Identifying and Measuring Technical Inefficiency Factors:Evidence from Unbalanced Panel Data for Thai Listed Manufacturing Enterprises

This study employs stochastic frontier analysis (SFA) and two-stage DEA approaches to predict firm technical efficiency and analyse an inefficiency effects model. Aggregate translog stochastic frontier production functions are estimated under the SFA approach using an unbalanced panel data of 178 Thai manufacturing enterprises listed in the Stock Exchange of Thailand (SET), covering the period 2000 to 2008. The maximum-likelihood Tobit model is used to conduct the second-stage of the two-stage DEA model to investigate the relationship between technical inefficiency and environmental variables. Both parametric and nonparametric approaches are found to produce consistent results. The empirical evidence from both approaches highlight that Thai listed manufacturing firms had been operating under decreasing returns to scale over the period 2000 to 2008. The SFA approach reports that technical progress decreased over time, and relied on labour input. Both estimation approaches suggest that leverage (financial constraints), executive remuneration, managerial ownership, exports, some types of listed firms (i.e., family-owned firm and foreign-owned firm), and firm size have a negative (positive) and significant effect on technical inefficiency (technical efficiency). The empirical results obtained from both approaches also suggest that liquidity, external financing, and research & development (R&D) have a significantly positive (negative) effect on technical inefficiency (technical efficiency)Stochastic Frontier Analysis (SFA); Data Envelopment Analysis (DEA);Technical Efficiency; Manufacturing; Thailand

Identifying and measuring technical inefficiency factors: evidence from unbalanced panel data for Thai listed manufacturing enterprises

This study employs stochastic frontier analysis (SFA) and two-stage DEA approaches to predict firm technical efficiency and analyse an inefficiency effects model. Aggregate translog stochastic frontier production functions are estimated under the SFA approach using an unbalanced panel data of 178 Thai manufacturing enterprises listed in the Stock Exchange of Thailand (SET), covering the period 2000 to 2008. The maximum-likelihood Tobit model is used to conduct the second-stage of the two-stage DEA model to investigate the relationship between technical inefficiency and environmental variables. Both parametric and nonparametric approaches are found to produce consistent results. The empirical evidence from both approaches highlight that Thai listed manufacturing firms had been operating under decreasing returns to scale over the period 2000 to 2008. The SFA approach reports that technical progress decreased over time, and relied on labour input. Both estimation approaches suggest that leverage (financial constraints), executive remuneration, managerial ownership, exports, some types of listed firms (i.e., family-owned firm and foreign-owned firm), and firm size have a negative (positive) and significant effect on technical inefficiency (technical efficiency). The empirical results obtained from both approaches also suggest that liquidity, external financing, and research & development (R&D) have a significantly positive (negative) effect on technical inefficiency (technical efficiency

Barriers and Factors Affecting the E-Commerce Sustainability of Thai Micro-, Small- and Medium-Sized Enterprises (MSMEs)

It is anticipated that e-commerce will contribute to achieving the 17th Sustainable Development Goal, which seeks to improve implementation mechanisms and revitalize global partnerships for sustainable development. However, MSMEs still face a digital gap compared to large enterprises, which affects their e-commerce sustainability. The study’s objective is to examine the factors and barriers affecting the e-commerce sustainability of Thai micro-, small- and medium-sized enterprises (MSMEs) based on a survey of retail and food and beverage (F&B) service MSMEs in metropolitan Bangkok. Estimations confirm the significance of the TOE framework for Thai MSMEs. Internal e-commerce tools (i.e., smartphones and websites) and external e-commerce platforms (i.e., social media, e-marketplaces, and food delivery platforms) can enhance e-commerce sustainability. However, the age of firms and owners (CEOs) affects e-commerce sustainability negatively. Exports, B2B e-commerce, and e-commerce experience can promote the e-commerce sustainability of Thai MSMEs. However, they perceive that many consumers are still not literate in using e-commerce. In addition, Thailand still has insufficient security to prevent hacking and malware. Therefore, Thai entrepreneurs’ e-commerce literacy is insufficient to enhance their e-commerce sustainability. On the other hand, sustainable e-commerce can increase customer satisfaction, loyalty, and trust through customer support, leading to more long-term online shopping. Hence, this study focuses on e-commerce sustainability-based economic dimensions, as measured by the percentage of e-commerce sales to total sales (e-commerce utilization/intensity)

Technical efficiency performance of Thai listed manufacturing enterprises

This thesis aims to measure the technical efficiency performance of Thai listed manufacturing enterprises over the period 2000 to 2008. It also aims to identify and measure firm-specific and business environment factors which significantly impact on the technical efficiency performance of Thai listed manufacturing enterprises. Unbalanced panel data for 178 Thai listed manufacturing enterprises over the period 2000 to 2008 is compiled and used to conduct an empirical analysis employing both parametric (Stochastic Frontier Analysis (SFA)) and non-parametric data Envelopment Analysis (DEA)) approaches. This provides a robust check of the empirical results to analyse technical efficiency performances as well as significant factors influencing the efficiency of Thai listed manufacturing enterprises including sub-manufacturing sectors. The empirical results of this study reveal that the mean technical efficiency scores of Thai listed manufacturing enterprises obtained from the SFA and DEAapproaches are found to be quite consistent, given by 0.812 and 0.887 respectively,indicating that they operated at a high level of technical efficiency. Even though their technical efficiency performance is high, the empirical evidence from both estimation approaches confirm that they had operated under decreasing returns to scale over the period 2000 to 2008. More specifically, the SFA approach reveals the existence of decreasing returns to scale for Thai listed manufacturing enterprises. Similarly, the DEA approach suggests that approximately 86 percent of Thai listed manufacturing enterprises, on average, operate under decreasing returns to scale. Theempirical results from the SFA approach also highlight that the production of Thai listed manufacturing enterprises is mainly contributed by intermediate inputs and labour input, but capital is found to be the least important input. Similarly, the empirical evidence from an estimated Translog production function confirm theexistence of labour-using and capital-saving technical progress for Thai listedmanufacturing enterprises, indicating that their technical progress relied on labour input over the period 2000 to 2008. Moreover, the rate of technical progress is found to be 0.0205 for Thai listed manufacturing enterprises, technical change only increased by 2.05 percent per year. As a result they must attain a higher production frontier to enhance their future technical efficiency performance. The empirical evidence from both the SFA and DEA approaches also revealthat financial constraints (leverage) have a significant and positive association with the technical efficiency of Thai listed manufacturing enterprises. To confirm thisem irical evidence the empirical results from both estimation approaches reveal that liquidity has a significant and positive impact on their technical efficiencyperformance. In addition, the empirical evidence from both estimation approaches indicate that both external and internal financing are found to have a negative association with the technical efficiency performance of Thai listed manufacturing enterprises, but only the empirical evidence from the SFA approach is found to be statistically significant. External financing, however, does not exert a significant impact on their technical efficiency due to the very small magnitude of the estimatedcoefficients. The empirical results from both estimation approaches also reveal thatresearch and development (R&D) has a significant and negative association with the technical efficiency of Thai listed manufacturing enterprises. The empirical resultsfrom both estimation approaches, however, reveal that controlling ownership has a positive association with the technical efficiency performance of Thai listed manufacturing enterprises, but only the SFA approach produces a significant result. There is strong evidence from both estimation approaches that managerial ownershiphas a significant and positive impact on the technical efficiency of Thai listedmanufacturing enterprises. Both estimation approaches also strongly confirm thatexecutive remuneration has a significant and positive influence on their technical efficiency performance. Focusing on different types of firm ownership there is strong evidence from both estimation approaches that foreign and family ownership exerts asignificant and positive effect on the technical efficiency of Thai listed manufacturing enterprises. According to the magnitude of the estimated coefficients of each type of firm ownership, there is strong evidence from both estimation approaches that foreign and family ownership exerts asignificant and positive effect on the technical efficiency of Thai listedmanufacturing enterprises. According to the magnitude of the estimated coefficients of each type of firm ownership, there is strong evidence from both estimation approaches that foreign-owned firms perform best, followed by family-owned firms, hybrid-owned firms and domestic- owned firms, given joint-owned firms as the base category. There is strong evidence of a learning-by-exporting hypothesis from bothestimation approaches, indicating exporting exerts a significant and positive effect one technical efficiency performance of Thai listed manufacturing enterprises. Vice versa, there is evidence of the self-selection hypothesis that a firm’s technicalefficiency predicted by the SFA approach has a significant and positive impact on theexport participation of Thai listed manufacturing enterprises. A positive result is also found from the DEA approach, but is not statistically significant. Finally, the robust results from this study can be used to provide empiricallybased policy implications and recommendations which are useful to both policymakersand entrepreneurs to enhance the long-term efficiency and competitiveness of Thai listed manufacturing enterprises

Measuring technical inefficiency factors for Thai listed manufacturing enterprises: A stochastic frontier (SFA) and data envelopment analysis (DEA)

This study employs stochastic frontier analysis (SFA) and two-stage DEA approaches to predict firm technical efficiency and analyse an inefficiency effects model for overall Thai listed manufacturing sector enterprises including sub-listed manufacturing sector enterprises using an unbalanced panel data for 178 Thai listed manufacturing enterprises over the period 2000 to 2008. Both estimation approaches are found to produce consistent results for overall Thai listed manufacturing sector enterprises. For sub-listed manufacturing sector enterprises both approaches empirically find quite consistent results in coefficient signs, but significance results from both estimation approaches may be different. Focusing on overall Thai listed enterprises both approaches suggest that leverage (financial constraints), executive remuneration, managerial ownership, exports, some types of listed firms (i.e., family-owned firm, foreign-owned firm, and hybrid-owned firm), and firm size have a negative (positive) and significant effect on technical inefficiency (technical efficiency).The empirical results obtained from both approaches also suggest that liquidity, external financing, and research & development (R&D) have a significantly positive (negative) effect on technical inefficiency (technical efficiency

Identifying and measuring factors of technical inefficiency: evidence from unbalanced panel data of Thai listed manufacturing enterprises

This study employs stochastic frontier analysis (SFA) and two-stage DEA approaches to predict firm technical efficiency and analyse an inefficiency effects model. Aggregate translog stochastic frontier production functions are estimated under the SFA approach using an unbalanced panel data of 178 Thai manufacturing enterprises listed in the Stock Exchange of Thailand (SET), covering the period 2000 to 2008. The maximum-likelihood Tobit model is used to conduct the second-stage of the two-stage DEA model to investigate the relationship between technical inefficiency and environmental variables. Both parametric and non-parametric approaches are found to produce consistent results. The empirical evidence from both approaches highlight that Thai listed manufacturing firms had been operating under decreasing returns to scale over the period 2000 to 2008. The SFA approach reports that technical progress decreased over time, and relied on labour input. Both estimation approaches suggest that leverage (financial constraints), executive remuneration, managerial ownership, exports, some types of listed firms (i.e., family-owned firm and foreign-owned firm), and firm size have a negative (positive) and significant effect on technical inefficiency (technical efficiency). The empirical results obtained from both approaches also suggest that liquidity, external financing, and research & development (R&D) have a significantly positive (negative) effect on technical inefficiency (technical efficiency

Finance, Ownership, Executive Remuneration, and Technical Efficiency: A Stochastic Frontier Analysis (SFA) of Thai Listed Manufacturing Enterprises

This study employs a stochastic frontier analysis (SFA) to predict the technical efficiency of Thai listed manufacturing enterprises, using unbalanced panel data for 178 enterprises covering the years 2000 to 2008. The empirical findings indicate that these enterprises have been operating under “decreasing returns to scale”, and rely heavily on labour input. Managerial ownership, controlling ownership, type of firm ownership, executive remuneration and firm size are found to have a significant and positive correlation with technical efficiency. Firm leverage is also found to be positively correlated with technical efficiency, but is not statistically significant, and liquidity is found to have a significant negative correlation with firm technical efficiency. Both internal and external financing have a significant negative correlation with technical efficiency, but external financing is less important. The paper also provides evidence-based policy recommendations to enhance the technical efficiency and competitiveness of Thai listed manufacturing enterprises

Thai manufacturing small and medium sized enterprise technical efficiency: evidence from firm-level industrial census data

Thai manufacturing small and medium sized enterprises (SMEs) face intense competition in domestic and foreign markets. Given their importance to the economic development of the country it is important to have a clear understanding of their readiness to face the rigors of international competition, including the barriers and specific problems that they face. This study uses a stochastic frontier analysis (SFA) and technical inefficiency effects model to analyze the technical efficiency of Thai manufacturing SMEs and key factors impacting upon it. Analysis of cross-sectional data from a 2007 census of Thai manufacturing SMEs indicates that their weighted average technical efficiency is approximately 50 percent, signifying a high level of technical inefficiency which is reducing potential output. The inefficiency effects model reveals that firm size, firm age, skilled labor, ownership characteristics and location are firm-specific factors that significantly affect the technical inefficiency of production. Key measures to improve the technical efficiency of Thai manufacturing SMEs are an adequate supply of inputs, access to credit facilities, extensive infrastructural development and training programs for employees

Estimating a Technical Inefficiency Effects Model for Thai Manufacturing and Exporting Enterprises (SMEs): A Stochastic Frontier (SFA) and Data Envelopment Analysis (DEA) Approach Technical Inefficiency Effects Model for Thai Manufacturing and Exporting E

Abstract This paper employs a stochastic frontier (SFA) and data envelopment analysis (DEA) to analyse inefficiency effect models for 3,894 Thai manufacturing and exporting small and medium size enterprises (SMEs), using 2007 Thai Industrial Census data. Thai manufacturing and exporting SMEs experience decreasing returns to scale even though their technical efficiency in production is found to be relatively high. Results from estimations using both approaches also reveal that firm age, medium-sized enterprises compared with small-sized enterprises, firm location in Bangkok, foreign investment and government assistance are significantly and positively related to firm technical efficiency. Focusing on the technical efficiency of SMEs exporting to different destinations, those exporting to OCEANIA perform the best, followed by SMEs exporting to ASEAN, East Asia, and North and South America, while SMEs exporting to Europe experience no significant effect upon their technical efficiency from doing so, where SMEs exporting to the rest of the world is the base exporting SME group. The results also reveal that SMEs in the chemicals and related products sector perform the best, followed by SMEs in the machinery and transport equipment sector, where the miscellaneous manufactured articles sector is used as the base sector. The manufactured goods sector and food, beverages and tobacco sector are also found to perform better than the miscellaneous manufactured articles sector. Finally, the paper also provides useful evidence-based policy recommendations aimed at enhancing the technical efficiency and competitiveness of Thai manufacturing and exporting SMEs

Factors affecting the technical inefficiency of Thai manufacturing and exporting small and medium sized enterprises: A stochastic frontier analysis (SFA)

This study employs a stochastic frontier analysis (SFA) and technical inefficiency effects model to predict the technical efficiency of 3,168 Thai manufacturing and exporting SMEs, analyze their returns to scale and key factors impacting on their technical efficiency. Analysis of cross-sectional data from a 2007 census of Thai manufacturing SMEs indicates that their average technical efficiency is approximately 69.72 percent, signifying a moderate level of technical inefficiency which is reducing potential output. With respect to each group of manufacturing and exporting SMEs, SMEs exporting to East Asia have a level of technical efficiency of 0.7081, followed by SMEs exporting to ASEAN (0.7038), North & South America (0.7005), OCEANIA (0.6979), South Asia (0.6828), Europe (0.6764), and Middle East & Africa (0.6679). Thai manufacturing and exporting SMEs extensively rely on labour rather than capital to increase their output, including almost all exporting SME groups, except those exporting to North & South America. Furthermore, the production of Thai manufacturing and exporting firms exhibit decreasing returns to scale (0.8837), including the production of SMEs exporting to ASEAN (0.9027), East Asia (0.9200), South Asia (0.7935), Europe (0.6487), North & South America (0.52118), and Middle East & Africa (0.7672). The production of Thai manufacturing SMEs exporting to Oceania, however, has increasing returns to scale (1.1965). The inefficiency effects model reveals that firm size, firm age, foreign ownership, location and government assistance are firm-specific factors that significantly affect the technical inefficiency of production. Finally, evidence-based policies are also provided to facilitate improvement in the technical efficiency performance of Thai manufacturing and exporting SMEs