Modeling Motivation Strategies for Enhancing Employee Management and Training Effectiveness in Guangxi Metal Processing Enterprises: A Structural Equation Modeling Approach

Wejie Yin; Watcharanan  Thongma; Surachai  Kungwon; Danaikrit  Inturit

doi:10.60027/iarj.2026.e291988

Authors

Wejie Yin Maejo University, Chiang Mai, Thailand https://orcid.org/0009-0009-8283-1361
Watcharanan Thongma Maejo University, Chiang Mai, Thailand https://orcid.org/0000-0001-9819-0065
Surachai Kungwon Maejo University, Chiang Mai, Thailand https://orcid.org/0009-0005-0168-4330
Danaikrit Inturit Maejo University, Chiang Mai, Thailand https://orcid.org/0009-0006-3961-0764

DOI:

https://doi.org/10.60027/iarj.2026.e291988

Keywords:

Cultural Dimensions, ICT Competence, Policy Support, Task Technology Fit, Intrinsic and Extrinsic Motivation, Training Effectiveness, Structural Equation Modeling, Guangxi Metal Processing

Abstract

Background and Aims: Guangxi’s metal processing sector is key to its economic shift but faces workforce challenges in adapting to Industry 4.0, particularly in digital readiness, motivation, and training. Guided by an integrated framework drawing on TAM, SDT, Hofstede’s cultural dimensions, and the TOE perspective, this study examines how culture, ICT competence, policy support, and motivation relate to employee management and training.

Methodology: A cross-sectional survey of 411 employees was collected via convenience sampling; content validity was established by three experts with an index of item objective congruence of 0.81, a pilot with 30 participants was conducted, and reliability was high with Cronbach’s alpha above 0.90. SEM and CFA were used to analyze key variable relationships.

Results: All four predictors—cultural dimensions, ICT competence, policy support, and intrinsic/extrinsic motivation—were positive and significant for employee management and training effectiveness (p < 0.001). Motivation showed the largest standardized effect (β = 0.234), followed by culture (β = 0.226), policy support (β = 0.221), and ICT competence (β = 0.220). Model fit was excellent, with CFI and TLI equal to 1.000 and RMSEA equal to 0.002, values that indicate near-perfect comparative fit and a close approximate fit, and the model explained 62.6% of the variance (R² = 0.626).

Conclusion: Motivation, both intrinsic and extrinsic, has the strongest effect on employee management and training effectiveness. Together with culture, ICT competence, and policy support, it constitutes a core set of levers for workforce development. Prioritizing motivation-centered practices alongside cultural alignment, clear policy signals, and role-relevant ICT skills supports sustained digital transformation in the metal processing sector.

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