a b s t r a c tThis study investigated how a job seeker self-presentation affects recruiter's hiring recommendations in an online communities and what categories of self-presentation contribute to fit perceptions for obtaining hiring recommendations. The study participants viewed potential candidates' LinkedIn profiles and responded to questions regarding the argument quality and source credibility of their self-presentations, fit perceptions, and hiring recommendations. The results show that recruiters make inferences about job seekers' person-job fit and person-organisation fit on the basis of argument quality in specific self-presentation categories, which in turn predict recruiters' intentions to recommend job seekers for hiring. Although certain specific categories of self-presentation offering source credibility have positive associations with person-person (P-P) fit perception, there is a non-significant relationship between perceived P-P fit and hiring recommendations.

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With the development of artificial intelligence (AI), the automatic analysis of video interviews to recognize individual personality traits has become an active area of research and has applications in personality computing, human-computer interaction, and psychological assessment. Advances in computer vision and pattern recognition based on deep learning (DL) techniques have led to the establishment of convolutional neural network models that can successfully recognize human nonverbal cues and attribute their personality traits with the use of a camera. In this paper, an end-to-end AI interviewing system was developed using asynchronous video interview (AVI) processing and a TensorFlow AI engine to perform automatic personality recognition (APR) based on the features extracted from the AVIs and the true personality scores from the facial expressions and self-reported questionnaires of 120 real job applicants. The experimental results show that our AI-based interview agent can successfully recognize the ''big five'' traits of an interviewee at an accuracy between 90.9% and 97.4%. Our experiment also indicates that although the machine learning was conducted without large-scale data, the semisupervised DL approach performed surprisingly well with regard to APR despite the lack of labor-intensive manual annotation and labeling. The AI-based interview agent can supplement or replace existing self-reported personality assessment methods that job applicants may distort to achieve socially desirable effects.INDEX TERMS Big five, convolutional neural network (CNN), personality computing, TensorFlow.

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Interpersonal communication skills and personality traits have been identified as critical success factors for job performance and organization effectiveness [1, 2]. Communication skills enable workplace members to effectively exchange, share, and feedback information to different stakeholders through verbal and nonverbal messages [3]. Verbal messages are used to convey exact words, and nonverbal messages, such as gestures, facial expressions, posture, and tone of voice, are helpful for understanding underlying emotions, attitude, and feelings [1, 4]. Personality traits refer to individual patterns of thinking, feelings, and behaviors that can be used to predict whether an individual is a good fit for a specific job context or organizational environment [2]. Face-to-face interviews are a common method of employment selection [5], and this method is a valid assessment tool for measuring interpersonal communication skills in a structured

Purpose The purpose of this paper is to propose a model to understand how and when employees’ perceived privacy violations and procedural injustice interact to predict intent to leave in the context of the use of social networking sites (SNSs) monitoring. Design/methodology/approach This study was conducted in a field setting of Facebook to frame the hypotheses in a structural equation model with partial least squares-structural equation modeling. Variables were measured empirically by administering questionnaires to full-time employed Facebook users who had experienced SNS monitoring. Findings The results showed that when an employee believed that he/she had more ability to control his/her SNS information, he/she was less likely to perceive that his/her privacy had been invaded; and when an employee believed that the transparency of the SNS data collection process was higher, he or she was more likely to perceive procedural justice in SNS monitoring. Research limitations/implications This research draws attention to the importance of intent to leave in the absence of perceived procedural justice under SNS monitoring, and the partial mediation of the perception of justice or injustice by perceived privacy violations. Practical implications For employers, the author recommends that employers come to know how to conduct SNS monitoring and data collection with limited risk of employee loss. Social implications For employees, the author suggests that SNS users learn how to control their SNS information and make sure to check their privacy settings on the SNS that they use frequently. Originality/value This study provided an initial examination and bridged the gap between employer use of SNS monitoring and employee reactions by opening a mediating and moderating black box that has rarely been assessed.

The aims of this study are to examine the effect of crowdsourced employer ratings and employee recommendations of an employer as an employer of choice, to examine which employer ratings that represent different employee value propositions can predict the overall employer rating through crowdsourcing, to examine whether the Fortune 500 ranking can also influence overall employer ratings, and to mine which keywords are popularly used when employees post a comment about the pros and cons of their employers on a crowdsourced employer branding platform. The study collected crowdsourced employer review data from Glassdoor based on 2019 Fortune 500 companies, and the results found that crowdsourced employer ratings are positively associated with “recommend to a friend,” while culture and values predominantly influence overall employer ratings. The rank of Fortune 500 has less predictive power for overall employer ratings than for other specific employer ratings, except for business outlook. The most popular keywords of Pros on Glassdoor are work–life balance and pay and benefits, whereas the most popular keywords of Cons on Glassdoor are work–life balance and upper management.

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