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Open AccessArticle

Malmquist Productivity Analysis of Top Global Automobile Manufacturers

1
Department of Industrial Engineering and Management, National Kaohsiung University of Science and Technology, Kaohsiung 80778, Taiwan
2
Electrical Engineering Department, Technological University of the Philippines Taguig, Taguig 1630, Philippines
*
Authors to whom correspondence should be addressed.
Mathematics 2020, 8(4), 580; https://doi.org/10.3390/math8040580
Received: 25 March 2020 / Revised: 9 April 2020 / Accepted: 12 April 2020 / Published: 14 April 2020
(This article belongs to the Special Issue Computational Methods in Analysis and Applications 2020)
The automobile industry is one of the largest economies in the world, by revenue. Being one of the industries with higher employment output, this has become a major determinant of economic growth. In view of the declining automobile production after a period of continuous growth in the 2008 global auto crisis, the re-evaluation of automobile manufacturing is necessary. This study applies the Malmquist productivity index (MPI), one of the many models in the Data Envelopment Analysis (DEA), to analyze the performance of the world’s top 20 automakers over the period of 2015–2018. The researchers assessed the technical efficiency, technological progress, and the total factor productivity of global automobile manufacturers, using a variety of input and output variables which are considered to be essential financial indicators, such as total assets, shareholder’s equity, cost of revenue, operating expenses, revenue, and net income. The results show that the most productive automaker on average is Volkswagen, followed by Honda, BAIC, General Motors, and Suzuki. On the contrary, Mitsubishi and Tata Motors were the worst-performing automakers during the studied period. This study provides a general overview of the global automobile industry. This paper can be a valuable reference for car managers, policymakers, and investors, to aid their decision-making on automobile management, investment, and development. This research is also a contribution to organizational performance measurement, using the DEA Malmquist model. View Full-Text
Keywords: data envelopment analysis (DEA); Malmquist productivity index (MPI); catch-up efficiency; frontier-shift; productivity index; technological change; total factor productivity data envelopment analysis (DEA); Malmquist productivity index (MPI); catch-up efficiency; frontier-shift; productivity index; technological change; total factor productivity
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Wang, C.-N.; Tibo, H.; Nguyen, H.A. Malmquist Productivity Analysis of Top Global Automobile Manufacturers. Mathematics 2020, 8, 580.

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