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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 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.

People usually try to avoid uncertainty. Recently, however, uncertainty has become an emerging marketing tool in the hedonic product industry. In the case of blind box consumption, for example, the consumers become addicted to the uncertainty created by businesses, leading to repeat purchases and even indulgences. Previous research has, yet, to focus on the impact of uncertainty on indulgence and the role of emotions.

This paper constructs and validates a chain mediation model of uncertainty triggering indulgent consumption based on the information gap theory, positive emotion theory and uncertainty resolution theory and examines the difference between resolved and unresolved uncertainty. This study also explores differences in the impact of whether uncertainty is resolved on emotions. The uncertainty-resolved group elicited a more positive emotional response than the uncertainty-unresolved group, leading to a more indulgent consumption.

The results of three studies show that uncertainty influences indulgent consumption through curiosity and positive emotion, and that curiosity and positive emotion play separate and chain mediating roles between uncertainty and indulgent consumption, respectively. We validate our central hypothesis with questionnaires among blind box consumer groups, examining the moderating role of perceived luck and risk preferences.

The findings shed new light on firms' use of uncertainty to promote consumer purchases.

The literature currently offers only fragmentary insights into the research on the relationship between employee emotions and human resource management (HRM). Therefore, further research is essential to bridge this knowledge gap. Our study aims to identify the mediating effects of positive employee emotions and exhaustion in the relationship between HRM and employee engagement.

Drawing on the literature review findings, a conceptual model was formulated to illustrate the relationship between HRM, employee emotions and engagement. A confirmatory analysis was conducted using structural equation modelling (SEM CFA) on a sample of 1,000 employees to validate the proposed model. The data were collected in 2021, with a particular emphasis on exploring the indirect influence of HRM on engagement through positive employee emotions and exhaustion.

The quantitative research aimed to test a model depicting the relationship between HRM and employee emotions. The findings indicate the robust effect of HRM on positive employee emotions and exhaustion. The authors observed significant variation in the level of impact depending on the size of the organisation (stronger in large firms) and the sector (stronger in the public sector).

The study bridges the gap in our understanding of the link between HRM and employee emotions. It would be advisable to further explore the specific impact of individual HRM practices on both positive and negative employee emotions. It is worth extending the scope of future research to explore components of the investigated constructs as well as mediators and moderators of the relationship between HRM and employee emotions.

Social media platforms are highly visible platforms, so politicians try to maximize their benefits from their use, especially during election campaigns. On the other side, people express their views and sentiments toward politicians and political issues on social media, thus enabling them to observe their online political behavior. Therefore, this study aims to investigate user reactions on social media during the 2016 US presidential campaign to decide which candidate invoked stronger emotions on social media.

For testing the proposed hypotheses regarding emotional reactions to social media content during the 2016 presidential campaign, regression analysis was used to analyze a data set that consists of Trump’s 996 posts and Clinton’s 1,253 posts on Facebook. The proposed regression models are based on viral (likes, shares, comments) and emotional Facebook reactions (Angry, Haha, Sad, Surprise, Wow) as well as Russell’s valence, arousal, dominance (VAD) circumplex model for valence, arousal and dominance.

The results of regression analysis indicate how Facebook users felt about both presidential candidates. For Clinton’s page, both positive and negative content are equally liked, while Trump’s followers prefer funny and positive emotions. For both candidates, positive and negative content influences the number of comments. Trump’s followers mostly share positive content and the content that makes them angry, while Clinton’s followers share any content that does not make them angry. Based on VAD analysis, less dominant content, with high arousal and more positive emotions, is more liked on Trump’s page, where valence is a significant predictor for commenting and sharing. More positive content is more liked on Clinton’s page, where both positive and negative emotions with low arousal are correlated to commenting and sharing of posts.

Building on an empirical data set from Facebook, this study shows how differently the presidential candidates communicated on social media during the 2016 election campaign. According to the findings, Trump used a hard campaign strategy, while Clinton used a soft strategy.

To reveal the emotions and information needs expressed by Chinese parents of children with autism spectrum disorder (ASD) in an online forum, and their relationship.

The 10,062 data were from “Yi Lin”, China’s largest online forum for ASD. Open coding identified parents’ emotions and information needs, and a chi-squared test explored the correlation.

First, parents’ emotions were categorized into four themes: emotions about coping with their child’s care, emotions about the parents’ own behavior, emotions about social support with other parents and emotions about anticipating the future. Parents’ overall emotions were negative (72.47%), while the tendency of emotions varied among the four themes. Second, five information needs topics were expressed: intervention and training of ASD, parenting experiences, schooling issues, social interaction and support and future development. Different information needs topics contained different themes of emotions. Third, the tendency of emotions and expression of information needs were significantly correlated. Negative emotions had a statistically significant correlation in expression of information needs.

This study reveals the relationship between the emotions and information needs expressed by parents of children with ASD. The ASD forum could develop emotional support modules and functions for parents and facilitate emotional communication between parents.