Labor-Saving Automation Technologies, Employee Engagement Analytics, and Job Performance Measurement in Artificial Intelligence Algorithm-based Workplace Environments
Labor-Saving Automation Technologies, Employee Engagement Analytics, and Job Performance Measurement in Artificial Intelligence Algorithm-based Workplace Environments

Summary/Abstract: The aim of this systematic review is to synthesize and analyze employee productivity measurement, algorithm-driven human resource management decision processes, big data mining-based employee knowledge accumulation, and artificial intelligence-based performance appraisals. With increasing evidence of artificial intelligence-enabled human resource management processes, big data algorithm-based employee performance, and artificial intelligence-driven workplace automation, there is an essential demand for comprehending whether deep and machine learning-based organizational support tools and decision support and task assignment systems can be deployed in algorithmic labor relation management, computer-based teamwork simulations, and artificial intelligence-based employee performance. A quantitative literature review of ProQuest, Scopus, and the Web of Science was carried out throughout January 2024, with search terms including “artificial intelligence algorithm-based workplace environments” + “labor-saving automation technologies,” “employee engagement analytics,” and “job performance measurement.” As research published in 2022 and 2023 was inspected, only 177 articles satisfied the eligibility criteria, and 40 mainly empirical sources were selected. Data visualization tools: Dimensions (bibliometric mapping) and VOSviewer (layout algorithms). Reporting quality assessment tool: PRISMA. Methodological quality assessment tools include: Citationchaser, Eppi-Reviewer, JBI SUMARI, Litstream, PICO Portal, and ROBIS.

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