• The Journal of the Korea Contents Association
• /
• v.21 no.11
• /
• pp.361-374
• /
• 2021

The purpose of this study is to analyze the efficiency and productivity of technological innovation activities of companies certified as innovative pharmaceutical companies by the government to diagnose their competitiveness and derive measures to strengthen them. This study collected pharmaceutical input (R&D expenditures and number of employees) and output (sale, operating profit and patent) data between 2017 and 2019 for 38 innovative pharmaceutical companies. This study analyzed them using the data envelopment analysis (DEA) method, Tobit model and the Malmquist Productivity Index (MPI). First, the DEA result of the innovative pharmaceutical companies show that between the value of the CCR model of the scale efficiency and the value of the BCC model to diagnose the internal operation efficiency is differences. Second, efficiency does not differ between corporate characteristics. Third, Tobit model shows that number of patents held have positive effects on efficiency. Forth, overall MPI is 0.89. This can be interpreted as the rate of TECI decreased 3%p and TCI has increased 4%p. The results of this study can be used as decision-making data for response strategies to improve efficiency by identifying the cause of inefficiency and presenting target values.

• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.21 no.11
• /
• pp.532-542
• /
• 2020

Due to abnormal weather conditions such as high temperature, the management of greenhouse rose farms is getting worse. In order to enhance the competitiveness of these farms, new measures are needed to improve their management performance. Therefore, this study suggests alternatives to improve the efficiency and productivity by identifying the causes of inefficiency of greenhouse rose farms in terms of management performance analysis through DEA analysis and MPI analysis. As a result of DEA analysis, the average TE of farmers increased from 0.867('16) to 0.905('17), but decreased to 0.850 in 2018, indicating that it was inefficient. In order to increase the management efficiency of farmers, efforts to preferentially reduce the costs (equipment, employment labor, fertilizer, facilities, seeds) that cause inefficiencies are needed. As a result of MPI analysis, TECI decreased from 1.044(T2) to 0.939(T3), which was the cause of the MPI decrease, and the TCI was rather increased from 0.958(T2) to 0.969(T3). In other words, it means that the decrease in productivity is due to insufficient utilization of potential production technology rather than the slowing of technological progress. This implies that it is important to provide technical guidance on utilization after technology dissemination.

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