Designing a Performance Evaluation Model for Knowledge Workers in the Atomic Energy Organization of Iran

Abstract

This study aimed to design and validate a comprehensive performance evaluation model for knowledge workers in the Atomic Energy Organization of Iran. An exploratory sequential mixed-methods design was employed. In the qualitative phase, semi-structured interviews were conducted with 12 academic and organizational experts selected purposively until theoretical saturation was reached. Interview data were analyzed through thematic analysis, thematic-network analysis, and the fuzzy Delphi technique using MAXQDA. In the quantitative phase, the statistical population consisted of 110 knowledge workers, managers, and supervisors, of whom 85 were selected through stratified simple random sampling based on Cochran’s formula. Data were collected using a researcher-made questionnaire whose content validity was confirmed by 10 experts. A pilot study was conducted with 30 employees, and the questionnaire demonstrated high internal consistency. Quantitative data were analyzed using SPSS and partial least squares structural equation modeling. Qualitative coding generated 60 indicators classified into eight dimensions: perceived supervisory support, team empowerment, perceived support for innovation, psychological safety, social cohesion, information sharing, internal communication, and external communication. Inter-coder reliability was confirmed by a significant Cohen’s kappa coefficient of 0.758 (p < .001). All indicators were retained after two fuzzy Delphi rounds, with inter-round differences below 0.80. Exploratory factor analysis was supported by KMO = 0.914 and a significant Bartlett’s test, χ²(1770) = 11,876.408, p < .001. Eight factors with eigenvalues greater than one explained 57.585% of the variance. All first-order factor loadings exceeded 0.40 and were significant at p < .001. Second-order loadings ranged from 0.730 to 0.867. Cronbach’s alpha values ranged from 0.733 to 0.947, composite reliability ranged from 0.849 to 0.952, and all AVE values exceeded 0.50. Discriminant validity was confirmed through the Fornell–Larcker criterion, cross-loadings, and HTMT values below 0.90. Model fit was acceptable, with SRMR = 0.071 and NFI = 0.800. Expert validation also confirmed the model’s internal, external, and overall validity (p < .01). The validated model provides a reliable and multidimensional framework for evaluating knowledge-worker performance by integrating managerial, team-based, innovative, psychological, social, and communicational dimensions.