Situational Influences in the Choice of Self-Service in a Multi-Channel Retail Context. Cheng Wang, University of New South Wales,

Page 1 of 9 ANZMAC 2009 Situational Influences in the Choice of Self-Service in a Multi-Channel Retail Context Cheng Wang, University of New South Wales, c.wang@unsw.edu.au Jennifer Harris, University of New South Wales, jennifer.harris@unsw.edu.au Paul Patterson, University of New South Wales, p.patterson@unsw.edu.au Abstract The topic of self-service technologies (SSTs) has been extensively researched in recent years. A general conclusion from previous research is that adoption is driven by SST characteristics and individual differences. However, few studies have focused on the influence of situational factors in people s actual choice of an SST in a multi-channel context. This paper argues that situational factors play an influential role when consumers are faced with the choice between self-service and personal service in a retail setting. Results from observations and interviews indicate that positive SST attitudes do not always lead to the actual use and in a multi-channel context, three situational factors waiting time, task complexity and group influence, have a significant impact on people s choice of self-service. Keywords: self-service technology, situational influence, multi-channel retailing, qualitative research

ANZMAC 2009 Page 2 of 9 Situational Influences in the Choice of Self-Service in a Multi-Channel Retail Context Introduction With the advances in information technology, recent years have seen the proliferation of selfservice technologies (SSTs) in the services industry (Bitner, Brown and Meuter, 2000; Bitner, Ostrom and Meuter, 2002). SSTs are those technological interfaces that enable customers to produce a service independent of direct service employee involvement (Meuter et al., 2000). Examples include supermarket self-checkout machines, automated teller machines, Internet banking, self check-in kiosks in airports, and ticket vending machines at railway stations. SSTs have dramatically changed the way in which service firms interact with customers. The traditional high-touch and low-tech personal service encounters have now been supplemented by the high-tech & low-touch technological interfaces (Froehle and Roth, 2004; Parasuraman, 1996). This means that customers nowadays have the choice of the way in which they want to receive services (self-service vs. personal service). While the flexibility is good for customers, it can be challenging for service organizations. To better allocate resources and keep a balance between service channels, it is important for a multi-channel service firm to understand when customers would use the self-service and when they would use the personal service channel. Thus, the purpose of this paper is to explore situational factors influencing customers choice between self-service and personal service. We are not looking at situational factors affecting the initial trial, but those influencing customers choice after their initial trial. First, previous SST research is reviewed and critiqued, from which our research questions are proposed. A description of the research methodology follows and empirical results summarized. The paper concludes with a discussion of the implications and future directions. Literature Review In the services literature, SST has occupied researchers interest in the last decade. It has been studied in a wide range of services contexts such as airlines (e.g., Harris, Mohr and Bernhardt, 2006; Liljander et al., 2006), banks (e.g., Curran and Meuter, 2007; Nilsson, 2007), retailing (e.g., Forbes, Kelley and Hoffman, 2005; Holloway and Beatty, 2003; Weijters et al., 2007), hotels (e.g., Beatson, Coote and Rudd, 2006; Oyedele and Simpson, 2007), and so on. In these studies, the key variable of interest is intention to use an SST and the main objective is to explore the determinants of that intention. A meta analysis of empirical results indicates that key determinants of SST intention can be generally classified into two broad categories: SST characteristics and individual differences (Meuter et al., 2005). So far in the literature, major SST characteristics that have been studied include usefulness (e.g., Lin, Shih and Sher, 2007; Walker and Johnson, 2006), ease of use (e.g., Dabholkar and Bagozzi, 2002; Timmor and Rymon, 2007), fun/enjoyment (e.g., Curran and Meuter, 2007; Weijters et al., 2007), risk (e.g., Curran and Meuter, 2005; Meuter et al., 2005), and control (e.g., Dabholkar, 1996; Lee and Allaway, 2002). In terms of individual differences, they can be further categorized into demographics and psychographics. Primary demographics that have been found to influence SST usage intention are age (e.g., Ding, Verma and Iqbal, 2007; Simon and Usunier, 2007), gender (e.g., Elliott and Hall, 2005; Meuter et al., 2005), education (e.g., Greco and Fields, 1991; Meuter et al., 2003), and income (e.g., Lee, Lee and Eastwood, 2003; Nilsson, 2007), whereas main customer psychographics include technology anxiety (e.g., Meuter et al., 2003; Oyedele and Simpson, 2007), technology readiness (e.g., Matthing et al., 2006; Parasuraman,

Page 3 of 9 ANZMAC 2009 2000), and need for human interaction (e.g., Dabholkar, 1996; Dabholkar and Bagozzi, 2002). In terms of the relative importance, a general conclusion is that SST characteristics are better predictors of intention than are individual differences (Dabholkar, 1996; Meuter et al., 2005). While previous research has significantly enhanced our knowledge of why people would use SSTs, two important issues have been largely ignored. One is that, with very few exceptions (e.g., Montoya-Weiss, Voss and Grewal, 2003; Simon and Usunier, 2007), SST has been studied as if it was the only available service channel. However, this is not the case in reality. Today, SST in a retailing context is often just one of multiple channels available to customers. Therefore, the decision to use self-service is more likely to be based on a comparative evaluation of all available channels rather than on an evaluation of the SST alone. Thus, examining the SST channel in isolation and neglecting the multi-channel context may hinder the investigation of relevant situational factors that may influence people s actual choice. Moreover, from the point of view of service organizations, the purpose of introducing selfservice is not to entirely replace the traditional personal service, but to give customers a choice and keep a balance between the two channels (Salomann, Kolbe and Brenner, 2006). Therefore, it is more important to understand when customers would choose self-service as opposed to personal service than to focus solely on self-service. The other issue is that most previous studies focus on the intention to use a SST rather than the actual usage (e.g., Curran, Meuter and Surprenant, 2003; Dabholkar, 1996; Dabholkar and Bagozzi, 2002). It is found that, in general, intention is determined by attitude, which is then determined by various SST characteristics. Although this relationship is well supported in the literature, the risk of focusing on intention instead of action is that intention does not always lead to action (e.g., Ajzen, 1991). This is especially true when SST is examined in a multichannel context, where customers actual choice of the SST channel might be influenced by the situation of other channels in addition to their SST attitude and intention. Moreover, for service organizations, it is the actual use rather than the attitude or intention that matters to them. Therefore, it has been suggested that SST research should go beyond the emphasis on attitude and intention and focus on actual behaviour (Meuter et al., 2005). To summarize, the multi-channel context and the actual behaviour have been largely ignored in the SST literature. Because of this, the focus has been on the effects of SST characteristics and individual differences, and situational influences have not been well addressed. We argue in this paper that when studying the actual use of an SST in a multi-channel context, attitude and intention alone may not be sufficient and situational factors may moderate the attitude behaviour link. Therefore, the objective of this paper is to explore relevant situational factors that impact people s actual use of an SST in a multi-channel retail context. The focus has now been shifted from a why question to a when question. Specifically, our research question is: what are the key situational influences and how do they moderate the attitude behaviour link in the context of customers choice of the self-service channel? Methodology Due to the need to build an understanding of actual influences on behaviour, qualitative research, in particular one-on-one interviews, is conducted. Non-participant observations are also conducted to provide supporting objective evidence. The context is offline retailing, with self-checkout machines in supermarkets the SST of interest. Supermarket self-checkout machines provide an ideal setting in that both self-service channel and personal service channel are present at the time of checkout and customers are free to choose either. They have

ANZMAC 2009 Page 4 of 9 only recently been introduced in some selected Australian supermarkets (Browne, 2008; Moses, 2008) and this offline retailing context has been rarely examined in the literature, providing an additional benefit of this setting. Considering the huge initial investments in launching the self-checkout and the possibility that the SST will become the next competitive focus in offline retailing (Preston, 2008; Vedris, 2008), an investigation is worthwhile. Five supermarkets in different areas were selected as the observation and interview venues in order to reach different customer segments. Two researchers conducted the fieldwork. One observed how a customer went through the self-checkout or the regular one and completed a checklist for each observation. The other researcher approached the observed customers after they completed the checkout and asked if they would like to help with a university study on SST. Once they agreed, they were then screened for eligibility. Eligible customers were those who shopped at this supermarket regularly and had used the self-checkout machine prior to this occasion. Interview topics included the good or bad aspects of using the self-checkout, and whether they always used the self-checkout, elaborating on the conditions. All interviews were digital recorded. After transcription content analysis was used to extract and classify interview data into meaningful categories. Category development and information coding were an ongoing effort. Two judges independently coded interviewees responses into categories which reflected either SST-specific characteristics or situation-specific factors. All disagreements were resolved through discussion. Observation data were analyzed through descriptive and comparative analysis using SPSS. Results 209 observations were obtained, including 110 customers who used self-checkout (users) and 99 who went through the regular checkout (nonusers) on this occasion. The sample covered a wide range of age groups, from young adults to seniors. In line with prior research, there was a significant difference in age between users and nonusers (p < 0.05), with more young adults in the user group and more seniors in the nonuser group. There was no significant difference in gender between users and nonusers (p = 0.61). However, overall there were more females than males in the sample. From the 209 observations, 57 customers were interviewed; 30 used the self-checkout on this occasion, 17 used the regular checkout on this occasion but had used the self-checkout before, and the remaining 10 had never used the self-checkout, hence were excluded from the analysis. Respondents varied in terms of their SST attitudes and usage. A favourable attitude towards the self-checkout was usually based on its usefulness ( It s quicker, faster than the regular checkout ), ease of use ( After the initial go, it s easy ; I just stand by the instructions. It s pretty easy. ), control ( I want to do it myself. I get control of what I am doing and paying ), and fun ( It s fun. I enjoy doing the scanning ). An unfavourable attitude, on the other hand, was often due to the fact that the self-checkout machine was hard to use ( I thought initially confusing; where do I put the money and where do I get the change ; wasn t sure where to find the type of the vegetable ), risky to use ( The note should not come down the bottom because someone else can come and take it when you are not watching ), and lacking in human interaction ( I enjoy saying hello to people, but you can t have a chat to a machine ). In terms of SST usage, very few interviewees stated that they always used the self-checkout; the vast majority used a combination of self-service and personal service. This means, even those who had a favourable attitude towards the self-checkout sometimes did not use it: Just sometimes it s a quicker line. There is no line there. But if there was no line in other registers, I will go there. Similarly, possessing an unfavourable attitude towards the self-checkout did

Page 5 of 9 ANZMAC 2009 not imply that the SST would never be used: For preference, no, but I would probably use it [the self-checkout machine] if I was really in a hurry and had only one or two items. This suggests that in a multi-channel context, the attitude behaviour link is not straightforward and customers choice of the self-service channel may be a complex interaction of attitudes, situational influences, personal characteristics (e.g., need for interaction) and past behaviour. Further analysis of the situational influences surrounding use or non-use indicated a number of possible categories, with three key ones emerging: perceived waiting time, perceived task complexity, and group influence. Each may enhance or decrease the actual usage of the SST, despite the strength or direction of prior attitudes. A multi-channel retail context Situational Factors Perceived waiting time Perceived task complexity Group influence SST Attitudes Usefulness Risk Ease of use Control Fun/enjoyment SST Behaviour Use or choice of SST Perceived waiting time relates to the queue length at the self-checkout. When choosing between the self-checkout and the regular checkout, customers simply look for the shortest queue. I will look at the quickest. If you have looked at this queue now, it s fairly long. So I wouldn t be waiting there. So really what we are trying to do is to find the quickest way out. If there were no queues for the other checkouts, I d probably use them more. But because of less queues for the self-service ones, you see now, there is no one waiting at the self-service, you just go straight in. Observation results verified average queue length at regular checkouts was significantly larger than that at self-checkouts (1.24 vs. 0.3, p < 0.05). However, when stating that the self-checkout because it is quicker, it is the perceived rather than the actual waiting time influencing choice. Actual waiting time is not solely determined by queue length. Slow processing by other customers can increase wait time. As people are still learning, it can be a little bit slow sometimes. Observation results showed that although self-checkout customers purchased fewer items than regular checkout customers, they took a similar time going through the checkout (1.87 vs. 1.58 min, p =.25). However, sometimes the actual waiting time can be shorter even if the queue is longer. People at the self-checkout usually have only a couple of items and you ve got one line but several machines. Perceived task complexity relates to the number and the type of items being purchased. Observation results verified that on average self-checkout customers purchased a significantly smaller number of items than regular checkout customers (4.29 vs. 10.97, p < 0.05). I would not say it [self-checkout] is better because if I had a lot of things, I would never use it, I will always go to the traditional one. But if I just purchased a few things, it s definitely better. When probed on why number of items mattered, interviewees revealed that as the number increases, they worry that they have too many things to do at the machine by themselves and they cannot control the whole situation. Because it is much harder to use the machine. If you have a lot of items, you have to scan them all. It s time consuming. And the machine is more likely to go wrong. Perceived complexity is also increased with more items since people are confused as to how to manage multiple bags or where to put them. The fact that the bagging

ANZMAC 2009 Page 6 of 9 area isn t big enough and you have to constantly stop because it [machine] keeps telling you return items to the bagging area, which is annoying and frustrating. Thus, personal checkout tends to be used for a large number of items even if they like and enjoy the selfcheckout. The type of items being purchased also impacted on perceived task complexity. Some stated that they would not use it if they had vegetables or fruit, because it is hard to find the right item on the screen. Observation also revealed proportion of fruit and vegetables purchased was much lower through self-checkouts than regular checkouts (p < 0.05). Group influence is the influence from others (e.g., family or friends) a customer shops with. Although observation revealed few shopped with company (20% vs. 80%), the impact of this factor was particularly strong for older customers. Many had a negative attitude towards using the self-checkout simply because they thought they were too old and they did not want to change. I am too old-fashioned to self-serve. I am used to the normal checkouts. I don t like change. Therefore, they usually did not use it when alone. However, some stated they had used it when they were with their children or friends who could show them how to do it. When my daughter is with me, we do [use the self-checkout], but other than that, I am a bit old-fashioned. It is not clear whether these older customers actually used the self-checkout machine themselves under the help of their children/friends or just watched others using it. This may have a significant impact on whether they subsequently use it when alone. Discussion Results show that in a multi-channel context most customers who use the SST do not stick to this service channel; they use a combination depending on the situation they are faced with. Therefore, focusing exclusively on SST characteristics and attitudes cannot effectively predict customers actual choices. Our findings indicate that three situational factors, namely waiting time, task complexity and group influence, moderate the attitude behaviour path in a multichannel context. Specifically, customers always compare queues and look for the shortest line in order to minimize their waiting time. The finding is consistent with Bateson s (1985) study, where perceived time taken was found to be the most important situational factor when faced with the choice between a self-service and personal service channel. Customers also think that self-service is good for simple tasks only and when the task becomes complicated they would use personal service. The theoretical argument for the impact of task complexity can be found in Theory of Planned Behaviour (Ajzen, 1991), which suggests that the actual behaviour is determined by both perceived behavioural control and behavioural intention. For older customers, their usage of self-service is often driven by others they are with. The results have some implications for managers. Though the introduction of an SST is most likely to be supply driven, which is sometimes out of managers control, our findings can help managers better handle the self-service channel in relation to other service channels. For example, in order to keep a balance between the self-checkout and the personal checkout, managers can monitor and change the queue length by opening or closing more self-checkout machines or checkout counters. Also, task complexity results suggest that the self-checkout may be a good alternative for the express line but not for the checkout counter. In future, it would be interesting to see if customers perception of waiting time changes as they get more experienced. Would they consider other factors in addition to the queue when estimating the waiting time? This may influence the design of an SST in the way that the perceived waiting time can be reduced. Another area could be empirically testing the moderating effects of the situational factors. More specifically, the varying moderating effects (direction vs. strength) of the situational factors under different conditions (positive vs. negative attitude) can be tested.

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