Impact of Production Factors on the Competitiveness of the Service Industry in Thailand

Abstract

This study investigates the impact of production factors on the competitiveness of Thailand’s service industry, aiming to examine the relationships among key inputs and their influence on competitiveness. Multiple regression analysis was employed, with capital (CAP), labor (LAB), average wage (WAGE), and technology (TECH) as independent variables, and competitiveness (COMP) as the dependent variable. This research utilizes secondary time-series data comprising 32 annual observations from 1993 to 2024. The LAB variable was sourced from the Ministry of Labor, CAP and COMP from the National Economic and Social Development Council (NESDC), and WAGE from the Bank of Thailand. TECH was calculated using the Solow residual method based on Total Factor Productivity (TFP), allowing for a more precise estimation of technological impact. The findings indicate that capital and average wage are positively and statistically significantly associated with competitiveness in the service sector. Accordingly, the government should promote capital formation through measures such as tax incentives, low-interest financing, and infrastructure development and oversee the adjustment of the labor compensation structure to ensure appropriateness to enhance sustainable competitiveness over the long term. In contrast, labor exhibits a statistically significant negative effect, which may reflect issues related to low labor productivity or mismatches between workforce skills and market demand. Therefore, policy efforts should prioritize reskilling initiatives, digital skills training, and strengthened vocational education to improve labor quality. Although, technology does not demonstrate a statistically significant relationship with service sector competitiveness in this analysis, it remains theoretically important. Policymakers should thus continue to support technological adoption in the service sector particularly automation, artificial intelligence, and data-driven systems to foster long-term innovation and efficiency.