DIGITAL TRANSFORMATION SUPPORT, JOB PERFORMANCE, AND SELF-REPORTED INCOME CATEGORY AMONG VIETNAMESE SME WORKERS: EVIDENCE FROM SURVEY DATA
DOI:
https://doi.org/10.62985/j.huit_ojs.vol26.no3E.489Từ khóa:
Digital transformation support, job performance, income category, digital skills, SMEs, PLS-SEM.Tóm tắt
This study examines the associations among firm-level digital transformation support, job performance, and self-reported income category among workers in Vietnamese small and medium-sized enterprises (SMEs), while assessing the measurement properties of five digital-skill dimensions based on the DigComp 2.1 framework. Data from 650 valid survey observations were analyzed using partial least squares structural equation modeling (PLS-SEM). The measurement results show satisfactory reliability, convergent validity, and discriminant validity for the five digital-skill dimensions and for the structural-model constructs digital transformation support and job performance. Digital transformation support is positively associated with job performance (β = 0.232; t = 6.265; p < 0.001), and job performance is positively associated with income category (β = 0.628; t = 25.860; p < 0.001). The indirect association between digital transformation support and income category through job performance is also positive and statistically significant (β = 0.146; t = 6.068; p < 0.001). The model explains 5.4% of the variance in job performance and 39.4% of the variance in income category. The findings suggest that firm-level digital support is related to better job performance, while job performance is the stronger correlate of income category. Because the study uses non-probability, self-reported, cross-sectional data and an ordered income measure, the results should be interpreted as associations rather than causal effects.
Tài liệu tham khảo
[1] G. S. Becker, Human Capital: A Theoretical and Empirical Analysis with Special Reference to Education. New York, NY, USA: National Bureau of Economic Research, 1964. [Online]. Available: https://www.nber.org/books-and-chapters/human-capital-theoretical-and-empirical-analysis-special-reference-education-first-edition. Accessed: Jun. 30, 2026.
[2] J. A. Mincer, Schooling, Experience, and Earnings. New York, NY, USA: National Bureau of Economic Research, 1974. [Online]. Available: https://www.nber.org/books-and-chapters/schooling-experience-and-earnings. Accessed: Jun. 30, 2026.
[3] D. Acemoglu, “Technical change, inequality, and the labor market,” Journal of Economic Literature, vol. 40, no. 1, pp. 7–72, 2002, doi: https://doi.org/10.1257/0022051026976
[4] D. H. Autor, L. F. Katz, and A. B. Krueger, “Computing inequality: Have computers changed the labor market?” The Quarterly Journal of Economics, vol. 113, no. 4, pp. 1169–1213, 1998, doi: https://doi.org/10.1162/003355398555874
[5] S. Carretero, R. Vuorikari, and Y. Punie, DigComp 2.1: The Digital Competence Framework for Citizens with Eight Proficiency Levels and Examples of Use. Publications Office of the European Union, 2017, doi: https://doi.org/10.2760/38842
[6] A. J. A. M. van Deursen, E. J. Helsper, and R. Eynon, “Development and validation of the Internet Skills Scale (ISS),” Information, Communication & Society, vol. 19, no. 6, pp. 804–823, 2016, doi: https://doi.org/10.1080/1369118X.2015.1078834
[7] V. Venkatesh et al., “User acceptance of information technology: Toward a unified view,” MIS Quarterly, vol. 27, no. 3, pp. 425–478, 2003, doi: https://doi.org/10.2307/30036540
[8] L. J. Williams and S. E. Anderson, “Job satisfaction and organizational commitment as predictors of organizational citizenship and in-role behaviors,” Journal of Management, vol. 17, no. 3, pp. 601–617, 1991, doi: https://doi.org/10.1177/014920639101700305
[9] L. Bergkvist and J. R. Rossiter, “The predictive validity of multiple-item versus single-item measures of the same constructs,” Journal of Marketing Research, vol. 44, no. 2, pp. 175–184, 2007, doi: https://doi.org/10.1509/jmkr.44.2.175
[10] J.-M. Becker, K. Klein, and M. Wetzels, “Hierarchical latent variable models in PLS-SEM: Guidelines for using reflective-formative type models,” Long Range Planning, vol. 45, nos. 5-6, pp. 359-394, 2012, doi: https://doi.org/10.1016/j.lrp.2012.10.001
[11] M. Sarstedt, J. F. Hair, J.-H. Cheah, J.-M. Becker, and C. M. Ringle, “How to specify, estimate, and validate higher-order constructs in PLS-SEM,” Australasian Marketing Journal, vol. 27, no. 3, pp. 197-211, 2019, doi: https://doi.org/10.1016/j.ausmj.2019.05.003
[12] J. F. Hair et al., A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 3rd ed. Sage, 2022.
[13] N. Kock, “Common method bias in PLS-SEM: A full collinearity assessment approach,” International Journal of e-Collaboration, vol. 11, no. 4, pp. 1-10, 2015, doi: https://doi.org/10.4018/ijec.2015100101
[14] P. M. Podsakoff et al., “Common method biases in behavioral research: A critical review of the literature and recommended remedies,” Journal of Applied Psychology, vol. 88, no. 5, pp. 879-903, 2003, doi: https://doi.org/10.1037/0021-9010.88.5.879
