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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 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.

The online brand advocacy behaviors of omnichannel shoppers, who mainly rely on integrated brick-and-mortar stores (recognized as a vital channel for delivering a seamless shopping experience and meeting the dynamic needs of the shoppers), are still understudied. This study aims to investigate how integrated store service quality (ISSQ) may elicit both positive and negative emotions that contribute to a memorable omnichannel shopping experience and have an impact on shoppers' attachment to the store, leading to their exhibition of online brand advocacy behaviors (brand defense, brand positivity, brand knowledge sharing and virtual positive expression).

The study is a cross-sectional, descriptive and quantitative investigation. The research participants were chosen using a purposive sampling technique. Using a validated self-administered questionnaire, data were gathered from 886 Indian omnichannel shoppers who often purchase at the integrated brick-and-mortar store. The proposed conceptual model was tested using Smart PLS software for partial least squares-structural equation modeling.

The results indicate that positive and negative emotions mediate the relationship between ISSQ and memorable omnichannel shopping experiences, subsequently impacting omnichannel shoppers' attachment to the store and leading to online brand advocacy behaviors. The relationship strength perceived by shoppers significantly positively moderated the relationship between store attachment and different online brand advocacy behaviors (brand defense, brand positivity, brand knowledge sharing and virtual positive expression).

The study relied upon single cross-sectional data from the Indian population, where omnichannel retailing is still nascent.

This study addresses the need to investigate the different emotions that arise while evaluating service quality in omnichannel retail purchase journeys leading to memorable shopping experiences. Emphasizing post-purchase behaviors like different online brand advocacy behaviors (brand defense, brand positivity, brand knowledge sharing and virtual positive expression), this study is the first to show that ISSQ might affect four different OBAs through memorable omnichannel shopping experience and the shopper's sense of attachment to the store. The moderating effect of relationship strength perceived by shoppers with the retailer on a few proposed hypotheses was also tested to give managerial recommendations.

The purpose of this study is to conduct a thematic content analysis of facial emotion recognition (FER) research within the context of the hospitality and tourism industry. Through this analysis, the study aims to identify key themes, trends and implications of the utilization of FER technology in enhancing customer emotions and experiences within hospitality and tourism settings.

This is qualitative research that utilizes thematic content analysis. The research data were obtained from the Scopus database. A total of 45 articles (titles, abstracts and keywords) were coded into MAXQDA and VOSWiever programs for data analyses and mapping.

Based on the analyses, the predominant term used in titles was emotion, indicating its centrality in the research domain. Moreover, the most prevalent concepts in this field were emotion and experience. Notably, facial emotion recognition emerged as the most frequently utilized term within this context. Within the hospitality and tourism industry, FER was primarily employed within the travel sub-branch. Finally, the research culminated in the visualization of the theoretical framework and conceptual background, offering a comprehensive overview of the field.

There is a growing demand for using FER technology specifically within the hospitality and tourism industry context; therefore, growing scientific research has been conducted on this topic recently. By conducting a thematic content analysis, this study uncovered novel insights into the utilization of this technology to enhance customer emotions and experiences, thereby contributing to a deeper understanding of its potential implications and applications within the hospitality and tourism industry.

The study aims to delve into the “phygital customer journey” (PCJ), which merges physical and digital interactions in customer experiences, using a practice-based lens to reveal the underlying dynamics of these blended encounters.

Feedback from 60 individuals established the groundwork for a qualitative analysis. They chronicled customer journeys through diaries and used UXPressia software for journey mapping. This strategy enabled a detailed exploration of the PCJ, focusing on customers’ lived experiences and perceptions.

The study presents an integrative framework for the PCJ, identifying four key elements: hybrid artefacts (the melding of digital and physical tools/interfaces), blended contexts (the seamless integration of digital and physical spaces), circular actions (the non-linear paths of customer engagement) and intertwined emotions (the complex emotional responses to phygital experiences). These elements underscore the intricate and interconnected nature of the PCJ.

This study advances the field by applying a practice-based approach to unravel the complexities of the PCJ, illuminating the nuanced interplay between digital and physical realms. This innovative lens foregrounds the significance of practices in consumer experiences, thereby contributing to a deeper academic and practical understanding of phygital integration.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

Using cognitive appraisal theory, this study aims to propose and test an integrated framework – comprising robotic service quality, robotic service authenticity, customer existential authenticity and customer revisit intention – on diners with experience using robotic technology in restaurants. The moderating role of robotic appearance is in the hypothesised relationship.

Data was gathered through a Web-based survey delivered to 428 diners who had experience using robotic services in restaurants in China. The hypotheses were analysed using a structural equation model and multi-group analysis was used to analyse the moderating effect.

The findings indicate that functional service quality positively influences robotic service authenticity and existential authenticity. However, technical service quality only affects existential authenticity, which leads to revisit intention. Robotic appearance moderated the relationship between functional service quality and service authenticity.

Restaurateurs should enhance robotic service authenticity, existential authenticity and revisit intention by improving robotic technical service collaborating with robot manufacturers and operators.

Focusing on cognitive appraisal theory, the findings serve as a starting point for investigating robotic service quality and authenticity in robotic service settings theoretically and empirically.

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 research investigates the role of customer forgiveness as the result of online service recovery transparency in predicting customer engagement. It also examines the moderating roles of timeliness and personalization in this proposed model.

An online survey study using retrospective experience sampling and a scenario-based experimental study were conducted to test the proposed hypotheses.

Customer forgiveness positively influences customer engagement and plays a mediating role in the relationship between service recovery transparency and customer engagement. Additionally, timeliness and personalization moderate the positive influence of service recovery transparency on customer forgiveness. The positive influence of service recovery transparency on customer forgiveness is more apparent when levels of timeliness and personalization decrease.

To retain focal customers' engagement after a service failure, firms must obtain their forgiveness. One of the firm's online complaint handling strategies to increase the forgiveness level of focal customers is to provide a high level of service recovery transparency (i.e. responding to their complaints in a public channel), especially when the firm is unable to respond to online complaints quickly or provide highly personalized responses.

This research provides new insights into the underlying mechanism of customer engagement by applying the concept of customer forgiveness. It also contributes to the social influence theory by applying the essence of the theory to explain how other customers' virtual presence during the online complaint handling influences the forgiveness of focal customers in order to gain their engagement. Additionally, it provides insight into the conditions under which the role of service recovery transparency can be very effective in dealing with online complaints.