Human Trust in AI-Driven Performance Evaluation Systems: A Structural Equation Modeling Approach

@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}
  }