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The use of service robots in the hospitality industry

The impact of robotic service acceptance on customer experience in the hotel industry, and the moderating effect of type of value (hedonic vs utilitarian) and

reason for stay (business vs leisure)

Master Thesis

MSc Business Administration – Consumer Marketing Track University of Amsterdam, Faculty of Economics and Business

Academic year 2021-2022

Lotte Gijsen – 13452851 EBEC approval nr.: 20211026061029

Thesis supervisor: Dr. H. Güngör Date of submission: 26-02-2022 (final version)

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Statement of originality

This document is written by Student Lotte Gijsen 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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Table of content

List of figures ... 4

List of tables ... 4

1 Abstract ... 5

2 Introduction ... 6

3 Literature review ... 11

3.1 Robotic service acceptance ... 11

3.2 Customer experience ... 13

3.3 Type of value (hedonic vs utilitarian) ... 15

3.4 Reason for stay (business vs leisure) ... 17

3.5 Control variables ... 18

4 Methodology ... 21

4.1 Research design ... 21

4.2 Data collection ... 21

4.3 Procedure... 22

4.4 Stimuli ... 23

4.5 Measures ... 24

4.6 Statistical procedure ... 27

5 Results ... 28

5.1 Descriptive statistics ... 28

5.2 Reliability analysis for scales ... 29

5.3 Correlation analysis ... 31

5.4 Differences between conditions ... 33

5.5 Hypotheses testing ... 35

5.3 Further investigation of PROCESS Models ... 38

6 Discussion ... 45

6.1 Theoretical implications ... 48

6.2 Managerial implications ... 49

7 Limitations and future research ... 51

8 Conclusion ... 53

9 References ... 54

10 Appendix ... 68

10.1 Questionnaire ... 68

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List of figures

Figure 1: Conceptual model Figure 2: Service robot Pepper Figure 3: PROCESS Model 2 Figure 4: PROCESS Model 4 Figure 5: PROCESS Model 7

List of tables

Table 1: Overview of experimental conditions Table 2: Measurements

Table 3: Reliability analysis

Table 4: Means, Standard Deviations, Correlations Table 5: One-way ANOVA descriptive statistics Table 6: One-way ANOVA

Table 7: 2 x 2 Factorial ANOVA descriptive statistics Table 8: 2 x 2 Factorial ANOVA

Table 9: Linear regression

Table 10: Results PROCESS Model 2 Table 11: Results PROCESS Model 4 Table 12: Results PROCESS Model 7

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1 Abstract

Robots and artificial intelligence (AI) technologies have a remarkable impact on our economies. They are becoming more and more prominent in the hospitality industry and are rapidly and radically changing the nature of service encounters, customers’ service experience and customers’ relationship with service providers (Ostrom et al., 2015; Rust & Huang, 2014).

So far, there has been limited research on AI-enabled customer experiences. Consequently, this research aims to analyse whether service robots in hotels have the potential to increase an individual’s customer experience. A conceptual model is proposed to draw on different types of values (hedonic vs utilitarian) and different reasons for stay (business vs leisure). This research made use of an experimental survey design based on validated scales. In total 231 respondents participated in this research. Results show that robotic service acceptance has a positive influence on the overall customer experience. Furthermore, the relationship between robotic service acceptance and customer experience is not moderated by the type of value (hedonic vs utilitarian) or reason for stay (business vs leisure). Nevertheless, results imply that women and older individuals tend to have a lower level of robotic service acceptance. Besides, age has a slightly negative impact on people's desire to use technology in general and therefore also has a negative impact on customer experience. Finally, results show an indirect relationship between the type of value and customer experience, mediated by robotic service acceptance.

Overall, these findings suggest that customer experience is a more complex construct that needs further examination in future research. For practice, these results underline that managers need to identify their target group, and their key characteristics in order to be able to provide a special and memorable customer experience.

Keywords: Robotic service acceptance, customer experience, type of value, reason for stay, age, service robots

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2 Introduction

Robots and artificial intelligence (AI) technologies are on the rise and have a remarkable impact on our economies. A report by the International Federation of Robotics (IFR) indicated that the sales of professional service robots have increased by 32 percent to $11.2 billion worldwide between 2018 and 2019 (International Federation of Robotics, 2020). The COVID- 19 pandemic will even further boost the market. With the advent of advanced digital technologies, AI in specific, and the current pandemic situation, technologies are increasingly replacing employees to provide contactless services (Chiang & Trimi 2020). Hence, AI capabilities are very promising and threatening the hospitality industry (Ruel & Njoku, 2020).

Even though AI will alter service jobs and may cause human talent to be replaced by technology in the future (Huang & Rust, 2018), it also brings some advantages. In the hospitality industry, robots and AI gain more and more attention as they are a predictable avenue for innovation, improved efficiency and profitability (Ivanov & Webster, 2019). Besides, AI helps people to work smarter which results in better business outcomes and develops new competencies and capabilities (Ruel & Njoku, 2020). These innovations are radically and rapidly changing the nature of service encounters, customers’ service experience and customers’ relationship with service providers (Ostrom et al., 2015; Rust & Huang, 2014). Therefore, a deeper understanding of the acceptance of these technologies is crucial for marketers and hotels as it determines their adoption and use by individuals (Ukpabi & Karjaluoto, 2017).

This study focuses on different hotel services (hedonic vs utilitarian) carried out by robots, different reasons for travel (business vs leisure), and the resulting customer experience individuals have during robotic service encounters. Previous research shows contradicting findings concerning consumers’ reactions to robotic hotel services. Whereas some studies demonstrated positive reactions towards hotels with robotic services (Ivanov et al., 2018; Qiu et al., 2020), other research discovered scepticism and potential problems with customer

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acceptance of robots (Io & Lee, 2020; Mori et al., 2012 ). The lack of human interaction or the effort required from customers to engage with the new technology may result in sacrifices regarding the overall customer experience (Ameen et al., 2021). Consequences of these and other potentially problematic issues relating to robotic service acceptance and the customer experience need to be better understood (Malle et al., 2015; Shank et al., 2019). Until today, much research has been done about AI, but what is less known is the potential for AI-powered experiences to cause changes in how customers perceive service quality, adjust their engagement in relationship to the service provider, and evaluate their entire AI-powered experience (Ameen et al., 2021). Despite the importance of these factors, previous research has primarily focused on the use of AI from a technological and organizational point of view (Jarrahi, 2018). As a result, there is a scarcity of research on how customers perceive AI technology, such as robots, as an important part of their service experience, and how this leads to a more pleasurable experience and stronger brand ties (Shank et al., 2019; Wang et al., 2020).

Moreover, there are several other issues related to the dynamics of customer experience that need to be investigated (Verhoef et al., 2009), such as the influence of type of value (hedonic vs utilitarian) (Klaus & Maklan, 2013) and reason for stay (business vs leisure) on the overall customer experience.

Robotic service acceptance. Service robots are defined as “system-based autonomous and adaptable interfaces that interact, communicate, and deliver service to an organization’s customers.” (Wirtz et al., 2018). The hospitality industry has great interest in experimentation with service robots and already applied social robots to assist hotel guests and shape their customer experience. However, guests might be sceptical regarding these service robots, and may therefore not accept the new technology. Robotic service acceptance is referred to as how people accept to adopt a specific technology for usage (Ghazali et al., 2020). Customer acceptance of service robots depends on three types of elements: functional, social-emotional,

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and relational (Wirtz et al., 2018). Whether guests accept or reject service robots might impact their overall customer experience.

Customer experience. Scholars refer to the term customer experience as an internal and subjective response customers have to any direct or indirect contact with the company, which develops over time and per service encounter (Meyer & Schwager, 2017; Holbrook &

Hirschmann, 1982). Gronroos (1988) discussed the notion of the customer experience as something that emerges from customers’ interaction with the service provider and thereafter leads to a perception of service quality. Customer relationship management, online big data, and AI could enable robots to know customers better than any human, and use this knowledge to create relationships that increase customer loyalty and engagement with a service provider (Murphy et al., 2019). Therefore, it can be assumed that the use of service robots in the hospitality industry could positively affect customer experience.

Hedonic vs utilitarian value. Previous studies refer to utilitarian products and services as being effective, necessary, helpful, functional, and practical, whereas hedonic products and services are more fun, enjoyable, thrilling, and delightful (Lee & Kim, 2018; El-Adly & Eid, 2016). Since hedonic motivated customers are more in search for fantasy, awakening, enjoyment, happiness, and sensuality (To et al., 2007), and service robots can provide personalized and unique services (Pinillos et al., 2016), it can be expected that the use of service robots will increase the customer experience for hedonically motivated customers.

Business vs leisure guest. Business travel is defined as work-related travel to an irregular place of work (Middleton & Clarke, 2001). This is usually obligatory and occurs within a small time frame, which leads to a preference for a quick service in terms of punctuality and reliability (Marin-Pantelescu, 2011). Leisure, on the other hand, is defined as a category of experiences with recreational and creative sub-categories, free from obligations and regarded

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as personally pleasurable (Leiper et al., 2008). Research found that leisure travellers value the hotel’s innovativeness more than business travellers do (Victorino et al., 2005). Therefore, it is expected that the usage of service robots will have a stronger effect on the customer experience for leisure guests, rather than business guests.

To conclude, this study attempts to empirically test whether the type of value (hedonic vs utilitarian), and reason for stay (business vs leisure) influences the relationship between robotic services and an individual’s AI-enabled customer experience. To achieve this, a new model is proposed, drawing on robotic service acceptance and customer experience literature.

The model integrates the type of value (hedonic vs utilitarian), and reason for stay (business vs leisure) as moderators affecting the relationship between the independent variable (robotic service acceptance) and the dependent variable (customer experience). The developed conceptual model is tested in an experimental survey design setting, with a 2 x 2 between- subjects design, using convenience sampling.

The research question of this study is as follows: What is the impact of robotic service acceptance on customer experience in the hotel industry and how is this effect moderated by the type of value (hedonic vs utilitarian) and reason for stay (business vs leisure)?

The contribution of this research is multifaceted. Firstly, this research contributes to filling the literature gap regarding research on automated technologies, which is still emerging (Belanche et al., 2020; Lu et al., 2020). Secondly, this research is one of the first studies that provides knowledge about consumers’ perceptions of AI-enabled customer experiences and enhances the understanding of human interaction with AI-enabled services. Thirdly, by emphasizing the role of type of value (hedonic vs utilitarian) and reason for stay (business vs leisure), the suggested conceptual model contributes to a better understanding of AI-enabled customer experiences.

On the managerial side, this research is essential as it provides and improves the

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understanding of how robots will impact the hospitality industry, and how customers will further assess their experiences with service robots in hotels. Furthermore, the study at hand will shed light on whether the type of value (hedonic vs utilitarian) and reason for stay (business vs leisure) will increase AI-enabled customer experiences in different service encounters.

Hence, this research will provide clear management guidelines, according to firms’ need to carefully consider how to use AI to engage customers in a more systematic and strategic way (Huang & Rust, 2021).

This paper will firstly give an extensive literature review which starts with an overview of robotic service acceptance, followed by an explanation of customer experience, type of value (hedonic vs utilitarian), and reason for stay (business vs leisure). This section includes the hypotheses that this research investigated. Secondly, the methodology section introduces the online survey with an experimental aspect. Additionally, all used variables and measurements will be verified. Thirdly, the results section will analyse and discuss the results of this research.

Fourthly, a discussion of the research is provided, including theoretical and managerial implications. Next, the limitations of this research and suggestions for future research are discussed. Lastly, this research ends with a short conclusion.

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3 Literature review

3.1 Robotic service acceptance

With the advent of advanced digital technologies and the current pandemic situation, technologies are increasingly replacing employees to provide contactless services (Chiang &

Trimi, 2020). Artificial intelligence (AI) replicates human behaviour and performs cognitive activities through computers, robots, and machines using automation, big data, and machine learning to fulfil stated goals and tasks (Haenlein & Kaplan, 2019). In the service context, AI primarily refers to the digital and robot services that are offered to customers to facilitate their purchase and consumption journey (Gursoy, 2018; Lu et al., 2019).

Recently, service robots are being given a considerable amount of attention, investment, and research. According to the International Federation of Robotics, a service robot is defined as a robot “that performs useful tasks for humans or equipment excluding industrial automation applications” (International Federation of Robotics, 2020). Wirtz and colleagues (2018) define service robots as “system-based autonomous and adaptable interfaces that interact, communicate, and deliver service to an organization’s customers.” In general, service robots can play two major roles in customer-facing service scenarios: augmentation (assisting and complementing human employees) and substitution (replacing human employees) (Larivière et al., 2017).

Service robots are able to complete tasks by following a service script (Huang & Rust, 2018) and can execute tasks without the need for human intervention (Colby et al., 2016). In addition, these robots are capable of making independent decisions based on the data they get from various sensors and other sources; they gain and store the knowledge, allowing them to learn from, and adapt to previous circumstances (Pagallo, 2013; Allen et al., 2000; Wirtz et al., 2018). Among these service robots, humanoids are a specific type that is assembled to highly resemble a human shape. Such robots represent one of the most evolved forms of its kind. From

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the initial levels of mechanical and analytical intelligence (systematic adaptation based on data), humanoid robots are evolving towards a highly intuitive and empathetic intelligence that includes learning and adapting emphatically based on experience (Huang & Rust, 2018).

In a frontline service setting, service robots constitute the interaction counterpart of a customer and co-create value. Due to voice or facial recognition, robots can identify customers and offer services according to their customer profiles, which they retrieve through the interconnectivity of systems (Paluch et al., 2020). In the context of social interaction, it is important that the robot can generate a certain level of automated social presence during the service encounter, which relates to the ability to make customers feel like they are in company of another social entity (van Doorn et al., 2017).

Recent developments illustrate that robots are increasingly capable of performing both more demanding physical and cognitive tasks (Lu et al., 2020). Therefore, the hospitality industry has great interest in experimentation with service robots and already pervasively applied social robots to assist hotel guests and shape their experiences. In the Mandarin Oriental Hotel in Las Vegas, a humanoid robot called Pepper has been placed in the hotel lobby, to greet guests, help them to find directions, and provide information in a fun and innovative way (Paluch et al., 2020). Another example is the Henna Hotel in Japan, which is the first robot- staffed hotel in the world. At this hotel, guests can choose to check in with an android device, a robot, or a dinosaur robot. After the check-in, a porter robot helps guests to bring their luggage to the hotel room and the concierge robot Tully turns the lights on and off (Paluch et al., 2020).

Robotic service acceptance is defined as how people accept to adopt a specific technology for usage (Ghazali et al., 2020). According to the Service Robot Acceptance Model (sRAM) (Wirtz et al., 2018), consumer acceptance of service robots principally might depend on three types of elements: functional elements, social-emotional elements and relational elements. The functional elements refer to the technology’s ease of use, usefulness and

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adherence to social norms. The social-emotional dimension contains three elements: perceived humanness, perceived social interactivity and perceived social presence. Finally, trust and rapport are two elements in the relational dimension that are crucial for the user’s acceptance of service robots (Heerink et al., 2010; Nomura & Kanda, 2016). In this sRAM model, it is essential to consider consumers’ needs for robot interaction in order to achieve a congruence among the three elements (Fuentes-Moraleda et al., 2020).

Until today, the existing literature provides little insight into how frontline robots may affect customers’ experiences. Studies acknowledge the role of technological advances and offer multiple typologies (Kunz et al., 2019), but more research is needed to understand customer motivations and factors that might influence service robot acceptance.

3.2 Customer experience

The customer experience is an important variable for service brands. Especially in today’s very competitive atmosphere in the service business, management of customer experience is considered as an important topic (Garg et al., 2012). The proper management of customer experience can turn customers into advocates which can lead to a long-term competitive advantage and profitability (Ren et al., 2016).

Depending on the circumstances and grounds for experience, many definitions of customer experience are used. According to Meyer and Schwager (2017) and Holbrook and Hirschmann (1982), customer experience is the internal and subjective response customers have to any direct or indirect contact with the company, which develops over time and per service encounter. Shaw (2015) defines customer experience as an interaction between an organization and a customer, where it is a blend of an organization’s physical performance, the senses stimulated, and emotions evoked across all moments of contact. Finally, Gronroos (1988) discussed the notion of customer experience as something that emerges from consumers’

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interaction with the service provider, and thereafter leads to perceptions of service quality. The concept of customer experience assumes that the customer becomes an active player during a marketing encounter (Codeluppi, 2001; LaSalle & Britton, 2003). Experiential marketing focuses on the integration of a variety of senses in different customer encounters with the goal to build a connection with the customer (Schmitt, 1999; Codeluppi, 2001). One of the key priorities of experiential marketing is to create a relevant and unique customer experience (Carù

& Cova, 2003; Ismail et al., 2011).

The customer experience encompasses multiple dimensions, namely cognitive, emotional, physical and sensorial, and social elements (Brakus et al., 2009; Lemon & Verhoef, 2016). Cognitive components refer to higher mental processes, such as memory, language, problem-solving and abstract thinking (Ameen et al., 2021). Besides, these cognitive components refer to the functionality, speed, and accessibility of the service (Keiningham et al., 2017). Previous studies highlighted the emotional elements of customer service which tend to be complex in nature, since these feelings can be positive (i.e. joy or surprise) or negative (i.e. regret or anger) (Ladhari et al., 2017). Physical and sensory aspects of a client’s experience, on the other hand, are frequently distinguished between those in an offline and online context.

Offline experiences include features like lighting, layout, and signage, while online experiences include more technology-related features, like a clear design (Keiningham et al., 2017). Lastly, social components of the customer experience refer to the influence of others, such as family, friends, and a customer’s wider social network (Verhoef et al., 2009). Social components also involve a customer’s social or mental identity of how they view themselves (Keiningham et al., 2017).

According to Gartner (2020), the use of AI technologies can help to analyse customer sentiment and customer feedback at scale, precision and speed that is not achievable through humans. This indicates that AI has the potential to become an essential tool for service providers

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to continuously enhance customer experience in order to stay competitive (Newman, 2019).

The use of service robots has benefits for customers, such as improved and efficient service delivery, as well as the customization of the service delivery process (Pinillos et al., 2016).

Furthermore, according to van Doorn and colleagues (2017), service robots are able to create new types of automated social interactions through which guests can feel accompanied by another social entity. In their study on hotel guests’ experiences with service robots, guests stated that they had a novel and memorable experience when the service delivery was done by service robots. It is therefore expected that the use of service robots has a positive and direct relationship with the overall customer experience. Hence:

Hypothesis 1: Robotic service acceptance has a positive, direct correlation with customer experience.

3.3 Type of value (hedonic vs utilitarian)

The hedonic and utilitarian values have extensively been investigated in marketing contexts to better understand the consumption process (Jones et al., 2006; Kronrod & Danziger, 2013; Voss et al., 2003).

Hedonic value refers to the enjoyment, fun, and positive feeling that consumers obtain from their experience (El-Adly & Eid, 2016). In the hospitality industry, the hedonic impact of a consumption experience arises within the pre-and post-purchase stage of the customer journey, and manifests through interactions with hosts and other guests as well as host- facilitated activities (Finkenauer et al., 2007). Customers who are hedonically motivated are more in search for fantasy, awakening, enjoyment, happiness, and sensuality (To et al., 2007), and are therefore more involved in the service (Babin et al., 1994; Mittal, 1989; Rothschild, 1984).

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Conversely, utilitarian value is derived from products or services, and is defined as the ability to serve functions in one’s daily life (e.g. value for money and quality) (Chaudhuri &

Holbrook, 2001). Furthermore, utilitarian motivations are more about the achievement of gaining what is necessary rather than for recreation. According to Lee and Kim (2018) utilitarian value is based on economic gains, convenience and home-related benefits. Hence, utilitarian consumption can be described as rational, decision effective, and specifically goal- oriented (To et al., 2007; Batra & Ahtola, 1991). All of the above shows that hedonic value is more experiential, while utilitarian value involves an informational emphasis and highlights the consumption process (Lee & Kim, 2018).

In terms of customer experiences, hedonic value refers to the uniqueness of a product or service and the emotional connection it inspires from the consumer, whereas utilitarian value refers to the product's or service's effectiveness, task-specificity, and cost structure (Overby &

Lee, 2006).

Nowadays, guests do not only perceive hotels as places to sleep but also as places to seek experiences (Carrington, 2016). With hotel experiences, customers are obtaining hedonic benefits when the experience improves their feelings of uniqueness, enjoyment, and the need to experience something different and memorable (Dedeoglu et al., 2018). Hence, customers would tend to be influenced by higher-value features of goods and services called monovalent satisfiers, which are seen as extra elements that influence satisfaction and also the customer experience in a psychological way (Roy, 2018).

Therefore, it seems logical to argue that the quality of the AI-enabled customer experience would have a stronger effect on customers in the case of a service setting with a higher hedonic component (e.g. a service robot making a dinner reservation), compared to the context of a more utilitarian service (e.g. a service robot doing the check-in). Hence:

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Hypothesis 2: The positive relationship between robotic service acceptance and customer experience is moderated by the type of value (hedonic vs utilitarian). Such that the relationship is stronger (weaker) when the customer is having hedonic (utilitarian) values.

3.4 Reason for stay (business vs leisure)

Two types of travellers can be distinguished, namely business and leisure guests.

According to Middleton and Clarke (2001), business travel is defined as work-related travel to an irregular place of work. This type of travel is usually obligatory, because of its connection to work (Leiper et al., 2008), and tends to occur within a small time frame (Marin-Pantelescu, 2011). Because business trips tend to be short, travellers have shown to suffer from jet lag and sleep deprivation (Burkholder et al., 2010), and often work more hours than they would at their home office (Radojevic et al., 2018). This explains why business travellers tend to be more stressed than leisure travellers (Chen, 2019), and therefore prefer a quick service in terms of punctuality and reliability (Marin-Pantelescu, 2011). Yavas and Babakus (2005) found that convenience is one of the most highly ranked attributes that are important to business guests concerning hotel choices. This includes the ease of checking in and -out, and making reservations at the reception.

Conversely, Leiper and colleagues (2008) defined leisure as a category of experiences with recreational and creative sub-categories, pursued with a relative sense of freedom from obligations and regarded as personally pleasurable. The sense of freedom explains how leisure travellers are driven by personal motivation and characterized by a combination of needs which Leiper et al. (2008) define as states of deprivation. Examples of such needs include rest, relaxation, nostalgia and social interaction. Furthermore, leisure travellers have the autonomy to book hotels themselves and hence, may pick hotels that suit their specific needs. According

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to Zins (1998), leisure travellers seek value for money when picking hotels. As their perception of price worthiness is higher during a hotel visit, they tend to be more critical about hotel attributes impacting their experience (Bi et al., 2020). Other research found that leisure travellers value the hotel’s innovativeness more than business travellers do (Victorino et al., 2005).

Therefore it is expected that the usage of service robots will have a stronger effect on the customer's experience for those who travel for leisure purposes, rather than for guests who travel for business purposes. Hence:

Hypotheses 3: The positive relationship between robotic service acceptance and customer experience is moderated by the reason for stay (business vs leisure). Such that the relationship is stronger (weaker) when the guest is travelling for leisure (business).

3.5 Control variables

This study makes use of some control variables. Control variables may not be measured but can have a significant effect on the outcome of the experiment. This research makes use of the following control variables: gender, age and prior experience with technology in hotels.

Gender

Research shows that men have a more positive attitude toward robotic technologies, consider robots as more beneficial, and are more eager to use robots in their daily lives. Women, on the other hand, are more hesitant to interact with robots, have a negative perception of them, and are less inclined to use them (de Graaf & Allouch, 2013). However, the study of Dabholkar (1992) showed that women are increasingly using technology, which is decreasing the impact of gender differences in technology adoption behaviour. Another research by Kim et al. (2011)

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found no significant effect of gender on customers’ intention to use self-service technology in hospitality settings.

Age

Age has been shown to have a negative impact on people's desire to use technology in general (Broadbent et al., 2009). It is clear that user attitudes and actions about technology acceptance and usage are influenced by age (Hong et al., 2013). Older people are more distrustful of technology and have a negative attitude about robots, thus they are less likely to use them (Blut et al., 2021).

Prior experience with technology

Prior experience refers to an individual’s opportunity to use a specific technology (Venkatesh et al., 2012). Several studies have found that one’s personal experience with a system or specific technology has a significant impact on one’s opinion of the system’s ease of use and utility. According to these findings, experience with a certain system or technology is positively associated with each individual’s perceived ease of use (Hackbarth et al., 2003). This means that experienced customers perceive the new technology as easier to use compared to the customers with less experience.

Figure 1 shows the conceptual framework of this study and illustrates the proposed relationships of Robotic Service Acceptance, Customer Experience, Type of Value (hedonic vs utilitarian), and Reason for Stay (business vs leisure).

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H1

H2 H3

Figure 1: Conceptual model

Robotic service acceptance

Customer experience

Type of value (hedonic vs

utilitarian)

Reason for stay (business vs

leisure)

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4 Methodology

This chapter is providing detailed information regarding the methodological part of the study. Firstly, the research design will be discussed, followed by a description of data collection. Next, the experimental survey procedure and stimuli used for this study are described. Lastly, measurement scales and the statistical procedure are discussed.

4.1 Research design

This quantitative research will be carried out by using a cross-sectional experimental survey design setting. To answer the research question, primary data has been used. A 2 (hedonic vs utilitarian values) x 2 (business vs leisure guest) experiment was conducted with a between-subject design, which means that every participant experienced only one condition.

This design was used to avoid a carryover effect between conditions. Table 1 illustrates the different experimental conditions.

Moderator

Business guest Leisure guest

Moderator

Utilitarian value

Use of service robot for a utilitarian service (check-in) for business guest

Use of service robot for a utilitarian service (check-in) for leisure guest

Hedonic value

Use of service robot for a hedonic service (restaurant

reservation) for business guest

Use of service robot for a hedonic service (restaurant

reservation) for business guest

Table 1: Overview of experimental conditions

4.2 Data collection

The experiment was set up in the form of an online questionnaire as it is an affordable and time-efficient method, which provides the opportunity to approach large groups of

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respondents (Kohavi, 2007; Reips, 2002). Besides, the accessibility of respondents is high whereby respondents are easily gathered under low costs. Furthermore, because of the ability to control factors and randomly assign participants to conditions, an online questionnaire ensures high internal validity (Evans & Mathur, 2005). In order to ensure reliability, it was important to make sure that all the participants were given the same information and were tested under the same conditions.

To develop the online questionnaire, the research followed the recommendations of Illum et al. (2010), such as keeping the questionnaire short and guaranteeing the anonymity of the participants. The experimental survey was conducted via Qualtrics and data was collected in November 2021. Participants have been acquired through convenience sampling, a non- probability sampling method in which the sample is chosen from a group of people that is easy to reach (Saunders et al., 2012). The student network of the University of Amsterdam, social platforms such as Facebook, LinkedIn and Surveyswap, as well as word-of-mouth were used to reach participants. This chosen sampling method ensured a quick and wide-ranging data collection.

4.3 Procedure

At first, participants were introduced to the topic and purpose of the survey. They were informed that the survey is completely anonymous, that it is possible to quit at any time, and that the answers will be confidential. Lastly, the estimated time for completing the survey (approximately 7 minutes) and contact information from the researcher was provided.

After the general introduction, a short introduction of the service robot Pepper was given, and the participants answered questions regarding the acceptance of such a service robot.

After the questions about robotic service acceptance, participants were randomly assigned to one of the four conditions as shown in Table 1. Next, participants of all conditions evaluated

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their customer experience, and answered questions that assessed their reason for stay and type of value. To check whether people carefully read the presented scenario, participants had to indicate which condition they saw at the end of the questionnaire. Finally, for control purposes, participants shared their personal information (gender and age), how often they stay at hotels, whether they normally travel for business or leisure purposes, and whether they had prior experience with technology in hotels (see Appendix 1 for the full questionnaire).

4.4 Stimuli

The use of images of service robots supplemented with a description of a service scenario is a common practice in hospitality research (Belanche et al., 2020) and was therefore also selected for this research. In order to give the participants an even better understanding of the service robot Pepper, a video of approximately 30 seconds could be viewed in addition to the picture.

Based on the Uncanny Valley Theory (Mori et al., 2012) there is a risk that consumers may feel uncomfortable in interacting with a high humanlike robot, and feelings of unease or discomfort may lead to rejection of such a humanlike service robot. Therefore, it was important to select the right service robot for this research in order to contain the right level of human likeliness. Since the service robot Pepper (see Figure 2) has been most frequently used for research in the hospitality sector, it was therefore used in this research. Pepper is the first social humanoid robot in the world that is able to recognize faces and basic human emotions. The robot has a stable body with tactile sensors on the arms, head, and chest. Due to his curvy design, it ensures a danger-free use and a high level of acceptance by users. Furthermore, it has advanced speech recognition and dialogue available in 15 languages, and it has a voice that can be tailored. Finally, the touchscreen on his chest is used to highlight messages and support speech (SoftBank Robotics, 2021).

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Figure 2: Service robot Pepper

4.5 Measures

Table 2 gives an overview of the measured variables Robotic Service Acceptance, Type of Value (hedonic vs utilitarian), Reason for Stay (business vs leisure), and Customer Experience.

Variable / Items Cronbach's

Alpha

Sources

Robotic service acceptance 0.78

Belanche et al., 2020 Wirtz et al., 2018 Davis, 1989 1. I find the robot enjoyable

2. I find the robot fascinating 3. I think the robot is easy to use 4. I would enjoy the robot talking to me 5. I think I can use the robot without any help 6. I think the robot is useful to me

7. I think the robot can help me with many things 8. I think the robot would be convenient for me 9. I can imagine the robot to be a living creature 10. I think the robot will be pleasant to interact with 11. I think the robot will understand me

12. I feel I can rely on the robot to do what is supposed to do

13. I believe the robot provides accurate information 14. I would trust the robot if it gave me advice 15. I will try to use the robot in the future

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Customer experience 0.89

Ameen et al., 2020 Oh et al., 2007 Otto & Ritchie, 1996 Foroudi et al., 2018 1. The service encounter would be memorable

2. The service encounter would be entertaining 3. The service encounter would be exciting 4. The service encounter would be educational 5. The service encounter would be an experience 6. The service encounter would make me feel important

7. The service encounter would make me feel respected

8. The service encounter would make me feel welcomed

9. The service encounter would make me feel safe 10. The service encounter would make me feel a sense of comfort

11. The service encounter would make me feel a sense of beauty

Type of value (hedonic vs utilitarian) 0.80 Babinet al., 1994 Voss et al., 2003 1. This service encounter would be fun

2. This service encounter would be exciting 3. This service encounter would be thrilling 4. This service encounter would be enjoyable 5. This service encounter would be pleasant 6. This service encounter would be playful 7. This service encounter would be amusing 8. This service encounter would be funny 9. This service encounter would be effective 10. This service encounter would be functional 11. This service encounter would be practical 12. This service encounter would be efficient 13. This service encounter would be productive 14. This service encounter would be handy 15. This service encounter would be successful

Reason for stay (business vs leisure)

Wind et al., 1989 Hair et al., 2006 Orme, 2006 1. This type of service encounter is preferred

2. This kind of service encounter would positively influence my experience

Table 2: Measurements

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Independent variable: Robotic service acceptance

To measure participants’ level of robotic service acceptance, this study used the scales of the Service Robot Acceptance Model (sRAM) (Wirtz et al., 2018) and Technology Acceptance Model (TAM) (Davis, 1989). All 15 items were measured on a seven-point scale ranging from 1 = strongly disagree to 7 = strongly agree. First, a general scenario about Pepper was given and the acceptance was measured with statements like “I find the service robot enjoyable” and “I would trust the robot if it gave me advice”.

Dependent variable: Customer experience

To measure participants’ customer experience, this study used trustworthy scales of prior studies (Foroudi et al., 2018; Oh et al., 2012; Otto & Ritchie, 1996). Participants answered 11 items that will determine how they perceive the service encounter. For example “This service encounter would be memorable” or “This service encounter would be exciting”. This study used a seven-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree. The scale provides the respondents with enough alternative options without overwhelming or confusing them (Lee, 1993).

Moderator: Type of value (hedonic vs utilitarian)

Based on the study of Voss et al. (2003) and Babin et al. (1994) participants answered 15 items that determined how they perceived the scenario according to hedonic or utilitarian values. Examples of hedonic values are “This service encounter would be fun” and “This service encounter would be playful”. Utilitarian values are measured with examples like “This service encounter would be effective” and “This service encounter would be practical”. For answering the statements, a seven-point scale has been used, ranging from 1 = strongly disagree to 7 = strongly agree.

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Moderator: Reason for stay (business vs leisure)

Similar to the study of Wind et al. (1989), this study used a typical conjoint analysis approach of presenting the participant with a scenario that they rate with a seven-point Likert scale (following Hair et al., 2006; Orme, 2006). In combination with the question of whether the participant normally travels for business or leisure, the scenario will be measured with statements like “This type of service encounter is preferred”.

4.6 Statistical procedure

The statistics tool IBM SPSS version 27 was used for descriptive statistics and data analysis. To test H1, whether Robotic Service Acceptance influences Customer Experience, a linear regression was conducted. To test H2 and H3, whether the relationship between Robotic Service Acceptance and Customer Experience is moderated by Type of Value (hedonic vs utilitarian) and Reason for Stay (business vs leisure), the PROCESS Model 2 of Hayes for IBM SPSS was executed.

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5 Results

This chapter analyses and discusses the results of the analysis of the data in SPSS.

Firstly, the examination of the descriptive statistics is discussed, followed by the analysis of reliability of the measures. Thirdly, a correlation analysis of all the variables was analysed, followed by a comparison of differences between the four conditions. Lastly, the three hypotheses were tested and analysed.

5.1 Descriptive statistics

Overall, a total of 251 people participated in the online experiment. However, after excluding responses, in which the participant did not finish the survey or less than two minutes have been spent to complete the survey, the number of respondents dropped to 231.

From the 231 participants, 148 (64.1%) were female and 79 (34.2%) were male respondents. One participant was identified as non-binary/third gender (0.04%), and three participants preferred not to share their identity (1.3%).

Since age is noted to have a negative impact on people’s willingness to use service robots (Broadrent et al., 2009), it was important to acquire participants of multiple ages. The average age was 30.8, where the youngest participant was 16 years and the oldest participant was 65 years old.

Besides, 188 of the 231 participants (81.4%) stayed 0-5 times in a hotel a year, 31 participants (13.4%) stayed 6-10 times in a hotel a year, and 12 participants (5.2%) stayed in hotels more than 10 times a year.

Among the 231 participants, 216 people (93.5%) normally travel for leisure purposes, whereas only 15 participants (6.5%) travel for business purposes.

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Finally, from the 231 participants, 66 participants (28.6%) already had prior experience with technology in hotels, whereas 165 participants (71.4%) mentioned that they had no prior experience with technology in hotels in the past.

5.2 Reliability analysis for scales

Reliability analysis for robotic service acceptance (see Table 3)

The respondents indicated their Robotic Service Acceptance through a 15 item scale created by Wirtz et al. (2012) and Davis (1989). Respondents answered items such as “I find the service robot enjoyable” and “I would trust the robot if it gave me advice” on a seven-point Likert scale (ranging from 1 = strongly disagree to 7 = strongly agree). In order to measure the internal consistency of the scale, Cronbach’s alpha was used for the reliability analysis. With Cronbach’s alpha = 0.915 > 0.70 the scale showed high reliability (Cronbach, 1951). In summary, all 15 items had a good correlation with the total score of the scale, and none of the items would have changed the reliability if they were excluded.

Reliability analysis for customer experience (see Table 3)

All respondents indicated their Customer Experience with the help of an 11-item questionnaire (Foroudi et al., 2018; Oh et al., 2012; Otto & Ritchie, 1996). It included statements such as “This service encounter would be memorable” or “This service encounter would be exciting”. These statements were answered on a seven-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree. Again, Cronbach’s alpha was used for the reliability analysis to measure the internal consistency of the scale. With Cronbach’s alpha = 0.891 > 0.70 the scale showed high reliability (Cronbach, 1951). All items had a good correlation with the overall score of the scale, and none of the items would change the reliability if excluded.

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Reliability analysis for type of value (hedonic vs utilitarian) (see Table 3)

The respondents indicated the Type of Value (hedonic vs utilitarian) with the help of a 15-item scale designed by Voss et al. (2003) and Babin et al. (1994). It contained statements such as “This service encounter would be playful” and “This service encounter would be effective”. These statements were answered on a seven-point Likert scale (ranging from 1 = strongly disagree to 7 = strongly agree). To measure the internal consistency, the scale is divided into a hedonic and utilitarian part, and Cronbach’s alpha was used for the reliability analysis of the scale. With Cronbach’s alpha = 0.930 > 0.70, the hedonic type of value scale showed high reliability (Cronbach, 1951). The utilitarian type of value scale showed a Cronbach’s alpha of 0.945 > 0.70, and therefore also showed high reliability. Finally, all 15 items had a good correlation with the total score of the scale, and none of the items would significantly change the reliability if they were excluded.

Reliability analysis for reason for stay (business vs leisure) (see Table 3)

All participants indicated the Reason for Stay (business vs leisure) through a 2-item scale created by Wind et al. (1989). Participants answered items such as “This type of service encounter is preferred” on a seven-point Likert scale, ranging from 1 = strongly disagree to 7 = strongly agree. Once more, Cronbach’s alpha was used to measure the internal consistency of the scale. With Cronbach’s alpha = 0.865 > 0.70 the scale showed high reliability (Cronbach, 1951). Lastly, both items had a good correlation with the overall score of the scale.

Variable Cronbach's alpha

Robotic service acceptance 0.915

Customer experience 0.891

Type of value (hedonic) 0.930

Type of value (utilitarian) 0.945

Reason for stay (business vs leisure) 0.865

Table 3 : Reliability analysis

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5.3 Correlation analysis

After conducting a bivariate correlation with all the variables of interest, the correlation matrix including overall means, standard deviations and correlations was provided (Table 4).

According to the correlation matrix, the IV Robotic Service Acceptance is positive and significantly correlated with the DV Customer Experience (r = .711, p < .001). Looking at Robotic Service Acceptance and the moderator Type of Value, it can be said that there is a small but significant correlation between the variables ( r = .130, p < .05). The other moderator, Reason for Stay, does not show a significant correlation with any of the variables.

The control variable Gender (Female) shows a negative correlation with Robotic Service Acceptance (r = -.157, p < .05), which means that females score lower on Robotic Service Acceptance, and therefore accept service robots less than non-females.

Regarding the control variable Age, it can be said that age is negatively correlated with Customer Experience (r = -.211, p < .01) and Robotic Service Acceptance (r = -.230, p < .001).

Meaning that older people are less likely to accept service robots, and will have less customer experience when the service is provided by a service robot.

All other control variables, such as whether people normally travel for business or leisure, prior experience, and the number of times spent in a hotel did not show a significant correlation with the dependent variable.

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Table 4 : Means, Standard Deviations, Correlations

Variable M SD 1 2 3 4 5 6 7 8

1. Customer Experience 4.55 0.95 -

2. Robotic Service Acceptance 4.81 0.93 .711*** -

3. Type of Value 0.49 0.50 .104 .130* -

4. Reason for Stay 0.49 0.50 -.103 -.100 .004 -

5. Female 0.64 0.48 -.050 -.157* .017 .029 -

6. Age 30.83 13.19 -.211** -.230*** -.017 .087 -.142* -

7. Business travel 0.06 0.25 .087 .063 -.155* -.047 -.169* .180** -

8. Prior Experience 0.29 0.45 -.039 .038 -.068 .014 .034 -.020 .222** -

9. Number of stays a year 0.19 0.39 .040 .038 -.072 -.068 -.013 .010 .370*** .165*

Note. N = 231. ***p <.001, **p <.01, *p <.05

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5.4 Differences between conditions

In order to test whether there are differences between groups in terms of Customer Experience, a one-way ANOVA was used. The results in Table 6 show that there is no statistically significant difference between the four different conditions, F = 1.81, p > .05. As shown in Table 5, all four groups did not differ significantly in terms of their Customer Experience.

Scenario M SD N

Business + hedonic 4.79 0.79 58

Business + utilitarian 4.52 0.89 60

Leisure + hedonic 4.51 1.03 56

Leisure + utilitarian 4.40 1.05 57

Total 4.55 0.95 231

Table 5: One-way ANOVA descriptive statistics

SS df MS F p

Between Groups 4.82 3 1.61 1.81 0.147

Within Groups 201.82 227 0.89

Total 206.63 230

Table 6: One-way ANOVA

A 2 x 2 Factorial ANOVA has been conducted to see if the main effect, interaction effect, and the covariates show any differences between the groups. The results in Table 7 and Table 8 show that there is no significant difference between the groups and that there is no interaction effect.

Value Reason M SD N

Utilitarian Business 4.52 0.89 60

Leisure 4.40 1.05 57

Total 4.46 0.97 117

Hedonic Business 4.79 0.79 58

Leisure 4.51 1.03 56

Total 4.65 0.92 114

Total Business 4.65 0.85 118

Leisure 4.45 1.04 113

Total 4.55 0.95 231

Table 7: 2 x 2 Factorial ANOVA descriptive statistics

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Table 8: 2 x 2 Factorial ANOVA

SS df MS F p Partial η2

Intercept 61.55 1 615.49 728.31 <0.001 0.77

Type of Value 2.86 1 2.86 3.39 0.067 0.02

Reason for Stay 1.10 1 1.10 1.30 0.255 0.01

Type of Value*Reason for Stay 0.70 1 0.70 0.83 0.364 0.00

Female 0.72 1 0.72 0.85 0.358 0.00

Age 11.19 1 11.19 13.24 <0.001 0.06

Prior Experience 0.79 1 0.79 0.94 0.334 0.00

Business guests 3.70 1 3.70 4.38 0.038 0.02

Number of stays a year 0.00 1 0.00 0.00 0.955 0.00

Error 187.61 222 0.85

Total 4998.39 231

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5.5 Hypotheses testing

There are multiple ways to conduct hypothesis testing when dealing with answers measured on a seven-point Likert scale. In order to test the three hypotheses, different analyses were conducted.

Direct effect: linear regression

To test hypothesis 1, a linear regression analysis was used, which examined the linear relationship between Robotic Service Acceptance and Customer Experience, after controlling for Gender, Age and Business travellers.

The results in Table 9 show that the first model was statistically significant (F = 3.35, p

= .006) and explained 6.9% of variance in Customer Experience. The second model includes the independent variable Robotic Service Acceptance and explains 52.1% of the model. The introduction of Robotic Service Acceptance explained an additional 45.2% of variance in Customer Experience. The total variance explained by the model as a whole was 52.2%. In the final model, only the IV Robotic Service Acceptance was statistically significant, recording a Beta value of β = .702 and a p-value <.001. In other words, for one unit increase in Robotic Service Acceptance, the Customer Experience will increase by .715.

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Table 9: Linear regression

Model 1 Model 2 Model 3

Variable B SE β B SE β B SE β

Female -.117 .130 -.059 .139 .095 .070 .139 .095 .070

Age -.018 .005 -.245*** -.004 .004 -.055 -.004 .004 -.054

Business .521 .279 .136 .326 .201 .085 .333 .204 .087

Robotic Service

Acceptance .720 .050 .707*** .715 .050 .702***

Type of Value .034 .090 .018

Reason for Stay -.048 .089 -.025

R2 0.069 0.521 0.522

R2 Change 0.069** 0.452*** 0.001

Note. N = 231. ***p <.001, **p <.01, **p <.05

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Moderating effect: PROCESS model 2

To check whether the influence of Robotic Service Acceptance on Customer Experience is moderated by Type of Value (hedonic vs utilitarian) or Reason for Stay (business vs leisure), PROCESS Model 2 was used for the analysis (see Figure 3).

Figure 3: PROCESS Model 2

Moderation effect (X on Y, moderated by M)

The results in Table 10 show that the regression coefficient for the interaction term (XM) was -.163 and was not statistically significant, t = -1.70, p = .0912. Even though the interaction effect was almost significant, the Type of Value (hedonic vs utilitarian) did not have a moderating effect and therefore H2 needs to be rejected. The relationship between Robotic Service Acceptance and Customer Experience was not dependent on Type of Value.

Moderation effect (X on Y, moderated by W)

The results show that the regression coefficient for the interaction term (XW) was -.049 and was not significant, t = -.51, p = .6137. This means that Reason for Stay (business vs leisure) did not have a moderating effect and therefore H3 needs to be rejected as well. The relationship between Robotic Service Acceptance and Customer Experience was not dependent on Reason for Stay.

X

Robotic Service Acceptance

Y

Customer Experience

M Type of Value (hedonic vs utilitarian)

W Reason for Stay (business vs leisure)

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Coefficient SE t p

Constant 4.633 0.14 32.27 0.0000

Robotic Service Acceptance (X) c1 0.715 0.05 13.99 0.0000

Type of Value (M) c2 0.036 0.09 0.40 0.6910

Int_1 (XM) c4 -0.163 0.10 -1.70 0.0912

Reason for Stay (W) c3 -0.045 0.09 -0.51 0.6127

Int_2 (XW) c5 -0.049 0.10 -0.51 0.6137

Female 0.146 0.10 1.53 0.1267

Age -0.044 0.00 -1.23 0.2212

Business 0.341 0.20 1.67 0.0956

Prior -0.175 0.10 -1.74 0.0837

Number -0.008 0.12 0.06 0.9501

R2 = 0.53, p <0.001

F(10, 220) = 24.73

Table 10: Results PROCESS Model 2

5.3 Further investigation of PROCESS Models

Based on the findings, hypothesis 2 and hypothesis 3 both needed to be rejected during the analysis. However, it is possible to examine the influence of Type of Value on Customer Experience, with a mediating effect of Robotic Service Acceptance. This simple mediation was tested with PROCESS Model 4. Another possibility is a moderated mediating effect, which is tested with PROCESS Model 7.

PROCESS Model 4

To check whether the influence of Type of Value (hedonic vs utilitarian) on Customer Experience is moderated by Robotic Service Acceptance, PROCESS Model 4 was used for the analysis (see Figure 4).

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Figure 4: PROCESS Model 4

Indirect effect (X on M = a1). The results in Table 11 show that the regression coefficient for a1 was .269 and was statistically significant, t = 2.277, p = .0237, with a 95%

confidence interval ranging from .036 to .502. This means that Robotic Service Acceptance is .269 higher when the customer has a hedonic value.

Indirect effect (X on M = b1). The effect b1 = .715 indicates that two people who experience the same Type of Value, but that differ by one unit in their level of Robotic Service Acceptance are estimated to differ by b1 = .715 units in Customer Experience. The sign of b1 is positive, meaning that those relatively higher in Robotic Service Acceptance are estimated to be higher in their Customer Experience. This effect is statistically significant, t = 14.190, p = .000, with a 95% confidence interval ranging from .616 to .814.

Total indirect effect (a1b1). Overall, there is a significant indirect effect (a1b1 = .192), and it is statistically different from zero. This is shown by a bootstrap confidence interval that is entirely above zero (95% CI: .019 to .369).

Direct effect (X on Y = c1’). The results in Table 11 show that there is no direct effect of Type of Value on Customer Experience (c1’ = .034, p = .7070). Moreover, the direct effect is not statistically different from zero, t = .376 and the 95% confidence interval ranges from -.413 to .211.

X Type of value (hedonic vs utilitarian)

M Robotic Service

Acceptance

Y

Customer Experience

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Total effect (c1 = c1’ + a1b1). Finally, the results show that there was no total effect, as c1 was .226 and was not statistically significant, t = 1.847, p = .0661. The 95% confidence interval ranged from -.015 to .468.

Control variables. In the case of the indirect effect (X on M = a1), the control variables Gender (Female) (p = .0055) and Age (p = .0001) appear to have a significant effect. Which means that females and older people will score lower on Robotic Service Acceptance.

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Table 11: Results PROCESS Model 4

Consequent

Robotic Service

Acceptance (M) Customer

Experience (Y)

Variable B SE p B SE p

Type of Value (X) a1 .269 .118 0.0237 c1' .034 .090 0.7070

Robotic Service Acceptance (M) --- --- --- b1 .715 .050 0.0000

Female -.349 .125 0.0055 .139 .095 0.1466

Age -.019 .005 0.0001 -.004 .004 0.2759

Business .338 .270 0.2117 .333 .204 0.1040

Prior Experience .059 .134 0.6604 -.181 .101 0.0734

Number of stays .015 .163 0.9251 -.009 .122 0.9448

Reason for Stay -.127 .118 0.2806 -.048 .089 0.5887

Constant i1 5.489 .198 0.0000 i2 1.188 .314 0.0002

R2 = .121 R2 = .089

F(7, 223 ) = 4.390 F(8, 222 ) =

30.319

p< .001 p< .001

Effect SE p LLCI ULCI

Direct effect c1' .034 .090 0.7070 -.143 .211

Total effect c1 .226 .123 0.0661 -.015 .468

Boot SE Boot LLCI Boot ULCI

Indirect effect a1b1 .192 .089 .019 .369

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PROCESS Model 7

To check whether the influence of Type of Value (hedonic vs utilitarian) on Customer Experience has a moderated mediation effect, PROCESS Model 7 was used for the analysis (see Figure 5).

Figure 5: PROCESS Model 7

Moderated mediation. The findings from the statistical analysis (see Table 12) reveal that Type of Value has a significant effect on Robotic Service Acceptance (a1 = .269, p < .05).

This means that the Robotic Service Acceptance is .269 higher when the customer has a hedonic value. However, there is no moderated mediation effect, as the interaction effect is not statistically significant (p > .05). This means that Reason for Stay (business vs leisure) does not moderate the relationship between Type of Value (hedonic vs utilitarian) and Robotic Service Acceptance. The b-path shows a significant effect of Robotic Service Acceptance (M) on Customer Experience (Y) (b1 = .034, p < .001). However the total indirect effect shows a negative and not significant effect (a1b1 = -.029, 95% CI: -.355 to .308). The direct effect of Type of Value (X) on Customer Experience (Y) is also not significant (95% CI: -.143 to .210).

This means that the Customer Experience is not different for a hedonic or utilitarian Type of X

Type of Value (hedonic vs utilitarian)

Y Customer experience M

Robotic Service Acceptance W

Reason for stay (business vs leisure)

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Value. Again, in the case of the indirect effect (X on M = a1), the control variables Gender (Female) (p = .0055) and Age (p = .0001) appear to have a significant effect, which means that females and older people will score lower on Robotic Service Acceptance. All these results confirm the findings of PROCESS Model 4, and complement the findings by showing that there is no moderated mediation effect.

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Table 12: Results PROCESS Model

Consequent

Robotic Service

Acceptance (M)

Customer

Experience (Y)

Variable B SE p B SE p

Type of Value (X) a1 .269 .185 0.0239 c1' .034 .090 0.7095

Robotic Service

Acceptance (M) --- --- --- b1 .717 .050 0.0000

Reason for Stay (W) a2 -.127 .118 0.2825 --- --- ---

Int_1 -.040 .236 0.8650 --- --- ---

Female -.351 .125 0.0055 .138 .095 0.1487

Age -.019 .005 0.0001 -.004 .004 0.2560

Business .340 .271 0.2103 .337 .204 0.0993

Prior Experience .060 .134 0.6569 -.183 .100 0.0698

Number of stays .017 .163 0.9192 -.005 .122 0.9671

Constant i1 5.560 .185 0.0000 i2 1.176 .312 0.0002

R2 = .121 R2 = .522

F(8, 222 ) = 3.829 F(7, 223 ) = 34.719

p< .001 p< .001

Effect SE p LLCI ULCI

Direct effect c1’ .034 .090 0.7095 -.143 .210

Boot SE Boot LLCI Boot

ULCI

Indirect effect a1b1 -.029 .169 -.355 .308

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