Data-driven decision-making in Hungary: Case of the University of Pécs, Faculty of Business and Economics - Haj Taieb, Sirine (Marketing & Menedzsment, 2026)
THE AIM OF THE PAPER This paper delves into data-driven decision-making in higher education, focusing on a business school in Hungary. The aim is to explore the incorporation of data into the decision-making processes, identify the tools employed, and uncover the challenges faculty members face in adopting data-driven decision-making. METHODOLOGY A qualitative study was conducted following a hermeneutic methodology. The research employs a qualitative approach, conducting 14 in-depth semi-structured interviews with faculty members. MOST IMPORTANT RESULTS Analysis of the interviews reveals insights into the faculty’s decentralized decision-making process across operations, marketing, and strategic directions. The study also explores DDDM practices in the classroom, emphasizing the continuous adjustment of courses based on feedback and the integration of DDDM into the curriculum. Six distinct DDDM tools are identified, ranging from classroom-related tools to financial accounting and marketing instruments. Challenges faced by the faculty include the impact of COVID-19, cultural and teaching challenges, data-related issues, management discrepancies, and systemic challenges. Furthermore, the study acknowledges the crucial role of international accreditation in promoting DDDM, which necessitates adherence to academic quality standards. The study also identifies opportunities to enhance capacity management, optimize data management, and develop interconnected systems at the university level. RECOMMENDATIONS A set of recommendations is generated for decision-makers at universities, including maintaining and enhancing accreditations, employing analysis tools that support individual-level data, and implementing a rewards-based system to boost survey response rates.