Decomposing Ecosystem Effects On Digital Entrepreneurial Intention Among International Students In Busan: A Pls-Sem Study

Authors

  • Sobirjonova Mohinur Shukhratjon Kizi Tongmyong University
  • Lama Tenzen Angmu Tongmyong University
  • Edward Yi Tongmyong University
  • Uturestantix Tongmyong University

DOI:

https://doi.org/10.24905/icobis.v2i1.1

Keywords:

Academic discipline, Digital entrepreneurship, Entrepreneurial ecosystem, International students, PLS-SEM

Abstract

Digital entrepreneurship offers international students a route to convert cross-border knowledge, platform access, and local resources into new venture opportunities. However, aggregate evidence can overstate ecosystem effects when student populations differ sharply by academic discipline. This study examines regulatory accessibility, digital ecosystem resources, cross cultural adaptability, and digital entrepreneurial intention among 120 international students in Busan, South Korea. A mixed measurement partial least squares structural equation model specified regulatory accessibility and ecosystem resources as formative composites and cultural adaptability and entrepreneurial intention as reflective constructs. The pooled model showed a strong association between ecosystem resources and entrepreneurial intention, whereas regulatory accessibility and cultural adaptability were not significant. Predictive assessment yielded positive Q²predict values for all intention indicators. However, a control-adjusted robustness model reduced the ecosystem coefficient to 0.081 (p = 0.392); academic discipline became the dominant correlate, with Engineering/IT positive and Humanities/Arts negative relative to Business/Logistics. The findings reveal an aggregation-driven composition effect: apparent ecosystem influence largely reflects disciplinary clustering rather than a general within-student mechanism. The study offers a cautionary perspective on measurement and modeling in digital entrepreneurial ecosystem research and recommends discipline-sensitive entrepreneurship programming, transparent data provenance, and longitudinal designs before drawing policy conclusions.

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Published

2026-09-24

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