Enhancing and measurement of student activity with the talent pool system at University of Pécs Faculty of Business and Economics

Authors

  • Gábor Balogh PTE KTK Vezetés- és Szervezéstudományi Intézet
  • Ferencné Farkas PTE KTK Vezetés- és Szervezéstudományi Intézet
  • Edit Bányai PTE KTK Marketing és Turizmus Intézet

Keywords:

talent points, talent profiles, higher education, performance evaluation, motivation

Abstract

AIMS OF STUDY

The Competence and Talent Development Center helps the talented students with new methods. The study examines the challenges in talent management and higher education and suggests new techniques to measure and enhance student’s performance. We have been using a new tool (point collection diary) for 3 semesters, so we can collect the activity' points of talents and we created a talent databank which allows us to follow up our student’s achievements and generate talent profiles. Our hypothesis is that there are significant differences between talent profiles by degree programs (BA, MA, PhD) and gender.

METHODOLOGY

We conducted an empirical research. The sample size 132 (talent database). The students collected 5,602 point. We used descriptive statistics and analysis of variance (ANOVA).

MOST IMPORTANT FINDINGS

More students are active in scientific activities in MA program than in BA program and more students are active in business activities in BA program than in MA program, so these two types of activities are in complementary relationship.

PRACTICAL SUGGESTIONS

This tool (talent point diary) can motivate the students and we can analyze their activities, so we can draw the talent profiles of students, programs and the Faculty.

Downloads

Published

2019-10-30

How to Cite

Balogh, G., Farkas, F. and Bányai, E. (2019) “Enhancing and measurement of student activity with the talent pool system at University of Pécs Faculty of Business and Economics”, The Hungarian Journal of Marketing and Management, 50(3-4), pp. 3–18. Available at: https://journals-test.lib.pte.hu/index.php/mm/article/view/882 (Accessed: 7 July 2024).

Issue

Section

-

Most read articles by the same author(s)

1 2 > >> 

Similar Articles

1 2 3 > >> 

You may also start an advanced similarity search for this article.