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AI Implementation Impact on Workforce Productivity: The Role of AI Training and Organizational Adaptation
Citation. Nurlia, N., Daud, I., & Rosadi, M. E. (2023). AI Implementation Impact on Workforce Productivity: The Role of AI Training and Organizational Adaptation. Escalate: Economics and Business Journal, 1(1), 01–13. https://doi.org/10.61536/escalate.v1i01.6
Summary. This survey study of all 70 employees at the Regional Secretariat Pontianak models associations among reported AI implementation, AI training, organizational adaptation, and workforce productivity using PLS-SEM. AI implementation was positively associated with training, adaptation, and productivity; training was the stronger reported direct predictor of productivity and mediated the implementation–productivity association. The organizational-adaptation indirect path was not statistically supported at the study’s stated threshold.
Key claims
- Reported AI implementation was positively associated with AI training (path coefficient 0.602, t = 4.890, p < 0.001), organizational adaptation (0.324, t = 2.562, p = 0.012), and workforce productivity (0.487, t = 3.678, p = 0.001) in this single-organization sample.
- Reported AI training (0.712, t = 5.215, p < 0.001) and organizational adaptation (0.427, t = 3.245, p = 0.002) were each positively associated with reported workforce productivity.
- The AI implementation → AI training → workforce productivity indirect path was supported (0.281, t = 2.146, p = 0.034), while the corresponding organizational-adaptation indirect path was not (0.196, t = 1.782, p = 0.086).
Evidence & limitations
- The study uses a questionnaire census of 70 employees in one Indonesian local-government secretariat and PLS-SEM; its cross-sectional, self-report design supports associations rather than causal effects of implementation, training, or adaptation on productivity.
- Measures and reported validity/reliability procedures are described briefly, so generalization beyond this organization and inference about objective productivity require caution.
- Crossref metadata and the publisher landing page identify this as a 2023 journal article with DOI 10.61536/escalate.v1i01.6; the public publisher page was reachable and its abstract matches the ingested PDF.