Artificial intelligence and public performance management in developing countries: towards an integrated conceptual framework beyond conventional approaches?

Abstract

In the context of global digital transformation, developing countries face multiple challenges in modernizing public performance management. This study proposes an integrated conceptual framework that seeks to overcome the limitations of traditional approaches focused exclusively on financial and operational indicators. A systematic literature review, based on over 70 scientific contributions indexed in databases such as Scopus, Web of Science and Google Scholar, identified five fundamental dimensions: technological, organizational, institutional, strategic and ethical.

These dimensions enable a holistic approach to the opportunities offered by artificial intelligence (AI) to improve the efficiency, transparency and responsiveness of public entities, particularly in contexts marked by scarcity of reliable data, lack of technical skills and structural weaknesses in governance. The proposed conceptual framework is based on the dynamic interaction between these dimensions and the actors involved (decision-makers, civil servants, citizens), integrating algorithmic tools into the evaluation, planning and performance management cycles.

This hybrid model is both an essential tool for understanding the challenges of digital governance and a sound theoretical basis for future empirical research. It also offers concrete orientations for public action, such as resource optimization, service personalization and continuous improvement through predictive analytics. The article fills a theoretical gap by exploring, using a multidisciplinary approach, the interrelationship between technological innovation, institutional performance and sustainable development. It presents a contextualized vision, adapted to the reality of developing countries, and contributes to the debate on responsible artificial intelligence and the reform of public systems.

Classification JEL : O33, H83, O17, H11, L86

Paper type: Theoretical Research