Data-driven decision-making in Hungary: Case of the University of Pécs, Faculty of Business and Economics
DOI:
https://doi.org/10.15170/MM.2026.60.01.02Kulcsszavak:
data-driven decision-making, faculty, challenges, data, toolsAbsztrakt
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.
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