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Purpose – We aim to elucidate the influence of leaders' emotion expressions on the social distance between leaders and followers in face-to-face and digital communication.

Design/methodology/approach – Literature review

Findings – Following functional theories on emotions, leaders' expressions of socially engaging emotions (e.g., guilt, happiness, gratitude, and compassion) lower social distance. Leaders' expressions of socially disengaging emotions (e.g., anger, contempt, disgust, and pride) increase social distance. In digital communication, we propose that the effect of socially engaging and disengaging emotions depends on the social presence that is provided by the different digital communication media.

Practical implication – Based on our theoretical model, we derive implications for (1) leaders' use of face-to-face communication, (2) the importance of digital communication with high social presence, (3) leaders' use of digital communication as a tool for emotion regulation, and (4) coping strategies when communicating via digital means with low social presence.

In this overview, the editors trace the history of 10 books they have helmed in what has become the legacy of the Emonet conferences. From the seeds planted in 1998 by a small group of international scholars assembled together at the first Emonet conference, the shift of the study of emotions in organizational studies from the almost “undiscussable” to mainstream scholarship is traced. Following this historical analysis, the story of “What have we learned? Ten years on,” the latest volume in the Emonet book series, is given. In a brief summary of each chapter in the current edition, the editors draw attention to eight topic areas to showcase the remarkable and broad-ranging advances in the field of organization studies that have been enabled by attention to the role of emotions in theory and practice in 10 years since the first publication in the book series. From advances in our knowledge and understanding of work, workers and consumers, to team behavior, leader-member exchange, and In Extremis work contexts, and methodological contributions in the assessment of noncognitive traits through to advances in knowledge of positive work environments, the reader is left in no doubt that organizational scholarship and practice has been deeply enriched through bringing emotions center stage.

The applications of Artificial Intelligence (AI) in various areas of professional and knowledge work are growing. Emotions play an important role in how users incorporate a technology into their work practices. The current study draws on work in the areas of AI-powered technologies adaptation, emotions, and the future of work, to investigate how knowledge workers feel about adopting AI in their work.

We gathered 107,111 tweets about the new AI programmer, GitHub Copilot, launched by GitHub and analysed the data in three stages. First, after cleaning and filtering the data, we applied the topic modelling method to analyse 16,130 tweets posted by 10,301 software programmers to identify the emotions they expressed. Then, we analysed the outcome topics qualitatively to understand the stimulus characteristics driving those emotions. Finally, we analysed a sample of tweets to explore how emotional responses changed over time.

We found six categories of emotions among software programmers: challenge, achievement, loss, deterrence, scepticism, and apathy. In addition, we found these emotions were driven by four stimulus characteristics: AI development, AI functionality, identity work, and AI engagement. We also examined the change in emotions over time. The results indicate that negative emotions changed to more positive emotions once software programmers redirected their attention to the AI programmer's capabilities and functionalities, and related that to their identity work.

Overall, as organisations start adopting AI-powered technologies in their software development practices, our research offers practical guidance to managers by identifying factors that can change negative emotions to positive emotions.

Our study makes a timely contribution to the discussions on AI and the future of work through the lens of emotions. In contrast to nascent discussions on the role of AI in high-skilled jobs that show knowledge workers' general ambivalence towards AI, we find knowledge workers show more positive emotions over time and as they engage more with AI. In addition, this study unveils the role of professional identity in leading to more positive emotions towards AI, as knowledge workers view such technology as a means of expanding their identity rather than as a threat to it.

Drawing on appraisal theories, this paper aims to attempt to demonstrate how augmented reality (AR) strengthens relationships in the cognitive-affective-conative mechanism to shape visitors' recommendation of awe-inspiring heritage experiences.

A mixed-method approach was undertaken. First, an exploratory pre-study (n = 98) analyzed the multidimensionality of awe appraisals, eliciting the most salient dimensions in tourism experiences for informing the main study. Subsequently, an on-site survey was used to examine the relationships in the cognitive-affective-conative sequence, based on 316 questionnaires collected at a highly acclaimed religious heritage equipped with a multisensory AR activity.

Two distinct appraisals underlined in the awe concept were elicited during the exploratory phase: altered emotions (aggregating positive self-transformative dimensions) and the need for accommodation. The survey results demonstrated that the influence of authenticity on recommendation is shaped by emotions. Altered emotions hold a central mediating role and are differentiated from other discrete emotions. AR and the need for accommodation are revealed as contingencies that regulate the mediated mechanism.

This paper provides the hospitality and tourism stakeholders with a better understanding of how immersive technologies can be used for boosting recommendations, by enhancing the emotional responses toward authenticity perceptions.

This study introduces a novel conceptual framework to broaden our understanding of how cognitive and affective mental processes are linked to visitors’ recommendations, and how AR amplifies the outcomes. To the best of the authors’ knowledge, it is the first research to distinguish and document the specific effects of two different types of awe appraisal.

Using sentiment analysis (SA), this study aims to examine the impact of COVID-19 on mental health and virtual learning experiences among 1,125 students at a public Argentinean faculty.

A study was conducted during the COVID-19 pandemic, surveying 1,125 students to gather their opinions. The survey data was analysed using text mining tools and SA. SA was used to extract the students’ emotions, views and feelings computationally and identify co-occurrences and patterns in related words. The study also examines educational policies implemented after the pandemic.

The prevalent emotions expressed in the comments were trust, sadness, anticipation and fear. A combination of trust and fear resulted in submission. Negative comments often included the words “virtual”, “virtual classroom”, “virtual classes” and “professor”. Two significant issues were identified: teachers’ inexperience with virtual classes and inadequate server infrastructure, leading to frequent crashes. The most effective educational policies addressed vital issues related to the “virtual classroom”.

Text mining and SA are valuable tools for decision-making during uncertain times, such as the COVID-19 pandemic. They can also provide insights to recover quality assurance processes at universities impacted by health concerns or external shocks.

The paper makes two main contributions: it conducts a SA to gain insights from comments and analyses the relationship between emotions and sentiments to identify optimal educational policies. The study pioneers exploring the link between emotions, policies and the pandemic at a public university in Argentina. This area of research still needs to be explored.

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.

This study aims to investigate the influence of perceived luxuriousness on consumers’ revisit intentions via the mediating effects of positive and negative emotions based on the Stimulus-Organism-Response (SOR) model. In this context, “luxuriousness” specifically refers to the richness of furnishings, including the visual allure of aesthetic design and the surrounding cues.

A quantitative approach using a survey method is employed to analyse the collected 289 data from consumers of bubble tea. Partial least squares structural equation modelling is chosen as the main analytical approach to examine the research model.

The results showed that perceived luxuriousness has a significant positive influence on positive emotion and a significant negative influence on negative emotion. Furthermore, positive emotion positively affects revisit intentions, whereas negative emotion negatively affects revisit intentions. Positive emotion mediates the relationship between perceived luxuriousness and revisit intentions, but negative emotion does not.

In terms of theoretical contributions, this study contributes to the SOR model by exploring the influence of perceived luxuriousness on revisit intentions via the mediating effects of emotions in the bubble tea context, which has not been previously examined by past studies. In terms of managerial implications, this study provides insights into how to leverage the element of luxury to encourage consumers to revisit bubble tea stores.

Despite being a seminal explanation of the workforce emotional experiences, capable of mapping the path from the antecedents to consequences, affective events theory (AET) only offers a “macrostructure” of a working environment. To date, little is known about the universal features of the work environment that may guide the understanding of imperative work aspects triggering employees’ emotions at work. Hence, the study proposes and validates that Stafford Beer’s viable system model (VSM) can provide a holistic view of the organizational work environment, enabling a comprehensive understanding of work events or factors triggering workforce emotions.

First, the VSM structural layout is used to fill in the “macrostructure” of the “working environment” in AET to diagnose the functional and relational aspects of the work and the related work events occurring within. Using a deductive approach, 31 work events were adopted to determine the impact of VSM-based work environment events on the employees’ emotional experiences and subsequent work attitudes (job satisfaction) and behaviors (citizenship behavior). To field test the proposed nexus of VSM and AET, the survey was conducted on two hundred and fifteen employees from 39 different organizations. PLS-SEM tested the explanatory power of the suggested VSM’s systemic approach for understanding the affective work environment in totality.

The findings confirmed that the VSM metalanguage provides a holistic view of the organizational functioning and social connectivity disposing of affective work events, helpful in assessing their aggregate influence on employees’ emotions and work-related outcomes.

The findings identify how employees' emotions can be triggered by everyday work operations and social relations at work, which can affect their extra-role behaviors and necessary work-related attitudes.

The study utilized Beer’s VSM framework based on the systemic principle of “holistic view” for ascertaining the affective work environment and its related features holistically, which filled in well the macrostructure of “work environment features” with micro-structures of organizational inter-related aspects which are yet to be known in AET – a seminal explanation for managing workforce emotions.

The focus in ICT development has shifted from performance to maximization of tangibility. In particular, the interests in emotion-based services increase and more attention is paid to improving the quality of service and maximizing tangibility. Hence, it is necessary to model human emotions and utilize inferred emotions for services in order to enhance the quality of emotion-based personalized services. To this end, this study aims to model personalized emotional space by means of V-A emotion model and to suggest a fuzzy-based emotion inference method.

This study takes advantage of V-A emotion model, AHP and fuzzy integral technique to implement an emotion inference based on the quantitative evaluation and systematic expression of human emotions. By means of AHP, the personalized V-A emotion model was established. Fuzzy measure and Choquet integral methods were applied for emotion inference based on the inputs of valence and arousal (V-A) values, with the results evaluated in an experiment.

Thayer's emotion model structures 12 human emotions in reference to V-A values. This structure, however, may differ depending on individuals, and thus a method to measure and express it appropriately is necessary. This study evaluates the relative weight of 12 emotions based on AHP and regularizes them to compose the personalized V-A emotional space by drafting the V-A model. In addition, fuzzy integral-based emotion inference has been tested in reference to the V-A values on the established V-A emotion space.

Fuzzy integral-based emotion inference has been tested in reference to the V-A values on the established personalized emotion space.

Highlights the power of positive emotions in helping to build individual and organizational success.

Explores the meaning of positive emotions, how they can be promoted at individual and organizational level and the benefits they can bring to the individual and organization.

Advances the view that positive emotions can benefit physical health, mental well-being and the ability to flourish, creativity, resilience, the mood of others, positive memories and relationships.

Argues that the positive emotions of the workforce can improve the organizational culture and improve organizational performance.

Demonstrates that around three positive emotions are needed to balance out each negative emotion and shows that positive emotions can be stimulated through having new experiences and through acts of kindness and gratitude.

Extends psychological thinking on positive emotions to the workforce and workplace.