Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model

Fang, Siran; Xue, Xiongzhi; Yin, Geping; Fang, Hongyuan; Li, Jialin; Zhang, Yongnian

doi:10.3390/su12187509

Cited by 35 publications

(20 citation statements)

References 47 publications

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“…The presented formulation is a general Tobit model that applies temporal and spatial scales data simultaneously. The expected effect of x i on y * is monotonic, and β T and σ 2 are consistent by the maximum likelihood estimation under the assumption of homoscedastic normal disturbances [57,58]. Using the two-stage estimation method suggested by Ozcan [59], ME was measured using metafrontier DEA in the first stage, after which a Tobit regression was performed in the second stage using the ME scores from the first step as the dependent variables and context/environmental variables as the independent variables.…”

Section: Determinants Of Meta-efficiency (Me)supporting

confidence: 58%

Evaluating the Service Operating Efficiency and Its Determinants in Global Consulting Firms: A Metafrontier Analysis

2021

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Knowledge consulting services are one of the fastest growing fields in the knowledge service industry since the 2010s and have been emerging as a core area of the knowledge economy. Accordingly, consulting services are actively sought and provided in various fields, including business strategy and management, accounting, and ICT, and global consulting firms have experienced rapid growth. However, previous research evaluating the performance or service quality of knowledge consulting services is relatively scarce. In particular, there are barely any studies that apply the data envelopment analysis (DEA) model to measure the relative operating efficiencies of consulting firms in the global consulting service field. This study measured the operating efficiency of 27 global consulting firms using DEA. As global consulting firms are managed differently depending on the characteristics of the country in which they operate, the 27 global consulting firms were classified into three groups by region (USA, Europe, Asia) to measure their meta-efficiency (ME), group efficiency (GE), and technology gap ratio (TGR) and identify the causes of inefficiency at global consulting firms. The contextual variables within consulting firms that affect efficiency were analyzed using Tobit regression. Based on the analysis results, this study suggests strategies for enhancing the operating efficiency and realizing sustainable growth in global consulting firms.

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Section: Determinants Of Meta-efficiency (Me)supporting

confidence: 58%

Evaluating the Service Operating Efficiency and Its Determinants in Global Consulting Firms: A Metafrontier Analysis

2021

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“…e capacity to pursue self-directed innovation has emerged as the single most important determinant of the success or failure of the vast majority of small and medium-sized businesses [10,11]. e primary focus of current research in this field is on analyzing and contrasting the levels of productivity achieved by businesses that are either privately or publicly owned.…”

Section: Introductionmentioning

confidence: 99%

Research on the Evaluation and Optimization of Innovation Ability of Private Enterprises Based on DEA Model

Fan

2022

Security and Communication Networks

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As private businesses are an integral part of China’s market economy, it is essential to foster their expansion in a positive manner. Even though some private enterprises have made significant strides in their ability to innovate independently, the majority of them have not yet established an effective endogenous mechanism of independent innovation, and their independent innovation capability and level are insufficient to meet the requirements of economic and social development in the new era. Therefore, it is essential to evaluate the innovation capacity of private businesses. Therefore, the first section of this paper examines the research on measuring enterprise productivity. Second, this paper employs a combination of endogenous dynamics to develop an endogenous dynamics mechanism that encourages innovation in private companies. In addition to employing the DEA model, this paper proposes an evaluation optimization mechanism for assessing the innovation capability of private enterprises in a Chinese province from 2015 to 2021. It concludes that the innovation capability of private enterprises in this province is generally low and that factors such as the number of technical employees, the R&D investment funds of private enterprises, and the number of granted patents have a significant impact.

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“…Similarly, the DEA-Tobit model has been used to construct a benchmark for enterprise in the new energy vehicle industry in terms of their technological innovation efficiency from 2013 to 2018. The analysis was completed using a sample of 23 related Chinese companies (Fang et al, 2020).…”

Section: Application Of Dea In Randd Efficiency Evaluationmentioning

confidence: 99%

Evaluating R&D efficiency of China’s listed lithium battery enterprises

Bai

Han

et al. 2022

Front. Eng. Manag.

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Promoting the growth of the lithium battery sector has been a critical aspect of China’s energy policy in terms of achieving carbon neutrality. However, despite significant support on research and development (R&D) investments that have resulted in increasing size, the sector seems to be falling behind in technological areas. To guide future policies and understand proper ways of promoting R&D efficiency, we looked into the lithium battery industry of China. Specifically, data envelopment analysis (DEA) was used as the primary approach based on evidence from 22 listed lithium battery enterprises. The performance of the five leading players was compared with that of the industry as a whole. Results revealed little indication of a meaningful improvement in R&D efficiency throughout our sample from 2010 to 2019. However, during this period, a significant increase in R&D expenditure was witnessed. This finding was supported, as the results showed that the average technical efficiency of the 22 enterprises was 0.442, whereas the average pure technical efficiency was at 0.503, thus suggesting that they were suffering from decreasing returns to scale (DRS). In contrast, the performance of the five leading players seemed superior because their average efficiency scores were higher than the industry’s average. Moreover, they were experiencing increasing scale efficiency (IRS). We draw on these findings to suggest to policymakers that supporting technologically intensive sectors should be more than simply increasing investment scale; rather, it should also encompass assisting businesses in developing efficient managerial processes for R&D.

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Evaluation and Improvement of Technological Innovation Efficiency of New Energy Vehicle Enterprises in China Based on DEA-Tobit Model

Cited by 35 publications

References 47 publications

Evaluating the Service Operating Efficiency and Its Determinants in Global Consulting Firms: A Metafrontier Analysis

Evaluating the Service Operating Efficiency and Its Determinants in Global Consulting Firms: A Metafrontier Analysis

Research on the Evaluation and Optimization of Innovation Ability of Private Enterprises Based on DEA Model

Evaluating R&D efficiency of China’s listed lithium battery enterprises

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