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In this chapter, we outline the background to the present volume, including the history of the Emonet group and the origins of the book series. We argue that the volume subtitle “A coat of many colors” reflects the diversity of approaches to studying emotion in organizational settings. We then provide a summary of the 11 contributor chapters in the volume, which illustrates the wide range of emotion-related topics covered in the volume.

This chapter provides an overview of the chapters in the volume, and gives a brief summary of each chapter, explaining how each fits into the overall theme of the volume and listing the key contribution of each chapter.

The introduction concludes with a summary of main findings of the chapters, and how they shape the future of the field, concluding that, since emotion-related topics nowadays are so integrated into the mainstream literature in organizational behavior and organization theory, maybe there is no longer a need to address emotions as a stand-alone topic.

The chapters in this volume address a wide range of emotion-related topics in the fields of organizational behavior and organization theory and point to the future of research in this field.

The purpose of this study is to comprehend how AI aids marketers in engaging customers and generating value for the company by way of customer engagement (CE). CE is a popular area of research for scholars and practitioners. One area of research that could have far-reaching ramifications with regard to strengthening CE is artificial intelligence (AI). Consequently, it becomes extremely important to understand how AI is helping the marketer reach customers and create value for the firm via CE.

A detailed approach using both systematic review and bibliometric analysis was used. It involved identifying key research areas, the most influential authors, studies, journals, countries and organisations. Then, a comprehensive analysis of 50 papers was carried out in the four identified clusters through co-citation analysis. Furthermore, a content analysis of 42 articles for the past six years was also conducted.

Emerging themes explored through cluster analysis are CE concepts and value creation, social media strategies, big data innovation and significance of AI in tertiary industry. Identified themes for content analysis are CE conceptualisation, CE behaviour in social media, CE role in value co-creation and CE via AI.

CE has emerged as a topic of great interest for marketers in recent years. With the rapid growth of digital media and the spread of social media, firms are now embarking on new online strategies to promote CE (Javornik and Mandelli, 2012). In this review, the authors have thoroughly assessed multiple facets of prior research papers focused on the utilisation of AI in the context of CE. The existing research papers highlighted that AI-powered chatbots and virtual assistants offer real-time interaction capabilities, swiftly addressing inquiries, delivering assistance and navigating customers through their experiences (Cheng and Jiang, 2022; Naqvi et al., 2023). This rapid and responsive engagement serves to enrich the customer’s overall interaction with the business. Consequently, this research can contribute to a comprehensive knowledge of how AI is assisting marketers to reach customers and create value for the firm via CE. This study also sheds light on both the attitudinal and behavioural aspects of CE on social media. While existing CE literature highlights the motivating factors driving engagement, the study underscores the significance of behavioural engagement in enhancing firm performance. It emphasises the need for researchers to understand the intricate dynamics of engagement in the context of hedonic products compared to utilitarian ones (Wongkitrungrueng and Assarut, 2020). CEs on social media assist firms in using their customers as advocates and value co-creators (Prahalad and Ramaswamy, 2004; Sawhney et al., 2005). A few of the CE themes are conceptual in nature; hence, there is an opportunity for scholarly research in CE to examine the ways in which AI-driven platforms can effectively gather customer insights. As per the prior relationship marketing studies, it is evident that building relationships reduces customer uncertainty (Barari et al., 2020). Therefore, by using data analysis, businesses can extract valuable insights into customer preferences and behaviour, equipping them to engage with customers more effectively.

The rapid growth of social media has enabled individuals to articulate their thoughts, opinions and emotions related to a brand, which creates a large amount of data for VCC. Meanwhile, AI has emerged as a radical way of providing value content to users. It expands on a broader concept of how software and algorithms work like human beings. Data collected from customer interactions are a major prerequisite for efficiently using AI for enhancing CE. AI not only reduces error rates but, at the same time, helps human beings in decision-making during complex situations. Owing to built-in algorithms that analyse large amounts of data, companies can inspect areas that require improvement in real time. Time and resources can also be saved by automating tasks contingent on customer responses and insights. AI enables the analysis of customer data to create highly personalised experiences. It can also forecast customer behaviour and trends, helping businesses anticipate needs and preferences. This enables proactive CE strategies, such as targeted offers or timely outreach. Furthermore, AI tools can analyse customer feedback and sentiment across various channels. This feedback can be used to make necessary improvements and address concerns promptly, ultimately fostering stronger customer relationships. AI can facilitate seamless engagement across multiple digital channels, ensuring that customers can interact with a brand through their preferred means, be it social media, email, or chat. Consequently, this research proposes that practitioners and companies can use analysis performed by AI-enabled systems on CEB, which can assist companies in exploring the extent to which each product influences CE. Understanding the importance of these attributes would assist companies in developing more memorable CE features.

This study examines how prominent CE and AI are in academic research on social media by identifying research gaps and future developments. This research provides an overview of CE research and will assist academicians, regulators and policymakers in identifying the important topics that require investigation.

This paper aims to explore the potential of implementing an action competence teaching model as a framework for achieving sustainable development goals (SDGs) in higher education. The paper seeks to draw insights from the students on the potential of this teaching model.

The study adopted a quantitative research design in exploring the potential of an action competence teaching model. This study used self-report measures to obtain insights into the objective of the study.

The action competence teaching model was seen as useful in equipping students with knowledge about a problem, confidence and willingness to act. However, some issues like the design of the projects, teamwork and instructional practices were identified and discussed.

To the best of the authors’ knowledge, this study is the first to implement an action competence teaching model to help draw insights from students on its potential. This paper documents certain aspects of action competence that require attention before being implemented in higher education. This information so far lacking in scientific literature contributes to ongoing discussions on SDGs while unveiling strengths and weaknesses to be considered.

This research employed two neurophysiological techniques (electroencephalograms (EEG) and galvanic skin response (GSR)) and machine learning algorithms to capture and analyze relationship-oriented leadership (ROL) and task-oriented leadership (TOL). By grounding the study in the theoretical perspectives of transformational leadership and embodied leadership, the study draws connections to the human body's role in activating ROL and TOL styles.

EEG and GSR signals were recorded during resting state and event-related brain activity for 52 study participants. Both leadership styles were assessed independently using a standard questionnaire, and brain activity was captured by presenting subjects with emotional stimuli.

ROL revealed differences in EEG baseline over the frontal lobes during emotional stimuli, but no differences were found in GSR signals. TOL style, on the other hand, did not present significant differences in either EEG or GSR responses, as no biomarkers showed differences. Hence, it was concluded that EEG measures were better at recognizing brain activity associated with ROL than TOL. EEG signals were also strongest when individuals were presented with stimuli containing positive (specifically, happy) emotional content. A subsequent machine learning model developed using EEG and GSR data to recognize high/low levels of ROL and TOL predicted ROL with 81% accuracy.

The current research integrates psychophysiological techniques like EEG with machine learning to capture and analyze study variables. In doing so, the study addresses biases associated with self-reported surveys that are conventionally used in management research. This rigorous and interdisciplinary research advances leadership literature by striking a balance between neurological data and the theoretical underpinnings of transformational and embodied leadership.

For human resource (HR) managers, the harmful outcomes of employees’ ruminations about external crises, such as a pandemic, represent important, timely concerns. This research postulates that employees’ perceptions of pandemic threats might diminish the extent to which they engage in change-oriented voluntarism at work. This negative connection may be attenuated by employees’ access to two personal (work-related self-efficacy and organization-based self-esteem) and two relational (goal congruence and interpersonal harmony) resources.

The theoretical predictions are tested with survey data collected among employees who work in a banking organization in Portugal.

Persistent negative thoughts about a pandemic undermine discretionary efforts to alter and enhance the organizational status quo, but this detrimental effect is mitigated when employees (1) feel confident about their work-related abilities, (2) have a positive self-image about their organizational functioning, (3) share a common mindset with coworkers with respect to work goals and (4) maintain harmonious relationships with coworkers.

This study pinpoints several ways HR managers can reduce the danger that employees’ worries about life-threatening crises may lead to complacent responses that, somewhat paradoxically, might undermine their ability to alleviate the suffered hardships.

The findings contribute to research on the impact of external crisis situations on organizations by providing an explanation of why employees may avoid productive, disruptive work activities, contingent on their access to complementary resources.

The purpose of this paper is to examine how different types of user experience in TikTok impact purchase intention via commitment to the influencer and commitment to the platform, with customer type included to determine moderating effects. Three types of user experience are considered: information experience, entertainment experience and parasocial-relationship-based experience.

This study collected 458 valid questionnaires from TikTok users, employing the structural equation modeling approach to examine the proposed research model.

Information experience, entertainment experience and parasocial-relationship-based experience are found to critically stimulate user commitment to the influencer and commitment to the platform, in turn driving TikTok-based purchase intention. Tests incorporating customer type reveal that commitment to the influencer more strongly influences the purchase intention of repeat customers, with commitment to the platform more likely to stimulate purchase intention among potential customers.

On a theoretical level, the paper is among the first to examine TikTok-based user purchase intention with customer type as a moderator. On a practical level, the results can guide marketers to effectively promote products using TikTok and inspire TikTok managers to develop customized strategies to stimulate initial and repeat sales.

TikTok is moving to the stage of commercialization and monetization by introducing e-commerce features. Although this move should cultivate particularly fertile ground for companies to sell products, TikTok user purchase behavior has yet to receive sufficient research attention, with little currently known about their purchase motivations. The current study uncovers the significant antecedents of users' purchase intention through TikTok, and further reveals the motivational differences among potential and repeat customers.

This paper tests the degree to which Sunstein's law of group polarization predicts the increase or decrease in polarization among individuals in an out-group during a polarizing event. The authors use the discourse on Parler surrounding the events of January 6th as a case study.

The study includes an overall sentiment analysis, a statistical analysis of emotions, along with eight other feelings, including anger, anticipation, disgust, fear, joy, sadness, surprise and trust. Specifically, the authors measure the differences in feelings related language used in posts as they pertain to Donald Trump and the Make America Great Again (MAGA) movement vs. Trump's Vice President Mike Pence both before and after January 6, 2021. The authors use this empirical analysis to show whether polarization in the Parler community increased or decreased after January 6th.

The authors find evidence that there is more complexity to polarization than Sunstein's theory would predict. The authors would expect a very polarized outed group to become more polarized relative to the general public after a central event; however, the authors see two extremes emerging within the Parler community (both strongly positive and strongly negative feelings toward Trump). The authors do not see unanimous consent across the Parler platform as Sunstein's theory would suggest; the out-group is becoming more polarized relative to the rest of the population. Instead, the authors observe a wide mix in reactions. The results of this study demonstrate that there is dissent even among the Parler echo chamber. For many themes surrounding the January 6th riots, Parler users express strong disagreement with each other and a lack of unity in their feelings for former President Trump.

The results suggest further research into polarization of outed groups and the policy implications of their polarization changes over time.

Increases in group polarization are often a motivator for public policy and are further becoming a major focus for research. Brookings' authors Stephanie Forrest and Joshua Daymude point to polarization as a substantial threat to American society, claiming “reducing extreme polarization is key to stabilizing democracy” (2022). Researchers Diana Epstein and John D. Graham demonstrate that polarized politics has impacted the “substance of rulemaking, judicial decisions, and legislation” along with “complicating long-term policy changes” (2007). The authors study how entrepreneurs have responded to this increase in polarization and its implications for public policy.

Not only does group polarization impact all types of groups, from the social to the economic, but also it has “particular implications for insulated ‘outgroups’” (Sunstein, 1999, p. 21). Groups that are excluded by either coercion or choice from dialog with other groups become even more polarized and extreme (Sunstein, 1999; Turner et al., 1989).

The authors have engaged in an empirical analysis that no other paper has addressed. This paper summarized the Parler sample data set and analyzed various themes associated with the events of January 6th, namely President Trump and MAGA themes and Vice President Pence. The analysis demonstrated a dramatic increase in negative sentiment and emotion related to Vice President Mike Pence after January 6th as well as mixed support for President Trump and an increase in disgust before and after the Capitol riot.

Extant research shows mixed results on the impact of expressed negative emotions on donations in online charitable crowdfunding. This study solves the puzzle by examining how different types of negative emotions (i.e. sadness, anxiety and fear) expressed in crowdfunding project descriptions affect donations.

Data on 15,653 projects across four categories (medical assistance, education assistance, disaster assistance and poverty assistance) from September 2013 to May 2019 come from a leading online crowdfunding platform in China. Text analysis and regression models serve to test the hypotheses.

In the medical assistance category, the expression of sadness has an inverted U-shaped effect on donations, while the expression of anxiety has a negative effect. An appropriate number of sadness words is helpful but should not exceed five times. In the education assistance and disaster assistance categories, the expression of sadness has a positive effect on donations, but disclosure of anxiety and fear has no influence on donations. Expressions of sadness, anxiety and fear have no impact on donations in the poverty assistance category.

This work has important implications for fundraisers on how to regulate the fundraisers' expressions of negative emotions in a project's description to attract donations. These insights are also relevant for online crowdfunding platforms.

Online crowdfunding research often studies negative emotions as a whole and does not differentiate project types. The current work contributes by empirically testing the impact of three types of negative emotions on donations across four major online crowdfunding categories.

There are numerous studies on satisfaction, but not enough on dissatisfaction when its consequences can be harmful. This study aims to examine different unsatisfactory situations during customer–supplier relationships in industrial markets combining the appraisal theory with the critical incident technique to identify potential problems and strategies to minimize their effect.

This research follows an exploratory qualitative approach based on 18 in-depth interviews with managers from business-to-business firms. The information obtained was object of a textual and conceptual analysis using the analytical software ATLAS TI 9.0.

The results show that negative cognitions have greater influence than negative emotions, and those dissatisfied customers may respond by expressing complaints, ending transactional relationships, reporting the other party legally, asking for explanations or continuing commercial relationships, even though they are dissatisfied. This will depend on the severity of the critical incident and the negative cognitions and emotions perceived. Proactivity and understanding of this situation will allow for understanding what specific actions to take to resolve conflicts and mitigate the negative effects among the parties.

This paper focuses on dissatisfaction, instead of satisfaction, in industrial markets through the appraisal theory. Furthermore, it applies the critical incident technique to understand the cognitions and emotions related with dissatisfaction in the commercial relationships. Finally, it provides ideas on what are the main source of dissatisfaction and how to manage them to anticipate and better manage those incidents.

The booming social media attracts construction professionals (CPs) to express emotions caused by work pressure (WP) through online behaviors. Previous works focus on the analysis of WP and emotions but do not adequately consider how WP can be reflected through online emotions. Thus, this study aims to attempt to explore the quantitative relationship between online emotional intensity and WP.

This study developed a linguistic-sticker (LS) model to quantitatively evaluate the sentiment intensity of posts published on social media. Moreover, the authors designed two econometric models of ordinary least squares regression and negative binomial regression to test the hypothesis.

The research found that posts with stronger negative sentiment (or positive sentiment) indicate that CPs face higher (or lower) WP. Besides, there is a negative bias between the sentiment intensity of posts and the comment quantity.

The positive correlation between sentiment intensity of posts and WP has been confirmed, which indicates that construction managers should pay more attention to CPs' behavior on social media, and take a more direct way to analyze work-related online behavior (e.g. posting, commenting). The dynamic monitoring of emotion-related posts also provides a direct basis for the management team to learn about CP's pressure status and propose measures to reduce their negative emotions. Furthermore, the emotional posts published by CPs on social media provide a direct basis for team managers to obtain their psychological state.

The research contributes to incorporating CPs' emotions into the LS model and to providing information systems artifacts and new findings on the analysis of WP and online emotions.