Performance Measurement Framework Using Human Resource Analytics for Private Higher Education Institutions
The purpose of this research is to provide a performance measurement framework with key performance indicators (KPIs) practiced by private higher education institutions (HEIs) in terms of instruction, research, and community extension which used human resource analytics (HRA) for objectively measuring academic performance. This is to provide an analytical scientific approach using Analytical Hierarchy Process (AHP) to measure the performance of academic staff of private HEIs in South Cotabato, Sarangani, and General Santos City (SOCSARGEN). This research used the sequential qualitative-quantitative research design methodology which conducted two (2) sessions of focus group discussions (FGD) from seven (7) participating schools with fifteen (15) participants and utilization of the developed performance measurement tool to three (3) private HEIs as basis to develop the performance measurement framework. The selection of participants consists of academic administrators and staff using purposive sampling. This study uses thematic analysis, AHP and ranking to obtain objective and reliable insights on performance evaluation relevant for human resource analytics. Based on results, in terms of instruction, sub-KPIs include work ethics, personality and values, teaching competence and performance, learning atmosphere, qualification, involvement and productivity. In terms of research, sub-KPIs include attendance and presentation, citations and publications, journal memberships and research involvement. In terms of community extension, sub-KPIs include participation and involvement in extension works and external linkages. This research also developed a general performance measurement framework which consists of setting goals and objectives, indicators, evaluation, analysis, reporting and review.
Authors
- Denise Meryl Robles
Publication Details
- Journal
- Business fora
- Published
- 2025-01-10
- DOI
- https://doi.org/10.62718/vmca.bf-baiij.3.1.sc-1224-007
- Primary Topic
- Big Data and Business Intelligence
- Type
- article
- Field-Weighted Citation Impact
- 0.00