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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.

Information Systems research on emotions in relation to using technology largely holds essentialist assumptions about emotions, focuses on negative emotions and treats technology as a token or as a black box, which hinders an in-depth understanding of distinctions in the emotional experience of using artificial intelligence (AI) technology in context. This research focuses on understanding employees' emotional experiences of using an AI chatbot as a specific type of AI system that learns from how it is used and is conversational, displaying a social presence to users. The research questions how and why employees experience emotions when using an AI chatbot, and how these emotions impact its use.

An interpretive case study approach and an inductive analysis were adopted for this study. Data were collected through interviews, documents review and observation of use.

The study found that employee appraisals of chatbots were influenced by the form and functional design of the AI chatbot technology and its organisational and social context, resulting in a wider repertoire of appraisals and multiple emotions. In addition to positive and negative emotions, users experienced connection emotions. The findings show that the existence of multiple emotions can encourage continued use of an AI chatbot.

This research extends information systems literature on emotions by focusing on the lived experiences of employees in their actual use of an AI chatbot, while considering its characteristics and its organisational and social context. The findings inform the emerging literature on AI.

This study seeks to understand the communication factors associated with effective social media for nonprofit organizations (NPOs). Specifically, the study investigated how interactive and emotional communication strategies influence public engagement in different ways, and how the effects differ by service-oriented and other types of NPOs.

Using computer-assisted textual and emotional analyses, the authors examined the functional interactivity, contingency interactivity and emotion elements of 301,559 tweets from the 100 largest US nonprofits. Negative binomial regression was applied to test the relationships among these elements and public engagement on Twitter (i.e. likes and retweets).

Findings revealed negative effects of functional interactivity on likes, negative effects of contingency interactivity on likes and retweets but a positive effect of functional interactivity on retweets. The findings also showed negative effects of emotion valence on likes and retweets but positive effects of emotion strength on likes and retweets. There were varying effects of interactivity and emotion on public engagement for service-oriented and other types of NPOs.

This study advances the nonprofit social media scholarship in several ways. First, this study suggests a clear yet largely ignored distinction in the effects of functional and contingency interactivity on public engagement. Second, this study is an early attempt to examine the role and impact of emotion elements in nonprofit social media success without downplaying the role of interactivity. Third, this study is one of the earliest attempts to include interaction effects for different types of NPOs. Last, this study contributes to the organizational social media use research by demonstrating the benefits of computer-assisted approaches in processing text data on social media. From a practical perspective, this study provides strategic guidelines for NPOs to design effective communication contents and improve their public engagement on social media.

Today, contactless businesses are becoming part of the “new normal” in daily life. Augmented reality-based services (ARBS) thus provide a mechanism for contactless commerce, offering customers access to sensory experiences, especially during the COVID-19 pandemic. However, privacy can be a key concern when consumers decide whether to continue using ARBS. Thus, drawing on the Appraisal Tendency Framework (ATF), the study aims to examine how augmentation quality (Aug-Q), discrete emotions (joy and frustration) and privacy perceptions influence users' ARBS continuing use intention.

A survey methodology with a well-designed online questionnaire was used for data collection. The data were analyzed using a structural equation model with Amos v. 22.0 software.

This study demonstrated that Aug-Q had a significant positive impact on joy and a significant negative impact on frustration. Additionally, joy was positively associated with the perception of privacy benefits and ARBS continuing use intention, while frustration was negatively associated with the perception of privacy benefits and ARBS continuing use intention. The results also indicate that (perceived privacy risks) PPR–benefits predict the likelihood of ARBS continuing use intention.

This study enhances understanding of users' ARBS continuing use intention from an integrative perspective based on the ATF, thus identifying the Aug-Q-induced emotions that subsequently influence privacy trade-offs and predict users' ARBS continuing use intention. The results provide evidence that privacy and emotions can be key determinants when consumers decide whether to continue using ARBS. The findings of this research may be beneficial for commercial companies in preventing the loss of ARBS users.

Despite a significant focus on customer evaluation and sentiment analysis, limited attention has been paid to discrete emotional perspective in terms of the emotionality used in text. This paper aims to extend the general-sentiment dictionary in Chinese to a restaurant-domain-specific dictionary, visualize spatiotemporal sentiment trends, identify the main discrete emotions that affect customers’ ratings in a restaurant setting and identify constituents of influential emotions.

A total of 683,610 online restaurant reviews downloaded from Dianping.com were analyzed by a sentiment dictionary optimized by the authors; the main emotions (joy, love, trust, anger, sadness and surprise) that affect online ratings were explored by using multiple linear regression methods. After tracking these sentiment review texts, Latent Dirichlet Allocation (LDA) and LDA models with term frequency-inverse document frequency as weights were used to find the factors that constitute influential emotions.

The results show that it is viable to optimize or expand sentiment dictionary by word similarity. The findings highlight that love and anger have the highest effect on online ratings. The main factors that constitute consumers’ anger (local characteristics, incorrect food portions and unobtrusive location) and love (comfortable dining atmosphere, obvious local characteristics and complete supporting services) are identified. Different from previous studies, negativity bias is not observed, which poses a question of whether it has to do with Chinese culture.

These findings can help managers monitor the true quality of restaurant service in an area on time. Based on the results, restaurant operators can better decide which aspects they should pay more attention to; platforms can operate better and can have more manageable webpage settings; and consumers can easily capture the quality of restaurants to make better purchase decisions.

This study builds upon the existing general sentiment dictionary in Chinese and, to the best of the authors’ knowledge, is the first to provide a restaurant-domain-specific sentiment dictionary and use it for analysis. It also reveals the constituents of two prominent emotions (love and anger) in the case of restaurant reviews.

The fundamental challenge for project management is dealing with people and their feelings. While there has been sporadic attention to the importance of emotions in project work, project management practices tend to neglect the role of emotions and emotional reflexivity. The authors use a symbolic interaction framework to present an in-depth exploration of emotions and emotional reflexivity in projects.

Empirical data was gathered in 19 semi-structured interviews with diverse project managers to assess their experience of emotion (15 male, 4 female, early 20s to late 50s, 3–38 years of expertise). Transcribed interviews were thematically analysed using a sociology of emotions informed, grounded theory, interactional framework.

The data revealed that emotional states are framed by factors specific to project management, including organisational change, project constraints and dealing with stakeholders. Explicitly managing emotions improved team engagement and project performance by acting as a catalyst for engaging in reflective practice and intuitive decision making.

Given the widely held misconceptions of emotion as maladaptive, project management education must focus on empathy in communication and leadership if practitioners are to master valuable soft skills. Techniques for emotional reflection and learning feeling lessons must be incorporated into practice.

The authors contribute to the emerging understanding that emotions matter in project management. The authors demonstrate the centrality of emotions in projects and the substantial impact they have on the wellbeing of practitioners and staff. Emotional reflexivity in practice, which is widely acknowledged yet tends to be ignored, is an essential part of the project manager's toolkit.

This paper aims, first, to examine artificial intelligence (AI) vs human delivery of financial advice; second, to examine the serial mediating roles of emotion and trust between AI use in the financial service industry and their impact upon marketing outcomes including word of mouth (WOM) and brand attitude; and third, to examine how political ideology moderates' consumers' reactions to AI financial service delivery.

A review of the extant literature is conducted, yielding seven hypotheses underpinned by affect-as-information theory. The hypotheses are tested via three online scenario-based experiments (n = 801) using Process Macro.

The results of the three experiments reveal consumers experience lower levels of positive emotions, specifically, affection, when financial advice is provided by AI in comparison to human employees. Secondly, across the three experiments, conservative consumers are shown to perceive somewhat similar levels of affection in financial advice provided by AI and human employees. Whereas liberal consumers perceive significantly lower levels of affection when serviced by AI in comparison to conservatives and human employee financial advice. Thirdly, results reveal affection and trust to be serial mediators which explain consumers' WOM and brand attitudes when financial services are provided by AI. Fourthly, the investment type plays an important role in consumers’ reactions to the use of AI.

To the best of the authors’ knowledge, this research is one of the first to study political ideology as a potential moderator of consumers’ responses to AI in financial services, providing novel contributions to the literature. It further contributes unique insights by examining emotional responses to AI and human financial advice for different amounts and types of investments using a comprehensive approach of examining both valence and discrete emotions to identify affection as a key explanatory emotion. The study further sheds insights relating to how emotions (affection) and trust mediate the relationship between AI and WOM, and brand attitudes, demonstrating an affect-attitude psychological sequence that explains consumers’ reactions to AI in financial services.

This paper aims to construct a spatio-temporal emotional framework (STEF) for digital humanities from a quantitative perspective, applying knowledge extraction and mining technology to promote innovation of humanities research paradigm and method.

The proposed STEF uses methods of information extraction, sentiment analysis and geographic information system to achieve knowledge extraction and mining. STEF integrates time, space and emotional elements to visualize the spatial and temporal evolution of emotions, which thus enriches the analytical paradigm in digital humanities.

The case study shows that STEF can effectively extract knowledge from unstructured texts in the field of Chinese Qing Dynasty novels. First, STEF introduces the knowledge extraction tools – MARKUS and DocuSky – to profile character entities and perform plots extraction. Second, STEF extracts the characters' emotional evolutionary trajectory from the temporal and spatial perspective. Finally, the study draws a spatio-temporal emotional path figure of the leading characters and integrates the corresponding plots to analyze the causes of emotion fluctuations.

The STEF is constructed based on the “spatio-temporal narrative theory” and “emotional narrative theory”. It is the first framework to integrate elements of time, space and emotion to analyze the emotional evolution trajectories of characters in novels. The execuability and operability of the framework is also verified with a case novel to suggest a new path for quantitative analysis of other novels.

Coronavirus disease 2019-related fake news consistently appears on social media. This study uses appraisal theory to analyze the impact of such rumors on individuals' emotions, motivations, and intentions to share fake news. Furthermore, the concept of psychological distance and construal level theory are used in combination with appraisal theory to compare toilet paper shortages and celebrity scandal rumors.

Data collected from 299 Taiwanese respondents to 150 toilet paper shortage-related and 149 celebrity gossip-related questionnaires were processed using partial least squares regression and multigroup analysis.

In both cases, surprise is felt most intensely. However, unlike in the celebrity fake news scenario, worry plays a prominent role in driving the altruistic sharing motivation related to the toilet paper shortage rumor. Furthermore, while emotional attributes (basic or self-conscious, concrete, or abstract) serve as a guide for how emotions change with psychological distance, the degree to which an emotion is relevant to the fake news context is key to its manifestation.

This study examines the impact of individuals' emotions on their motivations and intention to share fake news, applying the appraisal theory and the psychological distance concept in a single study to fake news sharing intention. It evaluates the relationship between psychological distance and emotions, revealing that it is not absolute and need not necessarily shift according to psychological distance change; rather, the relationship is context-sensitive.