Reevaluating the Meritocracy Myth: How Promotion Criteria, Algorithmic Scoring, and Generative-AI Screening Shape Long-Term Organizational Productivity

Abstract

This study re-examined the assumption that promotion systems and advanced HR technologies operate under neutral meritocratic principles. It explored how formal promotion criteria, algorithmic scoring tools, and generative-AI screening shaped long-term organizational productivity, with particular attention to perceptions of fairness, transparency, and legitimacy. A mixed-methods design was employed, combining survey data from employees, managers, and HR professionals with qualitative interviews from AI-enabled organizations. Quantitative analysis revealed that although algorithmic and AI-driven HR processes increased administrative efficiency and consistency, these technologies did not independently predict higher productivity. Instead, perceived fairness and transparency emerged as significantly stronger predictors of employee engagement and productivity outcomes. Regression analysis further indicated that fairness perceptions exerted the largest influence on productivity compared with algorithmic scoring and AI screening mechanisms. Qualitative findings supported these results, showing that employees were more likely to accept AI-mediated decisions when clear explanations, human oversight, and accessible appeals processes were present. Conversely, opaque or fully automated systems were associated with distrust, perceived bias, and weaker motivational outcomes. The study concluded that AI should complement, rather than replace, human decision-makers and that responsible governance frameworks were necessary to ensure equity and sustained performance benefits. Recommendations focused on transparency design, hybrid decision structures, bias audits, and digital literacy development. Future research was suggested in cross-cultural contexts, longitudinal adoption effects, and explainable-AI applications in HR decision-making.