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EFFECTS OF ARTIFICIAL INTELLIGENCE AND ROBOTIC TECHNOLOGIES ON THE FUTURE OF WORK: A META-ANALYSIS STUDY
- Truvace record
- TRV-2026-2720raw data (JSON)
- Version
- v1Published
- First published
- Evidence type
- Systematic review or meta-analysis
- Sources
- 1 sourcePeer-reviewed
- sha256
- c16300c0c6e3…check it
This meta-analysis examined the impacts of artificial intelligence and robotic technologies on the labor market through 18 empirical studies. Findings were evaluated under three main categories: (1) the average automation susceptibility rate was determined to be 21.3%, with methodological approach and publication year having significant effects on results; (2) while technology had a statistically insignificant slight negative effect on total employment, developed economies showed a decrease in routine manual tas…
The problem
A meta-analysis of 18 studies found that robot use in developed economies was linked to a significant decrease in employment in routine manual tasks (g = -0.441, p < 0.001).
What this doesn’t fix
The included studies differ in method, which makes comparison difficult, and the literature does not yet adequately cover generative AI or the long-term effects of shocks such as COVID-19.
Sources
1 on file- Journal of Global Strategic Managementdoi:10.20460/jgsm.2025.347
What the review found when it combined the studies
These are the review’s own pooled results, copied from its abstract. Truvace did not recalculate them.
Skill requirement changes with AI and robotic technologies
Fisher's z 0.382 (95% CI 0.072 to 0.691)
The review’s own words
The combined effect size (Fisher's z') of four studies examining skill requirements and changes using a random effects model was calculated as 0.382 (95% CI: [0.072, 0.691], p = 0.016).
Employment effect of robots and artificial intelligence on total employment
Hedges' g -0.155 (95% CI -0.323 to 0.013)
The review’s own words
The combined effect size (Hedges' g) of seven studies examining the employment effect (Graetz & Michaels, 2015; Bachmann et al., 2024; Acemoğlu et al., 2022; Klenert et al., 2023; Bordot, 2022; Fu et al., 2020; de Vries et al., 2020) using a random effects model was calculated as -0.155 (95% CI: [-0.323, 0.013], p = 0.070).
Automation susceptibility of jobs (log odds ratio, random effects)
Log odds ratio -1.312 (95% CI -2.042 to -0.581)
The review’s own words
The combined results of five studies reporting automation susceptibility rates (Frey & Osborne, 2017; Arntz et al., 2016; Nedelkoska & Quintini, 2018; Hawksworth et al., 2018; Manyika et al., 2017) using a random effects model show that the mean LogOR value is -1.312 (95% CI: [-2.042, -0.581], p < 0.001).
Code checks every number above against the review’s text before it is shown. The short labels are an AI model’s reading of that text, so the review’s own sentence sits under each one. A pooled figure is only as strong as the studies behind it. How Truvace uses pooled results
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v1Truvace Impact Record TRV-2026-2720, v1: “EFFECTS OF ARTIFICIAL INTELLIGENCE AND ROBOTIC TECHNOLOGIES ON THE FUTURE OF WORK: A META-ANALYSIS STUDY.” Truvace, 2026-11-12. /record/TRV-2026-2720 (accessed on the day you copy it). sha256 c16300c0c6e39cb7…
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