Belgium, Phone: , niels.schillewaert@vlerick.be.

2 2 DETERMINANTS AND OUTCOMES OF CUSTOMERS USE OF SELF-SERVICE TECHNOLOGY IN A RETAIL SETTING ABSTRACT Retailers are increasingly turning towards self-service technologies (SSTs) aimed at improving productivity and service quality while cutting costs. We identify a process model to understand the antecedents and consequences of SST usage by customers in an in-store retail setting. The model was validated on a combination of survey and observational data. Perceived usefulness, perceived ease of use, reliability and fun were identified as key drivers of customer attitude towards the SST. Customer attitude towards the SST predicted the actual usage of technology. We study the effects of SST usage on the actual time spent by customers in the store. Moreover, we investigate the impact of SST usage on customers perceptions of waiting time and consequently on their level of satisfaction with the shopping experience. Finally, we analyze the moderating effects of age, education and gender. Overall, the current study evaluates the benefits of SST introduction for both customers and retailers.

3 3 The rapid acceptance of modern information and communication technologies in daily business activities is the most important long-term trend in the business world (Rust 2001). Consequently, retailers are increasingly considering innovative options for delivering service to their customers (Bobbitt and Dabholkar 2001; Dabholkar, Bobbitt, and Lee 2003; Quinn 1996). As a result, the mode of service provision and production is increasingly turning towards the use of self-service technologies (SSTs), thereby allowing customers to create a service outcome independent of direct service employee involvement (Meuter et al. 2000). Prominent examples for the increased usage of SSTs in retail settings include online shopping (Childers et al. 2001) and self-scanning systems (Dabholkar, Bobbitt, and Lee 2003). Additionally, while online retailing is seeing an increasing level of acceptance in the marketplace, as evidenced by sales for retailers in the US amounting to $102.1 billion in 2006, marking a 24 % increase versus 2005 (comscore 2007), the usage of SST systems in retail settings has met with limited success (Dabholkar, Bobbitt, and Lee 2003). By introducing SSTs, retailers get the customers themselves to be productive resources involved in the service delivery process, which in turn helps retailers to overcome two major problems resulting from human interaction in traditional service encounters. First, the introduction of SSTs allows handling demand fluctuations without the expensive adjustment of employee levels (Curran, Meuter, and Surprenant 2003). Second, a major part of the service process is standardized owing to the technological interface, which leads to a more consistent service atmosphere independent of employees personality and mood (Hsieh, Yen and Chin 2004). As a result, the introduction of SSTs opens up for retailers the potential of improving productivity and service quality while cutting costs. Nevertheless, given the resource intensity of SST introduction and retailers struggle to increase the number of SST users (Curran, Meuter, and Surprenant 2003), retailers find themselves under increased pressure to

4 4 demonstrate the positive outcomes caused by the new SST offering (Rust, Lemon, and Zeithaml 2004). Surprisingly, to the best of our knowledge, little empirical research has gone into examining the pre-economic consequences associated with the usage of SSTs in retailing. Thus, in order to address this research gap, we explore the impact of SST usage on customer satisfaction with the current shopping trip. We choose customer satisfaction as an important pre-economic outcome of SST usage as it has proven to be a strong determinant of customer retention which in turn leads to higher profits (Anderson and Mittal 2000; Anderson, Fornell, and Lehmann 1994). Moreover, as studies building on queuing theory empirically confirm the importance of time for customers service evaluation, we further integrate perceived waiting time as a critical outcome variable (Czepiel 1980; Davis and Vollmann 1990; Taylor 1994; Tom and Lucey 1995). The importance of integrating perceived waiting time in SST research is also supported by Dabholkar (1996) and Dabholkar and Bagozzi (2002) who incorporate perceived waiting time as a situational moderator of the attitude and intention formation related to SST use. Additionally, we investigate whether SST use has a real effect on the total time that customers spend in-store. Expected time gain when shopping has been identified as an important motivation for customers to use SST (Bateson 1985; Childers et al. 2001), so it is essential to evaluate the extent to which this potential benefit is realized. 1 The investigation of the outcomes of SST usage is the primary contribution of the current study, since it remains an area in technology adoption that is under researched. The outcomes under study are important in determining the impact of introducing SSTs in a retail context, both on 1 We would like to thank an anonymous reviewer for pointing out this variable as an interesting outcome to investigate.

5 5 customer satisfaction and operational dimensions like customer flow through the store (Tom and Lucey 1995). In sum, we evaluate the benefits of SST introduction for both customers and retailers. Consequently, this study helps managers to make better decisions based on more realistic expectations concerning SST use. The second key contribution of our study is the examination of the moderating influence of demographics (education level, age and gender) on the SST acceptance process. As demographics serve as frequently used segmentation variables, a relevant question is whether the impact of drivers of SST usage are equal across different demographic groups (Chiu, Lin, and Tang 2005). Additionally, evidence for the importance of demographics is provided by the literature on technology adoption by employees within organizations which has established the key role played by demographics in this setting (Morris and Venkatesh 2000; Venkatesh and Morris 2000; Venkatesh et al. 2003). Finally, we identify important determinants of the attitude towards SST usage and connect attitude to actual behavior. Drawing on the Technology Acceptance Model (TAM), previous research has highlighted the role of attitude as an antecedent of SST usage. Yet, little empirical research has linked attitudes towards SST use to actual behavior in a real life setting. To address this issue, we collected a combination of survey and observational data in a retail setting (as done earlier in a study on self-scanning by Dabholkar, Bobbitt, and Lee 2003). This provides additional support for the relevance of the identified drivers of SST usage. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT Antecedents of Attitude towards SST A considerable part of the literature on SSTs examines determinants of SST acceptance (Childers et al. 2001; Curran, Meuter, and Surprenant 2003; Dabholkar 1994; 1996;

6 6 Dabholkar and Bagozzi 2002; Featherman and Pavlou 2003; Plouffe, Hulland, and Vandenbosch 2001). These studies are largely inspired by technology acceptance research including the Technology Acceptance Model (TAM) (Davis 1989) and Diffusion of Innovations Theory (Rogers 2003). TAM is based on the Theory of Reasoned Action (TRA) which asserts that an attitude towards a specific behavior and subjective norm have an impact on behavioral intention, which in turn determines the behavior displayed (Ajzen and Fishbein, 1980; Fishbein and Ajzen, 1975). According to TAM the amount of technology acceptance is reflected in the strength of attitude/intention towards using the technology (Davis, Bagozzi, and Warsaw 1989). An attitude can be defined as a person s negative or positive evaluation of performing the target behavior. Intentions are assumed to capture the motivational factors that influence a behavior, and thus indicate how hard people are willing to try or to what extent they are planning to make an effort, in order to perform the behavior (Ajzen and Fishbein, 1980). Within TAM, Davis, Bagozzi, and Warshaw (1989) identify two fundamental constructs for forecasting the acceptance of computer technology in an organizational setting: perceived ease of use and perceived usefulness. Ease of use refers to the process leading to a final outcome. Attainment of the said outcome itself (rather than the process leading towards it) is represented by perceived usefulness. Perceived usefulness reflects the utilitarian view on shopping, according to which consumers are concerned with buying products in a timely and efficient manner (Childers et al. 2001; Sherry, McGrath and Levy 1993). Dabholkar and Bagozzi (2002, p. 186) suggest that the perceived usefulness dimension is not relevant for technology-based self-services in which the consumer participates but does not own. Instead, Dabholkar and Bagozzi (2002) introduce the performance construct as a determinant of SST acceptance. According to Dabholkar and Bagozzi (2002), performance pertains to the extent to which the SST consistently and accurately performs the

7 7 expected task. In our study, we think that it is important that both of the above mentioned perspectives of perceived usefulness play a vital role in shaping customer s attitudes towards using the SSTs. Consequently, we refer to the consistency and accuracy of the SSTs as the reliability associated with using the SST, and perceived usefulness refers to the benefits customers associate with using the SSTs. We suggest that when faced with the choice of using SST, users tend to focus on the potential benefits that the technology has to offer (Bateson 1985; Meuter et al. 2000; Parasuraman, Zeithaml, and Malhotra 2005). This is in line with research conducted by Childers et al. (2001), who identify usefulness as a major driver of the attitude towards a SST in a retail shopping context reflecting the more instrumental aspects of shopping. In view of this, we suggest the following hypothesis: H1: Perceived usefulness of the SST is positively related to attitude towards the SST. As the literature review on individuals using technology reveals, the ease with which users can handle the technology positively affects their attitude towards it. This has proven to be true in research on organizational behavior (Davis, Bagozzi, and Warshaw 1989; Venkatesh et al. 2003), and in research on SSTs (Bateson 1985; Dabholkar 1996; Dabholkar and Bagozzi 2002; Dabholkar, Bobbitt, and Lee 2003). As a result, we identify ease of use as a key independent variable affecting customer attitudes to SST. H2: Perceived ease of use of the SST is positively related to attitude towards the SST. Drawing on insights from the literature on SST evaluation, we further suggest integrating the perceived reliability of the technology based self-service as a determinant of the attitude towards the SST (Dabholkar 1996; Dabholkar and Bagozzi 2002; Dabholkar, Bobbitt, and Lee 2003). This is further supported by research on service quality in general (Parasuraman, Zeithaml, and Berry 1988), and electronic service quality in particular

8 8 (Parasuraman, Zeithaml, and Malhotra 2005). Reliability represents a major determinant of overall electronic service quality and refers to the correct technical functioning of a SST and the accuracy of service delivery. Thus, in keeping with our arguments, we propose the following hypothesis: H3: Perceived SST reliability is positively related to attitude towards the SST. Still, as noted by Babin, Darden, and Griffin (1994) if shopping trips are assessed solely on the utilitarian benefits of products or services attained, the numerous intangible and emotional aspects related to a shopping experience are excluded. Therefore, a more recent stream of research has introduced the hedonic aspect of using self-services and focused particularly on the enjoyment aspect of using technology (Childers et al. 2001; Dabholkar 1994; Dabholkar and Bagozzi 2002; Dabholkar, Bobbitt, and Lee 2003). Enjoyment refers to the extent to which the activity of using technology is perceived to provide reinforcement in its own right, apart from any performance consequences that may be anticipated (Davis, Bagozzi and Warshaw 1989). Enjoyment has been reported to influence technology adoption for technology usage at the workplace (Davis, Bagozzi, and Warshaw 1992). While the utilitarian aspect is already represented by the more goal-oriented factor of perceived usefulness, enjoyment is added to reflect the hedonic aspect of using SSTs in a retail setting (Bauer, Falk, and Hammerschmidt 2006). As there is strong evidence in the literature for the significant effect the fun aspect has on the attitude formation towards using technology based self-services (Childers et al. 2001; Dabholkar and Bagozzi 2002; Dabholkar, Bobbitt, and Lee 2003), we propose that customers who perceive the aspect of using SST devices as a fun way of shopping are likely to have favorable attitudes towards the technology: H4: Perceived fun of using the SST is positively related to attitude towards the SST.

9 9 Use of SST The studies conducted by Dabholkar, Bobbitt, and Lee (2003), Venkatesh et al. (2003) and Meuter et al. (2005) go beyond the focus on attitudes for explaining SST acceptance and investigate actual SST use. Likewise, and in line with Mick s (2003) call to combine mental processes with actual behavior, we link the self-reported attitude towards the usage of SST to observed use of the SST. We hypothesize that: H5: Attitude towards the use of SST is positively related to the actual use of the SST. Moderating effects In marketing research, the conceptualization of innovativeness builds on psychological research concerning optimal stimulation level (Berlyne 1978). Optimal stimulation level refers to the observation that organisms may engage in activities merely for the sake of having exciting and novel experiences. Importantly, there are individual differences in the extent to which organisms in general and people in particular, feel intrinsically rewarded by such behavior. Exploratory tendencies thus motivate behavior, but not equally so among different individuals. Building on the work related to optimal stimulation as well as the work by Rogers (1962), Midgley and Dowling (1978), Raju (1980), Steenkamp and Baumgartner (1992), Baumgartner and Steenkamp (1996) translated these findings to a consumption context and proposed the concept of exploratory consumer buying behavior, later conceptualized as consumer innovativeness (Steenkamp, ter Hofstede, and Wedel, 1999). Hirschman (1980) proposed the concept of novelty seeking, i.e. the desire to seek out new stimuli. It is the latter aspect of innovativeness that has been pointed out as being especially relevant in the context of SSTs (Dabholkar and Bagozzi 2002). Within the innovativeness paradigm it has been common to focus on products and technologies that are particularly new. However, as SSTs are gaining acceptance, they are

10 10 automatically losing some of their newness. Consequently, SSTs are no longer new by necessity and their perceived newness is becoming more and more variable. For this reason, we posit newness as a variable attribute (rather than a given or constant) that may have a positive valence for some, a negative valence for others (Blythe 1999). The evaluation of newness consequently contributes to the overall attitude towards SST. In general, consumers' preference for newness (as an aspect of innovativeness) has been found to vary as a function of demographic variables (Robertson and Gatignon, 1991; Im, Bayus, and Mason, 2003). In general, we believe it is important to study how the technology usage process differs across demographic segments, as it has been done in an organizational context (Morris and Venkatesh 2000; Venkatesh and Morris 2000; Venkatesh et al. 2003). A better understanding of the moderating effects of demographics has both practical and theoretical value. While focusing on personality traits of users of SST is interesting (Dabholkar and Bagozzi 2002), demographic variables are more readily identifiable in practice and therefore more actionable 2 (Wedel and Kamakura 2000; Mittal and Kamakura 2001). Since generalization of specific domain-based findings to different settings is at the heart of theory development, we test whether findings from organizational behavior apply to the use of SST s by customers in a retail setting. As suggested above, since demographics are frequently used as segmentation variables, a relevant question is whether the determinants of SST use differ depending on the levels of education, gender, and age. Apart from the study conducted by Venkatesh et al. (2003) and Meuter et al. (2005), the issue of demographic influences on SST use hasn t been fully covered yet by the existing literature. An understanding of demographic differences in 2 By actionable we mean that the proposed segmentation (in terms of demographics) provides the opportunity for management to implement actions to target specific segments with specific propositions. For example, direct mailings with specific arguments and incentives to use SST can be addressed to different age groups.

11 11 perceptions and attitudes towards using SSTs in a real-life retail setting is valuable for the management of SSTs as demographics are generally acknowledged to profoundly influence response to marketing strategies (Meyers-Levy 1988, Meyers-Levy and Sternthal 1991; Mittal and Kamakura 2001). Education People differ in their sensitivity to time-related issues (Berry, Seiders, and Grewal 2002; Hui and Tse 1996). Durrande-Moreau and Usunier (1999) indicate that people who have more highly qualified jobs and education levels tend to display a more quantitative time orientation, as reflected in the statement time is money. We hypothesize that higher educated people will try harder to optimize their time allocation and thus feel more time pressure. This will lead them to attach more importance to the time gain that comes with using SST. H6a: The positive relation between perceived usefulness and attitude towards using the SST is stronger among higher educated customers than among lower educated customers. We also suggest that people exposed to higher levels of education are likely to have had more exposure to technology, not only at their workplace, but also in the course of their day-to-day activities. Additionally, the nature of the task at hand and its interaction with technology has been found to play an important role in the perceptions of the technology amongst individuals in an organizational setting (Rangarajan, Jones, and Chin 2005). In a retail setting, the task of using the technology is only secondary to the main issue of shopping for items. Given this, in conjunction with the exposure of educated individuals being more exposed to technology, we hypothesize that

12 12 H6b: The positive relation between perceived ease of use and attitude towards using the SST is weaker among higher educated customers than among lower educated customers. Based on an extensive review of the literature, Rogers (2003) concludes that earlier adopters have more years of formal education than do later adopters. As argued above, the defining characteristic of innovations obviously is their newness. Newness as an attribute may itself have some utility for customers (Blythe 1999), and apparently this is specifically the case for the highly educated, since this group has been found to more readily adopt new technologies (Im, Bayus, and Mason 2003). Taken together, these lines of thoughts lead to the hypothesis that perceived newness of an SST will positively affect attitudes towards the SST of higher educated people, while negatively affecting attitudes towards the SST of lower educated people. H6c: The relation between perceived newness of a SST and attitude towards using the SST is positive for higher educated people, and negative for lower educated people. The effect of education on user attitude towards technology has been documented in the organizational behavior literature as affecting an individual s attitude towards technologies in the workplace (Morris and Venkatesh 2000; Venkatesh and Morris 2000). However, to the best of our knowledge, little research has focused on the effect of education levels on subsequent usage of the technology. In line with Evanschitzky and Wunderlich (2006), we suppose that people with higher levels of education perform more comprehensive information gathering and processing efforts than less educated people. This is because better educated people draw on more information prior to decision making, whereas less well educated people rely more on fewer information cues (Capon and Burke 1980). Considering the important role of information processing in the formation of attitudes (Hoyer and MacInnis 2004), and the fact that better educated consumers feel more comfortable when

13 13 dealing with and relying on new information than people with lower educational attainments (Homburg and Giering 2001), we suggest the following hypothesis: H6d: Actual usage of the SST is more strongly related to attitude towards using the SST for customers with higher education levels than for customers with lower education levels. Age In a workplace setting, Morris and Venkatesh (2000) show that the relation between attitude and intention is not equally strong for all people. More specifically, they find that intention to use technology is more strongly driven by attitude among younger people as compared to older people. We propose that this finding is transferable to the attitude-behavior link in a retail setting and suggest: H7: Younger customers actual usage of the SST is more strongly related to their attitude towards using the SST than is older customers actual usage of the SST to their attitude towards using the SST. Gender Over the years, an increasing body of research has studied gender differences in shopping behavior. It has been found that males and females employ different information processing strategies while shopping (Meyers-Levy and Maheswaran 1991; Meyers-Levy and Sternthal 1991). Some researchers have suggested that females generally show higher involvement and more thorough information processing in shopping than do males (Laroche et al. 2000; Laroche et al. 2003). This might possibly translate into different priorities while shopping, in that males may want to minimize time and effort invested, while females may want to minimize distraction from the shopping task. In relation to SST use, this would imply that males attach more importance to making their shopping more efficient by using SST,

14 14 while females would not want to complicate their shopping task performance by having to use the SST. Evidence from a different context leads to similar conclusions. Venkatesh and Morris (2000) studied gender differences in the context of technology acceptance in the workplace. They found that, [ ] compared to women, men s technology usage decisions were more strongly influenced by their perceptions of usefulness. In contrast, women were more strongly influenced by perceptions of ease-of use [ ]. Based on these two lines of reasoning, we hypothesize a moderating effect of gender on the importance of perceived usefulness and perceived ease of use in the formation of attitude and intention. Thus, H8a: As compared to women, men s attitude towards using SST is more strongly related to perceived usefulness. H8b: As compared to men, women s attitude towards using SST is more strongly related to perceived ease of use. Outcomes of SST Usage As the literature review shows, research on exploring SST acceptance is a growing area of interest. Nevertheless, to the best of our knowledge, most studies except for Meuter et al. (2005) and Dabholkar, Bobbitt, and Lee (2003) have not focused on investigating post-usage behavior. This has to be seen as a major limitation as retailers have to show the economic consequences of their actions. Thus, the resource intensive introduction of SSTs must be made financially accountable by showing the contribution for enhancing a firm s financial performance (Rust et al. 2004). With customer satisfaction being a major driver of customer retention and profit, we integrate this variable as an important outcome of SST usage in our model (Anderson, Fornell and Mazvancheryl 2004; Anderson and Mittal 2000). As waiting time still remains a critical factor to customers shopping experience, we integrate this

15 15 measure along with customer satisfaction as an important outcome variable (Davis and Vollmann 1990; Tom and Lucey 1995). Zeithaml and Bitner (2003) suggest that customers often look for quick and efficient service and do not expect to spend a lot of time waiting. Therefore, it is crucial to investigate the effect of SST use on the perceived waiting time. We thereby follow a time perception approach (Hornik, 1984, p. 617) rather than measuring waiting time on the basis of standard measurement characteristics. This is in line with Tom and Lucey (1995) who promote that perceived time, more than objective time seems to form the basis of the reality for consumer experience and behavior. The construct of perceived waiting time seems also to offer more insights from a managerial point of view. Managers often face external limitations of ways of decreasing objective waiting times due to e.g. physical space which limits the maximum amount of possible check stands or increased customer traffic in rush hours (Tom and Lucey 1995). In the domain of SST, the perceived reduction in waiting time during the service experience is the main advantage delivered by using SSTs (Bateson 1985; Meuter et al. 2000). In a retail setting, SST delivers its main advantage by reducing waiting times at check out (Dabholkar, Bobbitt, and Lee 2003). As a result, we argue that SST users are likely to have lower perceived waiting times in the check-out counters. 3 H9: SST use has a negative effect on perceived waiting time. 3 It is not our intention to imply that the perception of waiting time is somehow biased either among non SSTusers or SST users. Rather, we opt for perceived waiting time (rather than actual waiting time) for two reasons: it is perceived not actual - waiting time that affects satisfaction, and asking about perceived waiting times leaves the customer the freedom to decide what is counted as waiting time and what is not, thus circumventing the conceptual and operational problem of delineating when waiting time starts and ends. We would like to thank an anonymous reviewer for drawing our attention to the potential unclarity in this regard.

16 16 Waiting times have been shown to strongly affect evaluations of service encounters by customers (Taylor 1994; Zeithaml and Bitner 2003). Durrande-Moreau and Usunier (1999, p. 177) point out, the wait is a minor but significant part of the overall service encounter that influences customers global evaluation of the service. In retail settings, customer perceived waiting time has been identified as a critical contributing factor to customer satisfaction with the retail outlet (Pruyn and Smidts 1998; Tom and Lucey 1995). When SST users and non-users perceive that they spend less time waiting in the counter, they exhibit higher levels of satisfaction with the overall shopping experience (Davis and Vollmann 1990). Consequently, we believe that the perceived waiting time will affect customer satisfaction with the shopping experience. H10: Perceived waiting time is negatively related to overall satisfaction with the shopping trip. Since one of the primary reasons for consumers to use SSTs is to save time, we expect the above mentioned relation to be even stronger among SST users (Childers et al. 2001). This indicates that SST users can be expected to attach more importance to perceived waiting time, implying a stronger relation between perceived waiting time and satisfaction. We therefore suggest a moderating role of SST use on the perceived waiting time-satisfaction relationship: H11: The relationship between perceived waiting time and satisfaction will be stronger for SST users than for non-users. While the effect of SST use on perceived waiting time is especially important for customers (since it is presumed to affect their satisfaction levels), from an operational point of view it is also crucial to know whether SST affects the actual time that is spent in the store. In other words, the question is whether using SST is indeed less time consuming and more

17 17 efficient. This is important because it affects the total number of customers in the store at any given moment and consequently the capacity that is needed in-store (Tom and Lucey 1995). It is believed that SST use reduces the time needed for shopping (Bateson 1985; Childers et al. 2001). H12: SST use leads to less time spent in-store. To ensure that the effect of SST use on the described outcomes is not due to the operation of confounding variables, the following two control variables are included: the number of items the customer purchases and number of customers at check-out. In particular, these two situational variables may affect perceived waiting time and actual total time spent in-store. Also, they may affect these outcome variables to a different extent for users and non-users of SST. All the relations proposed above are summarized in Figure 1. Insert Figure 1 about here EMPIRICAL STUDY Data collection Setting To test our model, we collected data in stores of a grocery retail chain in Western Europe. At the time of data collection, the self-scanning option had been in place for at least one year in each of the stores. The self-scanners were hand-held devices that were made available on a shelf at the entrance of the store. Customers walking in could choose between two options: either use a self-scanning device to aid them in their shopping trip or shop in the traditional method without the self-scanning device. Customers choosing the self-scanning option would then use the device throughout their shopping trip to scan the barcodes on all items they selected from the shelves. At the check-out, self-scanner users could then proceed

18 18 to separate lanes, where their bill was directly computed based on the purchases read-in by the device. The customers would then make the payments to a cashier at a check-out counter specifically reserved for SST users and, following this, would exit the store. In contrast, customers who did not use self-scanning devices would proceed directly to the check-out counter, have the products scanned by a cashier and subsequently pay the cashier for the goods purchased. Self-scanners are available only for customers in possession of a loyalty card. This policy has several reasons. First, the option to use self-scanning is considered an incentive to subscribe to the loyalty card system. Second, since only a sample of self-scanning customers purchases is checked by personnel at the check-out, the retailer reserves self-scanning for customers it chooses to trust. Finally, the retailer is constructing a behavioral database and only card holders can be readily identified in order to store their track record in terms of self-scanning use. Consequently, only loyal customers took part in this research. Survey Administration Six teams of research associates simultaneously collected data in six stores of the grocery retailer. Data were collected during a three day period to ensure a representative cross-section of shoppers. Data collection consisted of two stages. In the first stage, research associates addressed shoppers upon entering the store. A questionnaire with closed-ended questions was used for this data collection, which took place at the entrance. The next stage of data collection was after the customers had done their shopping and had checked out their items. They were requested to participate in an exit survey with closed-ended questions. The entry survey contained two filter questions to ensure (1) that we did not include people who were unaware of self-scanning devices and (2) that only customers with a loyalty card filled out our survey, given the retailer s policy of offering self-scanning devices only to

19 19 loyal customers. Upon provision of acceptable responses for the filter questions, the main questionnaire was administered to the respondents. The questionnaire consisted of a series of items measuring the perceived attributes of the SST as well as attitudes towards the SST. In addition, the demographic variables, educational level, year of birth and gender were measured. Participants were assigned unique identification numbers to enable us to match their responses between the entry and exit surveys. Also, the exact time of the entry and exit surveys were recorded. This allowed us to (1) measure the total amount of time spent instore, and (2) match the questionnaire data with observational data collected in the following way. At every one-minute interval, an accomplice recorded the number of non SST-users waiting at the cash registers. This number was then used as a proxy for crowding, and is henceforth referred to as the number of customers at check-out. At the end of the shopping experience, the identification numbers of the participants were recorded to enable us to match their responses at entry and exit. The respondents use or non-use of self-scanning was recorded. Respondents then answered additional questions about their perceptions of how long they waited in line (including processing time of purchases at check-out) and about their levels of satisfaction with the shopping experience. Additionally, the exact number of purchases was noted (based on the bill). Scale design Since our study focuses on use of a particular SST in Europe, we initiated a qualitative stage of research, which served two purposes. Our first purpose was to check whether relevant variables had to be included in the model in addition to those apparent from previous studies, specifically in the attitudinal model and the outcomes model. Our second purpose was to become familiar with the way customers feel, think and communicate about this issue. To this end, we did face-to-face interviews with a convenience sample of 30 customers. Of these respondents, 10 were male. Further, 9 respondents had ages from 20 through 29, 8 from

20 20 30 through 39, 9 from 40 through 49, and 4 had ages of 50 years and more. In our sample, 13 respondents had used self-scanning in the last year. Based on the literature and the qualitative interviews, we formulated indicators for the variables in the conceptual model. Attitudes were measured using a three item five-point semantic-differential scale. Perceived attributes of the SST were measured by means of five point Likert scales based on Dabholkar (1994), Dabholkar and Bagozzi (2002) and the qualitative interviews. Entry questionnaire Perceived usefulness was assessed by three items capturing efficiency, speed of shopping with self-scanning and SST use effect on waiting time at the cash register. Perceived ease of use was measured by two items capturing aspects concerning userfriendliness and effort related to using SSTs. Reliability was quantified with three items capturing the extent to which self-scanning is expected to work well and have a faultless result. Perceived fun was measured by two items capturing to what extent the use of selfscanning is perceived as being entertaining and enjoyable. Perceived newness was measured using three 5-point semantic differentials. Items of all attitudinal and attribute variables are presented in Appendix 1. Use of self-scanning was observed and notated as a dummy variable, with 0 equaling no use of self-scanning and 1 equaling use of self-scanning. Age was measured by asking respondents year of birth and subtracting it from the year of data collection. Gender was observed and notated as a dichotomous variable where 0 equals male and 1 equals female. Level of education was measured by means of an open question probing for the number of years of formal education after primary school. Exit questionnaire Satisfaction with the shopping trip was captured in terms of an overall, cumulative satisfaction on a scale from zero to ten, where zero is very dissatisfied and ten is very satisfied (Anderson, Fornell, and Lehmann 1994). Perceived waiting time was measured as

21 21 the perception of how many minutes the respondent had been waiting at the cash register (Hornik 1984). Perceived waiting time included the processing of the purchases. We consciously used single item measures in the exit survey in order to limit the burden on our respondents. This way, we intended to minimize non-response to the exit survey as well as bias due to irritation. Other researchers have made the same choice in a similar context (Taylor 1994) and perceived waiting time can be argued to be measurable by a oneitem measure (Rossiter 2002). The number of actual purchases was noted down based on the customers check-out bill. RESULTS Sample A total number of 1492 shoppers were approached for participation in the survey. Of these, 709 people responded favorably giving us a response rate of 47.1 %. Further, 632 respondents who answered favorably possessed a loyalty card (this was a first filter). After further filtering out respondents who were not familiar with self-scanning, this number dropped to 610 (this was a second filter). Each respondent was issued a ticket with a unique identification number and this ticket was then collected by a second team of research associates awaiting respondents at the exit of the store. Of the 610 respondents, 554 people (90.8 % of entry participants) participated in the exit survey. Out of these 554 responses at the exit, 548 responses could be matched with the same customer s response at the entry, since six customers failed to provide their numbered ticket. In the end, 497 questionnaires contained complete data of customers who had purchased at least one product, and could be used in the analysis. In this sample, 64.8 % (35.2 %) were female (male). Further, 62.6 % had had education after secondary school. As for age, 1.0 % were aged 12-19, 21.3 % 20-29, 21.1 % 30-39, 28.2 % 40-49, 18.5 % 50-59, 7.4

22 22 % 60-69, 1.6 % 70-79, and.8 % years. Finally, 36.2 % used self-scanning during the current visit to the store. This figure lies in the normal range of SST use rates commonly observed in the chain under investigation (this was stated thus by management). Data Analysis Test of attitudinal measurement model Before testing the structural model of interest, we evaluate the quality of the measurement model of the attitude and beliefs constructs. To this end, we perform a confirmatory factor analysis on attitude, perceived usefulness, perceived ease of use, fun, reliability and newness in MPlus version 3.13 (Muthén and Muthén 2004), using the MLR estimation (robust maximum likelihood). Although the chi square test is significant (χ² (89) =140.16, p= ), the alternative indices compare favorably to common criteria (Hu and Bentler 1999): CFI = 0.984; TLI= 0.979; RMSEA=0.034; SRMR= Moreover, there are no reasons to suspect that specific model modifications would enhance the quality of the parameter estimates. Table 1 provides a detailed evaluation of convergent and discriminant validity by listing each factor s average variance extracted and the shared variance between each pair of factors (Fornell and Larcker 1981). As can be read from this table, for all factors the average variance extracted was higher than their shared variances, providing evidence of good construct and discriminant validity. The correlation matrix of the attitudinal items is reported in Appendix 2. Insert Table 1 about here. Test of core model Building further on the above measurement model, we test the complete core model as depicted in Panel I of Figure 1 (i.e. without the moderating effects of the demographic variables). To this end, we specify a structural equation model in which attitude is regressed

23 23 on perceived usefulness, perceived ease of use, fun, reliability (for completeness, newness was included as a control variable). Use is regressed on attitude, by means of a logistic regression such that the regression weight, b, is interpreted as the increase in the log odds of using self-scanning versus not using self-scanning for a unit increase in attitude (as measured by a five point scale). The corresponding odds increase is b exponentiated, where odds refers to the ratio of the probability of using self-scanning to the probability of not using selfscanning (Muthén and Muthén 2004). The unstandardized regression weights are presented in Table 2. Insert Table 2 about here. It is clearly evident from the table that users attitudes towards the SST is significantly affected (p<0.05; one-sided) by perceived usefulness, perceived ease of use, reliability and fun. Thus, hypotheses H1 through H4 were supported. Additionally, our results indicate that user attitudes towards SST positively and significantly affected the actual use of selfscanning technology (p<0.001), thereby supporting H5. Test of moderating effects Next, we test the moderating effects of gender, age and education level on the core model (see Panel II of Figure 1). In choosing a method for these tests, we take into account the following characteristics of our data and model. First, while gender is a dichotomous variable, age and education are measured at a ratio level and should be treated as such (MacCallum et al. 2002). Second, the moderating variables are non-experimental in nature and are correlated to one another (age and education have a Pearson correlation of -0.21). Finally, the antecedents of attitude and attitude itself are latent variables, measured with error (Fornell and Larcker 1981). Therefore, we define interaction terms of each latent variable with each demographic variable. To make this operation feasible from a practical and computational perspective, we

24 24 subsequently test separate models, each of which contains the three interactions between all demographics and one latent variable, as well as the main effects of the demographic variables and all remainder beliefs. To illustrate, the first such model contains all variables of the core model, plus the demographics, plus the following interactions: (1) perceived usefulness by education level, (2) perceived usefulness by age, and (3) perceived usefulness by gender. In these analyses, education level and age are mean-centered. Also note that gender is coded as a dummy with a zero for males, a one for females. Consequently, the main effect regression weights correspond to the estimated average effect of an independent variable on a dependent variable for males of average age and education level in the sample. The interaction terms can be interpreted as the effect a unit increase in the demographic has on the former regression weight. We include interaction terms with all demographics even if not all of them are hypothesized. The reason for this is that not doing so might lead to spurious effects (Draper and Smith 1998). For example, if an effect is hypothesized for education but not for age, not including age might lead education to capture the effect that is in reality due to age. Table 3 presents the parameter estimates based on the interaction models, again estimated using the MLR estimator in Mplus Insert Table 3 about here. Age and education are expressed in units of ten years. While it was not the intended purpose of this study, we note that none of the demographic main effects is significant (these effects are not reported). As for the moderating effects, with a sample size of less than 500 power is rather limited (McQuitty 2004). While this reduces the number of significant interaction regression coefficients, leading to non-rejection of most null hypotheses (and thus no support for most of our directed hypotheses), it does focus attention on effects that are substantial. Three hypothesized moderation effects are significant (H6c, H6d and H8a). The other moderation hypotheses were not supported (H6a, H6b, H7 and H8b).

25 25 First, Figure 2 illustrates the relation between perceived usefulness and attitude towards the SST for female and male customers. In line with H8a, perceived usefulness is less important for female customers than it is for male customers, with unstandardized regression weights of respectively 0.27 and The former figure is obtained by adding the interaction coefficient of the gender dummy (-0.15) to the main effect (0.42) (see Table 3). Insert Figure 2 about here. Second, Figure 3 depicts the effect of perceived newness on attitude towards the SST for three different groups of customers: those with minimal education in our sample (0 years after primary school), those with average education (8 years after primary school) and those with the maximum number of years of formal education (16 years after primary school, the maximum level observed in our sample). The effect of newness on attitude towards the SST is significantly moderated by education level. More specifically, in line with H6c, this relationship is more positive among the highly educated. Note that the apparent absence of a main effect is due to the presence of a negative effect for some customers (with lower education levels) combined with a positive effect for others (with higher education levels). Insert Figure 3 about here. Finally, as proposed in H6d, the impact of attitude on use of the SST increases with education level. The relationship between attitude towards the SST and the probability of using the SST by education level is shown graphically in Figure 4. In line with the logistic regression specification of use on attitude, the dependent variable (use / non-use) is expressed as a probability. Insert Figure 4 about here. This figure shows that the probability of using the SST given a neutral to positive attitude is much higher for highly educated customers than it is for customers with a lower education: the graph for the highly educated crosses the 0.50 probability earlier on and

26 26 increases to the 0.75 probability in a steeper fashion. The graph representing the probability of using the SST for the less educated never reaches the 0.90 probability, even for very positive levels of attitude. This indicates that, in this segment, attitude towards the SST is not sufficient to explain use of the SST. Outcomes of SST Use To evaluate the impact of SST use on shopping outcomes, we specify a path model as presented in Figure 1, Panel III. Parameters and model fit indices are estimated using the Maximum Likelihood Estimator. Use versus non-use of SST is used as a grouping variable. This allows us to simultaneously study the direct effects (mean and intercept differences) and the moderating effects (differences in regression weights) of SST use on shopping outcomes. In the model, there are three endogenous variables: satisfaction, perceived waiting time and total time spent in-store. Satisfaction is regressed on perceived waiting time; perceived waiting time is in turn regressed on the exogenous variables number of purchases and number of customers at check-out. Similarly, total time spent in-store is also regressed on the exogenous variables number of purchases and number of customers at check-out. We simultaneously estimate the parameters for the model in both groups (users and non-users). The resulting chi square value indicates acceptable fit (i.e., the misfit between this model and the data is insignificant: χ² (6) =5.63, p=0.466). The parameters of interest are the intercepts of satisfaction, perceived waiting time and total time spent in-store, as well as the regression weight of satisfaction on perceived waiting time. A between-group difference in intercept would indicate that the use of SST leads to different values in perceived waiting time, satisfaction and/or total time spent in-store after controlling for covariates. A difference in the regression weight would indicate a different impact of perceived waiting time on satisfaction across groups.

27 27 None of the intercepts of satisfaction, perceived waiting time and total time spent instore are significantly different for the two groups. More specifically, perceived waiting time for non SST-users averages 0.88 minutes (s.e.=0.31) versus 1.23 minutes (s.e.=0.38) for SSTusers, resulting in a non-significant difference t-test (t=0.71, n.s.). Hence, the data do not lend support to H9. The intercepts (standard errors) of satisfaction (on a ten point scale) for the non SST-users and SST users respectively are 8.70 (0.08) and 8.75 (0.11), also resulting in a non-significant difference test (t=0.35, n.s.). Finally, the intercepts (standard errors) of actual total time spent in-store for non SST-users versus SST users are (1.82) versus (2.30), resulting in a non-significant difference test (t=-0.01, n.s.). Hence, there is no support for H12. This indicates there is no direct effect of SST use on any of the three endogenous variables after controlling for covariates. As hypothesized in H10, the effect of perceived waiting time on satisfaction is negative in both groups. However, in line with H11, satisfaction is more negatively affected by perceived waiting time among SST users. Specifically, the respective regression weights (and related standard errors and t-values) for non-users and users of SST were (s.e. = 0.022; t = -5.32) and (s.e. = 0.040; t = -4.99), resulting in a difference t-test of (one-sided p = 0.043). These regression weights reflect the decrease in satisfaction for every minute of perceived waiting time. Based on our observations in the retail setting under investigation, we further explore the potential moderating effect of SST use / non-use on the relations between perceived waiting time and actual total time in store on the one hand and their two situational antecedents that served as control variables in the above analyses on the other hand (i.e. the number of items purchased and the number of customers at check-out). The regression weight estimates of the time related outcomes on the situational antecedents are presented in Table 4. Insert Table 4 about here.