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This paper aims to explore and test the relationship between emotion recognition skill and assessment center performance after controlling for both general mental ability (GMA) and conscientiousness. It also seeks to test whether participant sex or race moderated these relationships.

Using independent observers as raters, the paper tested 528 business students participating in a managerial assessment center, while they performed four distinct activities of: an in‐basket task; a team meeting for an executive hiring decision; a team meeting to discuss customer service initiatives; and an individual speech.

Emotion recognition predicted assessment center performance uniquely over both GMA and conscientiousness, but results varied by race. Females were better at emotion recognition overall, but sex neither was related to assessment center performance nor moderated the relationship between it and emotion recognition. The paper also found that GMA moderated the emotion recognition/assessment performance link, as the former was important to performance only for people with low levels of GMA.

The results seem to contradict those who argue that E‐IQ is an unqualified predictor of performance. Emotional recognition is not uniformly valuable; instead, it appears to benefit some groups more than others.

The paper clarifies the emotional intelligence literature by providing further support for the predictive validity of emotion recognition in performance contexts, and by separating out how emotional recognition benefits certain population groups more.

The purpose of this paper is to explore uncertainty inherent in emotion recognition technologies and the consequences resulting from that phenomenon.

The paper is a general overview of the concept; however, it is based on a meta-analysis of multiple experimental and observational studies performed over the past couple of years.

The main finding of the paper might be summarized as follows: there is uncertainty inherent in emotion recognition technologies, and the phenomenon is not expressed enough, not addressed enough and unknown by the users of the technology.

Practical implications of the study are formulated as postulates for the developers, users and researchers dealing with the technologies of automatic emotion recognition.

As technologies that recognize emotions are becoming more and more common, and perhaps more decisions influencing people lives are to come in the next decades, the trustworthiness of the technology is important from a scientific, practical and ethical point of view.

Studying uncertainty of emotion recognition technologies is a novel approach and is not explored from such a broad perspective before.

Previous research has demonstrated the importance of emotion recognition ability in negotiations and leadership, but scant research has investigated the role of emotion recognition ability in service contexts. The purpose of this paper is to propose and test a compensatory model in which service employees’ emotion recognition ability helps enhance their job performance, particularly when employees score low on the agreeableness personality dimension or have low cognitive ability.

With a two-wave multisource dataset collected from a service center of a large retail bank, multiple regression analysis was used to test the moderating roles of agreeableness and cognitive ability on the relationship between service employees’ emotion recognition ability and their performance.

Service employees’ emotion recognition ability helped enhance their job performance. However, the positive effect of emotion recognition ability on job performance was only statistically significant when employees’ agreeableness or cognitive ability was low.

The findings have important implications for how service organizations select and recruit employees. In particular, service employees with low agreeableness or cognitive ability may still be able to perform well when possessing high emotion recognition ability. Therefore, emotion recognition ability should be considered in the selection and recruitment process.

Going beyond self-report measures of emotion recognition and using a performance measure from organizational records, this study is one of the first to examine how emotion recognition ability interacts with personality and cognitive ability in predicting service employees’ effectiveness in a service organization.

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 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.

Nowadays, the speech emotion recognition (SER) model has enhanced as the main research topic in various fields including human–computer interaction as well as speech processing. Generally, it focuses on utilizing the models of machine learning for predicting the exact emotional status from speech. The advanced SER applications go successful in affective computing and human–computer interaction, which is making as the main component of computer system's next generation. This is because the natural human machine interface could grant the automatic service provisions, which need a better appreciation of user's emotional states.

This paper implements a new SER model that incorporates both gender and emotion recognition. Certain features are extracted and subjected for classification of emotions. For this, this paper uses deep belief network DBN model.

Through the performance analysis, it is observed that the developed method attains high accuracy rate (for best case) when compared to other methods, and it is 1.02% superior to whale optimization algorithm (WOA), 0.32% better from firefly (FF), 23.45% superior to particle swarm optimization (PSO) and 23.41% superior to genetic algorithm (GA). In case of worst scenario, the mean update of particle swarm and whale optimization (MUPW) in terms of accuracy is 15.63, 15.98, 16.06% and 16.03% superior to WOA, FF, PSO and GA, respectively. Under the mean case, the performance of MUPW is high, and it is 16.67, 10.38, 22.30 and 22.47% better from existing methods like WOA, FF, PSO, as well as GA, respectively.

This paper presents a new model for SER that aids both gender and emotion recognition. For the classification purpose, DBN is used and the weight of DBN is used and this is the first work uses MUPW algorithm for finding the optimal weight of DBN model.

The purpose of this paper is to examine the impact of co‐workers receiving recognition on two types of responses, namely emotions (positive and negative) and behavioral intentions (interpersonal counterproductive behavior and interpersonal citizenship behavior).

This study is an experimental scenario study with a 2×2 between‐subjects design with 246 employees from a local health care organization.

The findings reveal that the relation between the recognition of others and positive or negative emotions was moderated by the quality of the relationship between both actors. Further, as hypothesized, the relation between the recognition of others and interpersonal counterproductive behavior was moderated by relationship quality. Contrary to the authors' expectations, relationship quality did not moderate the relation between employee recognition and interpersonal citizenship behavior.

This study provides useful suggestions for managers to diminish undesired (i.e. negative emotions and interpersonal counterproductive behavior) and enhance desired emotions and behaviors (i.e. positive emotions and interpersonal citizenship behavior).

This study is the first to show that employee recognition may have negative effects on the emotions of others and interpersonal behavior (i.e. interpersonal counterproductive behavior).

Various studies have examined the role of executive functions in autism, but there is a lack of research in the current literature on cognitive flexibility in autism spectrum disorders (ASD). The purpose of this study is to investigate whether cognitive flexibility deficits could be related to facial emotion recognition deficits in ASD.

In total, 20 children with ASD and 20 typically developing children, matched for intelligence quotient and gender, were examined both in facial emotion recognition tasks and in cognitive flexibility tasks through the dimensional change card sorting task.

Despite cognitive flexibility not being a core deficit in ASD, impaired cognitive flexibility is evident in the present research. Results show that cognitive flexibility is related to facial emotion recognition and support the hypothesis of an executive specific deficit in children with autism.

One of the limit is the use of just one cognitive test to measure cognitive flexibility and facial recognition. This could be important to be taken into account in the new research. By increasing the number of common variables assessing cognitive flexibility, this will allow for a better comparison between studies to characterize impairment in cognitive flexibility in ASD.

Investigating impairment in cognitive flexibility may help to plan training intervention based on the induction of flexibility.

If the authors implement cognitive flexibility people with ASD can have also an effect on their social behavior and overcome the typical and repetitive behaviors that are the hallmark of ASD.

The originality is to relate cognitive flexibility deficits to facial emotion.

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.

The purpose of this study is to conduct a thematic content analysis of facial emotion recognition (FER) research within the context of the hospitality and tourism industry. Through this analysis, the study aims to identify key themes, trends and implications of the utilization of FER technology in enhancing customer emotions and experiences within hospitality and tourism settings.

This is qualitative research that utilizes thematic content analysis. The research data were obtained from the Scopus database. A total of 45 articles (titles, abstracts and keywords) were coded into MAXQDA and VOSWiever programs for data analyses and mapping.

Based on the analyses, the predominant term used in titles was emotion, indicating its centrality in the research domain. Moreover, the most prevalent concepts in this field were emotion and experience. Notably, facial emotion recognition emerged as the most frequently utilized term within this context. Within the hospitality and tourism industry, FER was primarily employed within the travel sub-branch. Finally, the research culminated in the visualization of the theoretical framework and conceptual background, offering a comprehensive overview of the field.

There is a growing demand for using FER technology specifically within the hospitality and tourism industry context; therefore, growing scientific research has been conducted on this topic recently. By conducting a thematic content analysis, this study uncovered novel insights into the utilization of this technology to enhance customer emotions and experiences, thereby contributing to a deeper understanding of its potential implications and applications within the hospitality and tourism industry.