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Human Trust in AI-Driven Performance Evaluation Systems: A Structural Equation Modeling Approach
Subject area: Science,Engineering and Technology · Area of research: Artificial Intelligence
DOI: 10.64388/IREV9I12-1718542
Abstract
Artificial Intelligence (AI) is transforming Human Resource Management by enabling automated performance evaluation systems that improve efficiency, objectivity, and decision-making. However, employee trust remains a major challenge in the successful adoption of AI-driven appraisal systems. This research paper examines the determinants of human trust in AI-driven performance evaluation systems using Structural Equation Modeling (SEM). The study investigates the influence of perceived fairness, transparency, explainability, data privacy, and organizational support on employee trust, acceptance, and job satisfaction. A quantitative research methodology using structured questionnaires is proposed. The findings indicate that fairness, transparency, and organizational communication significantly influence employee trust in AI systems. The study contributes to the literature on AI-enabled HRM and provides managerial recommendations for ethical and transparent implementation of AI-driven performance evaluation systems.
Keywords
Artificial Intelligence, Human Resource Management, Trust in AI, Structural Equation Modeling, Performance Evaluation, Employee Acceptance, Algorithmic Fairness
References
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[3] Shin, D. (2021). The Effects of Explainability and Causability on Perception, Trust, and Acceptance: Implications for Explainable AI. International Journal of Human-Computer Studies, 146, 102551.
[4] Parasuraman, A Sheridan, T. B., & Wickens, C. D. (2000). A Model for Types and Levels of Human Interaction with Automation. IEEE Transactions on Systems, Man, and Cybernetics — Part A: Systems and Humans, 30(3), 286-297.
[5] Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate Data Analysis (8th ed.). Cengage Learning.
[6] Kline, R. B. (2015). Principles and Practice of Structural Equation Modeling (4th ed.). The Guilford Press.
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How to cite this paper
@article{1718542,
author = {Vaibhav Kumar, Dr. Mousmi Goel},
title = {Human Trust in AI-Driven Performance Evaluation Systems: A Structural Equation Modeling Approach},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {12},
pages = {1-4},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1718542.pdf},
abstract = {Artificial Intelligence (AI) is transforming Human Resource Management by enabling automated performance evaluation systems that improve efficiency, objectivity, and decision-making. However, employee trust remains a major challenge in the successful adoption of AI-driven appraisal systems. This research paper examines the determinants of human trust in AI-driven performance evaluation systems using Structural Equation Modeling (SEM). The study investigates the influence of perceived fairness, transparency, explainability, data privacy, and organizational support on employee trust, acceptance, and job satisfaction. A quantitative research methodology using structured questionnaires is proposed. The findings indicate that fairness, transparency, and organizational communication significantly influence employee trust in AI systems. The study contributes to the literature on AI-enabled HRM and provides managerial recommendations for ethical and transparent implementation of AI-driven performance evaluation systems.},
keywords = {Artificial Intelligence, Human Resource Management, Trust in AI, Structural Equation Modeling, Performance Evaluation, Employee Acceptance, Algorithmic Fairness},
month = {June},
doi = {https://doi.org/10.64388/IREV9I12-1718542}
}