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Research Paper | Digital Investigation | Volume 15 Issue 8, August 2026 | Pages: 1803 - 1816 | Zimbabwe
Developing an AI-Enabled Digital Transformation Framework for Anti-Corruption Operations: A Mixed-Methods Study of the Zimbabwe Anti-Corruption Commission
Abstract: Despite the constitutional mandate of anti-corruption agencies in emerging economies and growing international evidence of Artificial Intelligence (AI) efficacy in enhancing investigative operations, the design and implementation of context-sensitive AI-enabled digital transformation frameworks remain critically underexplored in resource-constrained institutional environments. This study addresses this gap by developing and empirically validating a comprehensive digital transformation framework specifically tailored to the Zimbabwe Anti-Corruption Commission (ZACC). Anchored in an integrated theoretical model combining Sociotechnical Systems Theory, the Unified Theory of Acceptance and Use of Technology (UTAUT), and adaptive change management perspectives, the research employs a convergent parallel mixed-methods case study design. Quantitative data from 100 ZACC personnel assessed digital maturity and technology acceptance through Structural Equation Modelling (SEM), while qualitative insights from 25-30 key stakeholders were analyzed via reflexive thematic analysis. Findings reveal that ZACC operates at an early-stage digital maturity level with significant infrastructure, governance, and capacity constraints. Performance expectancy, facilitating conditions, and institutional capacity emerged as the strongest predictors of AI adoption intention. The study contributes a theoretically grounded, four-layered digital transformation framework (with supporting visual models and diagrams) integrating AI governance, technology acceptance, organizational change management, and contextual adaptations for resource-constrained environments. A phased implementation roadmap with measurable milestones provides actionable guidance for practitioners. This research advances public sector digital transformation scholarship by demonstrating how AI-enabled frameworks must be co-designed with institutional capacity building, ethical governance structures, and culturally sensitive change management to achieve sustainable operational effectiveness in emerging economy anti-corruption institutions.
Keywords: Digital transformation, Artificial Intelligence, anti-corruption, public sector, mixed-methods research, UTAUT, sociotechnical systems, Zimbabwe, organizational change management, digital maturity assessment, technology acceptance, AI governance, emerging economies
How to Cite?: Arthur Murambiza, "Developing an AI-Enabled Digital Transformation Framework for Anti-Corruption Operations: A Mixed-Methods Study of the Zimbabwe Anti-Corruption Commission", Volume 15 Issue 8, August 2026, International Journal of Science and Research (IJSR), Pages: 1803-1816, https://www.ijsr.net/getabstract.php?paperid=SR26825201823, DOI: https://dx.doi.org/10.21275/SR26825201823