A Principal Component Analysis of Key Factors Influencing Enrollment Decisions in a Bachelor of Business Administration Program

Umaporn Chaisoong; Thitimaporn Waenphet; Nonthakan Nganjaturus; Jandara Suksam; Thanet Yothasiri; Waraluck Maprasom

doi:10.65205/jcct.2026.e3446

Authors

Umaporn Chaisoong Program in Digital Business Technology, Faculty of Management Technology, Rajamangala University of Technology Isan Surin Campus, Surin 32000, Thailand
Thitimaporn Waenphet Program in Digital Business Technology, Faculty of Management Technology, Rajamangala University of Technology Isan Surin Campus, Surin 32000, Thailand
Nonthakan Nganjaturus Program in Digital Business Technology, Faculty of Management Technology, Rajamangala University of Technology Isan Surin Campus, Surin 32000, Thailand
Jandara Suksam Program in Digital Business Technology, Faculty of Management Technology, Rajamangala University of Technology Isan Surin Campus, Surin 32000, Thailand https://orcid.org/0009-0006-3189-052X
Thanet Yothasiri Program in Digital Business Technology, Faculty of Management Technology, Rajamangala University of Technology Isan Surin Campus, Surin 32000, Thailand
Waraluck Maprasom Program in Digital Business Technology, Faculty of Management Technology, Rajamangala University of Technology Isan Surin Campus, Surin 32000, Thailand

DOI:

https://doi.org/10.65205/jcct.2026.e3446

Keywords:

Enrollment Decision, Principal Component Analysis, Data Mining, Higher Education, Surin

Abstract

This study aims to identify and extract the key factors influencing students’ enrollment decisions in higher education by adopting a data-driven analytical approach. Principal Component Analysis (PCA) was employed within the CRISP-DM framework to systematically analyze quantitative data collected from 571 prospective students across three provinces in Thailand. The survey instrument comprised 45 observed variables. Data suitability tests indicated strong adequacy for factor analysis, with a Kaiser–Meyer–Olkin (KMO) value of 0.963 and a statistically significant Bartlett’s Test of Sphericity. The PCA results, using Varimax rotation, revealed five latent components shaping enrollment decisions: financial readiness, institutional support and safety, student identity formation, technological and curriculum relevance, and peer guidance. The findings demonstrate that enrollment decision-making is multidimensional and hierarchical, extending beyond academic reputation to include economic, psychosocial, and experiential considerations. Academically, this study contributes by integrating PCA with educational data mining to provide a structured interpretation of student choice behavior. Practically, the results offer evidence-based insights to support curriculum development, targeted communication strategies, and enrollment policy design in higher education institutions.

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