AI and performance management efficiency: A quantitative survey
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DOIhttp://dx.doi.org/10.21511/ppm.24(3).2026.10
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Article InfoVolume 24 2026, Issue #3, pp. 130–144
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Type of the article: Research Article
Abstract
This study examines how artificial intelligence (AI) is associated with perceived performance management efficiency of organizations in the United Kingdom. Drawing on the technology acceptance model (TAM), the study tests a revised partial least squares structural equation modeling (PLS-SEM) model in which AI implementation level and AI sophistication predict perceived usefulness, AI training for HR teams predicts perceived ease of use, perceived ease of use predicts perceived usefulness, and perceived usefulness, together with AI utilization maturity, predicts performance management efficiency. Data were collected from 320 decision-makers in the United Kingdom using AI in performance management. The results support all six hypotheses: AI implementation level (beta = 0.188, p < .001), AI sophistication (beta = 0.222, p < .001), AI training for HR teams (beta = 0.256, p < .001), perceived ease of use (beta = 0.336, p < .001), perceived usefulness (beta = 0.324, p < .001), and AI utilization maturity (beta = 0.405, p < .001) were positively associated with their respective endogenous constructs. The model explained 36.0% of the variance in perceived performance management efficiency. The findings contribute to research on AI-enabled HR analytics by showing how organizational AI capabilities and TAM perceptions jointly predict perceived improvements in performance management.
- Keywords
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JEL Classification (Paper profile tab)M12, M15, O33
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References39
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Tables19
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Figures0
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- Table 1. Demographics
- Table 2. Survey response and screening summary
- Table 3. Non-response bias tests
- Table 4. Harman’s single-factor test
- Table 5. Full collinearity assessment
- Table 6. Data screening and final sample
- Table 7. Bootstrapping and PLS-SEM settings
- Table 8. Measurement model: Reliability and convergent validity
- Table 9. Discriminant validity: HTMT matrix
- Table 10. Multicollinearity: Inner VIF values
- Table 11. Structural model: Path coefficients and bootstrap inference
- Table 12. Explanatory power: R2 and adjusted R2
- Table 13. Effect sizes: f2
- Table 14. Predictive relevance: Q2 predict
- Table 15. Model fit
- Table A1. Questionnaire items
- Table A2. Content validity
- Table A3. Measurement model specification
- Table A4. Measurement model: Outer loadings
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