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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.

This study examines the impact of physical environmental factors on customer retention in fast-food restaurants/cafeterias. Furthermore, this study examines the mediating effect of customers’ positive emotions on this relationship.

In this study, the stimulus-organism-response model developed by Mehrabian and Russell (1974) is applied within the context of the fast-food restaurant/cafeteria industry. Data were collected from a sample of 250 consumers who have patronized establishments within this industry. The assessment model entailed employing the Partial Least Squares Structural Equation Modeling (PLS-SEM) approach, which involved a two-stage procedure: measurement model and structural model evaluation.

The study’s results underscore the key role of customer positive emotions in influencing customer retention. Notably, physical environmental factors, encompassing elements like table setting, layout, and service staff, emerge as significant determinants in shaping these positive emotions. Furthermore, the findings indicate that these physical environmental factors exert a direct influence on customer retention, with customer positive emotions acting as mediator role in the relationship between these factors and retention.

This study is the first to use physical environment, customer emotions and customer retention variables together in the fast food restaurant/cafeteria industry.

Digital voice assistants (DVAs) are revolutionising consumers’ interactions with technology and businesses. Whilst research on the adoption of these devices is rapidly expanding, few have explored post-adoption behaviour. To fill this gap, we investigate how functionality and human-like features shape customers’ emotions, engagement and loyalty towards DVAs.

The data were collected through a self-administered online survey from 509 DVA users. Structural equation modelling was employed for data analysis.

The results reveal that distinct human-like and functional factors of DVA independently explain customers’ positive emotions and engagement with DVAs. Positive emotions and engagement significantly impact customer loyalty to DVAs. The study shows that localisation of DVAs has a significant positive moderating influence on the service experience-customer engagement relationship but a negative moderating influence on the anthropomorphism-customer engagement relationship.

Unlike previous research, this study contributes to the literature by delving into post-adoption phenomena. It explains how DVAs’ human-like and functional attributes drive customers’ positive emotional responses, engagement and loyalty towards DVAs. The findings not only unveil new insights into the moderating role of localisation but also provide a crucial understanding regarding the boundary conditions of the influence of anthropomorphism and service experience on customer engagement.

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.

The rise of artificial intelligence (AI) applications has driven enterprises to provide many intelligent services to consumers. For instance, customers can use chatbots to make relevant inquiries and seek solutions to their problems. Despite the development of customer service chatbots years ago, they require significant improvements for market recognition. Many customers have reported negative experiences with customer service chatbots, contributing to resistance toward their use. Therefore, this study adopts the innovation resistance theory (IRT) perspective to understand customers’ resistance to using chatbots. It aims to integrate customers’ negative emotions into a predictive behavior model and examine users’ functional and psychological barriers.

In this study, we collected data from 419 valid individuals and used structural equation modeling to analyze the relationships between resistance factors and negative emotions.

The results confirmed that barrier factors affect negative emotions and amplify chatbot resistance influence. We discovered that value and risk barriers directly influence consumer use. Moreover, both functional and psychological barriers positively impact negative emotions.

This study adopts the innovation resistance theory perspective to understand customer resistance to using chatbots, integrates customer negative emotions to construct a predictive behavior model and explores users’ functional and psychological barriers. It can help in developing online customer service chatbots for e-commerce.

Previous research has studied interactive value formation (IVF) using resource- or practice-based approaches but has neglected the role of emotions. This article aims to show how emotions are correlated in problematic social media interactions and explore their role in IVF.

By combining a text mining algorithm, nonparametric Spearman's rho and thematic qualitative analysis in an explanatory sequential mixed-method design, the authors (1) categorize customers' comments as positive, neutral or negative; (2) pinpoint peaks of negative comments; (3) classify problematic interactions as detrimental, contradictory or conflictual; (4) identify customers' main positive (joy, trust and surprise) and negative emotions (anger, dissatisfaction, disgust, fear and sadness) and (5) correlate these emotions.

Despite several problematic social interactions, the same pattern of emotions appears but with different intensities. Additionally, value co-creation, value no-creation and value co-destruction co-occur in a context of problematic social interactions (peak of negative comments).

This study provides new insights into the effect of customers' emotions during IVF by studying the links between positive and negative emotions and their effects on different sorts of problematic social interactions.

The purpose of this paper is to expand and analyze deeply customer emotions, concretize the levels of positive or negative emotions with the aim of using machine learning methods, and build a model to identify customer emotions.

The study proposed a customer emotion detection model and data mining method based on the collected dataset, including 80,593 online reviews on agoda.com and booking.com from 2009 to 2022.

By discerning specific emotions expressed in customers' comments, emotion detection, which refers to the process of identifying users' emotional states, assumes a crucial role in evaluating the brand value of a product. The research capitalizes on the vast and diverse data sources available on hotel booking websites, which, despite their richness, remain largely unexplored and unanalyzed. The outcomes of the model, pertaining to the detection and classification of customer emotions based on ratings and reviews into four distinct emotional states, offer a means to address the challenge of determining customer satisfaction regarding their actual service experiences. These findings hold substantial value for businesses operating in this domain, as the findings facilitate the evaluation and formulation of improvement strategies within their business models. The experimental study reveals that the proposed model attains an exact match ratio, precision, and recall rates of up to 81%, 90% and 90%, respectively.

The study has yet to mine real-time data. Prediction results may be influenced because the amount of data collected from the web is insufficient and preprocessing is not completely suppressed. Furthermore, the model in the study was not tested using all algorithms and multi-label classifiers. Future research should build databases to mine data in real-time and collect more data and enhance the current model.

The study's results suggest that the emotion detection models can be applied to the real world to quickly analyze customer feedback. The proposed models enable the identification of customers' emotions, the discovery of customer demand, the enhancement of service, and the general customer experience. The established models can be used by many service sectors to learn more about customer satisfaction with the offered goods and services from customer reviews.

The research paper helps businesses in the hospitality area analyze customer emotions in each specific aspect to ensure customer satisfaction. In addition, managers can come up with appropriate strategies to bring better products and services to society and people. Subsequently, fostering the growth of the hotel tourism sector within the nation, thereby facilitating sustainable economic development on a national scale.

This study developed a customer emotions detection model for detecting and classifying customer ratings and reviews as 4 specific emotions: happy, angry, depressed and hopeful based on online booking hotel websites agoda.com and booking.com that contains 80,593 reviews in Vietnamese. The research results help businesses check and evaluate the quality of their services, thereby offering appropriate improvement strategies to increase customers' satisfaction and demand more effectively.

Although service researchers have long suggested that customer mistreatment adversely impacts service employees' outcomes, statistical integration of current empirical findings has been lacking. This meta-analysis aims to review and statistically synthesize the state of research on the relationship between customer mistreatment and service employees' affective, attitudinal and behavioral outcomes.

The authors included 221 effect sizes of 135 independent samples from 119 primary studies (N = 47,964). The authors used a meta-analytic approach to quantitatively review the relationship between customer mistreatment and service employees' affective, attitudinal and behavioral outcomes. Meta-analysis structural equation modeling was used to explore the mediation mechanism of service employees' affective outcomes on the relationships between customer mistreatment and employees' attitudinal and behavioral outcomes. Meta-regression was applied to explore the impact of contextual-level moderators (i.e. service provider type and service delivery mode) on these relationships. Furthermore, we compared the effects of customer mistreatment with the effects of other organizational-related factors on some commonly measured employee outcomes.

The results show that customer mistreatment has a significant negative impact on service employees' affective outcomes (i.e. negative emotions), attitudinal outcomes (i.e. job satisfaction, organizational commitment, work engagement and turnover intention) and behavioral outcomes (i.e. job performance, surface acting and emotional labor). Additionally, service employees' negative emotions mediate the association between customer mistreatment and employees' job satisfaction, turnover intention, surface acting and emotional labor. Furthermore, the relationships between customer mistreatment and service employees' negative emotions and job performance are influenced by a contextual-level moderator (i.e. service delivery mode).

The authors contribute to the literature by providing robust meta-analytic estimates of the effects of customer mistreatment on a variety of service employees' affective, attitudinal and behavioral outcomes, as well as the different magnitudes of the effect sizes between customer mistreatment and other job-related and personality-related factors by quantifying the true variability of the effect sizes. The authors draw on current theories underpinning customer mistreatment to test a theoretical model of the mediation mechanism of service employees' affective outcomes (i.e. service employees' negative emotions) on the relationships between customer mistreatment and employees' attitudinal and behavioral outcomes. The authors explore the effects of two contextual-level factors (i.e. service provider types and service delivery mode) related to the service delivery context that may account for the variability of effect sizes across empirical studies.

Consumers' perceptions of different aspects pertaining to servicescape and social servicescape at restaurants have received little consideration in the hospitality literature. To fill this gap, the authors develop a model that conceptualizes and empirically examines the impact of dissimilar attributes in restaurants on the development of negative emotions and the influence of negative emotions on consumers' dissatisfaction, which in turn determines consumers' behavioral intentions.

The authors used the moderating impact of restaurant attribute performance to support the link between negative emotions and dissimilar attributes. To achieve the study's goals, the authors conducted two investigations, Study 1 and Study 2, in Bangladesh and China, respectively. For study 1, 600 data were obtained from local Bangladeshi consumers, while for study 2, 396 foreign customers in China were surveyed. The collected data were examined by using Structural Equation Modeling (SEM) approach. The authors utilized IBM Analysis of Moment Structure (AMOS), version 24.0.

Both studies 1 and 2 found that dissimilar restaurant attributes had significant positive effects on the development of negative emotions, positive effects of negative emotions on consumer dissatisfaction and a positive influence of consumer dissatisfaction on consumers' behavioral intentions. Results of both studies 1 and 2 also showed that restaurant attributes performance positively moderate the relationships between dissimilar attributes and negative emotions.

The study's empirical results contribute to the body of knowledge in the domains of tourism, consumer psychology and consumer behavior. The study's findings can assist restaurant managers in better understanding how different features related to the servicescape and social servicescape dimensions cause unpleasant emotions and, as a result, influence consumer behavioral intentions.

No preceding research has looked at the link between dissimilar features and negative emotions in the restaurant setting to the authors' knowledge. Also, no previous research has looked at the moderating consequence of restaurant attributes in the association between dissimilar attributes and negative emotions. This research aims to fill those knowledge gap.

This study aims to discern and refine the role of emotional intelligence (EI) in the development of customer orientation among banking employees in Morocco. This analysis seeks to enhance understanding about the significance of this emotional skill within the Moroccan banking sector.

The research embraces an interpretivist philosophical perspective to gain insight into the subjective meanings and experiences of study participants. The methodology employed is qualitative, involving data collected from semi-structured interviews conducted with 21 front-office bank employees. The analysis of the data was conducted through employing thematic analysis.

The findings of this study conclude that emotional intelligence fosters and stimulates customer orientation for bank employees. The perception, understanding and effective management of emotions – both those of the contact personnel and their customers – enable employees to better comprehend customer reactions. They experience heightened empathy through the impact of accumulated professional experience, adapt their behaviors according to the emotional state of the customer, maintain a positive relationship with them and ultimately gain their confidence.

This study offers clear theoretical explanations and conceptualizations that have identified and linked pertinent literature on the topic. It focuses on a salient subject, investigating how emotional intelligence influences the customer-oriented behavior of front-office bank employees. Notably, this study represents one of the first attempts to explore this relationship within the Moroccan context. As a result, it contributes to the enhancement of managerial practices and human resource policies, thereby fostering a more productive and harmonious working environment.