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Emotion recognition and emotion expression/regulation are important aspects of emotional intelligence (EI). Although the construct of EI is widely used and its components are part of many investigations, there is still no sufficient picture set that can be used for systematic research of facial emotion recognition and practical applications of individual assessments. In this research we present a new Facial Action Coding System validated picture set consisting of six emotions (anger, disgust, fear, happiness, sadness, and surprise). Basic principles of stimulus development and evaluation process are described. The PFA-U can be used for future studies in organization for the assessment of emotion recognition, emotion stimulation, and emotion management.

This chapter examines EI, presents a history of EI including the various models, and a discussion of the three streams approach to classifying EI literature. The author advocates for the efficacy of the Stream One Ability Model (SOAM) of EI citing previous authors and literature. The commonly used SOAM instruments are discussed in light of recent studies. The discussion turns to alternate tests of the SOAM of EI including Situational Judgment Tests (SJTs). Recommendations include an analysis of SOAM instruments, a new approach to measurement, and increased use of SJTs to capture the four-branch ability model of EI.

Based on the situated focus theory of power, this chapter empirically investigates the relationship between an individual’s organizational power position and emotion recognition accuracy (ERA), and it examines individuals’ stress experiences at work as a boundary condition for this relationship.

Survey data were collected in a field sample of 117 individuals employed across various organizations in Germany. We used an established, performance-based test of ERA and applied hierarchical regression analysis to examine our model.

An individual’s power was negatively related with his or her ability to decipher others’ emotional expressions among individuals experiencing higher work stress, whereas this relationship was not significant for participants with lower stress.

Although the cross-sectional study design and data collection within one country are relevant limitations, the findings promote a better understanding of the complex relationship between power and ERA.

Given the relevance of accurate emotion perception, the results indicate that stressful work environments may be an important risk factor for organizational power holders’ personal and professional success.

The findings advance the literature on power and emotion recognition by highlighting the role of work stress as an important, heretofore neglected boundary condition that may explicate the ambiguous results in prior research.

Teams’ emotional skills can be more than the sum of their individual parts. Although theory emphasizes emotion as an interpersonal adaptation, emotion recognition skill has long been conceptualized as an individual-level intelligence. We introduce the construct of team emotion recognition accuracy (TERA) – the ability of members to recognize teammates’ emotions – and present preliminary evidence for its predictive validity. In a field study of public service interns working full-time in randomly assigned teams, taken together positive and negative TERA measured at the time of team formation accounted for 28.1% of the variance in team performance ratings nearly a year later.

Emotional appraisal is an act of sense making: What does a particular event mean for me? It is not the event itself – but rather an individual's subjective evaluation of the event – that elicits and shapes emotions (Scherer, 1997b). Thus, appraisal is the crucial first step in the emotion process, and describes how we attend, interpret and ascribe meaning to a given event or stimulus. First, emotional appraisal requires attention; given cognitive limits, we must prioritize which events are even worthy of our notice. Second, we must code the event, interpreting its meaning, and in particular its implications for the self (Mesquita & Frijda, 1992). If another person in a team environment is being rude, how one interprets the personal significance of this behavior may change significantly the emotional response – for example, whether the rude individual is a teammate, a customer, a supplier, or a competitor, and whether the rude behavior is directed at an innocent bystander or an instigator. Likewise, a bear approaching a campsite may elicit fear, but the same bear in a zoo could result in delight. Often the cognitive evaluation of stimuli associated with emotional appraisal occurs so quickly and automatically, before our conscious awareness, that we may be unaware of this individual component of the unfolding process. However, even in such cases, we can see the role of appraisal processes by examining, for example, how emotional reactions change over time and vary from person to person. An event that may have caused great embarrassment during youth might in adulthood leave one unfazed, and an event that makes one person angry might make another person sad. Indeed, it can be the lack of conscious awareness of the appraisal process – and the sense that appraisal is clear and lacking a subjective interpretive lens – that prevents individuals from questioning and evaluating it. This results in a particular challenge to reconciling colleagues’ often vastly differing emotional appraisals.

Purpose – The advancement of multimedia technology has spurred the use of multimedia in business practice. The adoption of audio and visual data will accelerate as marketing scholars become more aware of the value of audio and visual data and the technologies required to reveal insights into marketing problems. This chapter aims to introduce marketing scholars into this field of research.Design/methodology/approach – This chapter reviews the current technology in audio and visual data analysis and discusses rewarding research opportunities in marketing using these data.Findings – Compared with traditional data like survey and scanner data, audio and visual data provides richer information and is easier to collect. Given these superiority, data availability, feasibility of storage, and increasing computational power, we believe that these data will contribute to better marketing practices with the help of marketing scholars in the near future.Practical implications: The adoption of audio and visual data in marketing practices will help practitioners to get better insights into marketing problems and thus make better decisions.Value/originality – This chapter makes first attempt in the marketing literature to review the current technology in audio and visual data analysis and proposes promising applications of such technology. We hope it will inspire scholars to utilize audio and visual data in marketing research.

Theorists, such as Darwin and Aristotle, have long argued that facial expressions communicate information about a person's emotional state. Recently, validated coding strategies for facial expressions have been developed, which enable researchers to reliably assess a person's affect. Although social, health, and clinical psychologists have regularly employed these objective measures of facial expressions (OMFE), occupational stress and well-being researchers are yet to benefit from this method. The subsequent chapter integrates the facial expression and occupational well-being literature. Specifically, we discuss the advantages of OMFE over self-reports and implications of OMFE for future research on occupational well-being.

The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.

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.