SynthesisFrontiers in artificial intelligence2024
Exploring how AI adoption in the workplace affects employees: a bibliometric and systematic review.
Synthesis in Frontiers in artificial intelligence, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Artificial Intelligence and Occupational Health: Global Umbrella Review of Applications and Limitations.Safety and health at work · 2026Review
- When AI works, employees worry: the vigilance cost of AI delegation in hybrid work.Frontiers in psychology · 2026Article
- The rise of AI in the workplace: why perceived organizational support fails to protect long-term career satisfaction.Frontiers in artificial intelligence · 2026Article
- Algorithmic anxiety: AI, work, and the evolving psychological contract in digital discourse.Frontiers in psychology · 2026Article
- Artificial intelligence and Chinese university teachers' work performance: a synergistic or adversarial relationship.Frontiers in psychology · 2025Article
- Unlocking human potential in the AI Age: how employee-AI collaboration transforms work engagement through dual psychological pathways.Frontiers in psychology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: The adoption of artificial intelligence (AI) in the workplace is changing the way organizations function, and profoundly affecting employees. These organizational changes raise crucial questions about the employee's future and well-being. Our study aims to explore the intersection between artificial intelligence and employee well-being through a bibliometric review and a contextual analysis. Methodology: Carried out in May 2024, our study is divided into two phases. The first phase, dedicated to bibliometric review, was conducted using the PRISMA method, and explored the Scopus and Web of Science databases for the period from 2015 to 2024. A total of 92 articles were selected for quantitative analysis using VOSviewer software. The second phase is based on an in-depth systematic analysis of 25 articles selected from those previously identified. These articles were selected on the basis of their relevance to the research question, and were subjected to in-depth thematic analysis using NVivo software. Results: The bibliometric analysis results reveal a significant increase in publications starting from the year 2020, highlighting advancements in research, primarily in the United States and China. The co-occurrence analysis identifies four main clusters: ethics, work autonomy, employee stress, and mental health, thus illustrating the dynamics created by artificial intelligence in the professional environment. Furthermore, the systematic analysis has brought to light theoretical gaps and under-explored areas, such as the need to conduct empirical studies in non-Western cultural contexts and among diverse target groups, including older adults, individuals of different sexes, people with low education levels, and participants from various sectors, including primary and secondary industries, small manufacturing businesses, call centers, as well as public and private healthcare sectors. Conclusion: Existing literature emphasize the importance for organizations to implement supportive strategies aimed at mitigating the potential adverse effects of AI on employee well-being, while also leveraging its benefits to enhance workplace autonomy and satisfaction and promote AI-enabled innovation through employee creativity and self-efficacy.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.