The Impact of Smart Decision-Making Algorithms on Organizational Justice and Organizational Silence of Employees in Smart City Governance
DOI:
https://doi.org/10.63053/ijmea.89Keywords:
Smart Decision-Making Algorithms, Organizational Justice, Organizational Silence, Smart City Governance, Algorithmic ManagementAbstract
Introduction: With the expansion of smart city governance, smart decision-making algorithms have replaced traditional processes in organizational management. This study examines the impact of these algorithms on organizational justice and employee silence.
Methodology: This is an applied study using a descriptive-survey correlational method and structural equation modeling (SEM). The population included employees and middle managers of smart urban management organizations in Iranian metropolises (Tehran, Mashhad, Isfahan, Shiraz). A sample of 384 individuals was selected using Cochran's formula and stratified random sampling. Instruments included the Algorithm Perception Questionnaire (20 items), Organizational Justice Questionnaire (Niehoff & Moorman, 1993; 20 items), and Organizational Silence Questionnaire (Vakola & Bouradas, 2005; 15 items). Reliability was confirmed with Cronbach's alpha of 0.91, 0.89, and 0.87, respectively. Data were analyzed using SPSS and AMOS.
Findings: The mean score of algorithm perception (2.98 out of 5) was higher than the mean of organizational justice (2.67) and organizational silence (3.42). Algorithms had a negative effect on justice (β = -0.42, p < 0.01) and a positive effect on silence (β = 0.53, p < 0.01). Organizational justice had a negative effect on silence (β = -0.38, p < 0.01). The partial mediating role of organizational justice was also confirmed. Fit indices (GFI = 0.90, CFI = 0.92, RMSEA = 0.054) indicated acceptable fit.
Conclusion: Decision-making algorithms in smart city governance, despite their benefits in efficiency and speed, have negative consequences on perceived justice and increase organizational silence. Designing hybrid human-algorithm systems, algorithmic transparency, and effective feedback and grievance mechanisms is essential.
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