dr hab. Aleksander LOTKO – Uniwersytet Radomski im. Kazimierza Pułaskiego, Wydział Inżynierii Chemicznej i Towaroznawstwa, ul. Chrobrego 29, 26-600 Radom, Polska;
dr hab. inż. Krzysztof MELSKI – Uniwersytet Ekonomiczny w Poznaniu, Instytut Nauk o Jakości, al. Niepodległości 10, 61-875 Poznań, Polska; e-mail: firstname.lastname@example.org
dr hab. Małgorzata LOTKO – Uniwersytet Radomski im. Kazimierza Pułaskiego, Wydział Inżynierii Chemicznej i Towaroznawstwa, ul. Chrobrego 29, 26-600 Radom, Polska;
Received 2.10.2023. Accepted 23.10.2023
Purpose: Segmentation of students according to the values of predictors of choosing a field of study, determining the importance of these predictors and indicating its consequences for university marketing.
Design/methodology/approach: The research was carried out using an original questionnaire on a sample of 240 students of the Poznań University of Economics in the fields of product quality and development (JiRP) and production management and engineering (ZIP). Classification trees and the CART algorithm were used to develop the ranking of predictors and the characteristics of the obtained segments.
Findings/conclusions: A model for classifying students according to predictors related to the criteria for choosing a field of study was built. The most important predictors turned out to be: (1) the name of the field of study, (2) the possibility of obtaining a professional title of engineer and (3) sources of information about the future field of study.
Research limitations: Small sample size (240 students) and only 2 fields of study included.
Practical implications: Providing recommendations important for effective university marketing activities. In the JiRP field, the decisive predictor is the possibility of obtaining a professional engineering title, while in the ZIP field, the name of the field of study is such a predictor.
Originality/value: Application of classification trees in the study area. Obtained student segmentation, ranking of choice predictors and indication of marketing implications of the results of these analyses.
post commodity science, choosing a field of study, segmentation, university marketing