Beyond Efficiency: Developing and Validating a Multi-Dimensional Scale for AI-Induced Organizational

This study develops and validates the AI-Induced Organizational Performance Scale (AI-OPS), a 24-item instrument measuring six dimensions of AI's impact on organizations in emerging markets: Efficiency Gains, Innovation Enablement, Decision Quality Erosion, Deskilling, Ethical Drift, and Organizational Memory Loss. Drawing on survey data from 351 respondents across Nigerian fintech SMEs and Big Four audit firms (KPMG, EY, PwC, Deloitte), the scale demonstrates robust psychometric properties including high reliability (Cronbach's α = 0.76–0.89), convergent validity (AVE > 0.50), and discriminant validity. Confirmatory factor analysis confirms the six-factor structure (CFI = 0.93, RMSEA = 0.05), with full measurement invariance across both organizational contexts. The AI-OPS provides researchers with a rigorous tool to study AI's paradoxical effects empirically and offers managers a diagnostic to assess AI implementation holistically, moving beyond narrow efficiency metrics to capture both gains and unintended consequences. This preprint was submitted to the 11th African Conference on Information Systems and Technology (ACIST 2025).