ALGORITHMIC MANAGEMENT AND EMPLOYEE MENTAL WELL-BEING ACROSS INSTITUTIONAL CONTEXT: TOWARDS A HUMAN-CENTRIC CONCEPTUAL FRAMEWORK
DOI:
https://doi.org/10.69980/bz2jvj69Keywords:
AM, Employee mental well-being, Artificial intelligence, Institutional context, Responsible AI, Human-centered AIAbstract
The phrase "algorithmic management" (AM) denotes the transformation of modern workplaces through the automation of a variety of administrative duties, such as the proper distribution of jobs, performance tracking, scheduling, and assessment of performance, using artificial intelligence and data-driven systems. While the corpse of academic investigation into AM is expanding, the present corpus of investigation offers a fragmented comprehension of the impact of AM on the mental health of employees in a wide range of institutional settings. The mechanisms that underlie these relationships are not well-integrated. Through the implementation of a conceptual systematic literature review of 18 peer-reviewed studies, the objective of such a study is to establish a human-centric conceptual framework. The review includes nine studies that were identified through a structured Scopus database search and nine additional studies that were obtained through backward and forward snowballing. In order to improve the disparity, this is implemented. Based on the Job Demands-Resources (JD-R) Model, Socio-Technical Systems Theory, and Institutional Theory, the review incorporates current knowledge regarding AM, Human-Centered AI Implementation, employee mental-wellbeing, and organizational outcomes. The synthesis infers that the extent of AM's impact on the mental health of employees is contingent upon the institutional frameworks, governance practices, and organizational architecture. Algorithmic systems can mean more efficiency, flexibility and decision-consistency, but also more surveillance, labor intensification, less autonomy and psychological anguish. This paper also investigates Human-Centered AI Implementation, which is characterized by openness, human oversight, employee involvement, and ethical AI governance, to comprehend how enterprises can mitigate these adverse effects and promote positive employee outcomes. The report presents a conceptual framework and four research proposals for future empirical investigation based on the synthesized evidence. The present study enhances the current body of literature on AM by integrating the fragmented material into an exclusive theoretical model. It also offers concrete insights for managers and policymakers whose work is actively involved within the progression of AI-enabled management mechanisms designated as transparent, ethical, and human-centric. This is necessary to improve the mental well-being of employees and achieve sustainable organizational outcomes.
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