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      Job attitudes and career behaviors relating to employees' perceived incorporation of artificial intelligence in the workplace: a career self-management perspective

      ,
      Personnel Review
      Emerald

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          Abstract

          Purpose

          Artificial intelligence (AI) continues to be deployed in workplaces. While there are many positive outcomes of AI integration, understanding the extent of its consequences on employees is limited. Hence, this study examines employee perceptions of AI and the consequent influences on employee job attitudes and career behaviors. Utilizing the career self-management perspective, the authors explore the mechanisms related to employee perceptions of AI and potential career exploration behaviors.

          Design/methodology/approach

          The authors tested several hypotheses using employee survey data ( N = 345 call center agents) collected from a firm that recently integrated AI in their operations. The authors collected data on four occasions (one-week intervals between data collection) to determine employee perceptions of AI taking over jobs (Time 1); job insecurity (Time 2); psychological distress (Time 3); and career exploration behavior (Time 4).

          Findings

          The findings reveal that perceptions of AI taking over jobs are significantly associated with higher career exploration behaviors. In addition, the authors found job insecurity and psychological distress as pathways that explain why employees having perceptions of AI taking over their jobs influences their career exploration behaviors.

          Originality/value

          These findings fill a gap in the literature by revealing how AI integration in the workplace, despite its many positive outcomes for organizations, can have a negative influence on employees. The negative employee perceptions of AI can lead to career exploration behaviors. From the career self-management perspective, the authors offer novel insights that have practical implications for talent management, particularly the need to communicate effectively to employees about AI integration in the workplace to avoid them feeling threatened and leaving their jobs.

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          Most cited references54

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          Sources of method bias in social science research and recommendations on how to control it.

          Despite the concern that has been expressed about potential method biases, and the pervasiveness of research settings with the potential to produce them, there is disagreement about whether they really are a problem for researchers in the behavioral sciences. Therefore, the purpose of this review is to explore the current state of knowledge about method biases. First, we explore the meaning of the terms "method" and "method bias" and then we examine whether method biases influence all measures equally. Next, we review the evidence of the effects that method biases have on individual measures and on the covariation between different constructs. Following this, we evaluate the procedural and statistical remedies that have been used to control method biases and provide recommendations for minimizing method bias.
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            Fit indices in covariance structure modeling: Sensitivity to underparameterized model misspecification.

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              The MOS 36-ltem Short-Form Health Survey (SF-36)

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                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Personnel Review
                PR
                Emerald
                0048-3486
                April 25 2022
                May 15 2023
                April 25 2022
                May 15 2023
                : 52
                : 4
                : 1169-1187
                Article
                10.1108/PR-02-2021-0103
                bd83be74-9bd2-46bb-8eb6-ff66609ece72
                © 2023

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