Search results

Recent years have seen an explosion in the study of emotions in organizations, and although emotions play a central role in the job stress process, their role is largely neglected in empirical stressor–strain studies. Our chapter aims to build consensus in the literature by showing that discrete emotions provide a mechanism through which stressors exert their impact on well-being. By examining a larger domain of stressors, emotions, and well-being, we begin to develop and expand upon the nomological network of emotions. In an effort to build on the job demands–resources (JD-R) model, which includes both job demands (i.e., negative stimuli such as time pressure) and resources (i.e., positive stimuli such as autonomy), we include both negative and positive discrete emotions with the expectation that negative emotions will generally be linked to demands and positive emotions will be linked to resources. We also propose that there may be circumstances where demands trigger negative discrete emotions and lead to greater experienced strain, and conversely, where resources arouse positive discrete emotions, which would positively affect well-being. The model in our chapter sheds light on how discrete emotions have different antecedents (i.e., job demands and resources) and outcomes (e.g., satisfaction, burnout, performance), and as such, respond to calls for research on this topic. Our findings will be of particular interest to organizations where employees can be trained to manage their emotions to reduce the strain associated with job stressors.

Organizational behavior scholars have long recognized the importance of a variety of emotion-related phenomena in everyday work life. Indeed, after three decades, the span of research on emotions in the workplace encompasses a wide variety of affective variables such as emotional climate, emotional labor, emotion regulation, positive and negative affect, empathy, and more recently, specific emotions. Emotions operate in complex ways across multiple levels of analysis (i.e., within-person, between-person, interpersonal, group, and organizational) to exert influence on work behavior and outcomes, but their linkages to human resource management (HRM) policies and practices have not always been explicit or well understood. This chapter offers a review and integration of the bourgeoning research on discrete positive and negative emotions, offering insights about why these emotions are relevant to HRM policies and practices. We review some of the dominant theories that have emerged out of functionalist perspectives on emotions, connecting these to a strategic HRM framework. We then define and describe four discrete positive and negative emotions (fear, pride, guilt, and interest) highlighting how they relate to five HRM practices: (1) selection, (2) training/learning, (3) performance management, (4) incentives/rewards, and (5) employee voice. Following this, we discuss the emotion perception and regulation implications of these and other discrete emotions for leaders and HRM managers. We conclude with some challenges associated with understanding discrete emotions in organizations as well as some opportunities and future directions for improving our appreciation and understanding of the role of discrete emotional experiences in HRM.

The purpose of this study is to adopt a functional perspective to integrate and extend three streams of research, the first distinguishing between global affect and discrete emotional episodes, the second highlighting the capability of places to elicit emotions and the third demonstrating the differential impact of discrete emotions on consumer response. Doing so shows that four positive place emotions have a significant and variable influence on consumer purchase intentions for brands originating there.

A focus group pilot corroborates that places relate to contentment, enchantment, happiness and pride, which impact consumer response. Study 1 uses landscape photographs to show the four place emotions influence purchase intention for bottled water. Study 2 retests the impact of place emotions, using short vignettes and establishes the moderating role of product hedonic nature. Study 3 replicates emotion effects, corroborating their non-conscious nature and establishing their impact in the presence of place cognitions.

Together, the empirical studies provide evidence for effects of four discrete place emotions, especially with hedonic products and under conditions of cognitive load. Effects are robust when a person’s mood, buying volume, category knowledge, impulse buying tendencies and place cognitions are included as controls.

The study contributes to a better understanding of the emotional dimension of origin effects by adopting a novel, theory-based perspective on discrete positive place emotions impacting consumer response.

Managers invest substantially in places to elicit positive feelings, gravitating toward the view that all they need to do is create a global positive effect with consumers. The study informs this perspective by demonstrating how discrete emotions influence consumer response.

This study is among the first to examine discrete positive place emotions as possible drivers of consumers’ purchase intention.

This chapter proposes a conceptualization of joyful leadership as a unique leadership style and identifies a future research agenda to further explore the concept. While the concept of joyful leadership appears repeatedly in the nonacademic literature, including in blogs, vlogs, and podcasts, there is limited reference to joyful leadership in the academic literature highlighting a lack of academic rigor around the concept. Joyful leadership is proposed as a unique leadership style with specific patterns of behavior demonstrated by the leader. This research draws on understandings of emotion, positive affect, and leadership in the academic literature to develop a conceptualization of joyful leadership.

The proposed conceptualization is based on an extensive literature review drawing from both the leadership field and the study of emotions including various theoretical perspectives from these diverse fields.

Based on discrete emotion theory a conceptualization of joyful leadership as a unique leadership style is presented, identifying key patterns of behavior associated with joyful leadership including discrete autonomic patterns, actions, nonverbal signals, and identified feelings.

This research outlines a conceptual model to provide an understanding of the concept of joyful leadership as a unique leadership style. It draws on the current study of emotion, positive affect, and leadership and more specifically examines the concept of joyful leadership aligned to discrete emotion theory. This particular theory of emotion, when examined in relation to leadership, provides a basis for the concept of joyful leadership as a leadership style and the basis for its proposed characteristics and outcomes.

Although the importance and variety of emotions have been emphasized in existing literature, studies on discrete emotions remain limited. This study aims to propose a method for more precise recognition and calculation of emotions in massive amounts of online data on attraction visitor experiences and behaviour, by using discrete emotion theory.

Using HowNet’s word similarity calculation technique, this study integrated multiple generic dictionaries, including the sentiment vocabulary ontology database of the Dalian University of Technology, the National Taiwan University Sentiment Dictionary and the Boson Dictionary. Word2vec algorithm filters emotion words unique to hospitality and tourism in 1,596,398 texts from Sogou News, Wikipedia and Ctrip reviews about attractions, and 1,765,691 reviews about attractions in China.

The discrete sentiment dictionary developed in this study outperformed the original dictionary in identifying and calculating emotions, with a total vocabulary extension of 12.07%, demonstrating its applicability to tourism.

The developed new dictionary can be used by researchers and managers alike to quickly and accurately evaluate products and services based on online visitor reviews.

To the best of the authors’ knowledge, this study is the first to construct a sentiment dictionary based on discrete emotion theory applicable to hospitality and tourism in the Chinese context. This study extended the applicability of affective psychology to hospitality and tourism using discrete emotion theory. Moreover, the study offers a methodological framework for developing a domain-specific sentiment dictionary, potentially applicable to other domains in hospitality.

To reconcile theoretical discrepancies between discrete emotion, dimensional emotion (positive vs. negative affect), and the circumplex model, we propose the bifurcation model of affect structure (BMAS). Based on complexity theory, this model explores how emotion as an adaptive complex system reacts to affective events through negative and positive feedback loops, resulting in self-organizing oscillation and transformations between three states: equilibrium emotion, discrete positive and negative emotion in the near-equilibrium state, and chaotic emotion. We argue that the BMAS is superior to the extant models in revealing the dynamic connections between emotions and the intensity of affective events in organizational settings.

This study aims to establish a model for rapid and accurate emotion recognition in restaurant online reviews, thus advancing the literature and providing practical insights into electronic word-of-mouth management for the industry.

This study elaborates a hybrid model that integrates deep learning (DL) and a sentiment lexicon (SL) and compares it to five other models, including SL, random forest (RF), naïve Bayes, support vector machine (SVM) and a DL model, for the task of emotion recognition in restaurant online reviews. These models are trained and tested using 652,348 online reviews from 548 restaurants.

The hybrid approach performs well for valence-based emotion and discrete emotion recognition and is highly applicable for mining online reviews in a restaurant setting. The performances of SL and RF are inferior when it comes to recognizing discrete emotions. The DL method and SVM can perform satisfactorily in the valence-based emotion recognition.

These findings provide methodological and theoretical implications; thus, they advance the current state of knowledge on emotion recognition in restaurant online reviews. The results also provide practical insights into intelligent service quality monitoring and electronic word-of-mouth management for the industry.

This study proposes a superior model for emotion recognition in restaurant online reviews. The methodological framework and steps are elucidated in detail for future research and practical application. This study also details the performances of other commonly used models to support the selection of methods in research and practical applications.

The purpose of this study is to investigate the relations between emotion regulation (reappraisal and suppression), discrete emotions and emotional exhaustion among nurses.

A cross-sectional design was used with 168 nurses in South Korea. Structural equation modeling and path analysis were conducted for analysis.

Reappraisal correlated positively with enjoyment and pride and negatively with anxiety, anger and frustration, whereas suppression correlated negatively with enjoyment and positively with anxiety and frustration. Moreover, reappraisal was negatively associated with emotional exhaustion, whereas suppression was positively associated with it. Enjoyment was negatively related to emotional exhaustion, and anger and frustration were positively related to it. Enjoyment and frustration mediated the relation between emotion regulation and emotional exhaustion. Findings demonstrate the potentially beneficial influences of reappraisal as well as harmful impacts of suppression in the nursing context.

This paper expands research on nurses’ emotion management by applying Gross’s emotion regulation framework rather than Hochschild’s emotional labor framework. The mediating result suggests that not only nurses but also hospital administrators and nurse managers should pay attention to nurses’ emotional experiences to improve nurses’ well-being and ultimately better nursing practice. This research can provide the basis for developing practical interventions to efficiently regulate nurses’ emotions.

This is the first study exploring the mediating role of emotions in the link between nurses’ emotion regulation and emotional exhaustion. It contributes to interdisciplinary research by integrating perspectives from psychological emotion and emotion regulation research into the nursing field.

To clarify inconsistencies in the emotion elicitation process and to suggest avenues for advancing emotion research, this study aims to conduct a systematic review of emotion research in the subject area of hospitality and tourism management.

This study analyzes 178 emotion-related articles published in 37 journals from 2004–2019 in the context of hospitality and tourism, providing a systematic synthesis of publication outlets and trajectories, research settings, the conceptualization of emotion, emotion measurement, classifications of antecedents and consequences.

Regarding the elicitation of emotions, many studies ignore the developments in emotion research in the mainstream discipline and still consider external stimuli as the direct causes of emotion. Numerous studies conceptualize customer emotion as positive or negative, which overlooks the nuances between discrete emotions with the same valence. Additionally, emotion scales are largely borrowed from psychology without considering the specific characteristics of the hospitality and tourism context. Methodologically, most studies take a single-measure lens with either a self-report, physiological or expression behavior measure.

The analysis of the literature highlights three main areas for future emotion studies in the hospitality and tourism context.

Previous reviews are narrative and only address specific areas of interest, rendering them incapable of explaining how a systematic literature identification process was conducted. The present systematic review is among the first to provide an overview of emotion studies in hospitality and tourism over a 15-year period. By drawing insights from appraisal theories of emotions, this review addresses common misunderstandings concerning the emotion elicitation process in the current hospitality and tourism literature.

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.