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Service failures usually occur in front of third-party customers. Third-party customers react emotionally and behaviorally to service failure and recovery efforts aimed at focal customers. However, there is a gap in the literature on how third-party customers react to a service failures incident and a recovery over another customer, depending on how socially close or distant they are from. This study investigates the effect of third-party customers' emotions on consumer forgiveness, negative word-of-mouth (WoM) and repatronage intentions in the service recovery process by comparing close and distant third-party customers.

This study utilizes a 2 (social distance to the focal customer: close, distant) × 2 (service recovery: yes, no) between-subjects design. The authors used a scenario-based experiment to test the proposed hypotheses. A total of 576 respondents were involved in the study.

The results from the authors' scenario-based experimental study show that positive and negative emotions felt by distant third-party customers are higher than those of close third-party customers. In addition, the effect of positive emotions on customer forgiveness is more substantial for distant third-party customers. Third, moderated-mediation analysis indicates that social distance has a moderator effect only on the relationship between positive emotions and customer forgiveness.

This study contributes to the service literature by comparing socially close and socially distant third-party customers' reactions to service failure and recovery attempts.

Prior research has advanced several explanations for social media influencers' (SMIs’) success in the burgeoning computer-mediated marketing environments but leaves one key topic unexplored: the moderating role of SMIs in service failure and recovery strategies.

Drawing on a social constructivist perspective and an inductive approach, 59 in-depth interviews were conducted with millennials from three European countries (Italy, France and the United Kingdom). Building on social influence theory and commitment-trust theory, this study conceptualises four distinct pathways unifying SMIs' efforts in the service failure recovery process.

The emergent model illustrates how source credibility and message content moderate service failure severity and speed of recovery. The insights gained from this study model contribute to research on the pivotal uniqueness of SMIs in service failure recovery processes and offer practical explanations of variations in the implementation of influencer marketing. This study examines a perspective of SMIs that considers the cycle of their influence on customers through service failure and recovery.

The study suggests that negative reactions towards service failure and recovery are reduced if customers have a relationship with influencers prior to the service failure and recovery compared with the reactions of customers who do not have a relationship with the influencer.

This study aims to examine the effects of the perceived warmth and competence of humanoid robots on customer tolerance of service failure through the affective response and the boundary condition of relationship norms.

Two experimental studies were conducted to investigate the effects of perceived warmth and competence of humanoid robots’ physical appearances on tolerance of service failure and the mediating role of anger. The boundary influence of relationship norms is also explored.

The results reveal that the perception of warmth (vs. competence) robot leads to less (more) anger, which significantly results in tolerance of service failure. However, customer tolerance is insignificant under exchange norms, as the undelivered service violates the expectations of both warm and competent robots.

This study provides practical guidance for hospitality managers to implement humanoid robots in a way that minimizes the negative outcomes of service failure. Managers should also think about the appropriate match of different types of humanoid robots and relationship norms in which robots will be deployed.

This study contributes to the tolerance literature by taking a social cognition perspective to investigate the effect of humanoid robots’ physical appearances on customers’ reactions to service failure. The findings also reveal that its affective mechanism lies in the effect of expectancy violations of service failure on tolerance. Furthermore, this study extends the literature on relationship norms to the influence of company factors on effective humanoid robot implementation.

This paper aims to explore the matching effect of hospitality brand image (cool vs non-cool) and service agents (Artificial intelligence [AI] vs human staff) on brand attitude, with a focus on assessing the role of feeling right as a mediator and service failure as a moderator.

This paper tested the hypotheses through three experiments and a Supplementary Material experiment, which collectively involved 835 participants.

The results indicated that the adoption of AI by cool brands can foster the right feeling and enhance consumers’ positive brand attitudes. In contrast, employing human staff did not lead to improved brand attitudes toward non-cool brands. Furthermore, the study found that service failure moderated the matching effect between service agents and cool brand images on brand attitude. The matching effect was observed under successful service conditions, but it disappeared when service failure occurred.

The findings offer practical guidance for hospitality companies in choosing service agents based on brand image. Cool brands can swiftly transition to AI, reinforcing their modern, cutting-edge image. Traditional brands may delay AI adoption or integrate it strategically with human staff.

To the best of the authors’ knowledge, this paper represents one of the first studies to address the issue of selecting the optimal service agent based on hospitality brand image. More importantly, it introduces the concept of a cool hospitality brand image as a boundary condition in the framework of AI research, providing novel insights into consumers’ ambivalent responses to AI observed in previous studies.

User-generated content (UGC) and service failure have attracted considerable marketing inquiry over the last two decades. Previous studies primarily focused on the outcome of service failure and the impact of UGC on perceived failure severity. This article departs from previous studies as it examines the moderating role of UGC on the relationship between service failure recovery (SFR) and customer–brand relationship.

Building on commitment-trust theory and from a phenomenological hermeneutical perspective, this article explores this phenomenon through the interpretation of 60 in-depth interviews with millennials from three European countries: Italy, France and the UK. An analysis of the data was conducted using a qualitative approach to understand the main constructs and relationships derived from the data.

This study conceptualises four distinct moderating characteristics of UGC in the SFR process: satisfaction with experience and brand, dissatisfaction with experience and brand, satisfaction with brand and dissatisfaction with brand. The insights from the responsiveness, empathetic response, counterfactual thinking and brand salience (RECB) framework contribute to research on UGC and shed light on the relationship between SFR and consumer–brand relationships in the fashion industry.

Overall, this study demonstrates that customer interactions with UGC significantly affect their responses to, and relationships with, a brand. The proposed framework opens up interesting avenues for future research on the moderating role of UGC on the relationship between SFR and customer–brand relationships.

With the increasing use of robots in service scenarios in hospitality industries, service failure frequently occurs during the service process, and consumers may react differently toward humanoid vs. nonhumanoid robots due to different performance expectancies. This study focuses on consumers' reactions to service failures by humanoid vs. nonhumanoid robots and the different impacts on brand forgiveness and revisit intentions through performance expectancy for different genders.

The study used a sample of 280 participants to test the moderated chain mediation model. The participants were instructed to report their performance expectancies for humanoid/nonhumanoid robots and imagine a hotel check-in scenario in which a service failure occurs. Brand forgiveness, brand revisit intention and other demographic information were assessed.

The results show that consumers have higher performance expectancy for nonhumanoid robots. This performance expectancy generates brand forgiveness and revisit intentions for male consumers but does not affect female consumers' forgiveness and revisit behaviors.

This study contributes to the literature by taking a long-term perspective to investigate the outcomes after service failure, providing evidence for pending questions in previous studies and enriching studies of gender differences. Additionally, this study provides practical implications to consider the use of anthropomorphism in robots, advocate for functional confidence in robots and target consumers across genders.

The purpose of this research is to investigate the effects of service recovery experiences on customer engagement in negative word-of-mouth (WOM) in the hotel industry and explore the psychological motives and mediating mechanisms driving consumer behavior.

A scenario-based experimental design on Qualtrics was used, with a pre-test (N = 200). The main study data were collected using Amazon's Mechanical Turk platform.

Findings reveal that negative service experiences lead to higher engagement in negative WOM compared to positive and satisfactory recovery service experiences. Even well-executed recovery efforts may not completely eliminate negative WOM. The mediating role of emotional responses is substantiated, as heightened negative service experiences result in more intense negative emotional responses, leading to increased engagement in negative WOM.

The study emphasizes the importance of service recovery strategies and the need for businesses to consistently strive for exceptional service quality. It also highlights the complexity of customer reactions to service experiences, suggesting that further research is needed to explore the factors that minimize negative WOM across various service contexts.

This study explores how chatbot gender and symbolic service recovery may improve the satisfaction of angry customers in the context of service failures. It provides a strategy for companies to deploy chatbots effectively in customer anger.

This research relies upon a systematic literature review to propose three hypotheses, and we recruit 826 participants to examine the effect of chatbot gender on angry customers through one lab study and one field study.

This research shows that female chatbots are more likely to increase the satisfaction of angry customers than male chatbots in service failure scenarios. In addition, symbolic recovery (apology vs. appreciation) moderates the effect of chatbot gender on angry customers. Specifically, male (vs. female) chatbots are more effective in increasing the satisfaction of angry customers when using the apology method, whereas female (vs. male) chatbots are more effective when using the appreciation method.

The rapid advancements in artificial intelligence technology have significantly enhanced the effectiveness of chatbots as virtual agents in the field of interactive marketing. Previous research has concluded that chatbots can reduce negative customer feedback following a service failure. However, these studies have primarily focused on the level of chatbot anthropomorphism and the design of conversational texts, rather than the gender of chatbots. Therefore, this study aims to bridge that gap by examining the effect of chatbot gender on customer feedback, specifically focusing on angry customers following service failures.

The authors' research examines how negative electronic word-of-mouth (e-WOM) alters focal customers' post-recovery justice perceptions and attitudes to determine their future behavior with the service provider. Specifically, this paper develops and tests a conceptual model to investigate how negative e-WOM alters focal customers' perceptual and attitudinal outcomes after the service recovery experience. It also examines the post-recovery effect of negative e-WOM on focal customers’ willingness to patronize the service after their recovery experience.

To test the hypotheses, two pretests and two experimental studies with created scenarios in the retail context were conducted.

The authors' findings reveal that services are judged during and well beyond failure and recovery occurrences. To maintain a loyal customer base, service managers should develop processes that address service complaints both within and beyond the service consumption stage. The authors also find that despite a favorable recovery, focal customers gravitate toward the failure experience and develop unfavorable attitudes toward the service provider, leading to likely defections.

The authors' research demonstrates the persuasive power of negative e-WOM at the post-service recovery stage, making a unique contribution to the service recovery literature. This research also contributes to the persuasive effect of negative e-WOM, demonstrating message context as a boundary condition of negative e-WOM effects. In general, the authors' work highlights the importance of understanding the psychological processes involved in eliciting the persuasive influence of negative e-WOM in the post-service recovery stage that may lead to the defection of “so-called” successfully recovered customers.

Artificial intelligence (AI) agents have been increasingly applied in the tourism and hospitality industry. However, AI service failure is inevitable. Thus, AI service recovery merits empirical investigation. This study aims to explore how AI empathic accuracy affects customers’ satisfaction in the context of AI service recovery.

A moderated mediation model was presented to describe the effect of empathic accuracy on customer satisfaction via four scenario-based experiments.

The results reveal the positive impact of AI empathic accuracy on customer satisfaction and the mediating effects of perceived agency and perceived experience. Moreover, anthropomorphism moderates the empathic accuracy effect.

This paper expanded AI service studies by exploring the significance of empathic accuracy in customer recovery satisfaction. The results provide a novel theoretical viewpoint on retaining customers following AI service failure.

人工智能（AI）设备已越来越多地应用于旅游业和酒店业。然而, AI服务失败是不可避免的。因此, AI服务补救值得进一步实证研究。本研究探讨了AI共情准确性如何影响顾客对AI服务补救的满意度。

通过四个基于场景的实验, 提出了一个有调节的中介模型来描述共情准确性对顾客满意度的影响。

结果揭示了AI共情准确性对顾客满意度有积极影响, 感知能动性和感知感受性具有中介效应。此外, 拟人化调节了共情准确性的效应。

本文通过探讨共情准确性在顾客服务补救满意度中的作用, 拓展了AI服务研究。研究结果为AI服务失败后如何留住顾客提供了新的理论视角。

Las agentes de inteligencia artificial (IA) se aplican cada vez más en el sector del turismo y la hostelería. Sin embargo, los fallos de los servicios de IA son inevitables. Por lo tanto, la recuperación de servicios de IA merece una investigación empírica. Esta investigación explora cómo la precisión empática de la IA afecta a la satisfacción de los clientes con la recuperación del servicio de IA.

Se presentó un modelo de mediación moderado para describir el efecto de la precisión empática en la satisfacción del cliente mediante cuatro experimentos basados en escenarios.

Los resultados revelan el impacto positivo de la precisión empática de la IA en la satisfacción del cliente y los efectos mediadores de la agencia percibida y la experiencia percibida. Además, el antropomorfismo modera el efecto de la precisión empática.

Este artículo amplía los estudios sobre los servicios de IA al investigar el papel de la precisión empática en la satisfacción del cliente. Los resultados aportan un punto de vista teórico novedoso sobre la retención de clientes tras el fallo de un servicio de IA.