m

Retailers are increasingly turning toward self-service tech-nologies (SSTs) aimed at improving productivity andservice quality while cutting costs. The authors identify aprocess model to understand the antecedents and conse-quences of SST usage by customers in an in-store retailsetting. The model was validated on a combination of sur-vey and observational data. Perceived usefulness, per-ceived ease of use, reliability, and fun were identified askey drivers of customer attitude toward the SST. Customerattitude toward the SST predicted the actual usage of tech-nology. The effects of SST usage on the actual time spentby customers in the store were studied. The authors inves-tigate the impact of SST usage on customers’ perceptionsof waiting time and, consequently, on their level of satis-faction with the shopping experience. Finally, the moder-ating effects of age, education, and gender are analyzed.The current study evaluates the benefits of SST introduc-tion for both customers and retailers.

Keywords: self-service technology; retailing; consumerattitudes and behavior; technology adoption

The rapid acceptance of modern information and com-munication technologies in daily business activities is themost important long-term trend in the business world (Rust2001). Consequently, retailers are increasingly consideringinnovative options for delivering service to their customers(Bobbitt and Dabholkar 2001; Dabholkar, Bobbitt, and Lee2003; Quinn 1996). As a result, the mode of service provi-sion and production is increasingly turning toward the use ofself-service technologies (SSTs), thereby allowing cus-tomers to create a service outcome independent of direct-service employee involvement (Meuter et al. 2000).Prominent examples for the increased usage of SSTs in retailsettings include online shopping (Childers et al. 2001) andself-scanning systems (Dabholkar, Bobbitt, and Lee 2003).

Journal of Service Research, Volume 10, No. 1, August 2007 3-21DOI: 10.1177/1094670507302990© 2007 Sage Publications

Determinants and Outcomes ofCustomers’ Use of Self-ServiceTechnology in a Retail Setting

Bert WeijtersDevarajan RangarajanVlerick Leuven Gent Management School

Tomas FalkFraunhofer Institute for Systems and Innovation Research

Niels SchillewaertVlerick Leuven Gent Management School

In addition, while online retailing is seeing an increas-ing level of acceptance in the marketplace, as evidenced bysales for retailers in the United States amounting to $102.1billion in 2006, marking a 24% increase versus 2005(comScore 2007), the usage of SST systems in retail set-tings has met with limited success (Dabholkar, Bobbitt,and Lee 2003). By introducing SSTs, retailers get the cus-tomers themselves to be productive resources involved inthe service delivery process, which in turn helps retailersto overcome two major problems resulting from humaninteraction in traditional service encounters. First, theintroduction of SSTs allows handling demand fluctuationswithout the expensive adjustment of employee levels(Curran, Meuter, and Surprenant 2003). Second, a majorpart of the service process is standardized owing to thetechnological interface, which leads to a more consistentservice atmosphere independent of employees’ personalityand mood (Hsieh, Yen, and Chin 2004).

As a result, the introduction of SSTs opens up forretailers the potential of improving productivity andservice quality while cutting costs. Nevertheless, giventhe resource intensity of SST introduction and retailers’struggle to increase the number of SST users (Curran,Meuter, and Surprenant 2003), retailers find themselvesunder increased pressure to demonstrate the positive out-comes caused by the new SST offering (Rust, Lemon,and Zeithaml 2004).

Surprisingly, to the best of our knowledge, littleempirical research has gone into examining the pre-economic consequences associated with the usage ofSSTs in retailing. Thus, to address this research gap, weexplore the impact of SST usage on customer satisfactionwith the current shopping trip. We choose customer sat-isfaction as an important pre-economic outcome of SSTusage, as it has proven to be a strong determinant of cus-tomer retention, which in turn leads to higher profits(Anderson, Fornell, and Lehmann 1994; Anderson andMittal 2000).

Furthermore, as studies building on queuing theoryempirically confirm the importance of time for customers’service evaluation, we further integrate perceived waitingtime as a critical outcome variable (Czepiel 1980; Davisand Vollmann 1990; Taylor 1994; Tom and Lucey 1995).The importance of integrating perceived waiting time inSST research is also supported by Dabholkar (1996) andDabholkar and Bagozzi (2002), who incorporate perceivedwaiting time as a situational moderator of the attitude andintention formation related to SST use.

In addition, we investigate whether SST use has a realeffect on the total time that customers spend in-store.Expected time gain when shopping has been identified asan 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 real-ized.1 The investigation of the outcomes of SST usage isthe primary contribution of the current study, as it remainsan area in technology adoption that is underresearched.The outcomes under study are important in determiningthe impact of introducing SSTs in a retail context, both oncustomer satisfaction and operational dimensions like cus-tomer flow through the store (Tom and Lucey 1995). Insum, we evaluate the benefits of SST introduction for bothcustomers and retailers. Consequently, this study helpsmanagers to make better decisions based on more realisticexpectations concerning SST use.

The second key contribution of our study is the exam-ination of the moderating influence of demographics(education level, age, and gender) on the SST-acceptanceprocess. As demographics serve as frequently used seg-mentation variables, a relevant question is whether theimpact of drivers of SST usage is equal across differentdemographic groups (Chiu, Lin, and Tang 2005). In addi-tion, evidence for the importance of demographics is pro-vided by the literature on technology adoption byemployees within organizations, which has establishedthe key role played by demographics in this setting(Morris and Venkatesh 2000; Venkatesh and Morris2000; Venkatesh et al. 2003).

Finally, we identify important determinants of the atti-tude toward SST usage and connect attitude to actualbehavior. Drawing on the technology acceptance model(TAM), previous research has highlighted the role of atti-tude as an antecedent of SST usage. Yet little empiricalresearch has linked attitudes toward SST use to actualbehavior in a real-life setting. To address this issue, wecollected a combination of survey and observational datain a retail setting (as done earlier in a study on self-scanning by Dabholkar, Bobbitt, and Lee 2003). Thisprovides additional support for the relevance of the iden-tified drivers of SST usage.

LITERATURE REVIEW AND HYPOTHESISDEVELOPMENT

Antecedents of Attitude Toward SST

A considerable part of the literature on SSTs examinesdeterminants of SST acceptance (Childers et al. 2001;Curran, Meuter, and Surprenant 2003; Dabholkar 1994,1996; Dabholkar and Bagozzi 2002; Featherman andPavlou 2003; Plouffe, Hulland, and Vandenbosch 2001).These studies are largely inspired by technology accep-tance research including the TAM (Davis 1989) and dif-fusion of innovations theory (Rogers 2003). TAM isbased on the theory of reasoned action (TRA), which

4 JOURNAL OF SERVICE RESEARCH / August 2007

asserts that an attitude toward a specific behavior andsubjective norm have an impact on behavioral intention,which in turn determines the behavior displayed (Ajzenand Fishbein 1980; Fishbein and Ajzen 1975). Accordingto TAM, the amount of technology acceptance isreflected in the strength of attitude or intention towardusing the technology (Davis, Bagozzi, and Warsaw1989). An attitude can be defined as a person’s negativeor positive evaluation of performing the target behavior.Intentions are assumed to capture the motivational fac-tors that influence a behavior, and thus indicate how hardpeople are willing to try or to what extent they are plan-ning to make an effort, in order to perform the behavior(Ajzen and Fishbein 1980). Within TAM, Davis,Bagozzi, and Warshaw (1989) identified two fundamen-tal constructs for forecasting the acceptance of computertechnology in an organizational setting: perceived ease ofuse and perceived usefulness. Ease of use refers to theprocess leading to a final outcome. Attainment of the saidoutcome itself (rather than the process leading toward it)is represented by perceived usefulness. Perceived useful-ness reflects the utilitarian view on shopping, accordingto which consumers are concerned with buying productsin a timely and efficient manner (Childers et al. 2001;Sherry, McGrath, and Levy 1993).

Dabholkar and Bagozzi (2002) have suggested thatthe perceived usefulness dimension is not relevant fortechnology-based self-services “in which the consumerparticipates but does not own” (p. 186). Instead,Dabholkar and Bagozzi introduce the performance con-struct as a determinant of SST acceptance. According toDabholkar and Bagozzi, performance pertains to theextent to which the SST consistently and accurately per-forms the expected task. In our study, we think that it isimportant that both of the above-mentioned perspectivesof perceived usefulness play a vital role in shaping cus-tomer’s attitudes toward using the SSTs. Consequently,we refer to the consistency and accuracy of the SSTs asthe reliability associated with using the SST, and per-ceived usefulness refers to the benefits customers associ-ate with using the SSTs.

We suggest that when faced with the choice of usingSST, users tend to focus on the potential benefits that thetechnology has to offer (Bateson 1985; Meuter et al.2000; Parasuraman, Zeithaml, and Malhotra 2005). Thisis in line with research conducted by Childers et al.(2001), who identify usefulness as a major driver of theattitude toward an SST in a retail-shopping context,reflecting the more instrumental aspects of shopping. Inview of this, we suggest the following hypothesis:

Hypothesis 1: Perceived usefulness of the SST ispositively related to attitude toward the SST.

As the literature review on individuals using technologyreveals, the ease with which users can handle the tech-nology positively affects their attitude toward it. This hasproven 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, andLee 2003). As a result, we identify ease of use as akey independent variable affecting customer attitudesto SST.

Hypothesis 2: Perceived ease of use of the SST ispositively related to attitude toward the SST.

Drawing on insights from the literature on SST evalua-tion, we further suggest integrating the perceived reliabilityof the technology-based self-service as a determinant of theattitude toward the SST (Dabholkar 1996; Dabholkar andBagozzi 2002; Dabholkar, Bobbitt, and Lee 2003). This isfurther supported by research on service quality in general(Parasuraman, Zeithaml, and Berry 1988), and electronicservice quality in particular (Parasuraman, Zeithaml, andMalhotra 2005). Reliability represents a major determinantof overall electronic service quality and refers to the correcttechnical functioning of an SST and the accuracy of servicedelivery. Thus, in keeping with our arguments, we proposethe following hypothesis:

Hypothesis 3: Perceived SST reliability is posi-tively related to attitude toward the SST.

Still, as noted by Babin, Darden, and Griffin (1994), ifshopping trips are assessed solely on the utilitarian bene-fits of products or services attained, the numerous intangi-ble and emotional aspects related to a shopping experienceare excluded. Therefore, a more recent stream of researchhas introduced the hedonic aspect of using self-servicesand focused particularly on the enjoyment aspect of usingtechnology (Childers et al. 2001; Dabholkar 1994;Dabholkar and Bagozzi 2002; Dabholkar, Bobbitt, and Lee2003). Enjoyment refers to the extent to which the activityof using technology is perceived to provide reinforcementin its own right, apart from any performance consequencesthat may be anticipated (Davis, Bagozzi, and Warshaw1989). Enjoyment has been reported to influence technol-ogy adoption for technology usage at the workplace(Davis, Bagozzi, and Warshaw 1992). Although the utili-tarian aspect is already represented by the more goal-oriented factor of perceived usefulness, enjoyment isadded to reflect the hedonic aspect of using SSTs in a retailsetting (Bauer, Falk, and Hammerschmidt 2006). As thereis strong evidence in the literature for the significant effectthat the fun aspect has on the attitude formation toward

Weijters et al. / USE OF SELF-SERVICE TECHNOLOGY 5

using technology-based self-services (Childers et al. 2001;Dabholkar and Bagozzi 2002; Dabholkar, Bobbitt, and Lee2003), we propose that customers who perceive the aspectof using SST devices as a fun way of shopping are likelyto have favorable attitudes toward the technology:

Hypothesis 4: Perceived fun of using the SST ispositively related to attitude toward the SST.

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 SSTacceptance and investigate actual SST use. Likewise, andin line with Mick’s (2003) call to combine mentalprocesses with actual behavior, we link the self-reportedattitude toward the usage of SST to observed use of theSST. We hypothesize that:

Hypothesis 5: Attitude toward the use of SST ispositively related to the actual use of the SST.

Moderating Effects

In marketing research, the conceptualization of innov-ativeness builds on psychological research concerningoptimal stimulation level (Berlyne 1978). Optimal stimu-lation level refers to the observation that organisms mayengage in activities merely for the sake of having excit-ing and novel experiences. Importantly, there are individ-ual differences in the extent to which organisms ingeneral and people in particular, feel intrinsicallyrewarded by such behavior. Exploratory tendencies thusmotivate behavior but not equally so among differentindividuals. Building on the work related to optimal stim-ulation as well as the work by Rogers (1962), Midgleyand Dowling (1978), Raju (1980), Steenkamp andBaumgartner (1992), Baumgartner and Steenkamp(1996) translated these findings to a consumption contextand proposed the concept of exploratory consumer buy-ing behavior, later conceptualized as consumer innova-tiveness (Steenkamp, ter Hofstede, and Wedel 1999).Hirschman (1980) proposed the concept of novelty seek-ing, or the desire to seek out new stimuli. It is the latteraspect of innovativeness that has been pointed out asbeing especially relevant in the context of SSTs(Dabholkar and Bagozzi 2002).

Within the innovativeness paradigm it has beencommon to focus on products and technologies thatare particularly new. However, as SSTs are gainingacceptance, they are automatically losing some of theirnewness. Consequently, SSTs are no longer new by

necessity, and their perceived newness is becoming moreand more variable. For this reason, we posit newness as avariable attribute (rather than a given or constant) thatmay have a positive valence for some but a negativevalence for others (Blythe 1999). The evaluation of new-ness consequently contributes to the overall attitudetoward SST. In general, consumers’ preference for new-ness (as an aspect of innovativeness) has been found tovary as a function of demographic variables (Im, Bayus,and Mason 2003; Robertson and Gatignon 1991).

In general, we believe that it is important to studyhow the technology-usage process differs across demo-graphic segments, as it has been done in an organiza-tional context (Morris and Venkatesh 2000; Venkateshand Morris 2000; Venkatesh et al. 2003). A betterunderstanding of the moderating effects of demograph-ics has both practical and theoretical value. Althoughfocusing on personality traits of users of SST is inter-esting (Dabholkar and Bagozzi 2002), demographicvariables are more readily identifiable in practice andtherefore more actionable2 (Mittal and Kamakura 2001;Wedel and Kamakura 2000). Because generalization ofspecific domain-based findings to different settings isat the heart of theory development, we test whetherfindings from organizational behavior apply to the useof SSTs by customers in a retail setting.

As suggested above, as demographics are frequentlyused as segmentation variables, a relevant question iswhether the determinants of SST use differ depending onthe levels of education, gender, and age. Apart from thestudy conducted by Venkatesh et al. (2003) and Meuteret al. (2005), the issue of demographic influences on SSTuse hasn’t been fully covered yet by the existing litera-ture. An understanding of demographic differences inperceptions and attitudes toward using SSTs in a real-liferetail setting is valuable for the management of SSTs, asdemographics are generally acknowledged to profoundlyinfluence response to marketing strategies (Meyers-Levy1988; Meyers-Levy and Sternthal 1991; Mittal andKamakura 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) indicated that peoplewho have more highly qualified jobs and education levelstend to display a more quantitative time orientation, asreflected in the statement “time is money.” We hypothesizethat more highly educated people will try harder to opti-mize their time allocation and thus feel more time pressure.This will lead them to attach more importance to the timegain that comes with using SST.


Hypothesis 6a: The positive relation between per-ceived usefulness and attitude toward using theSST is stronger among more highly educated cus-tomers than among less educated customers.

We also suggest that people exposed to higher levelsof education are likely to have had more exposure totechnology, not only at their workplace but also in thecourse of their day-to-day activities. In addition, thenature of the task at hand and its interaction with tech-nology has been found to play an important role in theperceptions of the technology among individuals in anorganizational setting (Rangarajan, Jones, and Chin2005). In a retail setting, the task of using the technologyis only secondary to the main issue of shopping for items.Given this, in conjunction with the likelihood of educatedindividuals being more exposed to technology, wehypothesize the following:

Hypothesis 6b: The positive relation between per-ceived ease of use and attitude toward using theSST is weaker among more highly educated cus-tomers than among less educated customers.

Based on an extensive review of the literature, Rogers(2003) concluded that earlier adopters have more years offormal education than do later adopters. As argued above,the defining characteristic of innovations obviously istheir newness. Newness as an attribute may itself havesome utility for customers (Blythe 1999), and apparentlythis is specifically the case for the highly educated,because this group has been found to more readily adoptnew technologies (Im, Bayus, and Mason 2003). Takentogether, these lines of thoughts lead to the hypothesisthat perceived newness of an SST will positively affectattitudes of more highly educated people toward the SSTyet negatively affect attitudes of less educated peopletoward the SST.

Hypothesis 6c: The relation between perceivednewness of an SST and attitude toward using theSST is positive for more highly educated peopleand negative for less educated people.

The effect of education on user attitude toward tech-nology has been documented in the organizational behav-ior literature as affecting an individual’s attitude towardtechnologies in the workplace (Morris and Venkatesh2000; Venkatesh and Morris 2000). However, to the bestof our knowledge, little research has focused on the effectof education levels on subsequent usage of the technol-ogy. In line with Evanschitzky and Wunderlich (2006),we suppose that people with higher levels of educationperform more comprehensive information gathering and

processing efforts than less educated people. This isbecause those who are more well-educated draw on moreinformation prior to decision making, whereas less edu-cated people rely more on fewer information cues (Caponand Burke 1980). Considering the important role ofinformation processing in the formation of attitudes(Hoyer and MacInnis 2004), and the fact that better edu-cated consumers feel more comfortable when dealingwith and relying on new information than people withlower educational attainments (Homburg and Giering2001), we suggest the following hypothesis:

Hypothesis 6d: Actual usage of the SST is morestrongly related to attitude toward using the SSTfor customers with higher education levels than forcustomers with lower education levels.

AGE

In a workplace setting, Morris and Venkatesh (2000)show that the relation between attitude and intention is notequally strong for all people. More specifically, they findthat intention to use technology is more strongly driven byattitude among younger people as compared to olderpeople. We propose that this finding is transferable to theattitude-behavior link in a retail setting and suggest:

Hypothesis 7: Younger customers’ actual usage ofthe SST is more strongly related to their attitudetoward using the SST than is older customers’actual usage of the SST to their attitude towardusing the SST.

GENDER

In recent years, an increasing body of research hasfocused on gender differences in shopping behavior. Ithas been found that males and females employ differentinformation-processing strategies while shopping(Meyers-Levy and Maheswaran 1991; Meyers-Levy andSternthal 1991). Some researchers have suggested thatfemales generally show higher involvement and morethorough information processing in shopping than domales (Laroche et al. 2000; Laroche et al. 2003). Thismight possibly translate into different priorities whileshopping in that males may want to minimize time andeffort invested, whereas females may want to minimizedistraction from the shopping task. In relation to SST use,this would imply that males attach more importance tomaking their shopping more efficient by using SST, butfemales would not want to complicate their shopping taskperformance by having to use the SST. Evidence froma 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 technologyusage decisions were more strongly influenced by theirperceptions of usefulness. In contrast, women were morestrongly influenced by perceptions of ease of use” (p.115). Based on these two lines of reasoning, we hypoth-esize a moderating effect of gender on the importance ofperceived usefulness and perceived ease of use in the for-mation of attitude and intention. Thus,

Hypothesis 8a: As compared to women, men’s atti-tude toward using SST is more strongly related toperceived usefulness.Hypothesis 8b: As compared to men, women’s atti-tude toward using SST is more strongly related toperceived ease of use.

Outcomes of SST Usage

As the literature review shows, research on exploringSST acceptance is a growing area of interest. Nevertheless,to the best of our knowledge, most studies, except forMeuter et al. (2005) and Dabholkar, Bobbitt, and Lee(2003), have not focused on investigating postusagebehavior. This has to be seen as a major limitation, asretailers have to show the economic consequences of theiractions. Thus, the resource-intensive introduction of SSTsmust be made financially accountable by showing the con-tribution for enhancing a firm’s financial performance(Rust et al. 2004). With customer satisfaction being amajor driver of customer retention and profit, we integratethis variable as an important outcome of SST usage in ourmodel (Anderson, Fornell, and Mazvancheryl 2004;Anderson and Mittal 2000). As waiting time still remainsa critical factor to customers’ shopping experience, weintegrate this measure along with customer satisfaction asan important outcome variable (Davis and Vollmann 1990;Tom and Lucey 1995).

Zeithaml and Bitner (2003) suggested that customersoften look for quick and efficient service and do notexpect to spend a lot of time waiting. Therefore, it is cru-cial to investigate the effect of SST use on the perceivedwaiting time. We thereby follow “a time perceptionapproach” (Hornik 1984, p. 617) rather than measuringwaiting time on the basis of standard measurement char-acteristics. This is in line with Tom and Lucey (1995),who promote that perceived time, more than objectivetime, seems to form the basis of the reality for consumerexperience and behavior. The construct of perceivedwaiting time seems also to offer more insights from amanagerial point of view. Managers often face “external”limitations of ways of decreasing objective waiting timesbecause of factors like physical space, which limits the

maximum amount of possible check stands or increasedcustomer traffic in rush hours (Tom and Lucey 1995). Inthe domain of SST, the perceived reduction in waitingtime during the service experience is the main advantagedelivered by using SSTs (Bateson 1985; Meuter et al.2000). In a retail setting, SST delivers its main advantageby reducing waiting times at check-out (Dabholkar,Bobbitt, and Lee 2003). As a result, we argue that SSTusers are likely to have lower perceived waiting times inthe check-out counters.3

Hypothesis 9: SST use has a negative effect onperceived waiting time.

Waiting times have been shown to strongly affect eval-uations of service encounters by customers (Taylor 1994;Zeithaml and Bitner 2003). Durrande-Moreau and Usunier(1999) noted, “The wait is a minor but significant part ofthe overall service encounter that influences customers’global evaluation of the service” (p. 177). In retail settings,customer perceived waiting time has been identified as acritical contributing factor to customer satisfaction withthe retail outlet (Pruyn and Smidts 1998; Tom and Lucey1995). When SST users and nonusers perceive that theyspend less time waiting at the counter, they exhibit higherlevels of satisfaction with the overall shopping experience(Davis and Vollmann 1990). Consequently, we believe thatthe perceived waiting time will affect customer satisfactionwith the shopping experience.

Hypothesis 10: Perceived waiting time is negativelyrelated to overall satisfaction with the shopping trip.

Because one of the primary reasons for consumers touse SSTs is to save time, we expect the above-mentionedrelation to be even stronger among SST users (Childerset al. 2001). This indicates that SST users can beexpected to attach more importance to perceived waitingtime, implying a stronger relation between perceivedwaiting time and satisfaction. We therefore suggest amoderating role of SST use on the perceived waitingtime–satisfaction relationship.

Hypothesis 11: The relationship between perceivedwaiting time and satisfaction will be stronger forSST users than for nonusers.

Although the effect of SST use on perceived waitingtime is especially important for customers (as it is pre-sumed to affect their satisfaction levels), and from an oper-ational point of view, it is also crucial to know whetherSST affects the actual time that is spent in the store. Inother words, the question is whether using SST is indeed


less time consuming and more efficient. This is importantbecause it affects the total number of customers in the storeat any given moment and, consequently, the capacity thatis needed in-store (Tom and Lucey 1995). It is believedthat SST use reduces the time needed for shopping(Bateson 1985; Childers et al. 2001).

Hypothesis 12: SST use leads to less time spent in-store.

To ensure that the effect of SST use on the describedoutcomes is not because of the operation of confoundingvariables, the following two control variables areincluded: the number of items the customer purchasesand number of customers at check-out. In particular,these two situational variables may affect perceived wait-ing time and actual total time spent in-store. Also, theymay affect these outcome variables to a different extentfor users and nonusers of SST.

All the relations proposed above are summarized inFigure 1.

EMPIRICAL STUDY

Data Collection

SETTING

To test our model, we collected data in stores of a gro-cery retail chain in Western Europe. At the time of data

collection, the self-scanning option had been in place forat least 1 year in each of the stores. The self-scannerswere hand-held devices that were made available on ashelf at the entrance of the store. Customers walking incould choose between two options: either use a self-scanningdevice to aid them in their shopping trip or shop in thetraditional method without the self-scanning device.Customers choosing the self-scanning option would thenuse the device throughout their shopping trip to scan thebarcodes on all items they selected from the shelves. Atthe check-out counter, self-scanner users could then pro-ceed to separate lanes, where their bill was directly com-puted based on the purchases read by the device. Thecustomers would then make the payments to a cashier ata check-out counter specifically reserved for SST usersand, following this, would exit the store.

In contrast, customers who did not use self-scanningdevices would proceed directly to the check-out counter,have the products scanned by a cashier, and subsequentlypay the cashier for the goods purchased. Self-scannersare available only for customers in possession of a loyaltycard. This policy has several reasons. First, the option touse self-scanning is considered an incentive to subscribeto the loyalty card system. Second, because personnelcheck only a sample of self-scanning customers’ pur-chases at check-out, the retailer reserves self-scanningfor customers it chooses to trust. Finally, the retailer isconstructing a behavioral database, and only cardholderscan be readily identified to store their track record in


FIGURE 1Antecedents and Consequences of SST Use

terms of self-scanning use. Consequently, only loyal cus-tomers took part in this research.

SURVEY ADMINISTRATION

Six teams of research associates simultaneously col-lected data in six stores of the grocery retailer. Data werecollected during a 3-day period to ensure a representativecross-section of shoppers. Data collection consisted of twostages. In the first stage, research associates addressedshoppers on entering the store. A questionnaire withclosed-ended questions was used for this data collection,which took place at the entrance. The next stage of datacollection was after the customers had done their shoppingand had checked out their items. They were requested toparticipate in an exit survey with closed-ended questions.

The entry survey contained two filter questions toensure that (a) we did not include people who wereunaware of self-scanning devices and (2) only customerswith a loyalty card filled out our survey, given theretailer’s policy of offering self-scanning devices only toloyal customers. On provision of acceptable responsesfor the filter questions, the main questionnaire wasadministered to the respondents. The questionnaire con-sisted of a series of items measuring the perceived attrib-utes of the SST as well as attitudes toward the SST. Inaddition, the demographic variables, educational level,year of birth, and gender were measured. Participantswere assigned unique identification numbers to enable usto match their responses between the entry and exit sur-veys. Also, the exact times of the entry and exit surveyswere recorded. This allowed us to (a) measure the totalamount of time spent in-store and (b) match the ques-tionnaire data with observational data collected in the fol-lowing way. At every 1-minute interval, an accomplicerecorded the number of non-SST users waiting at thecash registers. This number was then used as a proxy forcrowding and is henceforth referred to as the “number ofcustomers at check-out.”

At the end of the shopping experience, the identificationnumbers of the participants were recorded to enable us tomatch their responses at entry and exit. The respondents’use or nonuse of self-scanning was recorded. Respondentsthen answered additional questions about their perceptionsof how long they waited in line (including processing timeof purchases at check-out) and about their levels of satis-faction with the shopping experience. Also, the exactnumber of purchases was noted (based on the bill).

Scale Design

Because our study focuses on use of a particular SSTin Europe, we initiated a qualitative stage of research,

which served two purposes. Our first purpose was tocheck whether relevant variables had to be included inthe model in addition to those apparent from previousstudies: specifically, in the attitudinal model and the out-comes model. Our second purpose was to become familiarwith the way customers feel, think, and communicateabout this issue. To this end, we did face-to-face inter-views with a convenience sample of 30 customers. Ofthese respondents, 10 were male. Furthermore, 9 respon-dents were aged 20 through 29, 8 from 30 through 39, 9from 40 through 49, and 4 were 50 years and older. In oursample, 13 respondents had used self-scanning in the pastyear. Based on the literature and the qualitative inter-views, we formulated indicators for the variables in theconceptual model. Attitudes were measured using a 3-item, 5-point semantic-differential scale. Perceivedattributes of the SST were measured by means of 5-point,Likert-type scales based on Dabholkar (1994), Dabholkarand Bagozzi (2002), and the qualitative interviews.

ENTRY QUESTIONNAIRE

Perceived usefulness was assessed by 3 items captur-ing efficiency, speed of shopping with self-scanning andSST use effect on waiting time at the cash register.Perceived ease of use was measured by 2 items that cap-tured aspects concerning user friendliness and effortrelated to using SSTs. Reliability was quantified with 3items capturing the extent to which self-scanning isexpected to work well and have a faultless result.Perceived fun was measured by 2 items capturing to whatextent the use of self-scanning is perceived as beingentertaining and enjoyable. Perceived newness was mea-sured using three 5-point semantic differentials. Items ofall attitudinal and attribute variables are presented inAppendix A. Use of self-scanning was observed and notedas a dummy variable, with 0 = no use of self-scanning and1 = use of self-scanning. Age was measured by askingrespondents’ year of birth and subtracting it from the yearof data collection. Gender was observed and noted asa dichotomous variable in which 0 = male and1 = female. Level of education was measured by meansof an open question probing for the number of years offormal education after primary school.

EXIT QUESTIONNAIRE

Satisfaction with the shopping trip was captured interms of overall, cumulative satisfaction on a scale rang-ing from 0 (very dissatisfied) to 10 (very satisfied;see Anderson, Fornell, and Lehmann 1994). Perceivedwaiting time was measured as the perception of howmany minutes the respondent had been waiting at the


cash register (Hornik 1984). Perceived waiting timeincluded the processing of the purchases.

We consciously used single-item measures in the exitsurvey to limit the burden on our respondents. This way,we intended to minimize nonresponse to the exit surveyas well as bias because of irritation. Other researchershave made the same choice in a similar context (Taylor1994) and perceived waiting time can be argued to bemeasurable by a 1-item measure (Rossiter 2002). Thenumber of actual purchases was noted based on the cus-tomers’ check-out bill.

RESULTS

Sample

A total of 1,492 shoppers were approached for partic-ipation in the survey. Of these, 709 people respondedfavorably, giving us a response rate of 47.1%. Furthermore,632 respondents who answered favorably possessed aloyalty card (this was a first filter). After further filteringout respondents who were not familiar with self-scan-ning, this number dropped to 610 (this was a second fil-ter). Each respondent was issued a ticket with a uniqueidentification number, and this ticket was then collectedby a second team of research associates awaiting respon-dents at the exit of the store.

Of the 610 respondents, 554 people (90.8% of entryparticipants) participated in the exit survey. Out of these554 responses at the exit, 548 responses could bematched with the same customer’s response at the entry,because six customers failed to provide their numberedticket. In the end, 497 questionnaires contained completedata of customers who had purchased at least one prod-uct and could be used in the analysis. In this sample,64.8% were female and 35.2% male. Also, 62.6% hadreceived education after secondary school. As for age,1.0% were aged 12 to 19; 21.3%, 20 to 29; 21.1%, 30 to39; 28.2%, 40 to 49; 18.5%, 50 to 59, 7.4%, 60 to 69;1.6%, 70 to 79; and .8%, 80 to 89 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 com-monly observed in the chain under investigation (this wasstated thus by management).

Data Analysis

TEST OF ATTITUDINAL MEASUREMENT MODEL

Before testing the structural model of interest, weevaluate the quality of the measurement model of the atti-tude and beliefs constructs. To this end, we perform a

confirmatory factor analysis on attitude, perceived use-fulness, perceived ease of use, fun, reliability, and new-ness 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 = .0004, the alternative indices compare favor-ably to common criteria (Hu and Bentler 1999): CFI =0.984; TLI = 0.979; RMSEA = 0.034; SRMR = 0.032.Furthermore, there are no reasons to suspect that specificmodel modifications would enhance the quality of theparameter estimates. Table 1 provides a detailed evalua-tion of convergent and discriminant validity by listingeach factor’s average variance extracted and the sharedvariance between each pair of factors (Fornell andLarcker 1981). As can be read from this table, for all fac-tors the average variance extracted was higher than theirshared variances, providing evidence of good constructand discriminant validity. The correlation matrix of theattitudinal items is reported in Appendix B.

TEST OF CORE MODEL

Building further on the above measurement model, wetest the complete core model as depicted in Panel I ofFigure 1 (i.e., without the moderating effects of thedemographic variables). To this end, we specify a struc-tural equation model in which attitude is regressed onperceived usefulness, perceived ease of use, fun, and reli-ability (for completeness, newness was included as a con-trol variable). Use is regressed on attitude by means of alogistic regression such that the regression weight, B, isinterpreted as the increase in the log odds of using self-scanning versus not using self-scanning for a unitincrease in attitude (as measured by a 5-point scale). Thecorresponding odds increase is B exponentiated, wherebyodds refers to the ratio of the probability of using self-scanning to the probability of not using self-scanning


TABLE 1Shared Variance and Average Variance

Extracted

SV/AVE PU PEU REL FUN NEW ATT

PU 0.50PEU 0.22 0.70REL 0.29 0.22 0.54FUN 0.06 0.22 0.00 0.84NEW 0.11 0.02 0.06 0.01 0.64ATT 0.39 0.34 0.13 0.33 0.07 0.84

NOTE: PU = perceived usefulness; PEU = perceived ease of use;REL = reliability; FUN = fun; NEW = newness; ATT = attitude. On thediagonal, average variance extracted of each factor is displayed; theother values display shared variance (i.e., r2) between two factors(Fornell and Larcker 1981).

(Muthén and Muthén 2004). The unstandardized regres-sion weights are presented in Table 2.

It is clearly evident from the table that users’ attitudestoward the SST are significantly affected (p < .05; one-sided) by perceived usefulness, perceived ease of use,reliability, and fun. Thus, Hypotheses 1 through 4 weresupported. In addition, our results indicate that user atti-tudes toward SST positively and significantly affectedthe actual use of self-scanning technology (p < .001),thereby supporting Hypothesis 5.

TEST OF MODERATING EFFECTS

Next, we test the moderating effects of gender, age,and education level on the core model (see Panel II ofFigure 1). In choosing a method for these tests, we takeinto account the following characteristics of our data andmodel. First, although gender is a dichotomous variable,age and education are measured at a ratio level andshould be treated as such (MacCallum et al. 2002).Second, the moderating variables are nonexperimental innature and are correlated to one another (age and educa-tion have a Pearson correlation of –0.21). Finally, theantecedents of attitude and attitude itself are latent vari-ables, measured with error (Fornell and Larcker 1981).

Therefore, we define interaction terms of each latentvariable with each demographic variable. To make thisoperation feasible from a practical and computationalperspective, we subsequently test separate models, eachof which contains the three interactions between alldemographics and one latent variable as well as the maineffects of the demographic variables and all remainderbeliefs. To illustrate, the first such model containsall variables of the core model, the demographics, andthe following interactions: (a) perceived usefulness by

education level, (b) perceived usefulness by age, and (c)perceived usefulness by gender. In these analyses, educa-tion level and age are mean-centered. Also, note thatgender is coded as a dummy with a 0 for males and a 1for females. Consequently, the main-effect regressionweights correspond to the estimated average effect of anindependent variable on a dependent variable for malesof average age and education level in the sample. Theinteraction terms can be interpreted as the effect that aunit increase in the demographic has on the formerregression weight. We include interaction terms with alldemographics even if not all of them are hypothesized.The reason for this is that not doing so might lead to spu-rious effects (Draper and Smith 1998). For example, if aneffect is hypothesized for education but not for age, notincluding age might lead education to capture the effectthat is, in reality, because of age. Table 3 presents theparameter estimates based on the interaction models,again estimated using the MLR estimator in MPlus 3.13.

Age and education are expressed in units of 10 years.Although it was not the intended purpose of this study, wenote that none of the demographic main effects is signifi-cant (these effects are not reported). As for the moderatingeffects, with a sample size of less than 500, power is ratherlimited (McQuitty 2004). Although this reduces the numberof significant interaction regression coefficients, leading tononrejection of most null hypotheses (and thus no supportfor most of our directed hypotheses), it does focus attentionon effects that are substantial. Three hypothesized modera-tion effects are significant (Hypotheses 6c, 6d, and 8a). Theother moderation hypotheses were not supported (Hypotheses6a, 6b, 7, and 8b).

First, Figure 2 illustrates the relation between perceivedusefulness and attitude toward the SST for female and malecustomers. In line with Hypothesis 8a, perceived usefulnessis less important for female customers than it is for malecustomers, with unstandardized regression weights of 0.27and 0.42, respectively. The former figure is obtained byadding the interaction coefficient of the gender dummy(–0.15) to the main effect (0.42; see Table 3).

Second, Figure 3 depicts the effect of perceived new-ness on attitude toward the SST for three different groupsof customers: those with minimal education in our sam-ple (0 years after primary school), those with averageeducation (8 years after primary school), and those withthe maximum number of years of formal education (16years after primary school, the maximum level observedin our sample). The effect of newness on attitude towardthe SST is significantly moderated by education level.More specifically, in line with Hypothesis 6c, this rela-tionship is more positive among the highly educated.Note that the apparent absence of a main effect is becauseof the presence of a negative effect for some customers


TABLE 2Regression Weights of the Core Model (Panel I

in Figure 1)

DV IV B SE t Value H

Attitude Perceived usefulness 0.325 0.061 5.361** H1(R2 = .55) Perceived ease of use 0.292 0.069 4.252** H2

Reliability 0.143 0.079 1.798* H3Perceived fun 0.195 0.048 4.032** H4

Usea (R2 = .62) Attitude 1.994 0.248 8.052** H5

NOTE: DV = dependent variable; IV = independent variable; B =unstandardized regression weight; SE = standard error; H = Hypothesis.Factors are expressed in the same metric as the original items by fixingone factor loading to 1.a. The regression weight between use and attitude is a logit term(Muthén and Muthén 2004). Here, a 1-point increase in attitude meansthe customer is 7.34 (= Exp[1.994]) times as likely to use the SST as acustomer without such an increase.*p ≤ .05. **p ≤ .01, one-sided probability tests.

(with lower education levels) combined with a positiveeffect for others (with higher education levels).

Finally, as proposed in Hypothesis 6d, the impact ofattitude on use of the SST increases with education level.The relationship between attitude toward the SST and theprobability of using the SST by education level is showngraphically in Figure 4. In line with the logistic regres-sion specification of use on attitude, the dependentvariable (use/nonuse) is expressed as a probability.

This figure shows that the probability of using the SSTgiven a neutral to positive attitude is much higher forhighly educated customers than it is for customers with alower education: The graph for the highly educatedcrosses the 0.50 probability earlier on and increases tothe 0.75 probability in a steeper fashion. The graph rep-resenting the probability of using the SST for the lesseducated never reaches the 0.90 probability, even forvery positive levels of attitude. This indicates that, in this


TABLE 3Interaction Effects: Unstandardized Regression Weights

DV IV Moderator B SE t Value H

Attitude PerceivedUsefulness Main effectb 0.416 0.079 5.30** H1

Education levelc 0.071 0.097 0.74 H6aFemaled –0.145 0.074 –1.95* H8a

Perceived easeof use Main effectb 0.368 0.091 4.03** H2

Education levelc –0.012 0.144 –0.09 H6bFemaled –0.117 0.080 –1.45 H8b

Newness Main effectb 0.110 0.080 1.38Education levelc 0.352 0.184 1.91* H6c

Use Attitudea Main effectb 2.183 0.406 5.38** H5Education levelc 0.899 0.493 1.83* H6dAgee 0.001 0.137 0.01 H7

NOTE: DV = dependent variable; IV = independent variable; B = unstandardized regression weight; SE = standard error of the estimate; H =Hypothesis.a. The regression weight between use and attitude is a logit term (Muthén and Muthén 2004).b. Main effects of the moderated independent variables are reported to enable interpretation of the moderating effects.c. Education level is expressed in units of 10 years of formal education after primary school.d. Female refers to a dummy variable, with 1 for female customers and 0 for male customers.e. Age is expressed in units of 10 years.*p ≤ .05. **p ≤ .01, one-sided probability tests.

FIGURE 2Differential Impact of Perceived Usefulness on Attitude Toward the SST by Gender

NOTE: Both the X and Y axis are mean-centered and scaled as their highest loading indicator (hence, the mean is zero and the scale is identical to thefive-point items in the questionnaire).

segment, attitude toward the SST is not sufficient toexplain use of the SST.

Outcomes of SST Use

To evaluate the impact of SST use on shopping out-comes, we specify a path model as presented in Figure 1,Panel III. Parameters and model fit indices are estimatedusing the maximum likelihood estimator. Use versusnonuse of SST is used as a grouping variable. This allowsus to simultaneously study the direct effects (mean and

intercept differences) and the moderating effects (differ-ences in regression weights) of SST use on shopping out-comes. In the model, there are three endogenousvariables: satisfaction, perceived waiting time, and totaltime spent in-store. Satisfaction is regressed on perceivedwaiting time; perceived waiting time is in turn regressedon the exogenous variables number of purchases andnumber of customers at check-out. Similarly, total timespent in-store is also regressed on the exogenous vari-ables number of purchases and number of customersat check-out.


FIGURE 3Differential Impact of Newness on Attitude Toward the SST by Education Level

FIGURE 4Impact of Attitude Toward the SST on the Probability of Using the SST by Education Level

NOTE: Both the X and Y axis are mean-centered and scaled as their highest loading indicator (hence, the mean is zero and the scale is identical to thefive-point items in the questionnaire).

NOTE: The main effect of education, though insignificant (B = 0.424, SE = 0.388; t-value = 1.094) is accounted for in this graph since it was part ofthe model on which the estimates are based.

We simultaneously estimate the parameters for themodel in both groups (users and nonusers). The resultingchi-square value indicates acceptable fit (i.e., the misfitbetween this model and the data is insignificant: χ2 (6) =5.63, p = .466. The parameters of interest are the inter-cepts of satisfaction, perceived waiting time, and totaltime spent in-store as well as the regression weight of sat-isfaction on perceived waiting time. A between-groupdifference in intercept would indicate that the use of SSTleads to different values in perceived waiting time, satis-faction, and/or total time spent in-store after controllingfor covariates. A difference in the regression weightwould indicate a different impact of perceived waitingtime on satisfaction across groups.

None of the intercepts of satisfaction, perceived wait-ing time, and total time spent in-store are significantlydifferent for the two groups. More specifically, perceivedwaiting time for non-SST users averages 0.88 minutes(SE = 0.31) versus 1.23 minutes (SE = 0.38) for SSTusers, resulting in a nonsignificant difference t test (t =0.71, n.s.). Hence, the data do not lend support toHypothesis 9. The intercepts (standard errors, or SE’s) ofsatisfaction (on a 10-point scale) for the non-SST usersand SST users, respectively, are 8.70 (0.08) and 8.75(0.11), also resulting in a nonsignificant difference test(t = 0.35, n.s.). Finally, the intercepts (SE’s) of actualtotal time spent in-store for non-SST users versus SSTusers are 12.14 (1.82) versus 12.12 (2.30), resulting in anonsignificant difference test (t = –0.01, n.s.). Hence,there is no support for Hypothesis 12. This indicates thatthere is no direct effect of SST use on any of the threeendogenous variables after controlling for covariates.

As noted in Hypothesis 10, the effect of perceivedwaiting time on satisfaction is negative in both groups.However, in line with Hypothesis 11, satisfaction is morenegatively affected by perceived waiting time amongSST users. Specifically, the respective regression weights(and related standard errors and t values) for nonusers

and users of SST were –0.120 (SE = 0.022; t = –5.32) and–0.198 (SE = 0.040; t = –4.99), resulting in a difference ttest of –1.72 (one-sided p = .043). These regressionweights reflect the decrease in satisfaction for everyminute of perceived waiting time.

Based on our observations in the retail setting underinvestigation, we further explore the potential moderatingeffect of SST use or nonuse on the relations between per-ceived waiting time and actual total time in store, on onehand, and their two situational antecedents that served ascontrol variables in the above analyses, on the other hand(i.e., the number of items purchased and the number ofcustomers at check-out). The regression weight estimatesof the time-related outcomes on the situationalantecedents are presented in Table 4.

The results indicate some significant differencesbetween SST users and nonusers. In particular, perceivedwaiting time is less affected by number of purchases forSST users. It is rather self-evident that SST users gain timeat the check-out because their purchases have already beenscanned during shopping. For non-SST users, on the otherhand, an employee individually scans all purchases at thecash register. Consequently, each product will take time toscan for non-SST users, whereas this is not the case forSST users. Among non-SST users, perceived waiting timeincreases by approximately 1 second per product that waspurchased. Among SST users, this effect is not signifi-cantly different from zero.

The effect of number of people at check-out on perceivedwaiting time is slightly less for SST users, but the differencedoes not reach significance (two-sided p = .107). Perceivedwaiting time may be a function of the number of other cus-tomers who have to wait in line at check-out. Becausecheck-out is much more efficient at the SST cash registers,it is reasonable that this impact would be less for SST users.However, the effect is small in the current data set.

Remarkably, this slight reduction in the dependence onsituational variables that is found for perceived waiting


TABLE 4Results of the Exploratory Analysis: Regression Weights of Perceived Waiting Time and Actual Time

in Store on the Situational Antecedents (Panel III of Figure 1)

Non-SST Users SST Users Difference

DV IV Estimate SE t Value Estimate SE t Value t Test

Perceived waiting time No. of items purchased 0.019 0.008 2.411* –0.009 0.010 –0.922 –2.206*Perceived waiting time No. of people at check-out 0.058 0.011 5.281** 0.033 0.011 3.085** –1.613Actual time in-store No. of people at check-out 0.105 0.064 1.645 0.040 0.065 0.622 –0.715Actual time in-store No. of items purchased 0.591 0.045 13.003** 0.655 0.061 10.782** 0.836

NOTE: DV = dependent variable; IV = independent variable; SE = standard error.*p ≤ .05. **p ≤ .01, two-sided tests.

time is not significant for actual total time spent in-store.In other words, although the effect of both number ofpeople at check-out and number of items purchased onactual time in-store is smaller among SST users thanamong non-SST users, the difference is not significant.

DISCUSSION

The purpose of this study was to propose and empiri-cally test a comprehensive process model of SST use bycustomers in a real-life retail setting. As part of themodel, key drivers of SST use along with outcomes asso-ciated with the same were identified and studied in a sin-gle shopping trip. We drew on previous literature on SSTuse (Dabholkar 1996; Dabholkar and Bagozzi 2002;Dabholkar, Bobbitt, and Lee 2003) to identify the driversof SST use and also replicated and extended the findingsof Dabholkar and Bagozzi (2002). We also built on pre-vious research by focusing on the moderating role ofdemographic variables on some key relationships in ourprocess model. In the model, perceived attributes of theSST were posited to relate to attitude toward using theSST. Drawing on the literature on the adoption of inno-vations and building on the work by Dabholkar andBagozzi (2002), the following attributes of SSTs weretaken into account as direct antecedents of SST adoptionin our model: (a) perceived usefulness, (b) perceived easeof use, (c) reliability, and (d) fun associated with usingthe SST. Apart from that, we modeled newness as a vari-able that may have a positive valence for some customers,although it may show a negative valence for others (Blythe1999). This corresponds to the fact that consumers’ prefer-ence for newness varies depending on demographic vari-ables (Im, Bayus, and Mason 2003; Robertson andGatignon 1991). Finally, we also studied the effect of atti-tude toward SST on the actual use of the SST.

Our results suggested that all four hypothesized directeffects on the attitude of customers toward using the SSTwere highly significant. Customers’ attitudes toward theSST, in turn, had a significant impact on actual SST usage.Subsequently, in specific conditions discussed below,using the SST affected the perceived waiting time at thecash register, which in turn was an important antecedent ofcustomer satisfaction with the shopping trip.

Our results also indicated that perceived usefulnessdemonstrated the highest explanatory power on attitude.We think that this is interesting considering the fact thatDabholkar and Bagozzi (2002) did not include this vari-able. A possible explanation for this could have beentheir definition of perceived usefulness of SSTs. In ourstudy, we operationalized perceived usefulness to reflectthe perceptions of benefits (e.g., time gain) that customers

are likely to attribute to the SST. Also, we operational-ized the performance attribute as studied by Dabholkarand Bagozzi (2002) to reflect the reliability associatedwith the SST. Thus, we not only looked at the perceivedusefulness of the SST but also at how the customerslooked at the reliability or performance of the SST. Wealso think that our results may differ because of the con-text of a real-life retail setting in which our study tookplace as compared to the experimental setting in a restau-rant as used by Dabholkar and Bagozzi (2002). In addi-tion, the role of perceived usefulness of technology hasbeen demonstrated in real-life settings before, and ourresults are in keeping with these results (Davis 1989;Venkatesh and Davis 2000). Hence, we believe that fur-ther research on adoption behavior of SST must alsoaccount for the effects of perceived usefulness associatedby users with the technology.

A major contribution of our study is the extension ofprevious research by focusing on not only actual usage ofSST by customers but also on the corresponding out-comes associated with using the SST. In doing so, it isessential to clarify the meaning of the results concerningthe shopping outcomes for non-SST users versus SSTusers. The findings regarding the intercept differencesshow that no differences occur between non-SST usersversus SST users in terms of perceived waiting time andtotal time spent in-store when the number of items pur-chased and the number of customers at check-out is keptat zero. Similarly, no difference in satisfaction occursbetween non-SST users and SST users when perceivedwaiting time is kept constant (at zero). However, amongnon-SST users, perceived waiting time increases as afunction of the number of items purchased (and slightlybut insignificantly with the number of customers atcheck-out), whereas this is not the case for SST users.Hence, the advantage of SST in terms of perceived wait-ing times is realized only when buying many items, prob-ably more so in crowded conditions. Also, as expected,perceived waiting time negatively affects satisfactionwith the shopping trip. It is interesting that this effect ofperceived waiting time is stronger for users than fornonusers of the SST. Remarkably, however, the actualtotal time spent in store is not affected by SST use ornonuse, even when buying many products in crowdedconditions. This indicates that the use of SST does notlead to an actual time gain overall. SST users probablyspend more time shopping around.

Another main contribution of our study was the focuson the moderating effects of demographic variables onthe previously described base model. It should be notedthat no main effects of demographics on SST-relatedbeliefs and attitudes were observed. However, in line withthe focus of the current research, our findings indicated


that demographic variables did seem to affect some of therelationships in the main process model. In other words,demographic segments will most probably hold largelysimilar beliefs regarding SSTs, but they will weight theirbeliefs differently when deciding whether or not to usethe technology. Although research on the use of technol-ogy in organizations has focused on the role of demo-graphic variables (Morris and Venkatesh 2000; Venkateshand Morris 2000), in the domain of SST acceptance,research still remains limited. Although age did not showany moderating effects, our study serves to highlight akey role played by education and gender of the cus-tomers. In particular, education level and gender of cus-tomers affect the importance that they attach to certainfeatures of SSTs when evaluating the technology.

Specifically, perceived newness has a widely varyingeffect on attitudes toward SST depending on the level ofeducation of the customers. Among less educated people,it is negatively related to attitude, but among more highlyeducated customers, it is positively related to attitude.Thus, more highly educated customers are likely to appre-ciate the innovativeness of the technology, whereas lesseducated customers might rather avoid novelty of suchtechnologies. This finding implies that the highly educatedare more likely to adopt SST when it is still perceived assomething new. For less educated customers, SST may bemore successful if the technology is presented as a triedand safe solution rather than a novel experience.

Research in an organizational setting has found thatage moderates the process leading to adoption of tech-nology. Although Morris and Venkatesh (2000) showedthe attitude–intention relation is stronger among youngerpeople than older people, in our study we actually com-pare younger and older respondents in terms of the atti-tude–behavior link. Contrary to our expectations, we findthat age does not moderate the attitude–use relation.However, education level does moderate this relation. Forcustomers with a higher level of education, attitude is abetter predictor of use than for customers with a lowerlevel of education. This finding also points to the impor-tance of controlling for education level when investigat-ing age effects. As in most samples and populations, agewas negatively related to education in the current study,and not including both variables in the same analysismight lead to a spurious effect.

We do find support for the differences between menand women on the relationship between perceived use-fulness and attitude toward the SST. In our study, the linkbetween perceived usefulness and attitude is stronger formen than for women. This finding is in keeping withVenkatesh and Morris (2000), who suggested that menare more likely to focus on the benefits of using thetechnology than are women, who are more likely to be

interested in making sure that the technology does nothinder their work.

Managerial Implications

The results of our study show that retail stores inter-ested in increasing the number of customers using SSTsmust focus on communicating to the customers the per-ceived benefits of using the SST, particularly the result-ing (perceived) efficiency. Attention must also be paid toensure that the SSTs not only are easy to use but also per-form reliably. The SSTs must be sturdy and should notbreak down in the middle of a shopping trip (Meuteret al. 2000). For situations when they do break down,contingency plans must be drawn up to ensure that thecustomer does not have to undergo the process again witha new scanner or have to choose to stand in line to beserved by a service employee (Holloway and Beatty2003). In addition, if the above conditions are met, thefun aspect of the SST may induce customers to use it dur-ing shopping. This is in line with Babin, Darden, andGriffin (1994), who noted that if shopping trips areassessed solely on the utilitarian benefits of products orservices attained, the numerous intangible and emotionalaspects related to a shopping experience are excluded.Bauer, Falk, and Hammerschmidt (2006) have supportedthis notion by pointing out the importance of hedonicaspects in technology-dominated retail settings. Thus,SST usage is not only triggered by extrinsic motives toshop in a more efficient manner but also by intrinsicmotives relating to the enjoyment of using modern tech-nologies (Childers et al. 2001).

Furthermore, managers of retail stores must ensurethat male shoppers are made aware of the potential bene-fits of using the SSTs. Also, managers need to communi-cate the newness of the technology to individuals withhigher educational levels, which in our study resulted inmore positive attitudes toward using SSTs and higherprobabilities of subsequent usage of the SSTs. However,our results suggest that managers need to focus on otherways of ensuring that individuals with lower educationallevels use the SSTs. One possible option is to offer incen-tives to get these individuals to try the SST.

One of the findings of the study was that perceivedwaiting time plays an important role as an antecedent ofsatisfaction. SST users are especially demanding in thisregard. Consequently, managers should allocate suffi-cient staff to the SST check-out counter to avoid waitinglines there. To create realistic expectations, customersshould be made aware that the potential benefit of SST isrealized only when purchasing many items.

Although SSTs in a retail setting are useful, there is noindication that the total time spent in-store per customer


is reduced. Hence, managers should be aware that theintroduction of SST will not necessarily lead to a betteroverall customer flow through the store.

LIMITATIONS AND DIRECTIONS FOR FUTURERESEARCH

Although this study has provided us with a deeperinsight in the process of customer interaction with SSTs,a number of limitations still remain. Together with thefindings discussed above, these limitations may indicateroutes for future research. In this study, perceived waitingtime and overall satisfaction were measured using onlyone item. Multiple items per construct generally areaccepted as the ideal or, in some cases, required method-ology (Churchill 1979). We made a trade-off in favor ofminimizing irritation among our respondents, thus alsodecreasing the risk of reactivity of measurement (Taylor1994). The expected outcome of this is that measurementerror would have attenuated the effects found in Part 3 ofour model. Thus, the tests of Hypotheses 10 and 11 weremore conservative than they would have been with theuse of multi-item measures for satisfaction.

Our study is cross-sectional and is limited to whathappens during one shopping trip. Needless to say, theattitude formation process extends to the period bothbefore and after the shopping trip. Previous experiencewill definitely influence the beliefs, attitude, and inten-tion of some customers or will do so in the future.Although past behavior has been proven to be a soundpredictor of current behavior (Sheeran, Orbell, andTrafimow 1999), we believe that the route we have takenin this study is more insightful in face of the questions athand. More specifically, we wanted to establish customerperceptions of SST attributes and their correspondingrelationship to attitudes and actual SST use.

An important part of our model is measured by meansof one questionnaire at one point in time. This might haveled to common method bias (McFarlin and Sweeney1992). Several arguments can be made against this possi-bility. First, we observed a regression weight of zero forthe main effect of novelty on attitude and a negativeeffect for a specific group (less educated). Second, dis-criminant and convergent validity was good (see Table 1).This supports the claim that factors in our model were notspuriously correlated as a consequence of yea–saying.Also, the relations between variables that are measured atdifferent stages of our data collection (entry survey,observation, exit survey) show effect sizes in the samerange as those of the other relations.

As noted in the Method section, in the setting understudy only loyal customers are eligible for use of the SST for

reasons explained earlier. Hence, it is not known to what extentthe current findings generalize to the general customer base.

Future research may focus on a longitudinal study inwhich the effect of the outcomes of using SST on oneoccasion has an impact on the attitudes toward the SST atthe next occasion. In addition, future research must studythe impact of the outcomes of SST use on the level ofloyalty displayed by the customers. The effect of per-ceived waiting time and the corresponding level of satis-faction experienced by SST users on their subsequentdecision to indulge in positive word of mouth about theSSTs might be of particular interest to investigate.

NOTES

1. We would like to thank an anonymous reviewer for pointing outthis variable as an interesting outcome to investigate.

2. By actionable, we mean that the proposed segmentation (in termsof demographics) provides the opportunity for management to imple-ment actions to target specific segments with specific propositions. Forexample, direct mailings with specific arguments and incentives to useSST can be addressed to different age groups.

3. It is not our intention to imply that the perception of waiting time issomehow biased either among non-SST users or SST users. Rather, we optfor perceived waiting time (rather than actual waiting time) for two rea-sons: It is perceived—not actual—waiting time that affects satisfaction,and asking about perceived waiting times leaves the customer the freedomto decide what is counted as waiting time and what is not, thus circum-venting the conceptual and operational problem of delineating when wait-ing time starts and ends. We would like to thank an anonymous reviewerfor drawing our attention to the potential lack of clarity in this regard.

APPENDIX A

Below, we list the items used in the entry survey tomeasure perceived SST attributes and attitude towardusing the SST.

Perceived Usefulness (5-point, Likert-type scale: completelydisagree to completely agree)

1. Self-scanning will allow me to shop fasterc

2. Self-scanning will make me more efficient whileshoppingc

3. Self-scanning reduces the waiting time at the cashregisterc

Ease of Use (5-point, Likert-type scale: completely disagreeto completely agree)

1. Self-scanning will be effortlessc

2. Self-scanning will be user friendlyc

Reliability (5-point, Likert-type scale: completely disagree tocompletely agree)

1. Self-scanning will be reliableb

2. I expect self-scanning to work wella

3. Self-scanning will have a faultless resultc


Fun (5-point, Likert-type scale: completely disagree to com-pletely agree)

1. Self-scanning will be entertainingb

2. Self-scanning will be enjoyableb

Newness (5-point semantic differential scales)

1. Self-scanning is outmoded–Self-scanning is progressivec

2. Self-scanning is old–Self-scanning is newc

3. Self-scanning is obsolete–Self-scanning is innovativec

AttitudeHow would you describe your feelings toward using self-

scanning technology in this store? (5-point semantic differentialscales)

1. Unfavorable–favorableb

2. I dislike it–I like itc

3. Bad–goodb

a. Item based on Dabholkar (1996).b. Items based on Dabholkar and Bagozzi (2002).c. Items written based on the qualitative study.


APPENDIX BCorrelation Matrix of the Variables in the Attitude-Use Model (Panel I of Figure 1)

Item 1 2 3 4 5 8 9 10 6 7 11 12 13 14 15 16

pu1 1.00 0.58 0.49 0.31 0.35 0.07 0.09 0.13 0.33 0.38 0.22 0.13 0.19 0.47 0.45 0.45pu2 0.58 1.00 0.39 0.29 0.31 0.15 0.19 0.17 0.40 0.42 0.28 0.18 0.22 0.42 0.41 0.43pu3 0.49 0.39 1.00 0.15 0.23 0.16 0.16 0.13 0.24 0.33 0.22 0.17 0.17 0.36 0.33 0.37peou1 0.31 0.29 0.15 1.00 0.68 0.24 0.31 0.25 0.30 0.31 0.17 0.10 0.06 0.39 0.40 0.36peou2 0.35 0.31 0.23 0.68 1.00 0.25 0.40 0.26 0.38 0.41 0.18 0.07 0.09 0.47 0.51 0.50rel1 0.07 0.15 0.16 0.24 0.25 1.00 0.61 0.52 0.14 0.17 0.08 0.10 0.03 0.21 0.22 0.23rel2 0.09 0.19 0.16 0.31 0.40 0.61 1.00 0.48 0.16 0.20 0.11 0.06 0.06 0.25 0.26 0.30rel3 0.13 0.17 0.13 0.25 0.26 0.52 0.48 1.00 0.19 0.19 0.09 0.10 0.09 0.27 0.27 0.27fun1 0.33 0.40 0.24 0.30 0.38 0.14 0.16 0.19 1.00 0.83 0.24 0.17 0.18 0.45 0.46 0.43fun2 0.38 0.42 0.33 0.31 0.41 0.17 0.20 0.19 0.83 1.00 0.22 0.13 0.17 0.50 0.51 0.48new1 0.22 0.28 0.22 0.17 0.18 0.08 0.11 0.09 0.24 0.22 1.00 0.59 0.68 0.20 0.24 0.28new2 0.13 0.18 0.17 0.10 0.07 0.10 0.06 0.10 0.17 0.13 0.59 1.00 0.66 0.08 0.12 0.12new3 0.19 0.22 0.17 0.06 0.09 0.03 0.06 0.09 0.18 0.17 0.68 0.66 1.00 0.18 0.22 0.22att1 0.47 0.42 0.36 0.39 0.47 0.21 0.25 0.27 0.45 0.50 0.20 0.08 0.18 1.00 0.86 0.80att2 0.45 0.41 0.33 0.40 0.51 0.22 0.26 0.27 0.46 0.51 0.24 0.12 0.22 0.86 1.00 0.85att3 0.45 0.43 0.37 0.36 0.50 0.23 0.30 0.27 0.43 0.48 0.28 0.12 0.22 0.80 0.85 1.00

NOTE: Use is a dichotomous variable and was analyzed as such; therefore, the correlation is not reported here.

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Bert Weijters is an assistant professor of marketing at theVlerick Leuven Gent Management School. He holds a PhD inmarketing (Ghent University), a Vlerick MA degree in market-ing management, and a master’s degree in psychology (GhentUniversity). His PhD dissertation discusses response styles inconsumer research. His other domains of interest are consumerbehavior, market segmentation, and the diffusion and adoptionof innovations.

Devarajan Rangarajan is an assistant professor of marketingat the Vlerick Leuven Gent Management School, where he isalso program director of the Master in General Management.He holds a PhD in marketing (C. T. Bauer College of Business,University of Houston) and a bachelor’s degree in mechanicalengineering (University of Madras). In 2003 he was awarded“the AMA Sales SIG Doctoral Dissertation Award” by theDirect Selling Educational Foundation and “the Direct SellingEducational Foundation Award for doctoral research” at theNational Conference in Sales Management. His research inter-ests focus on sales team composition, sales team effectiveness,and sales team learning.

Tomas Falk is a postdoctoral research fellow at the FraunhoferInstitute for Systems and Innovation Research in Karlsruhe(Germany). He has received his PhD from the University ofMannheim Business School (Germany). His areas of expertisecomprise the fields of service innovation, service quality, andindustrial services. He has received research grants from theEuropean Institute for Advanced Studies in Management(EIASM), Marcus Wallenberg Foundation, and the Julius-Paul-Stiegler-Gedaechtnis-Stiftung. His research work has been pub-lished in refereed journals including Journal of BusinessResearch and International Journal of Bank Marketing.

Niels Schillewaert is an associate professor of marketing at theVlerick Leuven Gent Management School. He obtained his PhDentitled “Information Technology Enabled Selling in BusinessMarkets. Studies on the Acceptance and Effects of InformationTechnology in the Sales Force” at Ghent University. He also holdsa Vlerick Master in Marketing Management degree and is cur-rently program director of the Master in Marketing Management.During his PhD period, he studied at the Pennsylvania StateUniversity (USA) and was nominated as ISBM DoctoralResearcher. He is cofounder and managing Partner of InSites, aconsulting and research office specialized in e-business. Hisresearch interests lie in business marketing, sales management,information technology, and e-business.


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