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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 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.

This paper aims to use the cognitive appraisal theory to investigate the effects of brand engagement and materialism on the positive emotions of joy and excitement evoked by a product at different stages of the purchase process.

Two complementary studies were conducted to achieve the research objectives. Study 1 used a longitudinal design, examining the pre-purchase stage and a moment shortly after the purchase. Study 2 adopted a cross-sectional approach, focusing on the post-purchase stage. Relationships among variables were analyzed through hierarchical regression and moderation analyses.

The findings indicate that materialism is not significantly associated with consumers’ positive product-evoked emotions (joy and excitement) before and shortly after purchase. At these two stages, brand engagement predicts positive emotions. In the post-purchase stage, where materialism is weakly related to joy and excitement, a robust positive link exists between brand engagement and positive product-evoked emotions. Moreover, higher levels of materialism reduce the experience of joy and excitement among participants with high levels of brand engagement.

Experiencing joy and excitement is crucial in consumer behavior. However, little is known about the determinants of these emotions in the consumption context, especially concerning internal consumption-related factors and different stages of the purchase process. This paper contributes to the field by shedding light on the role of brand engagement and materialism in the experience of positive product-evoked emotions. The findings provide evidence that brand engagement significantly enhances consumers’ positive product-evoked emotions, highlighting its importance in consumer behavior research.

The purpose of this study is to answer the following research questions. Does hostility shape the undesirable attitudinal consequences of perceived organizational politics (POP)? If so, does emotional intelligence play a role in this context? To answer these questions, the author relies on the affective events theory to present and empirically investigate a moderated mediation model in which: hostility mediates the relationships between POP and both job tension and turnover intentions; and emotional intelligence moderates these relationships.

The moderated mediation model was tested among a sample of 408 employees. The data was collected in three waves.

The results revealed that hostility mediated the relationships between POP and the two undesirable attitudes explored. In addition, one of the emotional abilities included in emotional intelligence, namely, self-emotion appraisal, moderated these relationships.

Interventions designed to increase the emotional intelligence level of employees might reduce the hostility they experience in response to POP, and consequently, its harmful implications.

Among the four emotional abilities included in emotional intelligence, only self-emotional appraisal moderates the relationship between POP and hostility. Such findings imply that in some cases, a thorough understanding about one’s emotions is more effective in regulating the hostility experienced in response to organizational politics than other emotional abilities that seem more relevant in this context.

Although numerous signal modalities are available for emotion recognition, audio and visual modalities are the most common and predominant forms for human beings to express their emotional states in daily communication. Therefore, how to achieve automatic and accurate audiovisual emotion recognition is significantly important for developing engaging and empathetic human–computer interaction environment. However, two major challenges exist in the field of audiovisual emotion recognition: (1) how to effectively capture representations of each single modality and eliminate redundant features and (2) how to efficiently integrate information from these two modalities to generate discriminative representations.

A novel key-frame extraction-based attention fusion network (KE-AFN) is proposed for audiovisual emotion recognition. KE-AFN attempts to integrate key-frame extraction with multimodal interaction and fusion to enhance audiovisual representations and reduce redundant computation, filling the research gaps of existing approaches. Specifically, the local maximum–based content analysis is designed to extract key-frames from videos for the purpose of eliminating data redundancy. Two modules, including “Multi-head Attention-based Intra-modality Interaction Module” and “Multi-head Attention-based Cross-modality Interaction Module”, are proposed to mine and capture intra- and cross-modality interactions for further reducing data redundancy and producing more powerful multimodal representations.

Extensive experiments on two benchmark datasets (i.e. RAVDESS and CMU-MOSEI) demonstrate the effectiveness and rationality of KE-AFN. Specifically, (1) KE-AFN is superior to state-of-the-art baselines for audiovisual emotion recognition. (2) Exploring the supplementary and complementary information of different modalities can provide more emotional clues for better emotion recognition. (3) The proposed key-frame extraction strategy can enhance the performance by more than 2.79 per cent on accuracy. (4) Both exploring intra- and cross-modality interactions and employing attention-based audiovisual fusion can lead to better prediction performance.

The proposed KE-AFN can support the development of engaging and empathetic human–computer interaction environment.

The study aims to examine the indirect relationships via application (app) brand self-relevance emotions and self-relevance that underlie the relationships between perceived value of mobile apps and (brand) love with respect to mobile apps. The study further investigates the moderating role of user–app relationship duration in the formation process of brand love for mobile apps from a dynamic and long-term perspective.

A multiple moderated-mediation model is developed and empirically tested with a sample of 396 users of popular Chinese mobile educational apps.

The study reveals that utilitarian value exhibits positive indirect relationships with brand love for mobile apps through increased positive self-relevance emotions. All three types of perceived value of mobile apps (utilitarian, hedonic and social) affect app brand love positively via self-relevance. These three types of perceived value were found to be serially linked to brand love through self-relevance and self-relevance emotions. Furthermore, empirical evidence is found for the moderating effects of user–app relationship duration.

By testing mechanisms simultaneously in an integrative model, this study investigates the reasons for app brand love that attract a user into a lasting relationship with an app and extends knowledge of the app brand love building process in inducing strong and positive brand–self connections. Our study also makes practical contributions by offering insights into delivering the most desired benefits to mobile app users according to different contextual conditions, in order to attract and retain users in a more cost-effective manner.