Template-Type: ReDIF-Article 1.0
Author-Name: Huong, Ngo Thi
Author-Name-First: Ngo Thi
Author-Name-Last: Huong
Author-Email: nthuong@utt.edu.vn
Author-Workplace-Name: University of Transport Technology, Hanoi, Vietnam
Author-Name: Tuyet, Vuong Thi Bach
Author-Name-First: Vuong Thi Bach
Author-Name-Last: Tuyet
Author-Email: vtbtuyet@utt.edu.vn
Author-Workplace-Name: University of Transport Technology, Hanoi, Vietnam
Author-Name: Le, Hoang Thi Hong
Author-Name-First: Hoang Thi Hong
Author-Name-Last: Le
Author-Email: hthle@utt.edu.vn
Author-Workplace-Name: University of Transport Technology, Hanoi, Vietnam
Author-Name: Do, Quang Hung
Author-Name-First: Quang Hung
Author-Name-Last: Do
Author-Email: dqhung@ptit.edu.vn
Author-Workplace-Name: Fintech Lab, Posts and Telecommunications Institute of Technology (PTIT), Hanoi, Vietnam
Title: Analyzing Determinants of Tax Management Effectiveness in Transportation Construction Enterprises in Vietnam: A Hybrid SEM-ANN Approach
Abstract: This study investigates the key determinants influencing tax management effectiveness among transportation construction enterprises in Vietnam. Drawing upon the Institutional Theory, Theory of Planned Behavior, and Technology Acceptance Model, a conceptual framework was developed incorporating six factors: Tax Policy, Tax Officers, Infrastructure for Tax Administration, Corporate Tax Compliance Awareness, Digital Technology, and Tax Administration Functions. Data were collected from 385 firms using structured questionnaires. A hybrid methodological approach combining Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN) was employed to capture both linear and nonlinear relationships among constructs. SEM results revealed that all six factors significantly influence tax management effectiveness, with Tax Policy and Tax Officers being the most impactful. The ANN analysis further confirmed these findings and ranked variable importance, highlighting Tax Policy as the strongest predictor. The study offers practical implications for policymakers aiming to enhance tax governance in project-based, capital-intensive sectors and demonstrates the value of hybrid modeling in public finance research.
Keywords: Tax Management Effectiveness, Transportation Construction, Structural Equation Modeling, Artificial Neural Network, Tax Policy, Public Finance
Journal: International Journal of Economics and Financial Issues
Pages: 119-129
Volume: 15
Issue: 6
Year: 2025
Month: 10
DOI: 10.32479/ijefi.20562
File-URL: https://econjournals.com/index.php/ijefi/article/download/20562/9271
File-Format: application/pdf
Handle: RePEc:eco:journ1:v:15:y:2025:i:6:id:20562