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Research suggests that understanding workers' attitudes towards artificial intelligence (AI) application is a prerequisite to successfully integrating AI into an organization. However, few studies have clarified the meaning of attitudes towards AI application at work (AAAW) as a multifaceted construct that can be assessed with psychometric validity. To address this issue, we developed and validated a scale to capture individuals' AAAW using three independent samples (total N = 2841). The resulting 25‐item scale covers an overall construct of AAAW as well as six dimensions that are subsumed under the construct (i.e., perceived humanlikeness, perceived adaptability, perceived quality of AI, AI use anxiety, job insecurity and personal utility). Our findings suggest that the AAAW scale has good psychometric properties and can be used to predict important recruiting outcomes. The scale offers opportunities to better understand and measure workers' attitudes towards AI application at work in a comprehensive and integrative manner. [ABSTRACT FROM AUTHOR]

Organisations are increasingly relying on people analytics to aid human resources decision‐making. One application involves using machine learning to automatically infer applicant characteristics from employment interview responses. However, management research has provided scant validity evidence to guide organisations' decisions about whether and how best to implement these algorithmic approaches. To address this gap, we use closed vocabulary text mining on mock video interviews to train and test machine learning algorithms for predicting interviewee's self‐reported (automatic personality recognition) and interviewer‐rated personality traits (automatic personality perception). We use 10‐fold cross‐validation to test the algorithms' accuracy for predicting Big Five personality traits across both rating sources. The cross‐validated accuracy for predicting self‐reports was lower than large‐scale investigations using language in social media posts as predictors. The cross‐validated accuracy for predicting interviewer ratings of personality was more than double that found for predicting self‐reports. We discuss implications for future research and practice. [ABSTRACT FROM AUTHOR]

Summary: The rising use of artificially intelligent (AI) technologies, including generative AI tools, in organizations is undeniable. As these systems become increasingly integrated into organizational practices and processes, understanding their impact on workers' experiences and job designs is critical. However, the ongoing discourse surrounding AI use in the workplace remains divided. Proponents of the technology extol its benefits for enhancing efficiency and productivity, while others voice concerns about the potential harm to human workers. To provide greater clarity on this pressing issue, this article presents a systematic review of empirical research that sheds light on the implications of AI use at work. Organized under five inductively generated themes within a multilevel framework, we uncover individual, group, and organizational factors that shape the interplay between humans and AI. Specifically, the themes are: (1) human–AI collaboration; (2) perceptions of algorithmic and human capabilities; (3) worker attitudes towards AI; (4) AI as a control mechanism in algorithmic management of platform‐based work; and (5) labor market implications of AI use. Our review offers insights into these themes and identifies five pathways for future research. Finally, we provide practical recommendations for organizational leaders seeking to implement AI technologies while prioritizing their employees' well‐being. [ABSTRACT FROM AUTHOR]