Abstract:

The main challenges that the Higher Education Institutions (HEI) faces across the globe are pressures from globalisation, changing supply of and demand for higher education and changes in higher education funding. The higher education is characterized by the extreme expansion of system, new global players, more diverse institutions, study programs and students, accepting and implementing IT technologies in learning and teaching, internationalization, growing costs and changing modes and roles of governance, increasing emphasis on performance, quality and accountability of the HEI. The results are more complex and more demanding missions, visions and strategic planning in HEI so the need for using methodology for comprehensive strategic planning and objectives prioritization has been expanded.

Higher education institutions (HEI) often use different quality systems, such as Balanced scorecard (BSC) or Total quality management (TQM), for strategic planning of their growth and development. In BSC, we create a strategic map of goals that are placed into one of several BSC perspectives. After the goals are placed into a strategic map, we identify connections between the goals respecting the influences between the goals of HEI. The rule is that the goals from a particular perspective can influence any goal from the same or higher BSC perspective. The reasoning related to the identification of connections between the goals have to be made by experts in the field and managers of higher education institution.

Many papers deal with the ranking of BSC goals. Often, methods such as the Analytic hierarchy process (AHP) and the Analytic network process (ANP), are used in the context of prioritization of BSC goals. The weak point of using the AHP is related to the fact that the influences between the goals in the strategic map are not considered. The weak point of BSC goals prioritization using the ANP is related to the reducibility of supermatrix that is generated in the ANP process. The reducibility does not ensure priorities – weights of all BSC goals equal to 0.0. To deal with this problem, some authors proposed introducing the fictive alternative. In this paper, we are analyzing the possibility of applying the centrality measures (that are initially used in the method called Social Network Analysis (SNA)). The centrality measures that are included in the analysis include centrality degree, betweenness centrality, closeness centrality, and the PageRank centrality. The main conclusion of the analysis is that the PageRank centrality can be efficiently used to rank BSC goals of higher education institutions and help them to set priorities.

Keywords:

Higher education, BSC, PageRank, ranking, AHP, ANP.