When service robots make mistakes: how does customers’ mood regulation affect their continuance intention to adopt?

1. Phenomenon under investigation

The rapid development and implementation of RAISA (service robots, artificial intelligence (AI) and service automation) have changed how services being delivered and experienced (Prentice et al., 2020). Thus, academics and practitioners propose that service robots become an integral part of human life (Tung and Law, 2017), and human labour will be replaced (Tuomi, Tussyadiah, and Stienmetz, 2021). However, customers’ adoption level of service robots is still quite low. Scholars attempt to examine factors that affect customer adoption of service robots, so that they can enhance their adoption. Factors related to robot characteristics, customer characteristics, and robot-customer interaction are found crucial in determining their adoption (Mende et al., 2019). Specifically, factors such as ease of use (Turja et al., 2020), gender (Seo, 2022), and anthropomorphism level (Chen et al., 2022) can affect customers’ service robot adoption to various degrees. However, this technology is still in its infancy and thus, unsuccessful outcomes of service robots, such as glitches, unexpected negative events and robots not living up to promises must be considered as they will significantly influence customers’ assessments of the service provider (Holloway and Beatty, 2003; Dabholkar and Spaid, 2012). A few scholars look into issues related to robot failure and how it affects customers’ adoption. For instance, Belanche et al. (2020) found that respondents made stronger attributions of responsibility for the service performance toward human employees than toward robot employees, particularly after a service failure. Moreover, Yang et al. (2022) examined the effectiveness of humour in robot failures suggesting that its effectiveness varies based on failure severity level. However, extant work fails to explore whether customer adoption of service robots will be affected after service failures and how customers’ affective states and individual traits affect their intentions to continue to adopt service robots.

2. Potential contributions and research questions

Factors such as a person’s affective state and mood (as well as their emotional reactions to them) largely impact their decisions to accept new technologies (Djamasbi et al., 2010). In particular, their continuous adoption of specific technology is also determined by their perceptions, such as trust (Tussyadiah et al., 2020). In reality, the affective state and “how people feel” when using technology are decisive factors, as emotional systems help define our rational decisions in conjunction with rational thought (Hanoch, 2002; Muramatsu and Hanoch, 2005). Recent studies have investigated the factors motivating customers to use these robots in service interactions (e.g., Lu et al., 2019; Liu et al., 2022). However, most of work lacks to take consumers’ affective states and individual characteristics into consideration when examining factors on their adoption intention after a service failure. Given the importance, this study aims to better understand how customers’ mood regulation influences their continued adoption of service robots after encountering unsuccessful service with a frontline robot. This research serves as a valuable contribution to the field of RAISA because it advances our understanding of the impacts that an individual’s mood has on their personality traits, which, in turn, influences their decisions to adopt new technology.

3. Theoretical Foundations

Customers’ mood regulation and adoption

Mood causes a differential impact on behaviour and advise that this has a complex relationship with people’s personality traits (Karimi and Liu, 2020). As non-specific affective states, moods can have a powerful impact on both behaviour and cognition, previous studies advising that mood can not only impact behaviour to a significant degree but also the cognition (Lischetzke and Eid, 2003; Das and Fennis, 2008). Biss et al. (2010) highlight that these enduring affective states may have positive or negative valence.

The process that people use to manage their affective states is known as mood regulation (Koole, 2010). According to Forgas (1995), the Affect Infusion Model (AIM), puts forward two different mechanisms that describe how mood impacts decisions and judgements, affect-as-information and affect-priming. In affect-as-information mechanisms, the affective state is used as a shortcut to infer evaluations and inform decisions. Therefore, consumer behaviour and choices are triggered by the behaviour that results from the influence of mood (Geen, 1995). Since consumers have different information processing behaviours (Karimi et al., 2018; 2020), their mood can affect their adoption decisions in various ways.

The mediating role of affective states
User perceptions of technology characteristics are impacted by affective states (Darban and Polites, 2016). Considering the acceptance and adoption of technologies, previous studies (e.g., Hoong et al., 2017; Verkijika, 2020) identified that people’s emotions and feelings are the most pivotal factor, outweighing other factors like the perception of risks or benefits (Chuah, 2021). Specifically, when people are in a positive mood, this affects their level of acceptance or support (Karimi and Liu, 2020). Meanwhile, Jobin et al. (2019) advise that research has not only found that people’s evaluation of information is guided by affective reactions but also that this consequently influences their acceptance of technology.

The moderating role of service failure
Service failure will become unavoidable as service robot technology becomes more commonplace; suggesting that customer satisfaction will be adversely affected by service robot failures such as preparing the wrong meal, providing incorrect directions or over-charging customers (Yam et al., 2021). Customers have certain expectations and when service falls below this standard, they will react accordingly (Hoffman and Bateson, 2010). Thus, when customers become dissatisfied through such service failures, they frequently respond with anger (Sliter et al., 2010; Wilson and Holmvall, 2013).

However, Schwarz and Clore (1983) propose that when people experience a positive mood, their affective state acts to inform their behaviour-related judgements; although satisficers may be affected by the informational impact, their pragmatism and judgement mean that they use their positive mood as information and decide to continue to use service-bots. Thus, customers who are experiencing a positive mood will be more forgiving of the failures of service robots (Yam et al., 2021). Building upon the above-mentioned conclusions and reasoning, we therefore propose the following hypotheses:

Hypothesis 1. In a positive (vs. negative) mood regulation, customers are more willing to adopt service robots after a service failure.
Hypothesis 2. The effect proposed is mediated by customers’ processed affective states.
Hypothesis 3. Robot service failure moderates the indirect effect of mood regulation on intention to adopt.

4. Methodology

This paper will conduct one correlational research (Study 1) and two experiments (Study 2A, Study 2B) to evaluate the hypotheses. Specifically, Study 1 investigates the correlations between the mood regulations that clients experience and their likelihood of continuing to use service robots. Study 2A uses an experimental method to investigate how different types of mood regulations affect participants’ affective responses, while Study 2B investigates how experimentally manipulated mood regulations facilitate customers’ intention to continue using service robots through the mediator (affective states) and the moderator (service failures). This study plans to conduct a lab experiment in a Chinese university and also an online experiment to hire samples from MTurk to take part this study.

5. Expected findings and conclusion

In conclusion, service recovery is one of the most critical initiatives required to address faults in the service delivery process and turn service failures into positive service outcomes (Chen and Tussyadiah, 2021). This study strives to add new knowledge to the pertinent literature in this field in several ways. This research will shed lights on how mood control can encourage customers’ continued intention to embrace service robots; the expected findings will experimentally evaluate and record the mediating function of customers’ affective states. Moreover, this study attempts to contribute to the literature via experiments to investigate the relationship between service failure and mood regulation. Expected findings will provide important insights into consumer behaviour and suggest that affective states plays a critical role in determining consumers’ preferences and behaviours when they encounter service failures.

Please see attached word file.

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.

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.

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

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.

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.

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).

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.

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
Hofstede, G. (2001). Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Sage publications.
Holthöwer, J., & van Doorn, J. (2022). Robots do not judge: service robots can alleviate embarrassment in service encounters. Journal of the Academy of Marketing Science. https://doi.org/10.1007/s11747-022-00862-x
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
Schweitzer, F., Belk, R., Jordan, W., & Ortner, M. (2019). Servant, friend or master? The relationships users build with voice-controlled smart devices. Journal of Marketing Management, 35(7-8), 693-715. https://doi.org/10.1080/0267257X.2019.1596970
Sembada, A., Tsarenko, Y., & Tojib, D. (2016). The Positive Effects of Customers’ Power on Their Behavioral Responses After Service Failure. Journal of Service Research, 19(3), 337-351. https://doi.org/10.1177/1094670516645188
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
Sturm, R. E., & Antonakis, J. (2015). Interpersonal Power:A Review, Critique, and Research Agenda. Journal of Management, 41(1), 136-163. https://doi.org/10.1177/0149206314555769
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
Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018). Brave new world: service robots in the frontline. Journal of Service Management, 29(5). https://doi.org/https://doi.org/10.1108/JOSM-04-2018-0119
Xu, H., Bolton, L. E., & Winterich, K. P. (2021). How Do Consumers React to Company Moral Transgressions? The Role of Power Distance Belief and Empathy for Victims. Journal of Consumer Research, 48(1), 77-101. https://doi.org/10.1093/jcr/ucaa067

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.

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.

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.

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.

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.