Green competitiveness research on Chinese automotive enterprises

Yuanhui Li

Abstract


Purpose: More and more executives of automobileindustry in China start to recognize the concept of green competitiveness recently. However, relatively less research attention has been devoted to the consideration of measurement. This paper aims to find empirical approach to quantify green competitiveness for automotive enterprises. The connotation of green competitiveness is explored and one suite of evaluation index system has been proposed with four dimensions including environmental, resource, capability and knowledge.

Design/methodology/approach: By introducing the factor analysis method, green competitiveness has been measured through an empirical analysis of 24 automotive enterprises within China.

Findings: The results indicate that those elements, such as enterprise resource possession and utilization; environment, responsibility and knowledge; profitability; management efficiency, have significant effect on the green competitiveness for automotive enterprises. The further analysis also unveils the advantages and disadvantages of green competitiveness for each company and the direction for improvement.

Research limitations/implications: Guide regulators and managers of automobile industry to take some measures to enhance their green competitive advantage.

Practical implications: Provide practical methods to measure green competitiveness for automotive enterprises.

Originality/value: This paper proposes an evaluation index system of green competitiveness for automotive enterprises. The suggestions of our research will be beneficial to enterprise executives and industry regulators.


Keywords


automotive enterprises; green competitiveness; competitiveness evaluation

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DOI: http://dx.doi.org/10.3926/jiem.1032


Licencia de Creative Commons 

This work is licensed under a Creative Commons Attribution 4.0 International License

Journal of Industrial Engineering and Management, 2008-2019

Online ISSN: 2013-0953; Print ISSN: 2013-8423; Online DL: B-28744-2008

Publisher: OmniaScience