Vollständiger Abstract
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This study investigates the effects of work–life balance and job satisfaction on professional commitment among employees in private dental clinics in Hanoi, Vietnam, within the context of digital transformation and AI-driven healthcare. Drawing on Social Exchange Theory, the Job Demands–Resources (JD–R) model, and Herzberg’s two-factor theory, the study develops a framework to explain how workplace conditions influence employees’ professional commitment. A quantitative research design was employed, with data collected from 305 employees using structured questionnaires. The proposed relationships were examined using Cronbach’s alpha, exploratory factor analysis (EFA), Pearson correlation analysis, and multiple regression analysis. The results reveal that both work–life balance and job satisfaction exert significant positive effects on professional commitment, with work–life balance demonstrating the stronger influence. These findings indicate that supportive working conditions and employee well-being are essential for strengthening workforce stability and professional attachment in private dental clinics. The study extends the literature by providing empirical evidence from an emerging healthcare market undergoing digital transformation and AI adoption. The findings offer practical implications for healthcare managers and policymakers in designing human resource strategies that enhance employee commitment, service quality, and sustainable organizational performance.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Ngo Thi Mai, Tran Thi Hong Phuong, Trang Nguyen Thi, Luong Tuan Anh, Trinh Viet Giang
- Quelle
- Edelweiss Applied Science and Technology
- Publikation
- 2026-08-18
- Band / Ausgabe
- 10 / 8
- Seiten
- 62-76
- ISSN / ISBN
- 2576-8484
- Zitationen
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Zitierfähiger Nachweis
Ngo Thi Mai, Tran Thi Hong Phuong, Trang Nguyen Thi, Luong Tuan Anh, Trinh Viet Giang (2026). Work–life balance, job satisfaction, and professional commitment in private dental clinics: Evidence in the ERA of digital transformation and AI-driven healthcare. Edelweiss Applied Science and Technology, 10 (8), 62-76. https://doi.org/10.55214/2576-8484.v10i8.13455