This paper explores how online networks (re) construct caring and nurturing practices of the family through the shaping of the female body as a body that produces human milk in ways that resemble old and gendered forms of labor and creates new ones. Through the analysis of three online communities of breastmilk exchange that afford both ease, access, and opportunity (Eats on Feets Facebook Groups, Facebook Market, and OnlyTheBreast.com), this essay explores how digital networks together with the technology of the breast pump and apps that keep track of breastmilk production, mobilize the female body and its milk into objects that are sometimes exchanged as commodities and others, as commerce-free pieces of labor in an era of economic relations led by the gig economy and augmented by digital platforms.
This paper studies the different elements that play a role in the exchange of breastmilk in families that sell, donate, buy or acquire breastmilk through online networks. These elements include features of the platform, type of users, network’s guidelines and values, safety measures, post content, price of the exchange, type of commitment, motivations for the exchange, and family dynamics. What opportunities for families is the online exchange of breastmilk bringing? How is that shaping, transforming, or imitating family life? What inequalities and power dynamics are being exposed and reshaped? The essay is not only putting three different sites/networks of breast milk dissemination in tension while exploring these questions, but by extension, it also shows the difference between a mutual aid social network and a commercial gig economy site as spaces embedded in family life. The paper illustrates that tension and difference, which becomes clear through the juxtaposition.
Purpose: As Internet technology evolves, electronic health (e-health) literacy gradually becomes a key factor in healthy behaviors how among adult how among adolescents. However, little is known about the influencing parental factors of adolescent e-health literacy in family. Thus, the objective of this study was to systematically review the status quo, assessment tools, and influencing parental factors of e-health literacy of adolescents.
Methods: We conducted a comprehensive search in several databases, including PubMed, Scopus, Web of Science between January 2006 and December 2022. The following search term was used E-health literacy scale. Inclusion criteria were: (1) English article published between 2006 and 2022. (2) Literature Free full text or Open Access. Exclusion criteria were: (1) Reviews, books, letters to the editor, and abstracts of speeches.
Results: A total of 61 articles were included in this review, all of which were studies about electronic health (e-health) literacy scale. The e-Health Literacy Scale (eHEALS) was the most used measurement for e-health literacy. The study has identified the influencing factors of e-health literacy among adolescents in family, including age, gender, domicile place, parental education level, information-seeking behavior, and social support. In this review, influencing parental factors were divided into predisposing factors, contributing factors, and enabling factors. These findings of this review provide new ideas for both family members and healthcare professionals to improve eHealth literacy among adolescents. Further research is needed to develop and implement an easy-to-use e-health literacy scale for adolescents.
Conclusion: In addition, there is a need to develop easy-to-use and highly accessible online information platforms and mobile applications for e-health literacy among adolescents. In addition, family members should improve communication and discussion with them on the use and acquisition of electronic resources for disease prevention and motivation for a healthy lifestyle. Families of adolescent also be encouraged to offer them more support. The findings highlighted parents as significant role models in adolescents’ healthy behaviors. Therefore, further research to examine the role of parental factors in development of adolescents’ e-Health Literacy is required.
Keywords: E-health literacy; adolescents; parental influencing factors; systematic review.
Despite numerous reforms over the years, intestate succession rules continue to privilege traditional, white, heterosexual families. It is evident that the one-size-fits-all scheme cannot truly reflect diversity of lifestyles and associations. This Article considers an innovative option that has become increasingly popular in recent years: using big data to create personalized rules, tailored to the personal characteristics of each decedent. This Article explores the promise and drawbacks of personalized intestacy, arguing that personalized default rules fall short in the realm of inheritance, because these rules are personal and inheritance law is inherently relational. It then offers preliminary guidelines for adapting big data techniques to the relational aspects of inheritance.
I use this framework to expand the study to critically evaluate other rules that can be personalized in the family: marital property division, will interpretation, and cohabitation.
Across East Africa, different regimes are experimenting with various ways of reclaiming control of the often-perceived ‘volatile’ social media and private messaging spaces. Yet, these platforms are the main ways of news exposure thus calling attention to how these tactics manifest in the patterns and practices of news consumption. This study positions trust within the broader discourses of surveillance as well as the socio-cultural context within which trust and privacy form part of public debates. While there is a widespread agreement that artificial Intelligence and algorithms have reconfigured the information—including news— distribution and consumption in the global south, it is not clear how they have shaped the understanding of trust and privacy among users its community of users. Point often overlooked, news is more than just ‘news’; it connects the ‘self’ to the immediate ‘world’, and at the same time brings the ‘world’ to the ‘self’. With this in mind, the inherent tensions of balancing how much information on/or about the ‘self’ the world need to know, and how much of the world the ‘self’ needs to know, raises fundamental issues on trust and privacy. Preliminary findings show that trust and privacy are two peas in a pod, they are inextricably linked and mutually reinforcing; two broad understandings of trust emerged, namely: vertical trust i.e. trust in societal institutions and ‘horizontal’ trust i.e. trust in each other. On the one hand, technical affordances, for instance, privacy settings and other provisions such as the use of passwords, Personal Identification Numbers (PIN), fingerprints, and voice commands among other security features emerged as the technical measures of guaranteeing online privacy and security.
Broadcasting moments of private life on YouTube has given rise to new narratives of the intimate, in individual terms, but also in the family sphere. In this way, events such as going back to school, the birth of a new family member, holidays, moving house, or a domestic problem, make up the themes of the channels through which all of this takes place: family vlogs. In addition, an apparent naturalness, and the ability to connect with audiences and thus influence their behaviour has put these family channels in the spotlight of advertisers. This research examines the intrinsic motivations (hedonic and eudaimonic), as well as the extrinsic motivations that drive these families to share their live on YouTube. For this purpose, the HEMA-RX questionnaire (Huta, 2016) was administered to N=11 families and N=101 vlogs were analyzed. Results reveal that we are dealing with a behaviour of self-affirmation, of personal commitment to one’s own family and the community of followers that they have generated with their videos and that, if money could sometimes be a motivator, as well as the psychosocial reward of knowing that they are micro-influencers, the fact is that motivation of a eudaimonic nature is also predominant.
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.
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
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).
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.
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