• The Journal of Industrial Distribution & Business
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• v.8 no.7
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• pp.13-20
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• 2017

Purpose - Performance appraisal has a significant influence on the development of low-carbon tourism distribution. Research design, data, and methodology - Data of this study are collected from 27 provinces (cities) of China. SBM-Malmquist model is used to measure the TFP and its dynamic changes of low-carbon tourism distribution; TOBIT model is used to discuss the factors of TFP of low-carbon tourism distribution. Results - The results show that, there are obvious differences among regional TFP of low-carbon tourism distribution, the average change tends to grow positively in general, and the western region grows fastest on average due to the improvement of technical efficiency and technical progress, while there are technical efficiency improvement but technical regresses in eastern and central regions. The economic scale, economic strength, structure of energy consumption, location quotient and government regulation have a significant positive effect on the TFP of low-carbon tourism; energy intensity, industrial structure and opening degree have a negative effect; investments in fixed assets, intensity of R&D fund and urbanization rate have no significant influence on the TFP of low-carbon tourism. Conclusions - Improving the productivity of low-carbon tourism and reducing regional differences are effective ways to develop low-carbon tourism and enhance tourism competitiveness.

• Journal of the Korea Safety Management & Science
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• v.12 no.4
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• pp.191-198
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• 2010

This paper selects length of berth, area of yard, unloading capacity and number of berth as the input indexes, and cargo turnover as output index to research the source of turnover growth of 22 main ports in Korea. We gains the following results: in general, the trade ports in Korea are the stage of expansion period, the sources of turnover growth depends on the growth of factor inputs and could not be supported by TFP and technical progress. Especially in the west and east coast, TFP and technical progress is the block to the turnover growth. Four major factors to the increase of TFP are following: competition between ports, reform of property system, harbor-hinterland economic and international trade, modeling, imitation and innovation in management, technology and system.

• Environmental Engineering Research
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• v.25 no.1
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• pp.71-79
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• 2020

This paper uses super-efficiency DEA model and Malmquist index to evaluate the carbon emission efficiency (CEE) values of the nine western provinces along the "Silk Road Economic Belt" for the period from 2000 to 2015, and analyses the influencing factors of the CEE. The major findings of this study are the following: (1) the overall CEE of the nine western provinces is not high, and there are significant inter-provincial differences in the CEE. Meanwhile, the provinces with higher levels of economic development generally have higher CEE. (2) The annual total factor productivity (TFP) of the nine western provinces, which is mainly determined by technological change, is greater than 1. Moreover, the total average growth rate of the TFP is 15.5%. (3) The CEE of the nine western provinces is not spatially dependent. In addition, the urbanization, openness, use of energy-saving technologies and research and development (R&D) investment have a significant positive impact on the CEE values, while the industrial structure, foreign direct investment, fixed asset investment, government expenditure levels and energy structure have a significant negative impact on the CEE. Among them, R&D investment is the primary factor in promoting the development of CEE, and the government expenditure has the greatest negative impact on the CEE.

• Journal of the Korea Safety Management & Science
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• v.12 no.4
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• pp.183-190
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• 2010

This paper selects length of berth, area of yard, unloading capacity and number of berth as the input indexes, and cargo turnover as output index to research the source of TFP(Total Factor Productivity) growth of 23 main ports in Korea. The major conclusions are as follows. The TFP of the trade ports in Korea is at the fluctuating stage, but it generally displays a rising trend, and it’s growth originate from the growth of technical efficiency. The Growth rates of TFP of trade ports in the different areas are different, and the sources of growth are also different, but the changing trends are basically the same. Four major factors to the increase of TFP are following: competition between ports, reform of property system, harbor-hinterland economic and international trade, modeling, imitation and innovation in management, technology and system.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.11
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• pp.1033-1039
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• 2020

Vietnam is conducting an export-led growth model and labour-intensive industries contributing majorly to the total export value. In the context of Industry 4.0, the labour-based industries are significantly affected; hence, enhancing productivity is the key measure to maintain these industries. The garment industry contributes significantly to the total export value of Vietnam. Based on meta-frontier framework, the approach of data envelopment analysis is used to measure technical efficiency of Vietnamese garment firms and the global Malmquist TFP index is utilised to identify productivity change and its components including efficiency, technology and technical gaps between different groups of firms. The data of Vietnamese garment firms from 2013 to 2018 collected from the Vietnam General Statistic Office is used in this study. The results show that: (i) The total factor productivity of Vietnamese garment firms growth, technical progress is the main contributor; (ii) The private garment sector is the leading group; (iii) There is a large technological gap among Vietnamese garment sectors. The private and FDI garment firms have experienced a growth in all components of total factor productivity change. Meanwhile, technological progress change is the main reason to constrain the productivity growth of state-owned garment firms.

• International Commerce and Information Review
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• v.16 no.4
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• pp.55-75
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• 2014

Non-parametric method such as technology efficiency, DEA/Window model and Malmquist Productivity Index (MPI) are used to measure efficiency and productivity of ICT (Information and Communication Technology) manufacturing industry and service industry over the period 2007-2011. The results of this paper indicate following: (1) Technology efficiency of the ICT manufacturing industry were found as the range of 0.34 and 0.39 over the sample period. Technology efficiency of the ICT service industry were found as the range of 0.16 and 0.20 over the sample period. (2) The geometric average of the Malmquist TFP indexes on ICT manufacturing industry indicated the productivity improvement an average of 8.3 percent. The geometric average of the Malmquist TFP indexes on ICT service industry indicated the productivity improvement an average of 1.6 percent. (3) TIER analysis result on ICT manufacturing industry showed that optimal bench marking made by storage devices${\rightarrow}$wireless communication equipment${\rightarrow}$broadcasting equipment${\rightarrow}$radio, recording and playback devices${\rightarrow}$computers, printers, video and audio-visual equipment path. TIER analysis result on ICT service industry indicated that optimal bench marking made by computers and packaged software${\rightarrow}$wired communication${\rightarrow}$communication, information, detection equipment${\rightarrow}$consulting and construction for computer systems integration${\rightarrow}$industrial machinery and equipment rental${\rightarrow}$telecommunications reseller${\rightarrow}$system software development and delivery${\rightarrow}$hosting path.

• Industry Promotion Research
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• v.8 no.4
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• pp.257-266
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• 2023

The research focuses on the provincial universities in the western region of China and investigates the research level of 12 provincial universities from 2017 to 2021, considering both static efficiency and dynamic efficiency. The static efficiency is examined using Data Envelopment Analysis (DEA), while the dynamic efficiency is analyzed using the Malmquist model. The analysis results are as follows: the scientific research efficiency of universities in the 12 western provinces is generally not high. Against the background of the "Double First-Class" construction, the overall efficiency of scientific research in universities is showing an increasing trend. The main reason for the increase in scientific research efficiency is the increase in scale efficiency in recent years. The total factor productivity (TFP) of research activities is influenced by the technology progress index and exhibits a pattern of initial increase, followed by a decline, and then an increase again. Research conclusion: Western colleges and universities should reasonably allocate resources for scientific research activities, perfect scientific research mechanisms, improve management standards, promote scientific innovation and corresponding achievements, and ultimately raise the scientific and technological level in western China.

• International conference on construction engineering and project management
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• 2013.01a
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• pp.315-320
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• 2013

The aim of this study is to compare efficiency and Total Factor Productivity(TFP) between Korean and Japanese construction firms in Korea and Japan over the period of 2005-2011. The results of this study are as follows. Efficiency scores of Korean construction firms are 0.797, and Japanese construction firms are 0.921. Second, annual total factor productivity growth of Korean construction firms is 0.5% and technical progress do much for TFP decrease. However Japanese construction firms marked annual increasing of 2.5% of TFP. Third, technical progress contributed in TFP increase of construction firms in Korea. Korean construction firms, however, relatively lagged behind Japanese construction firms in technical progress. Therefore, Korean construction firms need strategies to achieve technical advances including adopting new technology or process innovation to maintain competitiveness, survive, and develop in the future competition with Japan.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.10
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• pp.109-117
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• 2021

This study investigates the effects of FDI and other macroeconomic variables on Total Factor Productivity (TFP) in selected ASEAN+3 countries from 1981 till 2016. Total Factor Productivity (TFP) for each ASEAN+3 country was constructed using the Malmquist productivity index method. Then, a panel ARDL framework (dynamic heterogeneous panel), namely Pooled Mean Group (PMG), Mean Group (MG), and Dynamic Fixed Effect (DFE) are employed in examining the effects of FDI and other controlling variables on TFP. The new findings show that FDI has a significant and positive impact on TFP in the long run and the short run in ASEAN+3 countries. Besides, the results also reveal that in the long run, other variables such as the average number of years of schooling and the ratio of high-skilled to low-skilled labor also have a significant and positive effect on TFP. However, economic openness, government expenditure on R&D, and interaction between countries and FDI have a negative and significant impact on TFP. These findings implied that the recipient countries must ensure their political stability and offer various incentives to attract more investors. Furthermore, the influx of foreign investors, especially large ones and lower costs, should be encouraged by each country as FDI inflows can boost their TFP growth.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.2 no.4
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• pp.71-102
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• 2007

There were many comprehensive analyses conducted within the existing research activities wherein factors affecting technology progress including investment in R&D vis-${\Box}$-vis their influences act as the determinants of TFP. Note, however, that there were few comprehensive analysis in the industrial research performed regarding the impact of the economy of scale as it affects TFP; most of these research studies dealt with the analysis of the non -parametric Malmquist productivity index or used the stochastic frontier production function models. No comprehensive analysis on the impacts of individual independent variables affecting TFP was performed. Therefore, this study obtained the TFP increase rate of each industry by analyzing the factors of the existing growth accounting equation and comprehensively analyzed the TFP determinants by constructing a comprehensive analysis model considering the investment in R&D and economy of scale (smallness by industry) as the influencers of TFP by industry. First, for the TFP increase rate of the 15 industries as a whole, the annual average increase rate for 1993${\sim}$ 1997 was approximately 3.8% only; during 1999${\sim}$ 2000 following the foreign exchange crisis, however, the annual increase rate rose to approximately 7.8%. By industry, the annual average increase rate of TFP between 1993 and 2000 stood at 11.6%, the highest in the electrical and electronic equipment manufacturing business and IT manufacturing sector. In contrast, a -0.4% increase rate was recorded in the furniture and other product manufacturing sectors. In the case of the service industry, the TFP increase rate was 7.3% in the transportation, warehousing, and communication sectors. This is much higher than the 2.9% posted in the electricity, water, and gas sectors and -3.7% recorded in the wholesale, food, and hotel businesses. The results of the comprehensive analysis conducted on the determinants of TFP showed that the correlations between R&D and TFP in general were positive (+) correlations whose significance has yet to be validated; in the model where the self-employed and unpaid family workers were used as proxy variables indicating the smallness of industry out of the total number of workers, however, significant negative (-) correlations were noted. On the other hand, the estimation factors of variables surrogating the smallness of scale in each industry showed that a consistently high "smallness of scale" in an industry means a decrease in the increase rate of TFP in the same industry.