MSc Business Administration – Marketing Track
Self-service technologies, as complement or replacement?
The influence of information terminals on perceived service quality and
purchase intention.
Student: Martine Verhulst Student number: 10870989
Thesis supervisor: Jonne Guyt
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Statement of originality
This document is written by Martine Verhulst who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
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Content overview
Abstract ... 4
1. Introduction ... 5
2. Literature review ... 7
2.1 The multichannel retail environment... 7
2.2 Channel integration and SST usage ... 9
2.3 Perceived service quality ... 10
2.3.1 Complementary SST usage ... 10 2.3.2 Replacement SST usage... 12 2.4 Purchase intention ... 14 2.5 Technological anxiety ... 15 2.6 Demographic characteristics ... 16 3. Method ... 18
3.1 Design and Participants ... 18
3.2 Stimulus material ... 19 3.3 Procedure ... 20 3.4 Measures ... 21 4. Results ... 23 4.1 Preliminary analysis ... 23 4.2 Correlation matrix ... 24 4.3 Test of hypotheses ... 25
4.4 Discussion of the results ... 27
5. Conclusion and discussion ... 27
5.1 Theoretical and managerial implications... 29
5.2 Limitations and future research ... 31
6. References ... 34
7. Appendices... 41
8.1 Appendix A – The vignettes ... 41
8.2 Appendix B – The measures... 44
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Abstract
This research examines the impact of Self-Service Technologies (SSTs) in the retail industry, more specifically the use of information terminals. Existing literature on this topic mainly focuses on a customers’ attitude and acceptance towards such devices. This article goes one step further by distinguishing between SST usage as complement as well as replacement to traditional services, which has not previously been studied and will be of high practical value for managers in order to make the right channel-specific investments. Drawing on technology adoption research, a theoretical model is conceptualized where perceived service quality mediates the impact of SST usage on purchase intention. Given the heterogeneity in technological skills, technological anxiety is added as a moderating variable. An experimental vignette study is conducted to test the overall model. The results show a significant positive effect of perceived service quality on purchase intention. Although this effect cannot be explained by the
implementation of SSTs, an additional analysis did indicate that a portion of the consumers prefer consulting an information terminal over an employee. Therefore, this study implies evidence to suggest that the implementation of information terminals as a complement to traditional services is in line with the need of a segment of customers.
Key words: Self-service technologies, Information terminal, Perceived service quality,
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1. Introduction
Many traditional retailers in the Netherlands are currently having a hard time surviving in a highly competitive environment (e.g. Macintosh and DA Retailgroup). As a response to their customers wanting to interact with them anytime, anywhere and through multiple seamless interfaces, many retailers have started to provide real-time attributes of the physical store on their websites (i.e. product availability and reserving products). This can be seen as a first step in the channel integration process, which is defined as the degree to which different channels interact with each other (Bendoly, Blocher, Bretthauer, Krishnan & Venkataramanan, 2005). Sequel to this process is the current trend of retailers implementing online elements into their physical stores. These so called self-service technologies (SSTs) are defined as technological interfaces that enable customers to produce a service independent of direct service employee involvement (Meuter, Ostrom, Roundtree & Bitner, 2000). This study will focus on the potential effects of the implementation of such SSTs, more specifically in the form of information terminals,
distinguishing between SSTs as complement and replacement. Complementary SST usage refers to the use of SSTs as an adjunct to existing services. In case of replacement, the technology replaces current services such as an employee, as these technologies do not require direct employee assistance (Pantano & Di Pietro, 2012).
Nevertheless, it is important to note that the use of SSTs is not convenient for everyone. Whereas some consumers consider SSTs to be easy to use, others might feel uncomfortable using them and prefer to have contact with a salesperson (Dabholkar, Bobbitt & Lee, 2003). It is even stated that the attitude customers have towards using technology strongly influences their behavioural intentions (Kallweit, Spreer & Toporowski, 2014). Subsequently, technological anxiety may act as a moderating variable, measuring the state of mind of the consumer, regarding their ability and willingness to use technological tools such as an information terminal (Meuter,
Ostrom, Bitner & Roundtree, 2003). In order to get deeper insights in the previous mentioned dynamics that come with the use of SSTs, the following research question was developed:
6 What is the impact of complementary and replacement information terminals in the retail
industry? And how do they affect a customers’ perceived service quality and purchase intention? To answer this research question, an experimental vignette study was conducted in which participants read a scenario about the Dutch well-known Do-It-Yourself (DIY) store Praxis, and the services they provide. There are three versions, which differ only in the extent to which the participant can obtain product information. This is either by 1) asking an employee (benchmark), 2) asking an employee or consulting an information terminal (complement) or 3) only consulting an information terminal (replacement). By using Praxis as an example, this research touches upon an industry that is a relatively new adopter of information terminals, compared to fast-food and airlines businesses that have already successfully adopted such terminals on a large scale.
Moreover, information terminals are expected to be of growing importance in the future for other retailers too (Fishman, 2014). Moreover, by shedding light on both complementary and
replacement SST usage, this study provides insights on a current economic trend, described as: “The ability of businesses to do more and more with the same, or fewer, workers” (Fishman, 2004). Furthermore, as offline service quality has a strong cross-channel effect on perceived online channel service quality, it is important for managers to be aware of the critical role of SSTs (Yang, Lu & Chau, 2013). Our results are expected to provide insights on cross-channel effects and consequently provide suggestions on whether information terminals will be suitable for their specific target audience. Because, ultimately, when implementing SSTs in the right way this may reduce labour costs, improve productivity, increases firm’s performance and enhance service efficiency (Lee, Castellanos & Chris Choi, 2012a; Lee, Chiu, Liu & Chen, 2012b).
To address these issues, the article is structured as follows. First, the theoretical background will be explored, including the proposed hypotheses. Subsequently, the specified hypotheses will be empirically tested, followed by a description of the results and the discussion of the findings will be given. The paper concludes with the managerial implications, limitations and future research directions.
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2. Literature review
2.1 The multichannel retail environment
During the past decade commercial activities on the internet grew tremendously (Rangaswamy & Van Bruggen, 2005). Many new ways of information transition and exchange have arisen and customers have become more familiar with using these various interface technologies to interact with companies (Rangaswamy & Van Bruggen, 2005). This has stimulated market entry for a wide variety of firms with business models that are based on electronic platforms for customer interaction (Sorescu, Frambach, Singh, Rangaswamy & Bridges, 2011). Sorescu et al. (2011) describe a business model as a well-specified system of interdependent structures, activities, and processes that serve as a firm’s organizing logic for value creation (for its customers) and value appropriation (for itself and its partners). In order to survive in today’s competitive environment many existing retailers changed their business model and started to operate as multichannel firms (Sorescu et al., 2011). Levy and Weitz (2009) define multichannel retailing as the set of activities involved in selling merchandise over services to consumers through more than one channel.
By integrating the internet into their channel portfolios to interact with customers, companies transitioned from a brick-and-mortar (a traditional retail business models with only offline stores) into a brick-and-click organization (a business model which integrates both offline and online presences) (Yang et al., 2013; Bernstein, Song & Zheng, 2008). With over 80% of U.S. retailers indicating that they sell products through multiple channels, the transition to a brick-and-click organization is becoming a standard business strategy for many firms (Zhang et al., 2010; Yang et al., 2013). A McKinsey and Company publication even argues that something deeper is going on with information technology. They state that whereas processes once took place among human beings, they are now being executed electronically in an unseen domain that is strictly digital. This process is referred to as the formation of a second economy, which is characterized as vast, automatic and invisible – something that could be the biggest change since the Industrial Revolution (Arthur, 2011). In fact, these processes are integrated across all
8 industries today (i.e. applications for mobile phones, touch screen kiosks, automated hotel
checkouts etc.), and all work due to advanced technologies (Bitner, Ostrom & Meuter, 2002; Pantano & Viassone, 2014; Di Pietro, Pantano & Di Virgilio, 2014). In the end, this is in line with the demands of both customers and retailers. Namely, the implementation of information technologies in order to improve productivity and service quality while cutting costs (Weijters, Rangarajan, Falk & Schillewaert, 2007). The costs and benefits of the use of information technologies will be explored further in this paper.
Foremost, it is important to link the possibilities of these digitization practices with the current retail environment, as we see many retailers in the Netherlands having a hard time surviving in a highly competitive environment (e.g. Macintosh and DA Retailgroup). Although the majority of these traditional retailers did focus on the implementation of online retail
activities in recent years this seems not to be enough, as the numbers of bankrupt retailers is still growing. Remarkably, at the same time some retailers that started solely online and grew
tremendously in the last years (e.g. Coolblue and Neckermann) and are now opening offline stores, implying that physical stores can actually provide companies with a certain competitive advantage. The opportunities that lie in offline businesses are also recognized by Raul Vasquez, the chief Executive of Walmart.com, as he states:
“There was a time when the online and offline businesses were viewed as being different. Now we are realizing that we actually have a physical advantage thanks to our thousands of stores, and we can use it to become Number 1 online” (Vasquez, 2009 as cited in Bustillo & Fowler, 2009, p. 1).
Research by Herhausen et al. (2015) indicates this advantage could be the result of implementing a relatively new concept: channel integration.
9 2.2 Channel integration and SST usage
Channel integration is defined as the degree to which different channels interact with each other (Bendoly et al., 2005). There are two basic approaches to channel integration: 1) providing access to and knowledge about the internet store at physical stores (offline-online channel integration) and 2) providing access to and knowledge about physical stores at the internet store (online-offline channel integration). While most retailers have already made a first attempt in
implementing channel integration by providing real-time attributes of the physical store on their website (online-offline channel integration), we now see a trend of retailers implementing online elements into their physical stores (offline-online channel integration). These so called self-service technologies (SSTs) are defined as technological interfaces that enable customers to produce a service independent of direct service employee involvement (Meuter et al., 2000). Based on technical characteristics, SSTs can be classified in 3 main categories: 1) touch screen displays/in store terminals, 2) systems for mobiles (mobile applications) and 3) hybrid systems (Pantano & Viassone, 2014). Information terminals (category 1), are the most common form of SSTs. Hence, this study will focus on the implementation of information terminals. The main features characterizing these information terminals are their capability to provide detailed, personal and constantly updated information (i.e. price, promotions, availability, discounts etc.) and the possibility to instantly reserve and buy products. Bitner et al. (2002) describe several reasons why organizations are introducing SSTs at such a rapid pace: 1) to reduce costs, 2) to increase customer satisfaction and loyalty and 3) to reach new customer segments. Besides, SST usage leads to a more consistent service atmosphere as they are independent of employees’ mood and personality (Hsieh, Yen & Chin, 2004). Due to these valuable benefits and the high costs for companies emerging from the adoption of SSTs, companies are eager to understand the
mechanisms involved in SST usage (Chiu, Huang & Yen, 2010).
Whereas research on SSTs was first focused on transaction-related technologies, such as the placement of orders, scanning or paying, developments are nowadays increasingly focused on
10 customer-service or information-related technologies due to the growing importance of service quality (Meuter et al., 2000). Service quality functions more and more as a key differentiator for retailers since sales are increasingly initiated by information rather than the advantages of transaction-related technologies and transparency of the internet (Grewal, Iyer & Levy, 2004). Therefore, we adapted perceived service quality as a mediating variable in our model.
2.3 Perceived service quality
Lee, Kim, Ko and Sagas (2011) describe perceived service quality as a global judgement, or attitude relating to the superiority of a service. Though, when it comes to the multichannel environment and the shift in focus from commerce (mainly focused on the transactions) to e-service (focused on all cues and encounters that occur before, during and after the transaction), the definition of e-service gives us a more expanded understanding of the current retail
environment (Zeithaml, Parasuraman & Malhotra, 2002). E-service quality is defined as the extent to which a website facilitates efficient and effective shopping, purchasing, and delivery of products and services. As this definition states, the meaning of service is comprehensive and includes both pre- and post-website service aspects (Zeithaml et al., 2002). This definition is relevant to our topic of SST usage since the information terminals will provide access to a website, the internet store. As retailers are likely to be able to have an impact on service quality more than on price and product quality, the role of perceived service quality is strategically important for retailers (Sweeney, Soutar & Johnson, 1997). The literature describes several services-related effects as a result of the implementation of SSTs. In line with our study, these effects will be described distinguishing between complementary and replacement SST usage.
2.3.1 Complementary SST usage
Complementary SST usage refers to the use of SSTs as an adjunct to existing services (i.e. employees) and provides customers with several benefits. Most benefits are related to the improvement of currently offered services at different points of sale in the service-delivery process (Bitner et al., 2002). For example, some customers use SSTs in the orientation-phase
11 whereas others use them in the purchasing-phase. These channels are regarded as complementary, meaning that satisfaction with one channel enhances a customer’s intention to use another
channel (Verhoef, Neslin & Vroomen, 2007; Falk, Schepers & Hammerschmidt, 2007).
The literature describes several effects due to the use of complementary SSTs. First of all, SSTs provide customers with customized information and services which they can access
according to their own needs (Pantano & Di Pietro, 2012). This could reduce waiting times for customers that search for specific information while sales clerks are engaged in customer talks. This is especially important as time saving is one of the primary reasons for consumers to use SSTs (Meuter et al., 2000; Weijters et al., 2007). Accordingly, customers become more
independent of the know-how and availability of sales clerks (Pantano & Viassone, 2014). The findings of Van den Poel and Leunis (1999) state that many people perceive a benefit in obtaining information directly from a site rather than having to go through a salesperson. However, even though the diffusion of technology among retailers is changing the concept of the point of sale, Pantano and Viassone (2014) emphasize that this does not imply that the traditional point of sale will disappear. They state that the role of SSTs lies in generating a new customer experience to create more value.
Overall, stores seem to become more attractive and convenient in terms of time saving, place and purchasing modalities when implementing complementary SSTs, leading to an enhanced perception of service. (Weijters et al., 2007; Pantano & Di Pietro, 2012; Orel & Kara, 2014). Therefore, the following hypothesis was developed:
H1a: Offering SSTs as a complement to traditional services positively influences a customers’ perceived service quality, compared to offering traditional services only.
Though, it should be kept in mind that there is considerable heterogeneity between customers and not all will use SSTs and experience these kinds of advantages unless they feel comfortable with using the technology (Meuter, et al., 2003). Therefore, we added the moderating variable technological anxiety to our model, which will be discussed later in this paper.
12 2.3.2 Replacement SST usage
As most SSTs do not require direct employee’ assistance, they may be a substitute for employees (Pantano & Di Pietro, 2012). Even though not much research has been done concerning the actual effects of replacement SST usage, literature describes several mechanisms that should be kept in mind when implementing SSTs as replacement.
First of all, whereas replacing employees could reduce labour costs exponentially, it can be a drain on resources if the SSTs are not widely accepted by the consumers and therefore not used (Curran & Meuter, 2005). In fact, when changing a service, a potentially significant portion of the customers will opt not to participate in the new service format (Curran & Meuter, 2005). People might feel that their relationship with the vendors is changed and existing shopping routines and habits are threatened. This could negatively affect the image of a brand/firm
(Pantano & Di Pietro, 2012). Another pullback lies in the possible failures when interacting with SSTs, due to technical or human errors. These failures can result in technological abandonment, customer dissatisfaction and missed sales opportunities. Though, it is stated that recovery from such failures is possible (Zhu, Nakata, Sivakumar & Grewal, 2013).
Furthermore, replacement SST usage could have major downsides for employees too, as these technologies don’t just make the experience quicker, they make it better (Fishman, 2004). Brynjolfsson and McAfee (2011) state that the development of automation and software has been destroying jobs faster than it is creating them, leaving the typical worker worse off than before. They refer to this as the great paradox of our era since productivity is at record levels and innovation is faster than ever before, while at the same time we have fewer jobs and a falling median income (Brynjolfsson & McAfee, 2011). The routinization hypothesis describes this phenomenon as follows: “Technological change displaces human labour in tasks that can be described as routine, essentially so that a computer program can be written to mimic what a human would do” (Goos, Manning & Salomons, 2011, p.2). Though, it should be noted that the replacement of certain jobs by technology does not directly translate to people losing jobs.
13 Instead, those jobs will change (Fishman, 2014). For example, the self-service kiosks at
McDonald’s allow operators to move some workers away from cash registers into the kitchen to help speed up customer service.
In the end, a customer’s intention to use an online channel is expected to be diminished because of his/her preference for the offline channel. An underlying mechanism could be derived from the status quo bias theory, stating that individuals have a preference for sticking with the status quo alternative (the traditionally used channel). Moreover, individuals tend to put more weight on potential losses compared to potential gains (Falk et al., 2007). Based on previous mentioned effects, the following hypothesis was developed:
H1b: Offering SSTs as replacement for traditional services negatively influences a customers’ perceived service quality, compared to offering traditional services only.
As stated by Grewal, Levy & Kumar (2009), effective customer interaction (e.g. easy interactions between the customers and the firm, consistency of the message across
communication channels and providing multiple channels to interact and shop) increasingly determines the growth and profitability of retailers. They stated that when a company
accomplishes to deliver a superior customer experience, this should result in higher customer satisfaction, more frequent shopping visits and ultimately in higher profits. A customer
experience includes every touchpoint of contact at which the customer interacts with the business, product or service (Grewal et al., 2009). However, in their research they only focus on promotion, price, merchandise, supply chain and location. No form of customer service was integrated. In order to investigate whether customer service positively influences the growth and profitability of retailers we will measure the customers’ purchase intention. In this way, perceived service quality serves as a mediating variable in our model. In line with the findings of Grewal et al. (2009), it is suggested that offline and online store perceptions influence online purchase intention and that e-service quality affects satisfaction and the intention to purchase (Verhagen & Van Dolen, 2009; Zeithaml et al., 2002). Therefore, we developed the following hypothesis:
14 H2: The relationship between the use of SSTs and purchase intention is positively mediated by a customers’ perceived service quality.
2.4 Purchase intention
Purchase intention is defined as the likelihood that customers use a focal retailer or a certain channel for purchasing a certain product (Herhausen et al., 2015). According to Verhagen & Van Dolen (2009), existing literature on the impact of online stores on purchase intention has its limitations, due to the fact that they focus on stores as a single channel while most firms moved to a brick-and-click business model. Investigating the integration of offline and online stores is especially important as consumers can transfer their offline experience into online buying behaviour. This happens when consumers search for their product in offline stores, but buy it online (Verhagen & Van Dolen, 2009).
Theory suggests that lowering the rate of customer defection is a major key to the ability of a service provider to generate profits (Zeithaml, Berry & Parasuraman, 1996). As
complementary SST usage increases the overall convenience and perceptions of service, it is expected to positively influence a customers’ purchase intention. In addition, theory suggests that operating in multiple channels can positively affect the financial performance of a firm
(Geyskens, Gielens, & Dekimpe, 2002). Based on these findings, we developed the following hypothesis:
H3a: There is a positive relationship between complementary SST usage and purchase intention. In case of replacement SST usage a negative effect is expected to occur, due to previously
mentioned mechanisms on the adoption of replacement SSTs, resulting in the following hypothesis:
15 2.5 Technological anxiety
As briefly mentioned before, the usage of technological devices is not convenient for everyone. Whereas some consumers consider SSTs to be easy to use, others might feel uncomfortable using them and prefer to have contact with a salesperson (Dabholkar et al., 2003). As not all consumers choose to use new technologies nor do all consumers see these adjustments as improvements, it is expected that technological anxiety may act as a moderating variable to the effect of SST usage on both perceived service quality and purchase intention (Meuter et al., 2003). These two effects will now be further explained.
First of all, it is expected that technological anxiety influences the effect of SST usage on perceived service quality due to underlying feelings that might be involved. More specifically, there have been growing arguments that the usage of SSTs could result in a reduction of customer service due to a depersonalized atmosphere (Lee & Yang, 2013). Additionally, customers might perceive internet-based transactions as complex and intimidating (Zeithaml et al., 2002). This might even result in negative feelings that cause negative spill-overs from one channel to another (Herhausen et al., 2015). In contrast, a positive effect is expected for customers that score low on technological anxiety, implying a linear effect as grounded by findings of Bitner (1992), who states that a positive evaluation towards using technology is a prerequisite for a favourable perception of service quality. Moreover, if customers find technology easy to use, they consider the use of SST as an attractive alternative due to the fact that it requires them less effort
(Shamdasani, Mukherjee & Malhotra, 2008). Consequently we developed the following hypothesis:
H4: Technological anxiety negatively moderates the effect of SST usage on perceived service quality.
Related to the moderating effect on purchase intention, customers are likely to take the costs and benefits of learning to use the new technology into consideration, and switch
16 comfortable in the purchase process. According to Lee and Yang (2013), a possible explanation for the customers’ resistance to adopt SSTs lies in the need for human contact. Prior studies indicate that the need for interaction is negatively associated with the use of SSTs (Gelderman, Paul & Van Diemen, 2011). In fact, technological anxiety negatively influences the intention to re-use SSTs and the likelihood of participation in positive word-of-mouth (Meuter et al., 2013).
Though, there is a large group of people that do perceive benefits by the possibility of obtaining information directly from a site rather than having to go through a salesperson (Van den Poel & Leunis, 1999). Therefore, SSTs are particularly attractive for customers who are looking for a high level of individual control and who want to form their own opinion without being influenced by the interactions with sales clerks (Meuter et al., 2003). This low need for personal interaction can be a result of the habit of self-information seeking on the internet (Gelderman et al., 2011). Moreover, as Stewart (2003) found that the association with a trusted context positively influences the customers’ intention to purchase, we expect to find a linear effect, meaning that customers with a low level of technological anxiety will have higher purchase intention as they may approach the information terminal as something they know and trust, and vice versa. Consequently, we developed the following hypothesis:
H5: Technological anxiety negatively moderates the effect of SST usage on purchase intention.
2.6 Demographic characteristics
In the past, research focused on the development of profiles for the typical SST user based on demographic characteristics. For instance, it was found that non-adopters were older, less educated, and had lower incomes (Meuter et al., 2003). Indeed, older consumers often have the impression they are not competent enough to learn new things. They are not frequent online shoppers, do not prefer to use ATMs, feel less-confident and need more time to complete certain tasks on technological devices (Lee & Yang, 2013). Furthermore, better educated consumers seem to feel more comfortable when dealing with and relying on new information than people
17 Use of Self-Service Technologies (SSTs) H1 H2 H3 H4 H5
with lower educational backgrounds (Homburg & Giering, 2001). Besides, males and females are found to employ different information-processing strategies while shopping (Meyers-Levy & Sternthal 1991). It has been suggested that females generally show higher involvement and more thorough information processing than do males (Laroche, Cleveland, Bergeron & Goutaland, 2003). In the literature, such demographic variables have not consistently explained the usage of technology. Meta-analysis that investigated age and adoption of innovative technologies found that roughly half of the 228 studies established no relationship (Meuter et al., 2003). Findings of Meuter et al. (2003) even indicate that technological anxiety is a better and more consistent predictor of SST usage than demographic characteristics such as age and gender. In order to ensure our model measures the effects of technological anxiety, the covariates age, gender and level of education are added to our model.
Following on previous mentioned dynamics that come with the integration of SSTs, the following conceptual model was developed:
Conceptual model Purchase intention Perceived service quality
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3. Method
3.1 Design and Participants
To test the hypotheses, an experimental vignette study with three conditions was applied. Participants were randomly assigned to read one of the three vignettes as this was a between-subjects design (Aguinis & Brandley, 2014). The inclusion of survey questions directed us to a quantitative study with a minimum sample size of 50 participants per condition.
The population of interest was the Dutch consumer. As this is a large group and the sampling frame is unknown, non-probability convenience sampling was used. The respondents were selected through Facebook, by e-mail and in the train. The reason to approach people in the train was to ensure sufficient variance on the moderating variable technological anxiety. We tried to reach less experienced internet users by letting part of the respondents participate in an offline setting with pen and paper. The train was a stable environment since the respondents were sitting and waiting to arrive at their destination. Final, a total of 215 potential respondents were recruited of which 178 online and 37 offline. Though, the results show no significant differences on the level of technological anxiety between participants that filled in our survey offline (M = 2.64, SD = 1.17) or online (M = 2.64, SD = 1.27).
Off all contacted people, 184 respondents provided valid data (e.g., M = 184, response rate 85,58%), as the results of people who read the article for less than 15 seconds and who indicated that they did not see the information and/or pictures about the Praxis storage rack, were left out. Accordingly, version 1 of our study contained 58 respondents, version 2 contained 60 respondents and version 3 contained 66 respondents. The age of the participants ranged from 16 to 73 with a mean age of M = 35,80 (SD= 15.38), providing a wide range in age of participants. This is especially important, given the expectation that younger generations may have lower technological anxiety. Of these participants, 68,3% (N= 125) reported to be female and 31.7% (N= 58) to be male. The majority of participants completed an educational programme at the university of applied science (MBO = 13.7%, HBO = 47.8%). The remaining respondents either
19 completed a secondary education programme (VMBO, HAVO, VWO = 8.2%) or an education programme at the research university (WO = 30.2%).
3.2 Stimulus material
As mentioned before, this experiment is a vignette study with 3 conditions. By using vignettes, participants were presented a carefully constructed and realistic scenario to assess the dependent variable purchase intention. Enhancing experimental realism allows researchers to manipulate and control independent variables, in this case SST usage (Aguinis & Brandley, 2014). Moreover, an important characteristic of vignette studies, and implemented in this study, is that it is not restricted to present solely in written format, but can include images and other types of media too (Hughes & Huby, 2002).
The scenario was a description of a situation in the well-known Dutch DIY store Praxis. Praxis was chosen as example since self-service technologies are particular suitable for retailers with a large selling space and a relatively low number of sales clerks (Kallweit et al., 2014). Moreover, by using Praxis as an example, this research touches upon an industry that is a relatively new adopter of service terminals, compared to fast-food and airline businesses that have already successfully adopted such terminals on a large scale. Indeed, it is expected that information terminals will be of growing importance in various types of industries (Fishman, 2004). According to Hanjo Huizing, CEO of the Customer Interaction Group, information terminals are expected to be of valuable contribution for Praxis (which is one of their clients) as he states: “Information terminals can serve as a professional guide in a customers’ decision making process, lowering the threshold to purchase a product due to the availability of product advice/information, which ultimately increases sales” (H. Huizing, personal communication, June 20, 2016).
The first part of the scenario had the same structure for all versions. The participants were asked to imagine being interested in buying a certain storage rack. They were told that in order to
20 make a purchase decision, they wanted to get more information about the product. A picture of the storage rack was included to make the situation more realistic. This storage rack was chosen on the basis that it does not require too much knowledge and skills about DIY products, making the scenario more likely to occur for a large amount of people and therefore more realistic to imagine. Furthermore, as this product needs to be assembled, it is likely for people to have questions about this.
The second part of the scenario was different for all conditions. In condition 1, the participant was told that he/she could get answers to his/her questions by asking an employee, including a picture of a Praxis employee. As the majority of retailers have employees in their stores and the information terminal is excluded, this condition served as a benchmark. A benchmark study is referred to as an empirical experiment with the aim of comparing leaners with respect to a certain performance measure (Hothorn, Leisch, Zeileis & Hornik, 2005). In condition 2 (SST as complement), the participant was told that Praxis recently added information terminals to their stores, including information about its features. They could either use the terminal, or ask an employee to get answers to his/her questions. A picture of both the employee and the information terminal where included. In condition 3 (SST as replacement), the participant was told that Praxis recently added information terminals to the store, as a replacement for
employees. They were told about the features of these information terminals and that they could use them to get answers to his/her questions. A picture of the information terminal was added. The vignettes can be found in Appendix A. To check whether the participants were able to read the vignette and saw the images, a manipulation check was included in the experiment.
3.3 Procedure
First of all, a pre-test was conducted to ensure instrument clarity, clear wording of the questions and validity of the measures. 30 Individuals were taken as subjects and invited to comment on the questions and wordings. Based on the results and feedback, the survey was slightly adjusted.
21 The experiment was conducted in two settings. Part of the participants conducted the experiment online with Qualtrics, a software program for online surveys. The other part of the participants filled in the exact same questionnaire, but as a printed version in an offline setting. The experiment started with a brief introduction. They were told they were participants in a research study, that they were about to read a short scenario, that there were no right or wrong answers and that all answers would be based on complete anonymity and would only be used for the purpose of this research. Next, one of the three scenarios was shown, the version they saw was randomly determined. Afterward they were asked to answer questions about their perception of the services the DIY store Praxis provided, about their intention to re-purchase at Praxis, their experience with the use of technological devices and finally the respondents answered some general questions about their gender, age, level of education and whether they were able to read the information and saw any images. At the end of the survey the respondents were thanked for their participation and contact information was provided in case of questions or remarks.
3.4 Measures
As mentioned before, the population of interest of this study was the Dutch consumer. Since the items used in the questionnaire are derived from English studies, the items were back-translated into Dutch in order to ensure better understanding for the respondents and to make sure that the content of the items remained unchanged. The back-translations were carried out by two independent persons. This resulted in a small number of discrepancies between the original and the back-translated items which were corrected in the final Dutch version of the questionnaire. See Appendix B for an overview of all items including the translations.
Purchase intention. To measure people’s purchase intention, four items were assessed based on the validated scale of Cronin, Brady and Hult (2000), measuring behavioural intentions (α = .87). The items included statements such as “The probability that I will shop at Praxis again is” and “If I would buy a different product from the same category, I would go the same do-it-yourself shop”. To match the other measures, the original 9-point Likert scale was modified to a
22 seven-point scale (1= very low to 7= very high). The final scale of purchase intention resulted in a good Cronbach’s alpha of .83, (M = 5.01, SD = 1.21). The corrected item-total correlations indicate that all the items have a good correlation with the total score of the scale (all above .30). Also, none of the items would substantially affect the reliability of they were deleted.
Perceived service quality. Whereas the majority of researchers use the SERVQUAL scale by Parasuraman, Zeithaml and Berry (1988) to measure perceived service quality, which
conceptualizes service quality as a construct with five dimensions (tangibles, reliability, responsiveness, assurance and empathy), most of these items where not applicable to our
experiment. To be more precise, the SERVQUAL items are designed to be answered in a real-life shopping setting whereas this study uses a shopping scenario. For example, many questions refer to the interaction with employees, which is not relevant to our study as not all conditions include employees. Therefore, to measure perceived service quality a scale was developed in adaption of other commonly used scales that measure service quality (Brady & Cronin, 2001; Dabholkar, Thorpe & Rentz, 1995; Parasuraman, Zeithaml & Malhotra, 2005). The final scale consisted of 4 items and included statements like “I believe Praxis offers excellent service” and “Praxis
responds to my needs as a customer” (1= totally disagree, to 7 = totally agree). The perceived service quality scale has high reliability, with a Cronbach’s Alpha of .82, (M = 5.06, SD = 1.03). Again, the corrected item-total correlations indicated good correlation between all the items and the total score of the scale (all above .30) and none of the items would substantially affect reliability if they were deleted.
Technological anxiety. To measure technological anxiety, four items were applied form the 9-item scale of Meuter et al. (2003) that was verified with a Cronbach’s alpha score of .90. The items included statements like “I have difficulty understanding most technological matters” and “I am sure of my ability to interpret technological output” (1= totally disagree, to 7 = totally agree). Two of the four items were reverse coded meaning that a relatively low score indicates a relatively high level of technological anxiety. The scale of technological anxiety resulted in a
23 good Cronbach’s alpha of .84, (M = 2.64, SD = 1.25), meaning that the majority of respondents scored relatively low on technological anxiety.
Control variables. The results of this study are controlled for 3 variables. Gender and education were added as control variables based on the findings of Lee et al. (2011). They suggest that gender and education can have statistically significant effects on service quality dimensions. Furthermore, age will be added as a control variable as the literature does not consistently explain the relationship between demographic variables and the adoption of technology. Even though non-adopters are older and less educated, findings of Meuter et al. (2013) indicate that technological anxiety is a better and more consistent predictor of technology usage.
4. Results
4.1 Preliminary analysis
Before testing the structural model of interest, a pre-test was conducted in order to ensure instrument clarity, clear wording of the questions and validity of the measures. To this end, a principal axis factor analysis (PAF) and reliability analysis were conducted on the scale items measuring purchase intention, perceived service quality and technological anxiety. Based on these results several scale items were removed.
In order to test our conceptual model, various steps were carried out. First of all, a
frequencies check was done to examine if there were any errors in the data. No errors were found. The frequencies check did identify missing values, present for the covariates gender, age and education. Due to the sensitivity of these questions, participants were allowed to not answer these questions. As two of the four items measuring technological anxiety were reverse coded, these items were recoded to make sure that a high score indicated a high level of technological anxiety. After doing this, descriptive statistics, and normality tests were performed on all items.
24 Table 1 Table 2
Means, Standard Deviations, Correlations and Reliabilities Variables
Number
of items M SD 1 2 3 4 5 6 7 8
1. Gender
(1=male, 2=female, 3=other) 1 1.68 0.467
-2. Age 1 35.80 15.38 -.129 -3. Education 1 3.00 0.879 -.081 -.462** -4. P_INTtot 4 5.01 1.211 .152* -.029 -.093 (0.83) 5. PSQot 4 5.06 1.028 .213** .030 -.091 .612** (0.82) 6. TAtot 4 2.64 1.251 .258** .245** -.222** -.059 -.085 (0.84) 7. Version 2 (complementary) 1 .33 .47 .025 -.059 -.040 -.003 .068 .057 -8. Version 3 (substitutional) 1 .36 .48 -.075 .053 .065 -.069 -.131 -.002 -.520**
-Note: N = 184. Reliabilities are reported along the diagonal.
* Correlation is significant at the 0.05 level (two-tailed). ** Correlation is significant at the 0.01 level (two-tailed).
The Kaiser-Meyer-Olking measure verified the sampling adequacy for the analysis, KMO = .787. Bartlett’s test of sphericity χ² (X) = 1072.481, p <.001, indicated that correlations between items were sufficiently large for PAF. In agreement with Kaiser's criterion, examination of the scree plot revealed a levelling off after the third factor. Thus, three factors were retained and rotated with an Oblimin with Kaiser normalization rotation. The factor loadings after rotation can be found in Appendix C, suggesting that factor 1 represents perceived service quality, factor 2 technological anxiety and factor 3 purchase intention. Subsequently a reliability analysis was performed for the items of each construct. A Cronbach’s alpha coefficient of >.70 was considered acceptable to indicate internal consistency. As shown in Table 1, all three scales meet this
requirement and none of the items would substantially affect the reliability if they were deleted. In order to do a correlation analysis and test the hypothesis, the final scales were computed.
4.2 Correlation matrix
A correlation analysis was used to explore the relationship between the variables, their strength and direction. The correlation table is displayed in Table 1. Concerning the main variables purchase intention, perceived service quality and SST usage, the only significant correlation was found between purchase intention and perceived service quality, indicating a strong positive
25 relation between the service quality a customer perceives and their intention to purchase
r(184) = .61, p= <.01. Other significant correlations highlight the important role of the covariates in our model. Based on these findings and previously described theory, we decided to retain all three covariates into our model. A more formal test will now follow.
4.3 Test of hypotheses
The hypotheses were tested using SPSS, a software package used for statistical analysis (Field, 2013). To perform the regression analysis, PROCESS was used which is a computational tool for path analysis-based moderation and mediation analysis as well as their combination as a
“conditional process model” (Hayes, 2012).1 For the purpose of this work, the hypotheses were tested at a significance level of p<0.05. The results can be found in Table 2.
These results indicate no significant direct effect of SST usage on perceived service quality, as was suggested by hypothesis 1. This applies to both complementary (Version 2=.06, p= .742) as well as replacement SST usage (Version 3=.09, p= .821). However, the covariate gender did turn out to have a significant direct effect on perceived service quality, meaning that females score significantly higher on perceived service quality than do males (Gender=.58, p<.001). Surprisingly, no other significant effects where found concerning the covariates.
Regarding hypothesis 2, the regression coefficient for perceived service quality is
PSQ=.71 and is statistically different from zero, t(178) = 9.51, p<0.001, meaning that customers that score higher on perceived service quality will score significantly higher on purchase
intention. However, as hypothesis 1 did not find any significant effects, perceived service quality cannot be explained by SST usage according to our model. Therefore hypothesis 2 is not
supported. Hypothesis 3 investigated the direct effect of SST usage on purchase intention. Though, these effects did not turn out to be significant for both complementary (Version 2= -.17, p= .361) as well as replacement SST usage (Version 3= .05, p= .902).
1 When using PROCESS, one needs to indicate the right model number as there are 76 different types of statistical
26 Despite the fact that hypothesis 1 and 3 did not show significant effects, we took a closer look at the effects of the moderating variable technological anxiety. Hypothesis 4 suggested the relationship between SST usage and PSQ to be negatively moderated by technological anxiety, but this appeared not to be significant(Version 3 x TA= -.11, p= .388). Though, a marginally significant negative effect was found on the direct relationship between technological anxiety and PSQ, meaning that people that score higher on technological anxiety will have a lower PSQ (Technological Anxiety =-.13, p=.098). Similarly, hypothesis 5 predicted the direct relationship between SST usage and purchase intention to be negatively moderated by technological anxiety. This did not show significant effects either (Version 3 x TA=-.03, p= .788). As these interaction effects turned out not be significant, there was no need to look further at the conditional effects table.
Table 2 Table 3
Antecedent Coeff. SE p Coeff. SE p
Version 3 .09 .39 .821 Version 3 .05 .37 .902 PSQ - - - PSQ .71 .07 <.001 Technological Anxiety (TA) -.13 .08 .098 Technological Anxiety (TA) .00 .08 .973 Version 3 x TA -.11 .13 .388 Version 3 x TA -.03 .13 .788 Version 2 .06 .19 .742 Version 2 -.17 .18 .361 Gender .58 .17 <0.001 Gender .03 .17 .859 Age .01 .01 .286 Age -.01 .01 .337 Education -.06 .10 .527 Education -.08 .10 .391 constant 4.43 .59 <0.001 constant 1.87 .66 .005
Perceived Service Quality (PSQ) Purchase Intention (Y)
Consequent
R2 = .103
F (7,178) = 2.786, p = .009
R2 = .383
27 4.4 Discussion of the results
As the regression analysis did not show significant effects concerning the variable SST usage, simple statistics were explored by means of a post-hoc analysis in order to get more insights into customers’ channel preferences. These outcomes are based on a question people answered after reading the scenario, indicating which channel they would most likely use, to find answers to their questions. The outcomes show that in the benchmark situation, 77.6% of the people would most likely ask an employee for help, 19% would search on their own device for information and 3.4% would do nothing. Then, in version 2 (including an information terminal as complement) we see a shift towards the use of the information terminal. In this situation, 50.8% asks an employee, 33.9% would consult an information terminal, and 13.6% would search on their own device. On the one hand, this shift indicates that part of the consumers prefer using an
information terminal over asking an employee. On the other hand, we see a big part of consumers not switching to the new channel, which is in line with the literature, stating that a portion of the customers will not participate in new service formats (Curran & Meuter, 2005). An overview of these outcomes can be found in Appendix C.
5. Conclusion and discussion
In this study, we have attempted to deepen our understanding of the fast changing field of channel integration, more specifically: SST implementation. This study focused on a particular type of SSTs, namely in-store information terminals. We examined the effects of these terminals when implementing them as complement as well as replacement to current provided services.
The statistical analysis did not provide evidence for hypothesis 1, theorizing that complementary SST usage positively affects a customers’ perceived service quality. This is relatively unexpected given that the majority of the literature on this topic investigates some type of complementary SST usage and their findings imply related effects(Meuter et al., 2000;
28 hypothesis 3a, measuring the direct effect of complementary SST usage on purchase intention did not show significant results. A possible explanation could be found in the costs that are related to learning to use new technologies, which might be too high to be worthwhile for switching
(Curran & Meuter, 2005). In line with this resistance towards the adoption of SSTs lies the need for human contact (Lee & Yang, 2013). When customers have the option to ask their question to an employee or use a SST, the need for interaction might weigh more heavily. Additionally, the status quo bias theory states that a customer’s intention to use an online channel is expected to be diminished because of his/her preference for the offline channel (Falk et al., 2007). When related to our study, this implies that the effects of complementary SST usage did not show significant differences compared to the benchmark situation, possibly due to the availability for customers to still consult their default information channel (e.g. an employee) when having a question. In this situation, one might not feel the need to change their habits.
In order to investigate what happens when the status quo was left out, the effects of replacement SST usage were tested, implying a negative relationship between replacement SST usage and perceived service quality (H1b) and purchase intention (H3b). Nevertheless, our findings did not show significant results, which puts the status quo theory aside. Furthermore, these results do not provide evidence concerning the previous mentioned economic trend, stating that business are doing more and more with the same, or fewer, workers (Fishman, 2004). This trend might not apply for the retail industry as customers do not feel comfortable using
information terminals (yet). In the limitation and future research section we discuss alternative explanations and shortcomings of the current study.
The analysis did reveal a positive relation between perceived service quality and purchase intention (H2), confirming the important role of the service quality a company delivers. However, as hypothesis 1 was not significant, this effect cannot be explained as a result of SST usage which was the main focus of our study. Though, we did find a significant effect of gender on perceived service quality, indicating that females rate the perceived service quality of Praxis significantly
29 higher than do males. This is in line with the literature highlighting the importance of gender when measuring perceived service quality (Lee et al., 2011).
Regarding the moderating variable technological anxiety, results indicated no significant effects on both perceived service quality (H4) as well as the direct effect on purchase intention (H5). This is contradicting the literature that broadly agrees on the negative effects of
technological anxiety on SST usage (Dabholkar et al., 2003; Meuter et al., 2003; Zeithaml et al., 2002). Again, in the future research and limitation section we discuss potential explanations.
Worth mentioning are the outcomes of the post-hoc analysis, performed to get more insights into customers’ channel preferences. These outcomes indicated that a portion of the customers do prefer using (complementary) information channels over asking an employee, implying that information terminals can deliver an added value. Although, one should keep in mind that some customers will not participate in new service formats (Curran & Meuter, 2005).
5.1 Theoretical and managerial implications
This study contributes to the existing literature on SST usage by shedding light on two different approaches, complementary and replacement SST usage. The last one is of special interest as this is not previously studied yet and provides insights on current trends in the (rapidly changing) retail environment.
By using Praxis as an example, this research touches upon an industry that is a relatively new adopter of information terminals, compared to fast-food and airlines businesses that have already successfully adopted such terminals on a large scale. Moreover, as information terminals are expected to be of growing importance in the future for other retailers too, the results of this study can be helpful for many retailers (Fishman, 2014). Furthermore, as offline service quality has a strong cross-channel effect on perceived online channel service quality, it is important for managers to be aware of the critical role of SSTs (Yang et al., 2013). On the one hand, SST implementation can be very profitable due to the great amount of data collection. With the right
30 analytical skills, managers can collect strategically valuable information on consumers’ shopping behaviour, preferences and market trends (Chiu et al., 2010). Ultimately this could result in improved productivity, increased firm’s performance and enhanced service efficiency (Lee et al., 2012a; Lee et al., 2012b). Moreover, our findings on channel preference suggest that costs might be saved when implementing SSTs as a complement to current services. In that case, part of the consumers will consult an information terminal instead of an employee compared to the
benchmark, resulting in less work for the employee. Consequently, a manager could decide to work with fewer employees and reduce labour costs, or use their employees capabilities for other operations that might help the company grow.
Though, one should keep in mind that these information terminals will only be profitable as long as consumers will actually use them. Some customers might not switch to use information terminals as they do not perceive the benefits of learning to use the new technology higher than the costs (Curran & Meuter, 2005). Especially since the implementation of SSTs involves high costs and negative spill-overs that can harm your business. Acting on this, managers could position the benefits of the online channel against the disadvantages of the traditional format rather than communicating solely the benefits of the new channel option. By gradually communicating the disadvantages of the old service delivery mode to customers (e.g., lower flexibility, longer waiting time, higher costs), the perceived usefulness of the new channel might be enhanced (Falk et al., 2007). When doing this, they should focus on men, older people and less experienced internet users as they are more likely to display a strong status quo bias. A viable strategy would be to create positive word of mouth by providing special introduction programs for inexperienced users.
To conclude, this study shows relatively subtle cues about the effects of SST
implementation. These are cues that, on the one hand, can be very profitable for companies that want to be innovative, but on the other hand that show that SST implementation can be harmful to your company too, due to the high costs and risk of customers’ resistance towards using SSTs.
31 5.2 Limitations and future research
Even though our quantitative approach allows us to analyse a large amount of data in a
standardized and economical way, several limitations should be kept in mind. First of all, when using only self-reported measures, there is the possibility of common-method bias and social desirability in answers, which threatens the reliability. By emphasizing the anonymity of the respondents we intended to reduce this risk. Furthermore, by using non-probability sampling we might have excluded subjects who may be different from those who are included, which reduces the generalizability of our data. Moreover, due to the structure with three vignettes, the separated versions included not more than 66 respondents each, which might have caused a less
representative sample.
Regarding the used measurements, we decided to not use the SERVQUAL scale, which is used in many articles measuring perceived service quality. Instead, we developed a scale in adaption of other commonly used scales. Even though this scale showed high reliability, this might have affected our results. Moreover, the performed regression analysis with PROCESS did not fit perfectly to our model, as we had two independent measures (complementary and
replacement SST usage) and PROCESS only allows one. In order to run the analysis, the
complementary version was added as a covariate to the model. To check whether this influenced our results an additional analysis was run with the replacement version as independent measure, however this did not show significant differences.
Concerning the choice to conduct a vignette study to measure our conceptual model, there are several downsides that should be mentioned. For instance, since the participants were only presented with a single rather than multiple vignettes, they could not make a comparison that would help to contextually ground their responses. As the respondents did not have any reference point for their own judgements, the responses may not have accurately reflected their true
judgments, which might explain why the results did not show significant differences between the scenarios. As this is a common limitation to vignette studies, we tried to reduce this effect by
32 providing the participants with sufficient baseline information to provide a similar contextual background for all participants.
Regarding our choice for Praxis, one might argue that the DIY-industry meets solely utilitarian shopping values, as the majority of the products serve functional needs, whereas hedonic shopping values are derived from the perceived fun of the shopping (Sarkar, 2011). Therefore we acknowledge that the results might not be generalizable to the entire retail industry. To exclude the possibility that our results are biased by pre-existing assumptions about the service quality of Praxis, it is recommended to measure this prior to the scenario, something our study did not do. Hence, we suggest further research to investigate the usage of SSTs in different industries and control for any presumptions. When doing this, one might consider investigating SST usage in a situation that is focused on buying a product, instead of acquiring information, which was the focus of this study. The search for information being top of mind might have been problematic for our results, measuring purchase intention as dependent variable. Besides, many other factors will influence the effectiveness and usability of information terminals, such as software and appearance as all information terminals are different. As this was not the focus of this study, a basic information terminal was described, which might be too simplistic and therefore less representative.
Another aspect that could have affected our research, but did not fall within the objective of the present study, is the influence of time. Our study measured only one point in time where we rely on cross-sectional data instead of longitudinal data. Also, results might change over time as people get more familiar with using information terminals and the idea that the store they are familiar with is somewhat changing as well. Therefore, further research might investigate the SST-acceptance and implementation process over time. By doing this, one might include more categories of SSTs such as mobile applications and hybrid systems, or investigate more
sophisticated levels of SST usage, as our study was based on very rough distinctions.
33 and conduct of this research, the underlying motives of companies to implement information terminals are important to keep in mind too. Depending on how consumers perceive these motives, they either react positively as they believe the company implemented the terminals to provide them with better services, or the consumers react negatively as they believe the company used the terminals as a cheap way to cut on labour costs. The effects of these underlying motives may be an interesting angle for further research.
While we can acknowledge that there are some limitations to this research, the scales showed significant reliability, the vignettes differentiate substantially on the manipulated content and our assumptions were based on multiple theories. Even though most of the effects are not significant, our study does provide a framework for understanding the underlying mechanisms that influence SST usage.
34
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