Does China's new energy vehicle industry innovate efficiently? A three-stage dynamic network slacks-based measure approach

Yufeng Chen, Liangfu Ni and Kelong Liu

Technological Forecasting and Social Change, 2021, vol. 173, issue C

Abstract: Innovation ability is vital to the new energy vehicle(NEV) industry in China. To bridge the previous studies’ gaps, a dynamic network slacks-based measure(DNSBM) model, coupled with the three-stage innovation framework, is employed to evaluate the industrial innovation efficiency. Overall, dynamic efficiencies and the divisional efficiencies of the R&D, manufacturing, and marketing process are present, drawing on 17 listed companies’ data from 2012 to 2019. Additionally, the Malmquist index measures industrial productivity and its decomposition, including the Frontier-shift and Catch-up effect. The firm-level efficiencies’ analysis ends up following the types of enterprises and divisional performance. The findings reveal that the overall innovation efficiency was low, and marketing and manufacturing efficiency were higher than those of the R&D division. Then, the annual overall and divisional productivities were low, whereas the cumulative productivities were high. In the end, all enterprises’ performance differentiated, and commercial-vehicle firms’ efficiencies were higher than those of the passenger-vehicle firms except for the marketing division. Meanwhile, the state-owned companies’ efficiencies were higher than those of private companies’ except for the marketing process. The theoretical and practical implications are available for the policymakers, managers, and academic researchers from this study.

Keywords: New energy vehicle (NEV); Innovation efficiency; Three-stage innovation framework; DNSBM (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0040162521005941
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521005941

DOI: 10.1016/j.techfore.2021.121161

Access Statistics for this article

Technological Forecasting and Social Change is currently edited by Fred Phillips

More articles in Technological Forecasting and Social Change from Elsevier
Bibliographic data for series maintained by Catherine Liu ().