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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.

Facial expression recognition by human observers is affected by subjective components. Indeed there is no ground truth. We have developed Discrete Choice Models (DCM) to capture the human perception of facial expressions. In a first step, the static case is treated, that is modelling perception of facial images. Image information is extracted using a computer vision tool called Active Appearance Model (AAM). DCMs attributes are based on the Facial Action Coding System (FACS), Expression Descriptive Units (EDUs) and outputs of AAM. Some behavioural data have been collected using an Internet survey, where respondents are asked to label facial images from the Cohn–Kanade database with expressions. Different models were estimated by likelihood maximization using the obtained data. In a second step, the proposed static discrete choice framework is extended to the dynamic case, which considers facial video instead of images. The model theory is described and another Internet survey is currently conducted in order to obtain expressions labels on videos. In this second Internet survey, videos come from the Cohn–Kanade database and the Facial Expressions and Emotions Database (FEED).

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.

Purpose – This paper proposes a new procedure for measuring affective responses during social interaction using facial thermographic imaging.

Methodology – We first describe the results of several small pilot experiments designed to develop and refine this new measure that reveal some of the methodological advantages and challenges offered by this measurement approach. We then demonstrate the potential utility of this measure using data from a laboratory experiment (N=114) in which we used performance feedback to manipulate identity deflection and measured several types of affective responses – including self-impressions and emotions.

Findings – We find warming of the brow (near the corrugator muscle) and cheek (near the zygomatic major muscle) related most strongly to emotion valence and self-potency, with those whose brows and cheeks warmed the most feeling less positive emotion and less potent self-impressions. Warming in the eye area (near the orbicularis oculi) related most closely to undirected identity deflection and to positive self-sentiments. Positive self-views and strong identity disruptions both contributed to warming of the eyes.

Implications – The rigor of contemporary sociological theories of emotion exceeds our current ability to empirically test these theories. Facial thermographic imaging may offer sociologists new assessments of affect and emotion that are ecologically valid, socially unreactive, temporally sensitive, and accurate. This could dramatically improve our ability to test and develop affect based theories of social interaction.

Purpose: The purpose of the present study is to review and specifically examine the untested but common recommendation that leaders should give more effective positive feedback that is specific and mindful of nonverbal delivery. Study Design/Methodology/Approach: We used a dyadic interaction study where designated “leaders” interact with a “subordinate” in an idea generation and evaluation task. Leaders (n = 90) first received brief training in delivering positive feedback, and their subsequent feedback behavior during the dyadic interaction was coded for frequency, specificity, and both verbal and nonverbal affective delivery. Key dependent variables were subordinate affective reactions, perceptions of the leader, and subsequent task motivation. Findings: Frequency of leader positive feedback had significant positive impact on subordinate perceptions of the leader, but no impact on subordinate positive affect or task motivation. Positive feedback specificity and affective delivery, however, had no impact on subordinate affect, perceptions of the leader, or task motivation. Training effects were also observed – leaders were able to increase the specificity of their feedback, but were not able to modulate their affective delivery. Originality/Value: The design of the study allowed us to identify the leader positive feedback behaviors that were trainable and had the most impact on subordinates in terms of positive affect, perceptions of the leader, and subsequent task effort. We discuss the implications of these effects for positive feedback theory and application and commonly assumed “best practices.”

Purpose: The purpose of this chapter is to review and critically evaluate robots, artificial intelligence and service automation (RAISA) applications in the restaurant industry to educate professors, graduate students, and industry professionals.

Design/methodology/approach: This chapter is a survey of applications of RAISA in restaurants. The chapter is based on the review of professional and peer-reviewed academic literature, and the industry insight section was prepared based on a 50-minute interview with Mr. Juan Higueros, Chief Operations Officer of Bear Robotics.

Findings: Various case studies presented in this chapter illustrate numerous possibilities for automation: from automating a specific function to complete automation of the front of the house (e.g., Eatsa) or back of the house (e.g., Spyce robotic kitchen). The restaurant industry has already adopted chatbots; voice-activated and biometric technologies; robots as hosts, food runners, chefs, and bartenders; tableside ordering; conveyors; and robotic food delivery.

Practical implications: The chapter presents professors and students with a detailed overview of RAISA in the restaurant industry that will be useful for educational and research purposes. Restaurant owners and managers may also benefit from reading this chapter as they will learn about the current state of technology and opportunities for RAISA implementation.

Originality/value: To the best of the authors’ knowledge, this chapter presents the first systematic and in-depth review of RAISA technologies in the restaurant industry.

Advances in technology over recent years made it possible to use machines and artificial intelligence to develop commercially viable solutions for companies to listen to consumers, decode the meaning, and respond accordingly. In parallel, solutions have been developed that are able to automatically track facial expressions of consumers when reacting to a given marketing stimulus.

The authors look at how marketing executives can apply these technologies to generate enhanced customer insights, providing a realistic context for future applications. The focus is on bringing researchers and managers closer to those moments of truth and our ability to understand customer emotions, emotional reaction, everyday language, and ultimately brand engagement.

The chapter covers the application of commercially viable use cases for (1) the automated measurement of emotions through facial coding to optimize advertizing and content, and (2) the use of voice coding technology to design interactive chatbots as an alternative to traditional surveys. In the outlook, the authors describe the potential that these technologies provide for future research and further use cases.