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As self-service technologies (SSTs) become more prevalent, service providers are actively encouraging customers’ involvement with these technologies, sometimes even forcing their customers to use SSTs. This paper aims to examine the influence of the SST-only (vs full-service) mode on customers’ negative attitude toward SST providers through the mediating mechanism of powerlessness and explores how SST familiarity and SST anthropomorphism moderate the impacts of the SST-only mode on powerlessness.

Three experiments were performed, study 1 tested the main and mediating effect and studies 2 and 3 verified the moderating effects.

The results suggest that customer perceived powerlessness mediates the relationship between SST-only (vs full-service) mode and negative attitude toward SST providers. When the levels of SST familiarity and SST anthropomorphism are high, the impacts of SST-only on powerlessness are attenuated. Alternative mediating mechanism of powerlessness is examined and ruled out.

Future research should investigate other moderators that may reduce the impacts of SST on customer powerlessness. These moderators could be service-operating procedures, SST interface design, types of service situations and customer characteristics. In addition, other consequences of powerlessness, other than the negative attitude toward SST providers and intention to switch investigated here, should be investigated as well.

This research provides guidelines helping service providers to improve their customers’ SST usage experience by showing both SST familiarity and SST anthropomorphism may alleviate the negative effects of SST-only mode on customer perceived powerlessness more effectively.

This research examines the role of customers’ psychological reactions toward the SST-only mode, particularly from the perspective of power and control.

To enhance customer experiences, firms are increasingly adding human-like features to their self-service technology (SST) machines. To that end, the purpose of the present study is to examine customer interactions with an anthropomorphic machine in a service failure context. Specifically, the authors investigate the joint effects of machine voice, an individual’s sense of power and the presence of other customers in influencing customers’ switching intentions following an SST failure.

In this study, the authors used a quasi-experimental design in which they manipulated voice type (anthropomorphic vs robotic) and the presence of other customers (present vs absent) in video-based scenarios while measuring customers’ sense of power. The scenarios reflected a service failure experience with a self-service kiosk at an airport. The authors tested the hypotheses using PROCESS analyses with the Johnson–Neyman technique.

Consumer reactions to SST failures vary depending on the degree of anthropomorphism associated with an SST machine, an individual’s sense of power and the presence of other customers.

Field inquiry and an investigation in other SST contexts or of other anthropomorphic features are needed to generalize the findings.

Service providers targeting powerful consumers should consider the social presence of others when incorporating anthropomorphic features into their SST facilities.

This study is the first to examine consumer responses to service failures in an anthropomorphic SST context.

The purpose of this study is twofold: first, to identify the categories of artificial intelligence (AI) chatbot service failures in frontline, and second, to examine the effect of the intensity of AI emotion exhibited on the effectiveness of the chatbots’ autonomous service recovery process.

We adopt a mixed-methods research approach, starting with a qualitative research, the purpose of which is to identify specific categories of AI chatbot service failures. In the second stage, we conduct experiments to investigate the impact of AI chatbot service failures on consumers’ psychological perceptions, with a focus on the moderating influence of chatbot’s emotional expression. This sequential approach enabled us to incorporate both qualitative and quantitative aspects for a comprehensive research perspective.

The results suggest that, from the analysis of interview data, AI chatbot service failures mainly include four categories: failure to understand, failure to personalize, lack of competence, and lack of assurance. The results also reveal that AI chatbot service failures positively affect dehumanization and increase customers’ perceptions of service failure severity. However, AI chatbots can autonomously remedy service failures through moderate AI emotion. An interesting golden zone of AI’s emotional expression in chatbot service failures was discovered, indicating that extremely weak or strong intensity of AI’s emotional expression can be counterproductive.

This study contributes to the burgeoning AI literature by identifying four types of AI service failure, developing dehumanization theory in the context of smart services, and demonstrating the nonlinear effects of AI emotion. The findings also offer valuable insights for organizations that rely on AI chatbots in terms of designing chatbots that effectively address and remediate service failures.

Service robots can offer benefits to consumers (e.g. convenience, flexibility, availability, efficiency) and service providers (e.g. cost savings), but a lack of trust hinders consumer adoption. To enhance trust, firms add human-like features to robots; yet, anthropomorphism theory is ambiguous about their appropriate implementation. This study therefore aims to investigate what is more effective for fostering trust: appearance features that are more human-like or social functioning features that are more human-like.

In an experimental field study, a humanoid service robot displayed gaze cues in the form of changing eye colour in one condition and static eye colour in the other. Thus, the robot was more human-like in its social functioning in one condition (displaying gaze cues, but not in the way that humans do) and more human-like in its appearance in the other (static eye colour, but no gaze cues). Self-reported data from 114 participants revealing their perceptions of trust, anthropomorphism, interaction comfort, enjoyment and intention to use were analysed using partial least squares path modelling.

Interaction comfort moderates the effect of gaze cues on anthropomorphism, insofar as gaze cues increase anthropomorphism when comfort is low and decrease it when comfort is high. Anthropomorphism drives trust, intention to use and enjoyment.

To extend human–robot interaction literature, the findings provide novel theoretical understanding of anthropomorphism directed towards humanoid robots.

By investigating which features influence trust, this study gives managers insights into reasons for selecting or optimizing humanoid robots for service interactions.

This study examines the difference between appearance and social functioning features as drivers of anthropomorphism and trust, which can benefit research on self-service technology adoption.

Artificial intelligence (AI) has been widely used as a financial technology (fintech) in the mobile banking (M-banking) domain. However, in the literature, how AI affects users' perceptions of social support and the users' satisfaction and continuance intention (CI) remains unknown. To fill this gap, the two core characteristics of AI, perceived intelligence (PI) and perceived anthropomorphism (PA), are combined with social support theory (SST) (including informational support (IS) and emotional support (ES)) to develop a research model to investigate how PI and PA affect IS and ES, which in turn affect users’ M-banking satisfaction and CI.

This study adopted a random probability sampling method to collect a total of 360 valid responses to verify the proposed model. Partial least squares (PLS) was employed for data analysis.

The results showed that PI and PA both have a significant positive impact on consumers' perception of social support (IS and ES). IS was a direct driver of satisfaction and CI. Surprisingly, although ES was positively associated with satisfaction, the study found that higher levels of ES will decrease CI. This study exposed how AI affects consumers’ satisfaction and CI through SST, and the role of AI in M-banking applications has been further confirmed.

This study expanded the SST to creatively integrate with AI features to reveal the impact of PI and PA on IS and ES, which in turn influence users' M-banking usage.

Self-Service Technology (SST) is a disruptive technology that has reshaped customer interactions, increased efficiency, and enabled data-driven decision-making. Its impact continues to evolve as technology advances and customer expectations change, making it a key consideration for businesses in a dynamic landscape. This chapter delves into critical findings regarding the adoption and implications of SST in tourism and hospitality. The relevant studies are sourced from the Scopus database. A mixed literature review methodology was employed to review papers. The literature review findings show facets of SST adoption, shedding light on the intricate relationships between consumer readiness variables, context-specific influences, preferred SST features, and psychological attributes. The study reveals consumer preferences, including convenience, ease of use, and speed of service, as primary drivers of the adoption of SST. The bibliometric analysis reveals the scope for developing SST literature in tourism and hospitality. Collaborations among scholars, research and funding institutions could help provide the impetus. Research in SST security, sustainability, and resilience could help enhance the SST literature. Comparative studies evaluating SST's social and economic implications are also suggested.

Service providers and consumers alike are increasingly adopting artificial intelligence service agents (AISA) for service. Yet, no service quality scale exists that can fully capture the key factors influencing AISA service quality. This study aims to address this shortcoming by developing a scale for measuring AISA service quality (AISAQUAL).

Based on extant service quality research and established scale development techniques, the study constructs, refines and validates a multidimensional AISAQUAL scale through a series of pilot and validation studies.

AISAQUAL contains 26 items across six dimensions: efficiency, security, availability, enjoyment, contact and anthropomorphism. The new scale demonstrates good psychometric properties and can be used to evaluate service quality across AISA, providing a means of examining the relationships between AISA service quality and satisfaction, perceived value as well as loyalty.

Future research should validate AISAQUAL with other AISA types, as they diffuse throughout the service sector. Moderating factors related to services, the customer and the AISA can be investigated to uncover the boundary conditions under which AISAQUAL is likely to influence service outcomes. Longitudinal studies can be carried out to assess how ongoing use of AISA can change service outcomes.

Service managers can use AISAQUAL to effectively monitor, diagnose and improve services provided by AISA while enhancing their understanding of how AISA can deliver better service quality and customer loyalty outcomes.

Anthropomorphism is identified as a new service quality dimension. AISAQUAL facilitates theory development by providing a reliable scale to improve the current understanding of consumers’ perspectives concerning AISA services.

The purpose of this paper is to test the effect that anthropomorphic framing (i.e. robot vs automatic machine) has on consumers’ responses in case of service failure. Specifically, the authors hypothesize that consumers hold an unconscious association between the word “robot” and agency and that the higher agency attributed to self-service machines framed as robots (vs automatic machines) leads, in turn, to a more positive service evaluation in case of service failure.

The authors have conducted four experimental studies to test the framework presented in this paper. In Studies 1a and 1b, the authors used an Implicit Association Test to test for the unconscious association held by consumers about robots as being intelligent machines (i.e. agency). In Studies 2 and 3, the authors tested the effect that framing technology as robots (vs automatic machines) has on consumers’ responses to service failure using two online experiments across different consumption contexts (hotel, restaurant) and using different dependent variables (service evaluation, satisfaction and word-of-mouth).

The authors show that consumers evaluate more positively a service failure involving a self-service technology framed as a robot rather than one framed as an automatic machine. They provide evidence that this effect is driven by higher perceptions of agency and that the association between technology and agency held by consumers is an unconscious one.

This paper investigates a novel driver of consumers’ perception of agency of technology, namely, how the technology is framed. Moreover, this study sheds light on consumers’ responses to technology’s service failure.

The purpose of this paper is to explore what drives customer acceptance of frontline service robots (FSR), as a result of their interaction experiences with FSR in the context of retail banking services.

Applications of the unified theory of acceptance and use of technology and service robot acceptance model frame the exploration of customers’ interaction experiences with physical FSR to explain acceptance. A thematic analysis of information obtained through observations, focus groups and participant interviews was applied to identify themes.

This study identifies 16 dimensions that group into five main themes that influence customer acceptance of FSR in retail banking services: (1) utilitarian aspect, (2) social interaction, (3) customer responses toward FSR, (4) customer perspectives of the company brand and (5) individual and task heterogeneity. Themes 1 and 2 are labeled confirmed themes based on existing theoretical frameworks used; themes 3–5 are additional themes.

This study provides actionable suggestions to allow managers to reflect on their strategy and consider ways to design and improve the delivery of services that involve FSR.

This study adds to our limited knowledge of how human-robot interaction research in robotics translates to a relatively new research area in frontline services and provides a step toward a comprehensive FSR acceptance model.

This paper aims to examine the interaction effect among the subjective social class, service level and recovery type on post-failure service evaluations (recovery satisfaction and willingness to spread positive word-of-mouth).

A total of 270 US consumers were recruited via Amazon MTurk. This study adopted a 2 (Subjective social class: high vs low) × 2 (Service level: luxury vs mid-scale) × 2 (Recovery type: customer self-recovery vs joint recovery) between subjects’ factorial design using a scenario-based survey method.

The results from the three-way multivariate analysis of covariance confirmed that a joint recovery is ineffective for high subjective social class individuals in a mid-scale hotel setting. Moreover, the moderated mediation analysis revealed that this tendency can be explained by high subjective social class individuals’ tendency to attribute blame externally to self-service technologies (SSTs).

The results of this study suggest that mid-scale hotels should deploy employees in the SST service area based on the profile of their main customers. If a mid-scale hotel is positioning itself to appeal to high subjective social class customers, then employees should be aware of the fact that customers may not be highly satisfied if they receive assistance.

This study expands the current knowledge on customers’ psychological differences based on subjective social class. Furthermore, the findings of this study contribute to academia by providing evidence of external attribution among high subjective social class individuals.