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25th August 2022 by admin

Introduction and Research Question
Even though there is increased interest in service robots from both practitioners and scholars (Wirtz et al., 2018; Lu et al., 2020), the knowledge concerning how robots influence consumer behavior in retail settings is still scarce and thus much needed (Shankar, 2018; Biswas, 2019; Lu et al., 2020; Belanche et al., 2021). In particular, there is only little research on the impact of sensory information during the interaction with a service robot (Biswas, 2019). Haptic factors like touch have mostly been overlooked in research on human-robot interaction (Willemse et al., 2017; Law et al., 2021; Hayashi et al., 2022) despite their crucial role for human-robot bonding and communication (Andreasson et al., 2018) and customer experience (Lemon and Verhoef, 2016). Interaction between a service robot and the customer can take place through the modality of touch, in that not only customers touch robots but also robots touch customers (Law et al., 2021). Due to the importance of touch in everyday social interactions (Gallace and Spence, 2010), it is essential for retailers to understand how customers would react to being touched by a service robot. Trust has been considered one of the core responses when studying robots in socials contexts (Law et al., 2021) and is considered to influence customer experience (Lemon and Verhoef, 2016). Different types of touch might have different meanings for individuals and are thus evaluated differently by the customer. Moreover, robots, in general, might take over tasks during a human-robot interaction (De Gauquier et al., 2020). For instance, they might provide customers with information, guide them through the store, point them to the location of specific products and recommend products (Barnett et al., 2014). How customers respond to different touch types and shopping assistance by a service robot has not been answered yet in retail and service research.

Conceptual framework
We put forth hypotheses as to the effects of (1) touch type, (2) assistance by a service robot and (3) interaction between touch type and assistance by a service robot on consumers’ trust in the service robot. Moreover, we hypothesize that the effect of touch type can be explained by perceived interaction comfort.

Methodology
A usable sample of 245 German consumers (mean age = 28.38 years; 31 % male) was recruited to take part in the study. We employed a 3 (robot-initiated touch type: hug, handshake, no touch/waving) x 2 (assistance from the robot: yes, no) between-subjects factorial design where participants were randomly assigned to one of the six scenarios. Each participant received a scenario and a questionnaire. The scenario described a shopping situation and participants were asked to put themselves into the described situation. The humanoid robot Pepper developed by Softbank Robotics was chosen to portray the service robot in the present study (SoftBank Robotics, 2020). The questionnaire comprised realism checks, manipulation checks, perceived trust in the robot, perceived comfort as well was potential covariates and a standard set of socio-demographic questions. Wherever possible, we used seven-point Likert scales anchored by 1 (“strongly disagree”) and 7 (“strongly agree”). To test our hypotheses, we conducted an ANCOVA with trust in the robot as the dependent variable and the manipulations of touch types and assistance together with their interaction as independent variables. Based on prior research, we included gender (Stier and Hall, 1984) and technology readiness (Parasuraman, 2000) as covariates. We further tested whether the effect of touch type on trust was mediated by perceived interaction comfort. Mediation was performed according to Hayes (2013).

First Findings
Results indicate that touch types that violate social norms and/or a customer’s expectation as to what is appropriate in the shopping context leads to a decrease in trust in a humanoid service robot, which can be explained with the amount of (dis-)comfort felt. Moreover, participants trust the robot more, when the robot provides shopping assistance compared to no assistance. Further, providing assistance to the customer by the robot may change the effect of different touch types on trust in the robot.

Contributions
First, it is essential for service providers and retailers to understand how customers would react to being touched by a service robot and how and why they would react to different touch types. Different types of touch might have different meanings for individuals and are thus evaluated differently by the customer. We provide evidence of the associated underlying process. Second, robots might take over certain tasks during a human-robot interaction (De Gauquier et al., 2020) and thus offer assistance to the customers. This might lead to different evaluations as well. Third, we examine if different combinations of touch types and assistance may affect customer evaluations differently. Our findings are of importance to retailers and other service providers that want to know how robot-initiated touch and assistance can enhance customer interactions at the point of sale.

Practical implications
The findings of this study contribute to the emerging research field of human-robot interaction, with focus on human-robot touch, and provide important insights on how to employ service robots in retail stores, especially regarding physical interaction with the customer.

Research limitations and outlook
We examined the effects of robot-initiated touch types and assistance based on scenario descriptions and pictures. The participants of the study had to imagine the described interaction with the service robot. A better option would be that participants actually experience the interaction with the service robot and really feel the touch from the robot. Moreover, the study only included service robots as service agents. Although this procedure was useful in order to find out how to employ service robots, it does not reveal the differences compared to human employees in the store. Further research should also examine how the effects of robot touch differ from those caused by human touch in the retail setting

24th August 2022 by admin

Introduction
Service robots, which are system-based autonomous and adaptable interfaces that interact, communicate and deliver services to an organization’s customers (Wirtz et al., 2018), are increasingly adopted by service providers (Huang & Rust, 2018; Lu et al., 2020; Odekerken-Schröder et al., 2022; Wirtz et al., 2018). Consequently, there is a rapidly growing body of literature on the topic (Haenlein & Kaplan, 2021). While existing empirical studies have primarily focused on the technology’s adoption and acceptance (Mende et al., 2019), studies that investigate the post-purchase stage of robot-enabled services are scarce. For example, if a hotel service robot quickly and reliably brought Joyce an ordered coffee to her room, will this experience still influence her feelings about her stay when she is back home? Will it maybe even impact future purchase decisions? What if the robot had taken excessively long? With our study, we seek to understand how such different experiences shape customer attitudes and behaviors post-purchase (e.g., emotion, re-usage, repeat purchase).
To gain an initial understanding of how service robot-related post-purchase attitudes and behaviors are formed, we have analyzed 1107 reviews from online hotel booking websites and review platforms. All these reviews (partly) reflect on the customer’s experience with a hotel’s service robot. Based on these reviews, we see that customers’ primary and secondary appraisals play a crucial role in the formation of post-purchase attitudes and behaviors. For example, one customer writes “Wally the robot butler is pretty awesome, he frightened my husband at first, but after he got used to it, he ended up looking for reasons to have Wally deliver something to our room”. In this review, we can clearly observe a primary appraisal (i.e., fear), a secondary appraisal (i.e., accommodation), and ultimately post-purchase behavior (i.e., reuse).
Based on these reviews, we have built a model drawing on cognitive appraisal theory (Lazarus & Folkman, 1984) and coping strategies (Mick & Fournier, 1998) to better understand post-purchase attitudes and behaviors after robot-enabled services. We have planned field and lab experiments to validate and extend this model. In this way, we extend on from previous research and determine how real-life (rather than imagined) experiences with service robots shape actual post-purchase attitudes and behaviors as well as what role customers’ primary appraisals and coping strategies play in this process.

Background
Appraisal theory posits that primary appraisals are driven by user perceptions (Fadel & Brown, 2010). That is, a primary appraisal is formed by assessing what is personally at stake in a given situation, resulting in irrelevant, pleasant/positive and stressful/negative outcomes (Lazarus & Folkman, 1984). The second appraisal is then concerned with identifying a suitable coping strategy (Lazarus, 1991). Applying appraisal theory to employee interactions with service robots, Paluch et al. (2022) demonstrate that these interactions can be modelled as multistage appraisal processes. We build on these findings and suggest that appraisals are also key in understanding the formation of customers’ service robot-related post-purchase attitudes and behaviors. More specifically, we suggest that customers deal with their service robot related appraisals through either avoidance (e.g., neglect, abandonment, distancing) or confrontation strategies (e.g., accommodation, partnering, mastering; Mick & Fournier, 1998).
Existing research has thus far predominantly focused on customer acceptance as a predictor of actual use of service robots (Wirtz et al., 2018). However, based on the analyzed reviews, we see that actual use of services robots is often not intentional. Instead, customers often merely order the service and are then surprised when a service robot is delivering it. For example, one customer writes “Our best experience at this hotel was ‘Wally’ [..]. We ordered some towels and to our surprise, this beautiful robot was at our doorstep bringing what we requested. This definitely is one notch up in customer’s satisfaction.” This observation highlights the need for analyzing customers’ appraisal and coping processes to understand how they feel and behave after a robot-enabled service. Applying appraisal and coping theory in a context of customer–service robot interactions is one core contribution of our study.

Methodology
We started our investigation by analyzing 1107 reviews from online hotel booking websites. All reviews referred to the service robots employed by different hotels. These hotels used the same service robot which is characterized by a high level of autonomy, substituting the frontline employee in autonomously operating the elevator and delivering hotel amenities and missing items (e.g., toiletries, personal care, phone charger, coffee) to the guest’s hotel room. Following an inductive critical incident approach, we discovered the reoccurring theme of appraisal, coping and re-purchase (use) within the reviews. This led us to construct our initial model (see Figure 1).
In the next stages of our research, we validate and extend this model in multiple lab and especially field experiments. Here, we will especially focus on including robot-specific elements. We already collaborate with industry partners that are eager to support our research in hospitality contexts.

Preliminary Findings
Figure 1 shows the conceptual model. Table 1 provides an illustrative overview of reviews.
Insert Figure 1 here
Insert Table 1 here

Conclusion and outlook
Thus far, most studies investigating service robots are concerned with the pre-purchase (e.g., intention to use; Wirtz et al., 2018) and purchase stages (e.g., acceptance, customer satisfaction; Choi et al., 2020). However, as service robot interactions become more frequent, it is increasingly important to understand how these interactions impact customers’ attitudes and behaviors post-purchase. Consequently, our theoretical model suggests that a multistage process of appraisal and coping underlies customers’ service robot-related post-purchase attitudes and behaviors. Fitting in with the theme of this special issue, these post-purchase behaviors are often not only unanticipated, but also unintended (e.g., customers only ordering food to spend time with the robot).
In the next steps, we will validate and extend our model by using field and lab experiments. In this way, we seek to contribute to the service robot literature by furthering our understanding of the thus-far underexplored post-purchase stage. Additionally, by observing real behaviors and attitudes in the field rather than during hypothetical experiments (de Keyser & Kunz, 2022), we aim to understand the social complexity of the real world. By doing so, we complement current state-of-the-art literature by investigating the real-life impact of these robots (Lu et al., 2020).

23rd August 2022 by admin

Brands are increasingly incorporating humanoid robots into frontline services (Brengman et al., 2021; Choi et al., 2021; Song & Kim, 2022). Due to their high level of human-likeness, humanoid robots induce a higher degree of anthropomorphism and hence engender more automated social presence among consumers than nonhumanoid robots (van Doorn et al., 2017). Yet, successfully integrating humanoid robots into customer service is a major challenge for most brands because they can trigger negative feelings and compensatory behaviours from consumers (Mende et al., 2019). While previous empirical research has identified contextual factors (Holthöwer & van Doorn, 2022; Pitardi, Wirtz, et al., 2022) and robotic design features (Belanche et al., 2021; Pitardi, Bartikowski, et al., 2022) that may attenuate such negative effects, literature on how brands can strategically frame the social relationship between a humanoid service robot and consumer to mitigate these effects remains scant (Chang & Kim, 2022). Moreover, like all technologies, humanoid robots are not error free (Choi et al., 2021), causing service failures that precipitate unfavourable consumer responses. Hence, it is imperative to examine how brands can proactively mitigate negative effects of a humanoid service robot failure. In this paper, we examine how framing the social relationship between consumers and humanoid robots can reduce attribution of globality (the extent to which consumers generalize robot-led failure to the brand as a whole) and negative word-of-mouth. Our second objective is to examine the variations of this effect in different cultures by drawing on the cultural dimension of power distance belief (Hofstede, 2001).
Previous research suggests that consumers perceive robots as social agents and during interactions experience their automated social presence (van Doorn et al., 2017). Novak and Hoffman (2019) conceptualize that consumers can form master-servant and partner-partner relationships with smart objects. Meanwhile, Schweitzer et al. (2019) provide empirical evidence that consumers form various relationships with smart devices, including servant, friend, or master. Building on this body of work, we conceptualize two types of consumer-humanoid relationship in frontline services: robot as servant to consumer and robot as partner with consumer. We posit that the two relationship types activate different states of power in consumers.
As a psychological state, power is defined as a sense of discretion to asymmetrically enforce one’s will over other others (Sembada et al., 2016; Sturm & Antonakis, 2015). A high power state triggers an agentic orientation whereas a low power state triggers a communal orientation. One consequence is that high power consumers develop a greater psychological distance to others and are more prone to use stereotypical beliefs in evaluations than low power consumers (Galinsky et al., 2006). Moreover, previous research suggests that consumers make inferences about a brand from a service employee’s negative behaviours (Folkes & Patrick, 2003) and this attribution of globality leads to dissatisfaction with the brand (Hess et al., 2007). Thus, we hypothesize that priming consumers to perceive a robot as partner leads to less attribution of globality than priming consumers to perceive a robot as servant, and this relationship is mediated by power. Furthermore, high power consumers are more action oriented than low power consumers (Galinsky et al., 2003). In a service failure, consumers can experience negative emotions like anger and frustration, prompting them to form coping responses (Gelbrich, 2010). We argue that high power consumers are more likely than low power consumers to express and act on their negative emotions. Therefore, we hypothesize that priming consumers with robot as partner leads to less negative WOM than robot as servant, mediated by power.
Distinct from power, power distance belief (PDB) is the degree to which individuals embrace and expect inequalities in power; eastern societies like Japan and China have higher PDB than western countries (Hofstede, 2001; Xu et al., 2021). We posit that consumers high on PDB will perceive a greater power differential between servants and partners, because they are more cognizant of social hierarchy and receptive of power disparity between social classes. In other words, the difference between consumer power states activated by the two relationship types is greater for high PDB consumers. Hence, we hypothesize that the mitigating effects of robot as partner (versus robot as servant) on attribution of globality and negative WOM are greater for high PDB than for low PDB consumers.
A pre-test with 60 administrative staff and students in Norway (female = 48%, average age = 32) enabled us to establish a baseline for our conceptualization that without any cues, robot as servant and partner relationship types are prevalent amongst consumers, and that consumers perceive a spectrum of power disparity between service robots and themselves. We have planned three experiments onward. To test the main and mediation effects, we will conduct an online experiment using Prolific in late August 2022. In line with previous studies (Choi et al 2021), we are currently developing a 3D animation as stimuli, which is more effective than pictorial stimuli. To enhance externality validity, we have planned a quasi-field experiment in early November, where a humanoid service robot will provide students with information on a fictional student travel insurance scheme. The quasi experiment will happen at a pop-up campus event. To test the mediated moderation, we will conduct an additional online experiment with one sample from US/Europe on Prolific and one sample from China on Wenjuanxing (wjx.cn) in October 2022.
Our research contributes to literature by showing that framing consumer-robot relationship as a partnership in frontline services can allow brands to attenuate negative effects of robot-led failures. We use a dynamic triadic framework that combines the micro-level of consumer traits and the meso-level of brands and markets (Wirtz et al., 2018). We also answer the call for more cross-cultural analyses in studying consumer frontline encounters with service robots (Belanche et al., 2020; Chang & Kim, 2022). Our finding will shed light on the role of a fundamental cultural factor and help firms market their service robots appropriately in different consumer markets.

References
Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020). Service robot implementation: a theoretical framework and research agenda. The Service Industries Journal, 40(3-4), 203-225. https://doi.org/10.1080/02642069.2019.1672666
Belanche, D., Casaló, L. V., Schepers, J., & Flavián, C. (2021). Examining the effects of robots’ physical appearance, warmth, and competence in frontline services: The Humanness-Value-Loyalty model. Psychology & Marketing, 38(12), 2357-2376. https://doi.org/https://doi.org/10.1002/mar.21532
Brengman, M., De Gauquier, L., Willems, K., & Vanderborght, B. (2021). From stopping to shopping: An observational study comparing a humanoid service robot with a tablet service kiosk to attract and convert shoppers. Journal of Business Research, 134, 263-274. https://doi.org/https://doi.org/10.1016/j.jbusres.2021.05.025
Chang, W., & Kim, K. (2022). Appropriate service robots in exchange and communal relationships. Journal of Business Research, 141, 462-474. https://doi.org/https://doi.org/10.1016/j.jbusres.2021.11.044
Choi, S., Mattila, A. S., & Bolton, L. E. (2021). To Err Is Human(-oid): How Do Consumers React to Robot Service Failure and Recovery? Journal of Service Research, 24(3), 354-371. https://doi.org/10.1177/1094670520978798
Folkes, V. S., & Patrick, V. M. (2003). The Positivity Effect in Perceptions of Services: Seen One, Seen Them All? Journal of Consumer Research, 30(1), 125-137. https://doi.org/10.1086/374693
Galinsky, A. D., Gruenfeld, D. H., & Magee, J. C. (2003). From Power to Action. Journal of Personality and Social Psychology, 85(3), 453-466. https://doi.org/10.1037/0022-3514.85.3.453
Galinsky, A. D., Magee, J. C., Inesi, M. E., & Gruenfeld, D. H. (2006). Power and Perspectives Not Taken. Psychological Science, 17(12), 1068-1074. https://doi.org/10.1111/j.1467-9280.2006.01824.x
Hess, R. L., Ganesan, S., & Klein, N. M. (2007). Interactional service failures in a pseudorelationship: The role of organizational attributions. Journal of Retailing, 83(1), 79-95. https://doi.org/https://doi.org/10.1016/j.jretai.2006.10.010
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Mende, M., Scott, M. L., van Doorn, J., Grewal, D., & Shanks, I. (2019). Service Robots Rising: How Humanoid Robots Influence Service Experiences and Elicit Compensatory Consumer Responses. Journal of Marketing Research, 56(4), 535-556. https://doi.org/10.1177/0022243718822827
Novak, T. P., & Hoffman, D. L. (2019). Relationship journeys in the internet of things: a new framework for understanding interactions between consumers and smart objects. Journal of the Academy of Marketing Science, 47(2), 216-237. https://doi.org/10.1007/s11747-018-0608-3
Pitardi, V., Bartikowski, B., Osburg, V.-S., & Yoganathan, V. (2022). Effects of gender congruity in human-robot service interactions: The moderating role of masculinity. International Journal of Information Management, 102489. https://doi.org/https://doi.org/10.1016/j.ijinfomgt.2022.102489
Pitardi, V., Wirtz, J., Paluch, S., & Kunz, W. H. (2022). Service robots, agency and embarrassing service encounters. Journal of Service Management, 33(2), 389-414. https://doi.org/10.1108/JOSM-12-2020-0435
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Song, C. S., & Kim, Y.-K. (2022). The role of the human-robot interaction in consumers’ acceptance of humanoid retail service robots. Journal of Business Research, 146, 489-503. https://doi.org/https://doi.org/10.1016/j.jbusres.2022.03.087
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van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. A. (2017). Domo Arigato Mr. Roboto:Emergence of Automated Social Presence in Organizational Frontlines and Customers’ Service Experiences. Journal of Service Research, 20(1), 43-58. https://doi.org/10.1177/1094670516679272
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22nd August 2022 by admin

Robots, including in-store physical robots and virtual chatbots, have been increasingly employed to substitute human employees in customer service provision. These customer-facing service robots (SRs) can be defined as “system-based autonomous and adaptable interfaces that interact, communicate and deliver service to an organisation’s customers” (Wirtz et al., 2018, p.909). For these roles, SRs are often programmed to simulate customer-facing employees, engaging in different tasks with different intelligences and mimicking human-human interaction (Huang and Rust, 2021; Pantano and Scarpi, 2022). As a part of this, SRs may also be designed with physical and/or non-physical humanlike features, which can be stereotypically feminine or masculine, and which can affect customers’ emotional, cognitive, and behavioural responses (e.g., Blut et al., 2021; Tay et al., 2014). Despite a growing number of empirical studies, there is currently a lack of comprehensive synthesis of the extant literature related to the impacts of anthropomorphic design and gendering of SRs on consumer behaviour, which is vital to determine how further research can make meaningful contributions.

Anthropomorphism in SR literature refers to either perceived human-likeness (Blut et al., 2021) or design attributes of a robot (Lu et al., 2020; Wirtz et al., 2018). Anthropomorphism can affect customers’ reactions towards SR interactions in various service contexts, including public service (van Pinxteren et al., 2019), home service (Letheren et al., 2021), hospitality (Lv et al., 2021; Yam et al., 2021; Yoganathan et al., 2021), and retail (Whang and Im, 2021). Customers’ resultant emotional, cognitive, and behavioural responses can be positive or negative. For example, customers served by humanoid robots have a higher tendency of adoption (Blut et al., 2021; Sheehan et al., 2020) and re-use intention (Moriuchi, 2021); higher satisfaction (Yam et al., 2021); more positive evaluation (Li and Sung, 2021); closer emotional connection (Araujo, 2018); and more willingness to purchase (Yoganathan et al., 2021). On the other hand, researchers reveal that anthropomorphism of robots has the potential to harm human-robot interactions, causing greater discomfort and negative attitudes toward the robot (e.g., Kim et al., 2019; Mende et al., 2019). However, research on anthropomorphism of SRs has focused more on positive than negative effects such that less is known about the latter, especially the outcomes brought by the uncanny valley theory in customer-SR interactions (Wirtz et al., 2018).

Robot gendering refers to assigning gender onto a robotic platform through characteristics such as name, voice, and physique (Bryant et al., 2020; Robertson, 2010). Prior research examining effects of anthropomorphism of SRs often conflates gender with anthropomorphism by using gender-stereotypical features or characteristics to convey human-likeness (Crolic et al., 2021; Yam et al., 2021). However, gender-stereotypes have long been recognised in a technology context (e.g., Nass et al., 1997) and can impact how customers perceive and evaluate services (Pitardi et al., 2022a). For instance, Tay et al. (2014) demonstrate that participants show more positive evaluation and greater acceptance of a female-gendered healthcare robot and a male-gendered security robot due to gender-occupational role stereotypes. Broadly speaking, male-gendered robots are perceived to deliver a sense of intelligence, dominance, and competence while female-gendered robots provide a feeling of affection, communality, and particularly warmth (Ahn et al., 2022; Borau et al., 2021; Eyssel and Hegel, 2012; Seo, 2022) which plays a crucial role in customer-SR interactions (Choi et al., 2021). These findings raise important questions regarding how researchers and practitioners imbue robots with humanlike features.

Research on SRs in specific situations is beginning to emerge. During service failure, customers attribute more blame to a human employee than a SR (Belanche et al., 2020a; Leo and Huh, 2020) due to the robot’s perceived low level of agency (Gray et al., 2007). However, it is less clear how blame would be attributed to a humanlike versus non-humanlike SR, considering the impact of perceived thoughtfulness and/or agency brought by human-likeness (Waytz et al., 2014; Yam et al., 2021). While humanoids are more able to recover service failure than non-humanoids (Choi et al, 2021), it is unclear whether there would be any differences caused by gendering. Another important context to note is embarrassing service encounters (e.g., body measurement) in which a humanlike SR can have unfavourable effects on consumer embarrassment due to its resemblance with humans (Pitardi et al., 2022b). However, it is unclear whether human-robot gender congruity versus incongruity (Pitardi et al., 2022a) would have more or less favourable effects in embarrassing service encounters.

The field of SR research is in its infancy (Wirtz et al., 2018) and prior research is fragmented (Lu et al., 2020). Pioneering studies have systematically identified key dimensions of robot-delivered frontline service (Wirtz et al., 2018) and synthesised the impacts of SRs on customers and service employees (Lu et al., 2020), indicating the non-negligibility of anthropomorphic design in customer-SR interactions. Although van Pinxteren et al. (2020) innovatively conducted a systematic review of humanlike communications in conversational agents, a comprehensive literature analysis of anthropomorphism and gender of SRs and their impacts on human-robot service interactions is lacking. Aiming to overcome the gap and highlight the importance of discussing human-likeness of non-human service agents, we will undertake a systematic review to investigate the effects of anthropomorphism and gendering of SRs. The paper will provide a definition of gender of a SR which is missing in previous studies, describe the relationship between anthropomorphism and gender, and clarify the role of anthropomorphism and gender in human-robot service interactions. It will also identify knowledge gaps and key areas of interest via a research agenda for humanlike SRs. The paper will contribute to management and marketing literature as well as the SR literature by answering several research calls for understanding a robot’s design in general (Lu et al., 2020) and the robot’s humanlike characteristics (Blut et al., 2021; Choi et al., 2021; Xing et al., 2022) including gender (Belanche et al., 2020b; Choi et al., 2021) in particular. It could also provide theoretical support for organisations to deliver better service for consumers.

20th August 2022 by admin

This research paper formulates role of virtual service robots in view of relationship marketing. The paper draws special attention to some underlying premises of adoption of virtual service robots in managing relationships. Although, robots in service encounters witnessed substantial attention from scholars in recent years, more attention is needed especially related to adoption of service robots in managing customer relationships. Post examining practitioner literature from the IT industry (global consulting firms and CRM vendors), the paper elaborates on three research avenues. First, identifying customer barriers and motivators in case of service robot adoption. Second, to examine robots as interaction partners in actual field setting and third, to evaluate different characteristics of relationships styles and how these might diminish or enhance customer experience. In closing, based on the study’s findings theoretical and practical implications are offered.

by admin

Artificial intelligence (AI) is expected to change what we know about customer satisfaction and service quality. This review reveals that in addition to the typical antecedents of satisfaction described in the literature (i.e. expectations, performance, and disconfirmation), new drivers of customer satisfaction with AI-driven services have emerged. While service quality remains one of the most important determinants of satisfaction, AI has influenced the traditional dimensions of service quality and this influence can be seen in the new dimensions of service quality and the new models for measuring AI service quality.

by admin

Service companies are striving to improve the customer experience and provide personalized services (Grewal, 2020). For example, companies are introducing innovative digital technologies (e.g., artificial intelligence and robotics) to improve customer service encounters (Lariviere et al., 2017; Marinova et al., 2017; Wirtz & Jerger, 2016; Wirtz et al., 2021). Customers are therefore becoming more accustomed to robots in their daily lives (Kunz et al., 2019) and perceive robots as independent social entities with an automated social presence (van Doorn et al., 2017), consequently referred to as social service robots. Social service robots are one example of such a frontline technology that companies employ to enhance the service encounter.
Engaging in social interactions with service robots has a significant impact at all phases of the customer’s service experience. Indeed, robot proactivity (i.e., robots mimicking human frontline employees’ proactive behaviors) elicits different customer reactions compared to human-human interaction, such as positive/negative responses or various levels of satisfaction (Krishna et al., 2019; Pitardi et al., 2021). Understanding which emotions service robots evoke is particularly important for designing the service encounter, especially in the pre-purchase phase (Tielman et al., 2014; Lerner et al., 2015). This phase is delicate because here the customer anticipates the final purchase choice through a series of considerations and evaluations (Ashman et al., 2015; Lemon & Verhoef, 2016). In this phase, a service robot can help customers to better identify their own needs and find appropriate product options, by pointing out and picking items (Roggeveen & Sethuraman, 2020).
Despite the development of research on service robots during the service encounter, further research is needed during the pre-purchase phase in retail contexts (Ashman et al., 2015). Although several researchers have conceptualized human-robot interactions as a moment of co-creation of value in the service encounter and have acknowledged the social presence of robots along with the benefits to the customer experience itself (Fernandes & Oliveira, 2021), there is still a need for empirical research on the effects of the proactive behavior of different types of robots and the unexpected emotional consequences of this type of interaction (Lu et al., 2020; Puntoni et al., 2021). Therefore, the present study aims to investigate customers’ reactions (i.e., facial expressions) to two types of social service robot behaviors (approaching vs non-approaching). Specifically, this study aims to determine whether the approaching behavior (or the sole presence) of a social service robot elicits positive or negative customer emotions. With two additional conditions including a human, the research study determines whether the reactions elicited by different approaches of the service robot are consistent with customers’ responses to relationships with front-line employees.

This study adheres to the concept of human-robot shared experience (Gaggioli et al., 2021) and responds to the call for a shift in research from robots perceived solely as objects to robots perceived as active participants in the co-creation of experiences (Rancati & Maggioni, 2021). Therefore, we use a non-functionally oriented service setting. Unlike previous studies, the service robot is not primarily a goal-directed tool that performs a specific task (e.g., guiding the consumer’s choices), but rather serves in a social context to enhance the service experience.
For this study, we used the social robot Misty II (https://www.mistyrobotics.com/). 150 participants (75M, 75F) volunteered for the study. In addition to participants’ demographics, we recorded their facial expressions during the service encounter to determine participants’ emotional states during the object observation. Additionally, we registered a self-reported measure of emotions. We capture the Positive Affect and Negative Affect Schedule (PANAS) (Terraciano et al., 2003) which assesses participants’ experience before and after the object observation.
In this study, we investigate whether the presence of another social agent (human/robot) increases the aesthetic experience of a painting when visiting an art museum. Our experimental study applies a between-subjects design and participants were randomly assigned to one of five conditions: I) robot approaching condition; II) robot only present; III) human partner approaching condition; IV) human partner only present; V) customer alone.
In the approaching conditions I and III, the social service robot or the human partner interacts with the study participants, showing some degrees of agency. The robot or human physically approaches the participants (reduces spatial distance) and looks at the participants while observing an object. In the non-approaching conditions II and IV, the social service robot or the human partner is only present; they do not approach the participants as in conditions I and III. The service robot and the human partner behave similarly, comparable to other bystanders. They show body and eye movements, but they do not demonstrate an intent to engage with the participants, for example, in order to communicate their observation of the object. Condition V is a control condition in which participants observe the object alone. In all conditions, the visual stimulus selected as the observed object is “The Starry Night” by Vincent van Gogh. It was chosen because it has been shown to elicit complex emotions (Chirico et al., 2021). We can also assume that the popularity of the painting does not induce a “surprise effect” due to its initial exposure.
We expect that conditions I and III, which relate to the approaching behavior (i.e., agency) of the robot and the human, will differ in the facial expression analysis or in the PANAS, thereby establishing a positive effect in the customer viewing the object. Similarly, we expect that conditions II and IV, in which the robot or the human is only present (i.e., presence), will not differ. Finally, we expect that conditions I and III will differ from each other as well as from the passive control condition V. Additional results will be presented during the workshop.
Our study implements previous studies on artificial intelligence and extends previous research with a multidisciplinary approach that combines neuro-tools (facial expressions) with self-reports in the study of positive and negative emotions, proposing that consumers perceive service robots (approaching vs non-approaching differently), and examines whether this affects the service encounter during the pre-purchase phase.

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Extended Abstract
Due to the rapid digital transformation, service robots are increasingly replacing human service agents (Lewnes, 2021). As a particular form of service robot powered by natural language processing to simulate human conversations (either written or spoken) (Hoyer et al., 2020), chatbots have been put in the spotlight – due to the need for fast contactless online customer service (Stoilova, 2021). They can be implemented on a website, in a messenger, in a mobile App, in a robot, or elsewhere providing users with assistance and information through text, voice (e.g., Siri and Alexa), and a novel way – video. As the first touchpoint for customers on 7/24, service chatbots are widely adopted by firms for saving costs and improving online service. However, when issues get more complex and require individual attention or recovery service, human employees still need to be involved in supporting the problem-solving process with their emotional and social skills. Research reveals that not all applications in service delivery are widely adopted or successful (Davenport et al., 2020). In some cases, the statements suggesting the benefits of chatbots are “self-evident” (Alami et al., 2020). The changes in consumer behaviour and the challenges of service strategies have raised the need for research (Lu et al.,2019). From a theoretical perspective, a comprehensive understanding of consumers’ perceptions of service chatbot adoption is required (Gursoy et al., 2019).

We review the most well-known theories and the most frequent independent variables used in technology adoption studies (see Appendix A). Regarding service chatbots’ adoption, current literature arguably falls short in at least three research areas. First, traditional technology adoption models, such as TAM and UTAUT, were well developed but in the context of technologies whose features and abilities are vastly different from modern AI (Artificial Intelligence) (Lu et al., 2019). Some critical factors are inappropriate for investigating service robots, for instance, perceived ease of use and effort expectancy. Service chatbots possessing human intelligence powered by AI will not hinder adoption (Gursoy et al., 2019), as consumers do not need to learn how to operate chatbots. This limitation suggests that updated constructs and models are required to understand service chatbots’ adoption, i.e., Performance Efficacy (Lu et al., 2019). Beyond the ability to perform a task (i.e., usefulness), performance efficacy addresses specific areas where service chatbots are more competent than human agents (i.e., accuracy and efficiency). Second, prior studies mainly investigated from a technology perspective to understand consumer behaviour. However, adoption is influenced by not only technology characteristics, but also user characteristics (i.e., innovativeness). Drawing on the Diffusion of Innovation Theory and Social Identity Theory, the greater strength of identity makes consumers more sensitive to information relevant to identity, more likely to adopt identity-relevant products and more likely to engage in behaviours that directly implicate the identity (Reed et al., 2012). Consumer innovativeness sheds light on the individual differences when considering service targeting and positioning. Third, scholars call for research on consumer responses to different service types (Paluch and Wirtz, 2020), given service tasks can be distributed between human and robots. According to the Task Technology Fit theory, individuals will adopt a technology based on matching the task requirement and technology characteristics (Goodhue and Thompson, 1995). It is possible that, although consumers perceive technology as being advanced, they do not adopt it if they think it is unfit for their task and cannot improve their performance (Junglas et al., 2008). Little research has been conducted concerning consumers’ perceptions on task technology fit of chatbot services.
In line with the research gaps in the literature, this ongoing research brings TAM, DOI and TTF theories into the chatbot service domain for the first time, aiming to investigate consumers’ perception of service chatbots adoption. An integrated research model (Fig. 1) was proposed to test the key determinants: Perceived Performance Efficacy, Consumer Innovativeness, Trust, Perceived Autonomy, and Perceived Task Technology Fit. Prior usage experience and knowledge of chatbots were control variables. All measurement items were adapted from literature to ensure reliability and measured on a 5-point Likert scale to enable flexibility and prevent the target audience from being too neutral and overwhelmed (Colman et al., 1997).

Fig.1 Research model

This study employed a survey to collect data for examining the research model. A total of 360 responses were collected in January 2022, and 356 responses were identified as valid and then used for data analysis. Structural Equation Modelling was used to test the research model based on the systematic process developed by Hair et al. (1998). We used software SPSS 27 and Amos 27 as data analysis tools.
The initial findings of this ongoing research mostly support the significant effects of the proposed model of consumers’ service chatbot adoption. The results theoretically contribute to the knowledge of Technology Adoption and Task Technology Fit theories in the service marketing domain. Performance efficacy has the strongest and most direct effect on the intention of service chatbot adoption. Whether a chatbot’s performance can meet consumers’ expectations is the most critical factor of adoption in online service. Most importantly, the study confirms the effect of perceived task technology fit on chatbot adoption for the first time. Findings suggest consumers have their preconceived belief on the appropriateness of tasks for service chatbots. The research also found more evidence on the relationship between consumers’ innovative characteristics and adoption behaviour. Surprisingly, prior knowledge and usage experience of chatbots don’t hinder the intention of service chatbot adoption in this study. For practical implications, findings benefit marketing practitioners in deciding on effective service technology strategies for improving consumers’ online service experience. When firms develop and implement service chatbots, they must be aware of consumers’ higher expectations of AI performance. Despite consumers’ concern on chatbot performance, firms still can provide satisfying chatbot services by targeting the right consumers and delivering the right service tasks. For those consumers who are inclined to resist the adoption of new technologies, firms must provide different service options for them to avoid service failure.

19th August 2022 by admin

1. Purpose
Academics are increasingly concerned about how AI and service robots are shaking up the business world (Chang and Kim, 2022; Haenlein et al., 2022; Hollebeek et al., 2021). Most studies have examined the likelihood of service robots replacing human service personnel, and research into the robot characteristics that customers care about has focused on the one-on-one interactions between customers and robots. However, less studies have explored how the interaction between customers and service personnel affects the customers’ intentions and general experiences of receiving services from service robots.
In this study, we explored the contexts in which customers require robot services from the perspective of integrated services. Specifically, we examined the contexts in which robots can compensate for the skills that human service personnel lack to provide the highest quality of service to customers. We assumed that the customers’ attachment styles with service personnel either strengthen or weaken their perceptions of the usefulness, social capability, and appearance of robots, which in turn affect their perceptions of the quality of robot services and their intentions to replace human service personnel with service robots. Attachment styles, which originated from attachment behavior theory (Bowlby, 1979), refer to people’s adjustment of their own behavior to establish relationships with their primary caregivers during childhood according to the messages that such caregivers convey to them. Ainsworth et al. (1978) divided attachment styles into three types: secure, anxious, and avoidant. Attachment styles are relevant to specific interpersonal relationships.
Mende, Bolton, and Bitner (2013) applied the theory of attachment styles to examine the relationship between business companies and customers. The results obtained indicated that customers with avoidant attachment styles consider certain companies untrustworthy and themselves as undeserving of favor from service personnel. They may also believe that they can look after themselves and do not need help from companies. Customers with anxious attachment styles exhibit uncertainty toward their self-worth and are unsure of whether companies would provide them with the help they need in a timely manner because the behaviors of others are unpredictable. Consequently, they have a low sense of control toward the services provided by companies. To provide high-quality services, these relationships between customers and service personnel can be supplemented using robots. The research framework is presented in Fig. 1.
2. Methodology
A questionnaire was distributed to the customers of service companies that use service robots. The respondents were confirmed to have received robot services before the questionnaire, such as hotel tours, store services, smart ordering services, and hospital services. A total of 345 responses were received, 58% and 42% of which were provided by female and male respondents, respectively. In total, 40.9% of all respondents were aged 21–30, and 73.3% had received college or university education (see Table 1). Regarding the reliability and validity of the responses, all the questionnaire dimensions exhibited Cronbach’s α higher than 0.88, average variance extracted no lower than 0.65, and factor loadings attaining the level of significance. Discriminant validity analysis (Table 2) indicated that the square root of the average variance explained for each dimension was higher than the correlation coefficient between each pair of dimensions, indicating satisfactory overall variable reliability and validity.
3. Findings and conclusions
Regression analysis revealed the following results (see Table 3):
(1) The usefulness, social capability, and appearance of robots reinforce the quality of their services, which in turn increases the customers’ intentions to replace human service personnel with service robots. Thus, H1–H4 are supported (p < 0.01). Customers using service robots care about whether these robots provide services with an increased efficiency, answer questions appropriately, and are attractive.
(2) Customer attachment avoidance considerably reinforces the effect of robot usefulness and social capability on the quality of their services but weakens that of their appearance on the quality of their services. Accordingly, H5 is partially supported (p < 0.01). In other words, the usefulness and social capability of service robots are required aspects for human service personnel to deal with customers with attachment avoidance. However, more attractive robots are regarded as more questionable in terms of the quality of their services.
(3) Customer attachment anxiety considerably weakens the effect of robot appearance on the quality of their services but does not substantially weaken that of their usefulness and social capability on the quality of their services. Thus, H6 is partially supported (p < 0.01). Customers with attachment anxiety are unlikely to be concerned about the appearance of service robots. Accordingly, appearance may not be a robot feature complementary to human service personnel.
4. Originality/Value
This study explored how service robots complement human service personnel in providing a satisfactory customer experience from the perspective of integrated services and the relationship between customers and service personnel. According to various perspectives, the attachment styles of customers with service personnel substantially affect their perceived importance of the quality of robot services, including their usefulness, social capability, and appearance. Business owners should note that customers who have avoidant attachment styles with human service personnel are particularly sensitive to the usefulness and social capability of service robots; that is, instead of the appearance of robots, they are more concerned about whether robots can efficiently solve their problems, hence compensating for the lack of efficiency in human service personnel. The results of this study provide a key reference to academics and service industries for improving their customers’ service experience by using robots or artificial intelligence with customer-centered thinking. Promoting the interactions among customers, human service personnel, and service robots is of utmost importance.

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1. PHENOMENON UNDER INVESTIGATION
Service robots are increasingly implemented in service industries, including the hospitality industry (Ivanov & Webster, 2017; Naumov, 2019). Service robots taking over frontline tasks will inevitably impact guests’ experience. While the success of robots is frequently evaluated based on their task execution, in hospitality settings, robots’ impact on guest experience is of predominant interest. Recently, researchers contributed insights based on hypothetical scenarios (Belanche et al., 2021; Choi et al., 2020; Hoang & Tran, 2022), or analysis of review data (Huang et al., 2021). Consequently, there is a need for research in real-life experiments, capturing the effects that human-robot interaction has on guest experience compared to traditional human-human interaction. Our study presents insights from cross-context field experiments to measure the impact of service robots on the hospitality guest experience.
2. THEORETICAL FOUNDATIONS & POTENTIAL CONTRIBUTIONS TO THE FIELD
This study contributes to two streams of discussion. First, we contribute to the debates about digital transformation in the hospitality industry. Digital technologies such as artificial intelligence, automation, and robots are transforming service industries. Due to the importance of the ‘frontline’, researchers position the hospitality industry as a fruitful research context to further understand digital transformation in service industries (Buhalis et al., 2019; Wirtz et al., 2018). To this end, we follow recent studies and conceptualise digital transformation as a “socioeconomic change across individuals, organizations, ecosystems, and societies that is shaped by the adoption and utilization of digital technologies” (Dabrowska et al., 2022, p. 3). Specifically, we study the co-existence and interdependence of humans and service robots on an individual level (Dabrowska et al., 2022; Tung & Law, 2017).

Second, we contribute to the literature at the crossroads of service robots and guest experience. Customer experience management has become the most promising approach for creating loyalty and other positive result variables and has been addressed in academic literature and practice through a wide array of industries (Homburg et al., 2017; Lemon & Verhoef, 2016). Designing, managing, and measuring experiences has become a key topic in the experience driven hospitality industry. As such, this requires understanding of stakeholder experiences along the touchpoints within the entire customer journey, their distinct roles and behaviours and the influencing factors (Kranzbühler et al., 2018). These insights address possible cause-effect relationships for effective interventions on the touchpoint level in the journey, such as the encounter with a service robot.

Given the need to understand the impact of service robots on the overall guest experience along the entire customer journey, we complement recent qualitative and conceptual studies (Belanche et al., 2021; Choi et al., 2020; Ivanov & Webster, 2017), and specifically contribute to the call for comparing “customer experiences across different contexts using quantitative research” (Huang et al., 2021). By measuring the touchpoint experiences ‘in the moment’, as the real time experiments of this study do, we contribute to the understanding of the guest experience as a response to managerial stimuli (Becker & Jaakkola, 2020). We provide insights how the application of service robots impacts hospitality experience, ultimately affecting guest satisfaction and loyalty (Pijls et al., 2017).
3. RESEARCH QUESTION
To what extent does a robot-human service encounter, compared to a human-human encounter, impact the guest’s hospitality experience?
4. METHODOLOGY
We conducted two experiments addressing hospitality company-owned touchpoints (Lemon & Verhoef, 2016), i.e., human encounters with a service robot.

Experiment A (n=135) concerns the implementation of an information provision robot in a hotel lobby. Experiment B (n=151) evaluates the guest experience of robots that deliver food and beverages in a fast-food restaurant. In both experiments, guests were randomly assigned to either a human-human or a robot-human interaction. Data on the guest experience were collected via surveys provided to guests soon after experience of the interaction. We build on established constructs to assess hospitality experience in a service context (Pijls et al., 2017). Hence, we measured experiential factors inviting, care and comfort and relate it to overall experience and satisfaction.
5. FINDINGS
In the information provision experiment (A), in comparison to human-human interaction, robots have a significant negative effect on the experiential factor ‘inviting’ and the outcome variable of perceived ‘overall hospitality’. In the restaurant experiment (B), food delivery robots influence the overall hospitality guest experience positively on the dimensions of ‘overall satisfaction’ and ‘overall experience’ of hospitality.
6. DISCUSSION

Our findings from real-life experiments suggest a confirmation of earlier hypothetical studies arguing that guests deem robots more capable of functional, repetitive work such as transporting items and not of social, interactive tasks, such as speaking with guests (Ivanov & Webster, 2019).

Moreover, the two field experiments measure the impact of robot-human (compared to human-human) service encounters on touchpoint hospitality experiences and the evaluative outcomes of it. Interestingly, the direction of the impact is different in both experiments. Also, the experiential dimensions involved are different. In both studies an impact on the overall outcomes in terms of satisfaction and experience has been found. The different directions of the outcome underline that the robot-human encounter experience is subjective and context-specific. We provide empirical evidence for earlier conceptual contributions, underlining the importance of considering customer, situational, and sociocultural contingencies (Wirtz et al., 2018). Future research should investigate key contingencies and specify different responses in relationship to different stimuli in human-robot interaction.

Lastly, our experimental study joins discussions on digital transformation in the hospitality industry. We deliver empirical insights how service robots can enhance (or reduce) guest experience in the frontline (Liu & Hung, 2021). Considering the relevance of guest experiences in hospitality settings, we present an early attempt to understand how to design effective service robots and where to implement them in frontline touchpoints. Thereby, we enable hospitality professionals to better design and manage effective robot driven service encounters. Our study provides first cues regarding guests’ utilization of service robots, thereby presenting first insights into the adoption of digital technologies in a hospitality context (Dabrowska et al., 2022; Tussyadiah et al., 2020).

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Comply or resist? The use of service robots for law enforcement

Qingxuan Zhang and Liliana L. Bove
The University of Melbourne, Australia

The global pandemic increased the need for monitoring and enforcement of COVID-related policies such as mandatory vaccinations, face mask-wearing, travel restrictions, and quarantine which were largely carried out by law enforcement officers (Kugler et al. 2021). This increase in demand for law enforcement officers, in addition to their greater risk of exposure brought about by their close contact with the public (Jennings and Perez, 2020) resulted in many countries, such as Australia (Opie, 2022), the US (Lee, 2021), and Canada (Piapot et al., 2022) experiencing severe staff shortages. At times officer shortage was so acute that police stations were forced to close (The Age, 2021). A potential solution to this chronic shortage is the use of robots to monitor and enforce COVID compliance.

The use of robots in the law enforcement sector was estimated to be US$ 1.6 billion in 2020 and is expected to increase to US$ 4.2 billion by 2027 (GIA, 2021). With recent advancements in technology, robots can carry out tasks that were previously only reserved for human law enforcement officers. For instance, robots, equipped with state-of-the-art cameras, sensors, tracking technology, and connectivity capabilities, can now conduct autonomous surveillance without the presence of human officers (Market Trends, 2021).

Despite the large number of studies that explore how service robots can enhance customer comfort (Lin et al., 2022), bring enjoyment (Van Pinxteren et al., 2019), and elicit compensatory consumption (Mende et al., 2019) in multiple settings, scant attention has been paid to contexts in which the primary role of the service robot is to gain customer compliance. The overall service experience is largely unexplored, and the unintended consequence of such robot uses on consumers are unclear.

In response to these gaps, this study aims to explore citizen attitudes and emotional reactions towards service robots in law enforcement settings. Drawing on a Norwegian study where fishers had a lower acceptance rate of modern control activities (e.g., remote monitoring with drones) compared to traditional means of control (e.g., physical inspection by coastguards) (Diekert et al., 2021), we anticipate citizens will feel and act similarly towards service robots in law enforcement settings.

Of further interest is understanding citizen behavior in law enforcement settings where the rules and regulations are deemed to be unfair and not legitimate. Underpinned by reactance theory, we predict that when an individual’s freedom is threatened, the threatened behavior becomes more attractive (Brehm, 1966) and reactance, manifested in negative emotions such as offense (Heide et al., 2007), hostility, aggression, anger and frustration (Clee and Wicklund, 1980; Miron and Brehm, 2006) is aroused. In these situations, the affected individual may be motivated to restore freedom by seeking opportunism to misbehave towards the source of the threat (Dillard and Shen, 2005) i.e., the robot.

Using in-depth interviews of Chinese citizens who have experienced interactions with service robots charged with COVID-related control activities, we will explore how robot characteristics such as level of anthropomorphism and/or biomimetricity impact customer emotional attitudes, and compliance or reactance behaviors.

Robots as law enforcers
Singapore appears to have led the way in the use of robots for law enforcement possibly due to the high level of trust their citizens place on the government (Rieger and Wang, 2022). It first trialed the use of a robot dog called Spot, which carried a loudspeaker to broadcast coronavirus-related messages to enforce social distancing in parks (BBC, 2020). It also deployed a Boston Dynamics robot dog with sensors and speakers at a reservoir to warn people about the laws against loitering and gathering (Su, 2020). More recently, Singapore introduced a pair of patrol robots, dubbed Xavier, to augment the work of public officers by helping to enforce COVID-19 protocols and deter other undesirable civic habits (e.g., smoking in banned areas) in a busy shopping district (Barrett, 2021). When Xavier detects any undesirable behaviors, it sends real-time alerts to the command-and-control center, and officers can choose to respond in person or remotely by displaying an appropriate message on the robot’s interactive dashboard (Adams, 2021; HTX, 2021).

Similarly, humanoid anti-pandemic robots, dubbed Jasiri, are put to work at an international airport in Nairobi, Kenya (Reuters, 2021). Jasiri, checks passengers’ temperatures, tells those not wearing masks to put them on, and enforces social distancing rules with a camera mounted on an extendable neck (Reuters, 2021).

Methodology
Given the scarcity of research as to how consumers react to robots used in law enforcement settings exploratory research in the form of in-depth interviews will be used. A phenomenological approach will be taken to obtain a first-person description of personal experience with a law-enforcement robot.

The first context investigates several eastern Chinese cities where robot dogs, referred to as Preserved Egg, a famous Chinese dish, patrol the streets three or four times a day and help the authorities spread Covid-related messages, such as calling for mandatory COVID-19 tests or encouraging people to vaccinate (McMorrow, 2022). The second context is in several major airports in China where service robots like Xavier are used to check travelers’ body temperature and QR codes related to past traces.

Participants will be recruited through snowball sampling beginning with personal contacts and then following up with informant referrals (Noy, 2008). They will also be encouraged to use their native language to allow freer, flowing conversation. The messaging app WhatsApp will be used as the platform for communication because of its free access and assured anonymity to participants with its end-to-end encryption (WhatsApp, 2022). While videoconferencing allows the interview to be recorded, due to the perceived sensitivity of the topic, informants will be asked if interviews can be restricted to audio recording to allow verbatim transcription. Participants will also be assured of anonymity, referred only to by their pseudonym during the interview. If audio recording approval is not given, extensive notes will be taken during the interview.

To ensure a generalizable sample across various age groups, educational backgrounds, and risk preferences, individuals such as elderly people who do not use WhatsApp will be interviewed in person via an intermediary family member who is a WhatsApp user.

References
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Barrett, C. (2021). “Exterminate bad habits”: Meet Xavier, Singapore’s COVID-compliance robot. The Sydney Morning Herald. Retrieved August 6, 2022, from https://www.smh.com.au/world/asia/exterminate-bad-habits-meet-xavier-singapore-s-covid-compliance-robot-20210908-p58q1c.html
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18th August 2022 by admin

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THEORETICAL BACKGROUND
How would consumers feel when service robots fail and what are the behavioral consequences? When people perceive that a negative event cannot be resolved and altered in the future, they feel helpless (Gelbrich, 2010). Past research has documented that consumers show more negative responses when a service failure is attributed to stable causes (Van Vaerenbergh et al., 2014; Weiner 1985). In the service technology context, Belanche et al. (2020) found that consumers tend to make stronger attribution of stability to service failures caused by service robots than by humans. This could be because people tend to perceive automated systems as less flexible and adaptable (Leo & Huh, 2020) and require a substantial amount of time and effort to fix functional issues (Joyeux & Albiez, 2011). Thus, consumers may expect that a service robot’s error is more likely to be stable (i.e., occur constantly in the future), thereby feeling helpless (Gelbrich, 2010; Luse & Burkman, 2022).
Extending this notion, we argue that consumer helplessness will be contingent upon the failure type they encounter. Based on the theory of mind perception (Gray et al., 2007), past research suggests that while humans are seen as having greater minds (both high in agency and experience), robots are typically perceived as having moderate levels of agency but low levels of experiential mind (Gray & Wegner, 2012). In other words, people expect service robots to possess a great level of functional capabilities, while lacking social-emotional capabilities, which are key determinants of outcome and process failures, respectively (Choi et al., 2021; Smith et al., 1999). Also, while mechanical and thinking intelligence can be easily replaced by robots, feeling intelligence is hardly carried out by them (Huang & Rust, 2021). As a result, consumers who encounter a process failure by a robot view that the issue can hardly be fixed, resulting in a high level of helplessness. In contrast, an outcome failure by a service robot can be perceived as easily resolved by, for example, a quick system check or machine learning (Heller, 2019). In other words, a process (vs. outcome) failure by a robot is perceived to be nearly irresolvable, eliciting stronger helplessness.
Further, the helpless consumers are more likely to engage in negative word-of-mouth, but less likely to directly complain to the company because they doubt the service provider can remedy the service failure while still needing to vent their negative emotions (Gelbrich, 2010). Thus, we hypothesize:

H1. Consumers are more likely to engage in NWOM, but less likely to directly complain to the organization when service process (vs. outcome) failure is caused by a robot.
H2. The effect of robot’s service failure type (process vs. outcome) on complaint behavior (NWOM, direct complain) will be mediated by helplessness.

How can managers let consumers believe that their robot is capable of fixing social-emotional functions and expect the same error would not occur in the future? Empirical evidence has shown that subtle cues (e.g., appearance) make robots to be perceived as warmer, and consumers tend to respond less negatively to their mistakes (Xu & Liu, 2022; Yam et al., 2021). Moreover, adding warmth cues to robots can enhance perceived social-emotional skills (Choi et al., 2021). Therefore, we propose that adding warmth cues to robots can make consumers to believe their social mistakes (i.e., process failures) could be resolved, mitigating helplessness.

H3. In a process failure, the effect of robot’s failure on coping strategy mediated by helplessness will be mitigated when the robot has warmth cues. There will be no such effect of warmth cue in an outcome failure.

METHODOLOGY
Study 1
To test H1-2, Study 1 will employ a 2 (failure type: process vs. outcome) between-subjects experimental design. Participants will first read a scenario describing a service failure at a restaurant deploying service robots. Failure type will be manipulated by adapting previous empirical study scenarios (e.g., Choi et al. 2021). After reading the scenario, participants will be asked to indicate their intention to complain directly to the manager (Evanschitzky et al., 2011) and NWOM intention (Zeithaml et al., 1996). Also, helplessness will be measured (Gelbrich, 2010), followed by anger and frustration as alternative explanations (Gelbrich, 2010), and perceived severity of the service failure and propensity to complain as potential control variables.

Study 2
To test H3, Study 2 will employ a 2 (warmth cues on a robot: present vs. absent) by 2 (failure type: process vs. outcome) between-subjects experimental design. Participants will first read a scenario describing a process or outcome failure by a service robot at a hotel (Choi et al. 2021). Warmth cues will be manipulated by adding a broad smile and cute appearance to the robot image, while such cues will not be applied in the cue absence condition, following Liu et al. (2022). Pretest will be conducted to verify the stimuli. After reading the scenario, the same items will be measured as in Study 1.

EXPECTED CONTRIBUTION
When a robot fails, consumers may not complain directly to the firm but spread negative words particularly when they experience a process failure. We argue that this is due to consumer helplessness such that consumers do not expect the problem to be resolved. Thus, managers should pay extra attention in monitoring how their robots “behave” towards customers. By adding warmth cues to robots, managers can expect direct complaints from customers, which allow them to take immediate action and ultimately improve service quality.
The current research expects to add to the literature on service robot failure by examining the role of helplessness, which has been underexplored in the service robotics research, and its unique consequences (Gelbrich, 2010).
References

Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020). Robots or frontline employees? Exploring customers’ attributions of responsibility and stability after service failure or success. Journal of Service Management, 31(2), 267-289.

Choi, S., Mattila, A. S., & Bolton, L. E. (2021). To err is human (-oid): how do consumers react to robot service failure and recovery?. Journal of Service Research, 24(3), 354-371.

Evanschitzky, H., Brock, C., & Blut, M. (2011). Will you tolerate this? The impact of affective commitment on complaint intention and postrecovery behavior. Journal of Service Research, 14(4), 410-425.

Gelbrich, K. (2010). Anger, frustration, and helplessness after service failure: coping strategies and effective informational support. Journal of the Academy of Marketing Science, 38(5), 567-585.

Gray, H. M., Gray, K., & Wegner, D. M. (2007). Dimensions of mind perception. science, 315(5812), 619-619.

Heller, M. (2019), “Machine Learning Algorithms Explained,” InfoWorld (May 9), https://www.infoworld.com/article/3394399/machine-learning-algorithms-explained.html.

Huang, M. H., & Rust, R. T. (2021). Engaged to a robot? The role of AI in service. Journal of Service Research, 24(1), 30-41.

Leo, X., & Huh, Y. E. (2020). Who gets the blame for service failures? Attribution of responsibility toward robot versus human service providers and service firms. Computers in Human Behavior, 113, 106520.

Liu, X. S., Yi, X. S., & Wan, L. C. (2022). Friendly or competent? The effects of perception of robot appearance and service context on usage intention. Annals of Tourism Research, 92, 103324.

Luse, A., & Burkman, J. (2022). Learned helplessness attributional scale (LHAS): Development and validation of an attributional style measure. Journal of Business Research, 151, 623-634.

Smith, A. K., Bolton, R. N., & Wagner, J. (1999). A model of customer satisfaction with service encounters involving failure and recovery. Journal of marketing research, 36(3), 356-372.

Van Vaerenbergh, Y., Orsingher, C., Vermeir, I., & Larivière, B. (2014). A meta-analysis of relationships linking service failure attributions to customer outcomes. Journal of Service Research, 17(4), 381-398.

Gray, K., & Wegner, D. M. (2012). Feeling robots and human zombies: Mind perception and the uncanny valley. Cognition, 125(1), 125-130.

Weiner, B. (1985). An attributional theory of achievement motivation and emotion. Psychological review, 92(4), 548-573.

Xu, X., & Liu, J. (2022). Artificial intelligence humor in service recovery. Annals of Tourism Research, 95, 103439.

Yam, K. C., Bigman, Y. E., Tang, P. M., Ilies, R., De Cremer, D., Soh, H., & Gray, K. (2021). Robots at work: People prefer—and forgive—service robots with perceived feelings. Journal of Applied Psychology, 106(10), 1557-1572.

Zeithaml, V. A., Berry, L. L., & Parasuraman, A. (1996). The behavioral consequences of service quality. Journal of marketing, 60(2), 31-46.

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Theoretical background

The increased use of service robots has caused remarkable changes in service delivery across various contexts. Service robots are “system-based autonomous and adaptable interfaces that interact, communicate and deliver service to an organization’s customers” (Wirtz et al. 2018, p. 4). Unlike self-service technology, service robots are empowered with artificial intelligence to deliver socially and emotionally interactive services. In human-robot interactions, consumers can feel they are interacting with another social agent (Doorn et al., 2017). Thus, robots’ adoption can not only be for their competence but also their emotional and social presence (Wirtz et al. 2018). The global market for robots is growing (Xiao and Kumar 2021) and robots are expected to be used massively in delivering frontline hospitality services by 2025 (Huang and Rust, 2018). However, research on service robots is mainly conceptual and focuses on the drivers of their adoption (Filieri et al., 2022). Empirical research on how consumers respond to unintended human-robot interaction failure and how firms can mitigate these unintended consequences is scant (Doorn, 2017; Honig and Oron-Gilad 2018). Thus, understanding how consumers respond to different attributes of human-robot interaction failure is needed. Human-robot interaction failure can influence consumers’ intentions to reuse the service and perceptions of service robots (Gompei and Umemuro, 2015). Thus, mitigating these unintended consequences can have substantial managerial and academic contributions. Human-robot interactions failure has rarely been studied in services marketing and is considered a future developmental plan for services research.

Research questions

RQ1: How would consumers respond to unintended service failure caused by a robot vs caused by a human misuse of a robot?
RQ2: Do consumers’ responses to an unintended human-robot interaction failure differ based on service failure type, service failure stability, robot design and the social presence of others?
RQ3: What is the most effective recovery strategy in handling service failures caused by a robot vs caused by human misuse of a robot?

Expected contributions

In line with the premises of attribution theory, we argue that failure causal attributions are related to consumers’ cognitive and emotional reactions (Vaerenbergh et al., 2014). Consumers’ reactions to failure caused by robots will be stronger than those caused by human misuse of robots due to the external vs internal locus of causality (Weiner 1985). We expect to make several theoretical and managerial contributions to the literature on human-robot interactions in the service failure and recovery context. Specifically, this study responds to services scholars’ calls (Pitardi et al., 2022) for additional research on the unintended consequences of service delivered by smart technologies such as service robots to improve consumers’ overall service experience. Meaningful technological engagement that can develop lasting relationships with humans has substantial implications for customers’ experiences (Doorn, 2017).

Methodology

Figure 1 provides an organizing framework that captures our conceptual and empirical work. Study 1 examines how failure caused by a robot versus a human misuse of a robot affects consumers’ cognitive, emotional and behavioral reactions following a technical versus interactional failure (testing Hypotheses 1). Study 2 examines how failure caused by a robot versus a human misuse of a robot) affects consumers’ reactions following a permanent versus temporary failure (testing Hypothesis 2). Study 3A examines the moderating effect of interacting with a very human-like vs a mechanical robot (testing hypothesis 3a). Study 3B examines the moderating effect of interacting with thinking vs relational robot (testing hypothesis 3b). Study 3C tests the moderating effect of the social presence of others (testing hypothesis 3c). Study 4A examines the moderating effect of expressing regret by a robot vs a human (testing hypothesis 4a). Study 4B examines the moderating effect of offering options to the consumers by a robot vs a human (testing hypothesis 4b), and study 4C tests the moderating effect of offering a collaborative solution with the help of a human (testing hypothesis 4c).

Studies 1, 2, 3 and 4
Participants and procedures
Study 1 design is a 2 (service failure source: robot vs. human misuse of a robot) x 2 (failure type: technical vs. interactional). Study 2 design is a 2 (service failure source: robot vs. human misuse of a robot) x 2 (failure stability: permanent vs temporary) between-subjects design. Study 3A design is a 2 (service failure source: robot vs. human misuse of a robot) x 2 (robot appearance: very human-like vs. mechanical) between-subjects design. Study 3B design is a 2 (service failure source: robot vs. human misuse of a robot) x 2 (robot type: relational vs. thinking) between-subjects design. Study 3C design is a 2 (service failure source: robot vs. human misuse of a robot) x 2 (social presence of others: present vs. absence) between-subjects design. Studies 4A, 4B and 4C designs are a 2 (recovery source: robot vs. human) x 3 (recovery type: expressing regret vs. offering options for the consumer vs. collaborative solutions with the help of a human). Participants will first read a scenario describing a service failure at a restaurant. Failure source, failure type, and failure stability will be manipulated by pictorial stimuli that will be presented along with written scenarios adopted from previous empirical studies (e.g., Choi et al. 2021). These scenarios will be verified and pretested in separate pretests before using them to manipulate our stimuli. A random sample of UK adult consumers will be recruited from Amazon MTurk’s consumer panel and randomly assigned to one of our experimental conditions. For example, participants in study 1 will be asked to imagine themselves in a restaurant scenario where either a technical failure (robot malfunction caused by a robot vs a human misuse of a robot) or social norm violation (inappropriate social behavior by a robot vs. a human) has occurred. Participants will be asked to indicate their perceived cognitions, emotions and behavioral outcomes. They will also be asked to respond to background information, including the frequency of dining out, failure controllability, failure severity and consumers’ technology readiness (used as potential control variables in a subsequent analysis).

References

Choi, S., Mattila, A. S., & Bolton, L. E. (2021). To err is human (-oid): how do consumers react to robot service failure and recovery?. Journal of Service Research, 24(3), 354-371.
Filieri, R., Lin, Z., Li, Y., Lu, X., & Yang, X. (2022). Customer Emotions in Service Robot Encounters: A Hybrid Machine-Human Intelligence Approach. Journal of Service Research, 10946705221103937.
Gompei, T., & Umemuro, H. (2015, August). A robot’s slip of the tongue: Effect of speech error on the familiarity of a humanoid robot. In 2015 24th IEEE International Symposium on Robot and Human Interactive Communication (RO-MAN) (pp. 331-336). IEEE.
Honig, S., & Oron-Gilad, T. (2018). Understanding and resolving failures in human-robot interaction: Literature review and model development. Frontiers in psychology, 9, 861.
Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of Service Research, 21(2), 155-172.
Pitardi, V., Heirati, N., Jayawardhena, C., Kunz, W., Paluch, S., Wirtz, J., Unanticipated and Unintended Consequences of Service Robots in the Frontline. Journal of Business Research [ accessed on 25 July 2022] https://www.journals.elsevier.com/journal-of-business-research/call-for-papers/unanticipated-and-unintended-consequences-of-service-robots-in-the-frontline
Van Doorn, J., Mende, M., Noble, S. M., Hulland, J., Ostrom, A. L., Grewal, D., & Petersen, J. (2017). Domo arigato Mr. Roboto: Emergence of automated social presence in organizational frontlines and customers’ service experiences. Journal of service research, 20(1), 43-58.
Van Vaerenbergh, Y., Orsingher, C., Vermeir, I., & Larivière, B. (2014). A meta-analysis of relationships linking service failure attributions to customer outcomes. Journal of Service Research, 17(4), 381-398.
Wirtz, J., & Zeithaml, V. (2018). Cost-effective service excellence. Journal of the Academy of Marketing Science, 46(1), 59-80.
Xiao, L., & Kumar, V. (2021). Robotics for customer service: a useful complement or an ultimate substitute?. Journal of Service Research, 24(1), 9-29.

17th August 2022 by admin

INTRODUCTION
For retail owners, it is crucial to deliver high-quality and innovative service to stay competitive. High-quality service is a valuable asset since it leads to customer satisfaction and loyalty, which contributes to the financial performance of the company . The so-called service encounter, concerning the direct interaction between customer and organization at the moment of service delivery, is central to high-quality service .
Humanoid service robots are one of the most dramatic innovations in the retail service encounter . Softbanks’ Pepper is a typical example of a possible retail innovation: a 120 centimeters humanoid with a friendly face, moving arms including hands and fingers, and a human-like locomotion. Even though humanoid service robots like Pepper could play an important role in a high-quality service encounter , empirical knowledge about the effects on customers and store employees is still lacking .
Previous studies show that the vast majority of research done on robots in retail has an conceptual nature, mainly focuses on the first adoption phase of consumers, and has therefore paid little or no attention to, for example, the role of store employees . The empirical research that has been done has mostly been conducted in controlled environments, which makes the practical usefulness for retailers limited and misses the complexity of a real-life setting . Therefore, a field experiment in an actual store is crucial.
RESEARCH QUESTION
What happens if you measure the added value of a service robot in an uncontrolled store setting?
METHODOLOGY
We conducted a field experiment where we placed Pepper in an actual grocery store during a 3-week period. Pepper was located at the entrance of the store, welcoming customers and suggesting information about the app, store discounts or meal inspiration. We asked store visitors about their robot experience: if they noted the robot, how they perceived the interaction (if applicable), and their idea of how Pepper could add value to the store.
To complement the data, we asked store employees about their experience with working next to Pepper. We used a structured interview to obtain this data.
FINDINGS
– Customers
Out of 139 customers, 46 customers (33.1%) did not notice the robot, 43 customers (30.9%) noticed the robot but did not stop at the robot, the other 50 customers (36%) noticed the robot and stopped. It is important to mention that this percentages only contain the customers we interviewed. To obtain a more accurate stopping rate, we counted 8 times for 15 minutes the customers entering the store, and how many of them interacted with Pepper.
The average stopping rate during these days was 14,7%.
The customers interviewed gave us interesting insights. 35 (25.2%) of them mentioned that Pepper could be valuable for entertainment, 22 (15.8%) customers thought Pepper can mean something for children and another 22 (15.8%) did not see any perspective for a service robot in the store. Lastly, 12 customers (8.6%) had no clue about what Pepper could mean for them or the store.
In addition, 15 (10.8%) people mentioned not to be interested in the robot because they were in a hurry.
– Employees
The three employees we interviewed all seemed quite skeptical about Pepper in the store. They did not experience a real decrease in workload and could not really see great advantages yet. Again, the effect on children was mentioned. Another employee pointed out that the robot was a good solution for welcoming customers. A third employee was slightly annoyed that they had to make sure Pepper was charged, which costed them extra work.
CONCLUSIONS
Empirical research into service robots and human-robot interaction perceptions in a real-life setting is still underdeveloped. In this study, we examined Pepper in an actual grocery store for a longer period, so we could identify unforeseen effects.
Firstly, we found that customers vary in their thoughts about the added value of Pepper. Recurring topics are entertainment and/or children, and no added value or idea. Since children seem to play a major role, it is important to take this into consideration: it might not be the actual target group of the retailer’s investment. Prior research focused on the effect of humanoids on children with autism , hearing disabilities , but the (unintended) effects for retail stores is undefined.
In line with the above, it is important for both customers and employees to know what Pepper does for the store. Store owners should have a clear goal what they would like to obtain before they invest in a service robot. The importance of instructing employees is highlighted.

16th August 2022 by admin

Relevance of research
Humanoid service robots (HSR) are on the rise. Designated by scholars and practitioners as the workforce of the future, their introduction into many service contexts and industries is fueled by advancing technological developments in artificial intelligence (AI) and automation (Rust & Huang, 2014). HSR are becoming an integral part of frontline service operations (FSO) to fulfill socially assistive positions across several service sectors such as healthcare (Holland et al., 2021), hospitality (Tuomi et al., 2021), and retail (Amelia et al., 2022). Here, they support or even replace frontline service employees. The increasing relevance of the topic in practice is also reflected in the remarkable increase in research in the field of service robots in recent years (Lu et al., 2020).
Nevertheless, prior studies on the interaction with and acceptance of humanoid service robots were conducted primarily with a focus on a HSR vs. human employee perspective by applying comparative (experimental/quantitative) methods. Thus, the current discussion of HSR in frontline service encounters lacks consideration of the fact that “human-robot interaction analysis requires a different approach that recognizes that humans’ relationship with technologies is multifaceted and context-dependent” (De Keyser & Kunz, 2022, p.68). This goes along with the identified lack of scholarly research on the ethical and moral considerations of de-humanizing service encounters by introducing HSR. These considerations range from privacy concerns and biased consumer outcomes to concerns related to loss of customer autonomy, social isolation and more (Mariani et al., 2022).

Research objectives and research question
Against the background of the blind spots regarding the impact of human-robot service interaction listed above, this research seeks to answer the following research question:

How can humanoid service robots in frontline service encounters put the customer experience and well-being in jeopardy?

To narrow this research question down and make it more approachable and assessable during our empirical design, we further established two guiding questions for our two empirical studies (see “methodology” below) to facilitate the course of our two-study research. The guiding questions helped us collect the relevant insights and knowledge to successively answer the research question above.

These guiding questions were:
(1) What are the ethical concerns and negative outcomes experienced by customers interaction with HSR?
(2) How should service providers manage HSR in frontline service operations to account for customers’ concerns and prevent threats to their well-being?

Research methodology
Our methodological approach acknowledges the lack of conceptual underpinnings regarding the contextual and relational character of human-robot service interaction (HRSI) and its impact on customers’ service experience and well-being. We thus used an exploratory research design with two complimentary studies to (1) understand what constitutes the service experience for customers interacting with HSR and what factors negatively influence the service experience and (2) determine what service providers using HSR in FSO can do to actively address ethical concerns and threats to customer well-being.
The first study was designed as a problem-centered interview study with service customers (n=41, age range 19 to 71 years old) who had had either prior experience with HSR in a service setting themselves or were familiar with the technology through prior research or experience in other contexts. Thus, a purposive sampling approach was applied to account for the novelty and distinctness of the topic.
The second study was conducted as an exploratory study using expert interviews (n=27) with company representatives working for companies that either actively use HSR in their FSO already or manufacture HSR for service application. Service providers from various fields were integrated, among others hospitality, gastronomy, human care, and transportation.
All interviews were transcribed verbatim and checked for correctness and accuracy and then exported to atlas.ti 22, a qualitative data analysis software. We followed a systematic stepwise recursive process in the thematic analysis of the data (Boyatzis, 1998). Transcripts were coded independently by both members of the research team. A code system was established and built inductively, based on the in-depth textual analysis. New codes were created in an iterative fashion to capture the meaning of initial code groups (Thomas & Harden, 2008). Co-occurrence matrices in atlas.ti were applied to hierarchically organize individual codes in the shape of a coding tree. In an iterative process, the data material was merged, and the two members of the research team independently formed the main categories, discussed the content and labeling and, after several rounds, agreed on a final set of themes.

Preliminary findings
The preliminary findings (analysis ongoing) decode the major concerns faced by customers interacting with HSR across various service encounters. They reveal customer concerns addressing the personal (e.g., privacy, rapport), the social (e.g., fear of substituting human labor), or the interactive (e.g., service quality) level of the HRSI. As customers interact with a HSR in a specific service encounter, they form expectations about the performance level of the service. Their evaluation of the service experience is based on the perceived performance level in terms of contextual (type of service, i.e., information-processing vs. people-processing), transactional (i.e., task complexity, convenience), and relational (i.e., empathy, emotionality) performance.

Originality of the paper
This research is among the first to openly address critical components of customers’ service experience with HSRs to assess ways in which they can put the customer experience and well-being in jeopardy. It presents negative consequences of unfavorable human-robot service interactions to pinpoint current boundaries of HSR-implementation in service settings.
Scholarly (re)search for determinants and interdependencies of emotionally and psychologically stimulating service experiences with HSR is still in its infancy. This research thus motivates scholars to strive for a better understanding of the ways in which HRSIs can cause negative impacts on customer well-being to inform technological development and contextual implementation in service settings. It reveals interdependencies of personal, technological, and contextual determinants to lead to concerns and threats to customer well-being, thereby creating awareness and leading service providers and managers towards a more customer-oriented design and application of HSR.

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Currently, businesses use a variety of artificial intelligence (AI) applications, such as service robots (Wirtz et al., 2018). Aside from the innumerable benefits, their quick and broad deployment has also led to a number of problematic issues (Honig & Oron-Gilad, 2018). For example, several studies focused on how people reacted to failing algorithms (Srinivasan & Sarial-Abi, 2021). Even fewer studies investigated how people react when robots fail (i.e. Choi et al., 2021). Prominent marketing strategies involved depicting resilient and well-engineered robots in states of falling, failing, beating up, and aiming at evoking various feelings (i.e. empathy, warmth, or comfort). Despite it being a significant phenomenon, almost no previous research investigated how consumers react to robots depicted as falling, failing, beaten up, or lost (see Table 1 for a list of popular robot failures and falls).
The most prominently portrayed type of robot failure in popular media is “the fall.” In this research, our goal is to investigate what people think and feel about the phenomenon of “failing robots” in the context of a “fall, as consumers evaluate the same technology (i.e., robots) in somewhat diverse ways (Siino & Hinds, 2005; Gretzel & Murphy, 2019). We present the initial findings of our content analysis to pinpoint the specific concepts consumers focused on when formulating their thoughts and feelings on falling robots.

We started out our in-depth exploratory research by gathering open-ended consumer verbatims from a convenience sample of university students. They responded to a specific robot fall news item, accompanied by a visual, which aided in the discovery of new and relevant issues centered on the “failing robots” theme. Visuals are frequently used in exploratory studies to aid in the probing of meanings and reactions (Christodoulides et al. 2021).
Participants
A convenience sample of eighty-eight (42 female) undergraduates studying business at a major European university took part in the current study in partial fulfillment of their course requirements. We have not prioritized generalizability and scale, which are not key aspects in qualitative sampling (Holloway & Jefferson, 2000). The average age of the participants turned out to be 23.09 (SD = 2.504, ranging between 19-34) and on average they reported medium income.
Procedure
Participants were first shown a piece of news about a robot falling (Appendix 1). The photograph was captioned, “The fall of one of a robotics company’s robots at a trade show.” They were asked to answer a few questions about their evaluation of the robot and the news depiction, as well as some control variables like their involvement in robotics, anthropomorphism level for the robot, and demographics.
Findings
As two researchers, we concurrently open-coded the short-essay reactions to the robot and the news. We used manual coding to gain insight into the characteristics and dimensions of attitudes toward malfunctioning robots. The initial results of consumer verbatim analysis revealed several broad themes of forming an opinion or attitude toward the issue. The following are some of the most notable examples in the three thematic categories: (1) seeing robots as the futuristic technological advancement of humans and getting upset with them falling; (2) seeing robots as yet another simple machine and not minding much about them falling; and (3) in-between: having mixed feelings toward the robots’ falling
Seeing robots as a futuristic technological advancement for humans:
“The sad part is that this shows that we are behind in terms of technology”
“They can develop themselves and improve their technology. Every success comes from after failure.”
“It wasn’t just a falling robot; the ideas and experiences fell, too.”
“I am not interested in how and why it fell off the stage at all, but I am curious about what is planned for the future of this robot in terms of AI improvements.”
Seeing robots as yet another simple machine:
“I feel nothing about the robot’s fall”
“I have no emotion about the robot’s fall”
“Since it’s a machine, a fault could appear at any time, which, on the other hand, shows that we cannot rely on robots 100% and that human interaction is gonna be always needed.”
“Since that was a robot, I did not feel anything.”
Having mixed feelings:
“This news is partly fun and partly sad”
“They’re exciting, but a little scary”
The first group got depressed with the robot’s fall and took it as a sign that the technological advancement and efforts set forth for such were wasted, or at best, they would like to understand what the key learning was to make sure that desired progress can be achieved in future technologies. The second group did not take the robot’s fall as something to be of importance as it was yet another machine that surrounds modern daily life; however, they still felt a bit disappointed that humans were needed to compensate for the robot’s falls. Lastly, the third group had mixed feelings and reported confusion when they saw a robot fall.
Conclusion
Consumers may experience unexpected and mixed emotions after witnessing robotic failures, and these emotions may then influence their attitudes and related behavioral intentions. For example, previous research demonstrated that using service robots attenuated consumers’ embarrassment (Pitardi et al. 2021). It is yet unknown which emotions and mechanisms are in place for evaluating failing robots in a favorable or unfavorable light by the consumers. The apparent polarity reflected in the verbatims points out that using robot fall strategies is a double-edged sword for robotic service providers, producers, and even retailers, and the involved mechanisms need to be analyzed in-depth in order to avoid unanticipated and unintended consequences.

12th August 2022 by admin

The deployment of AI chatbots is driving significant changes in the way that service is delivered and experienced (Belanche, Casaló and Flavián, 2021; Ostrom et al., 2021). The co-creation literature emphasises actor-to-actor networks that create value for each other (Vargo and Lusch, 2004); however, the introduction of AI chatbots in the frontline adds an uncharted dimension to the study of co-creation, as AI developments promise value co-creation that changes as the AI adapts to other actors (such as customers), and these actors then adapt to the AI. AI chatbots take an active part in the service encounter, and as pseudo frontline employees, become important actors in the value co-creation process (Verhagen et al., 2014).

However, chatbots often fall short of customer expectations, resulting in service failure (Sheehan, Jin and Gottlieb, 2020). In this manner, the failure can also be perceived as co-created, since customers invest considerable time and energy (resources) in order to interact with the chatbot and co-create the interaction (Heidenreich et al., 2015). The negative effects of such service failures may be further compounded if customers are not allowed to choose their preferred customer representative: a human agent or a chatbot; or if customers perceive to be interacting with a human, but would be interacting with a chatbot instead (Robinson et al., 2020). Although previous literature extensively examined the determinants of consumer use and adoption of novel technologies, extant research has largely been conducted in voluntary co-creation contexts, and has neglected the investigation of less harmonious forms of co-creation, such as forced co-creation contexts.

Different co-creation contexts are also likely to be influential in forming customer expectations regarding the forthcoming co-creation experience, the chatbot’s performance, as well as possible chatbot limitations (Crolic et al., 2022). The nature of such expectations can have a significant impact in shaping customer evaluations about their participation in co-creation (Nijssen, Schepers and Belanche, 2016) and eventually in assigning attributions for failure (Belanche et al., 2020). However, the literature only offers contradictory findings regarding the possible impact of different co-creation contexts on customer expectations, and responsibility attributions.

Against this background, the aim of this study is to investigate not only the customer perception of chatbot interactions in different co-creation contexts, but also to understand how such contexts may influence customer expectations and the resulting responsibility attributions when chatbot failure occurs.

Guided by a Pragmatism research philosophy, a mixed methods approach was adopted in order to be able to effectively address the research aim. Specifically, an exploratory sequential design was employed, consisting of the collection and analysis of qualitative data in the first stage, followed by a quantitative phase which built on the findings obtained from the qualitative study.

The qualitative study comprised a total of 39 semi-structured interviews conducted in a face-to-face format. This study demonstrated how customers perceive three distinct co-creation contexts when interacting with chatbots. One context, volitional co-creation, is more aligned with the concept of voluntary participation and collaboration that is prevalent in the co-creation literature. Yet, the other two contexts, coercive and deceptive co-creation, are novel to the co-creation literature and as such, are worthy of further investigation. As a result, the qualitative study advanced a research framework and accompanying hypotheses regarding the impact of distinct co-creation contexts on expectations and attribution of responsibility.

Focusing on the investigation of coercive co-creation contexts, the hypotheses were tested through two experimental research studies conducted in a customer service setting. Data was collected from US participants (N = 315; N = 324), who, as part of the experimental research, were asked to interact with a chatbot that was specifically programmed for this study. Half of the participants were forced to interact, whereas the other half were given the illusion of choice between interacting with a chatbot and an alternative customer service channel. Structural Equation Modelling (SEM) was used to evaluate the experimental data.

The experimental studies found that in situations which result in service failure, customers who were forced to interact with chatbots, attribute more negative responsibility towards the company, than customers who were given a choice among several contact options. In such cases, customers blame the company directly for the failure. When compared to those customers who were offered a choice, customers who were forced to interact with the chatbot showed stronger controllability and stability attributions towards the company; in other words, they perceived the company as having had the ability to prevent the failure, whilst also perceiving the failure as more permanent and likely to happen again. These results were replicated within the two different service settings that were included in the experimental research. These results show that despite all the benefits associated with chatbots, it is important to understand why customers may feel forced into interacting with a chatbot, and the resulting customer evaluations following such interactions. Although the co-creation literature has shown a strong reliance on the notion of co-creation as a harmonious, voluntary collaborative activity, this study challenged this view and contributed to the co-creation literature by demonstrating the possibility of discordant forms of co-creation, which may especially arise in human-to-non-human interactions. Additionally, this study also contributes to the service failure literature by assessing the impact of service failures in an AI context and uncovering the underlying psychological processes following a co-created service failure between a customer and an AI chatbot.

The experimental research also found support for the mediating effect of disconfirmation of expectations. Forcing customers to interact with chatbots results in a more negative disconfirmation of expectations (the experience rated as worse than expected) than those customers who were given a choice. This, in turn, leads to stronger attributions of controllability, stability and causality towards the company. It is likely that forced involvement represents a more comprehensive level of mental involvement for customers (Reinders, Dabholkar and Frambach, 2008), in the process expecting more from the interaction, and ultimately being more disappointed when the service results in failure. This result confirms the role of customer expectations as a reference point when judging the actual service (Oliver, 2015) and contributes to the co-creation literature by establishing disconfirmation of expectations as an important link between the perceived freedom of choice when co-creating and the resulting attributions of responsibility.

The findings also suggest a number of strategic implications for managers who are considering the partial or complete replacement of human staff with chatbots in customer service settings.

10th August 2022 by admin

The service sector is at an inflection point, with potential industrialisation arising from robotics creating new opportunities for innovation and the potential for a service revolution (Wirtz et al., 2018). A service revolution through technology may address issues including labour shortages (Beesley, 2021). In Ireland, for example, although tourism generates €9.2 billion annually and employs 265,000 people, there is a skills crisis with approximately 40,000 vacant positions (McGowan, 2021). Inflation in wages means businesses struggle to operate below 40% labour cost (McGowan, 2021), and 90% of businesses highlight difficulties in recruiting staff (Finn, 2021). In addition, workers in the gig economy face erratic working hours and uncertainty about pay (Martyn, 2021), with human costs of the gig economy including isolation without supportive colleagues or mentors, affecting perceptions of human dignity at work (Lillington, 2019).
Cognisant of these challenges, we recently conducted research based on a sample of 805 employees in the Irish hospitality sector (Wallace and Coughlan, 2022). Drawing on Conservation of Resources theory (COR), we proposed affective commitment and perceived leader support (specifically LMX) as resources against burnout. We found that the emotional exhaustion component of burnout was associated with counterproductive workplace behaviour, and affective commitment and LMX are effective resources against burnout, and against CWB. We also found that those who perceived they were on zero-hour contracts (or in the ‘gig economy’) were less able to draw on affective commitment or LMX as resources when experiencing burnout, but they were also less likely to ‘act out’ when they experienced burnout. We cautioned that zero-hour workers in the hospitality sector may internalise their stress and cope with the challenges of work in other ways.
One solution to these challenges would be a greater reliance on service robots; to support staffing, reduce uncertainty and work, and allow hospitality to deliver a consistent, efficient offering. Yet extant literature has focused less on the implications of frontline employees working with robotics (De Keyser et al., 2020). For example, if a service encounter is positive experience for the customer, the service robot may receive praise while the human employee remains unacknowledged, negatively impacting employee commitment (Robinson et al., 2020). Yet when technology such as service robots are involved in repetitive work, this may free up time to allow frontline employees to engage in more exciting and varied work (Wirtz et al., 2018). Also, if employees are no longer dealing with trivial requests, this may allow them to deal with higher-level tasks (Robinson et al., 2020).
Building on our earlier research, the first objective of our current study is to examine how service robots could be integrated in services by considering managers’ and employees’ views about these technologies. One could suggest that adding service robots would help to alleviate workload issues leading to burnout, support zero-hour workers in providing more assurance regarding hours worked, and support employees to allow them to engage with customers in a more genuine way. Yet while robots are predicted to have a profound impact on the sector (Lu et al., 2020), they have some weaknesses relative to service employees (Huang and Rust, 2018; Wirtz et al., 2018). For example, although robots offer an advantage of homogeneity in the delivery of repetitive services, customisation may be required to meet specific customer needs, and a heterogeneous delivery may be more appropriate (Palmer, 2011). Additionally, robots’ feigned emotion may be easily distinguished as not genuine, especially over longer- or high-involvement service encounters (Wirtz et al., 2018). Furthermore, customers may be relationship-motivated and expect social relationships with frontline employees, and this form of rapport, rich communication and emotional expression may engender customer trust and satisfaction, which could be lost in inhuman interactions (Robinson et al., 2020).
Rust (2020 p.18) highlighted that we are in a transformation, where artificial intelligence may compete with human intelligence, and this could dramatically change the skillset that humans need to remain relevant in the workplace. Huang et al. (2019) assert that human intelligence must emphasise empathy – a ‘feeling economy’ – where the empathetic dimensions of work are emphasised as mechanical and analytical tasks are replaced by AI and Robotics, especially in the services sector where interpersonal relationships are critical.
A consideration of the ‘feeling economy’ is particularly relevant in researching the implementation of service robots in Ireland’s hospitality sector. In Ireland, frontline service employees are a ‘secret ingredient’ in the sector. The Irish hospitality and tourism industry has built campaigns around ‘Ireland of the welcomes’ (McGrath, 2018). McGrath (2018) cites Niall Tracey of Failte Ireland, who explains “…what visitors constantly come back to is the people they engaged with,” he says. “When we ask visitors what it is about the people, it’s the smile. Irish people will smile at you, and without a word, that smile uniquely says, ‘I’m only here to help, how are you getting on?’”. He adds: “it’s very relaxed. It’s not manufactured friendliness, it’s a really authentic connection…it really makes Ireland quite magical compared to other destinations.” Yet when robots takes over a task, these human skills are displaced (Rust, 2020). How can, and should, the service be best delivered without losing that ‘magic’ that is unique and special within the sector?
The second objective of this study therefore is to explore how to best engage robots to alleviate challenges in the hospitality sector, while retaining the ‘magic’ of its service delivery and build an advantage in the ‘feeling economy’. Drawing on the Irish context provides unique insights into employee response to service robots, where frontline employees are the point of difference. Utilising survey, experimental design and in-depth discussions with managers and employees in hotels in particular, findings elicit their attitudes about service robots. We investigate the relationship between burnout, employee’s’ resources, and their response to service robots working alongside them. We also investigate managers’ views about integrating service robots in a sector famous for its friendly, human face.

10th June 2022 by admin

Metabolism of pathogens in infectious diseases is important for their survival, virulence and pathogenesis. Mycobacterial pathogens successfully scavenge multiple host nutrient sources in the intracellular niche. It is therefore important to identify the intracellular nutrient sources and their metabolic fates in these pathogens. Metabolic phenotype of an organism is defined by metabolic fluxes. We quantified in vivo fluxes of the pathogens and probed host-bacterial metabolic cross talks in tuberculosis (TB) and leprosy using systems-based strategies and techniques of isotopic labelling, metabolic modelling and metabolic flux analysis (MFA). We show that the TB pathogen metabolizes a number of carbon and nitrogen sources in human macrophages and identified vulnerable nodes such as glutamine and serine biosynthesis as potential drug targets. Mycobacterium leprae, the leprosy causing pathogen, uses host cell glucose in infected schwann cells and the enzyme, phoenolpyruvate carboxylase is a potential drug target. Our research provides an understanding of the intracellular diets and metabolism of these important human pathogens and identified vulnerable metabolic nodes that can be used for developing innovative chemotherapies in TB and leprosy.