An extension from network DEA to copula-based network SFA: Evidence from the U.S. commercial banks in 2009

Huang, Tai–Hsin; Chen, Kuan Chen; Lin, Chung I.

doi:10.1016/j.qref.2017.04.007

Cited by 19 publications

(7 citation statements)

References 52 publications

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“…DEA, multi index comprehensive scoring, and single input-output SFA methods have been widely used to study internet enterprises [ 42 , 43 ]. This work draws on the study of Afza T et al [ 17 ], Lutz et al [ 18 ], Hu et al [ 44 ], and Tai-Hsin Huang et al [ 45 ], and combines the distance function and the stochastic frontier method to construct a multiple input-output framework of a stochastic frontier model that can directly measure TIE.…”

Section: Data and Technological Innovation Efficiency (Tie)mentioning

confidence: 99%

“…However, the model is constrained by a single output, and previous studies typically used only a single output or multiple models of multiple output. This paper draws on the studies of Afza T et al [ 17 ], Lutz et al [ 18 ], Hu et al [ 44 ] and Tai-Hsin Huang et al [ 45 ], and combines the distance function and the stochastic frontier method to construct a multiple input-output framework of a multi stochastic frontier model that can directly measure TIE. The output distance function based on the Cobb-Douglas function is: …”

Section: Data and Technological Innovation Efficiency (Tie)mentioning

confidence: 99%

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Financing innovation and enterprises’ efficiency of technological innovation in the internet industry: Evidence from China

Piao

Yueqin

2020

PLoS ONE

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This study empirically examined the impact of financing innovation on technological innovation efficiency of select internet companies, that were affiliated with China between 2008 and 2017. Analysis was based on their patent and annual report data and used multiple input-output SFA model, system GMM, and panel fixed-effect model. The results are as follows. (1) There is significant variation in overall technological innovation efficiency of listed companies in the internet industry, and there is a downward trend. The technological innovation efficiency of business that use financing innovation methods is higher than those that do not. (2) The number of patents and intangible capital investment of internet businesses increase obviously every year, but there is no corresponding increase in the efficiency of technological innovation, and little intangible capital investment of non-financing innovation businesses. Thus, determining how to effectively improve the overall quality of patents and the efficiency of intangible capital investment is essential to improve the efficiency of technological innovation for Chinese internet businesses. (3) There is a term mismatch in the investment and financing of internet businesses in China. The financing structure between the financing innovation and non-financing innovation businesses has different impacts on the efficiency of technological innovation. And nowadays, more financing channels are short-term debt financing channels which invest in projects to improve the efficiency of technological innovation due to the pressure of debt repayment and the need to protect shareholders' interests. (4) In the panel regression, the coefficients of Icd and Roa are significantly negative, suggesting that the investment efficiency of internet businesses needs to be improved.

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Section: Data and Technological Innovation Efficiency (Tie)mentioning

confidence: 99%

Section: Data and Technological Innovation Efficiency (Tie)mentioning

confidence: 99%

Financing innovation and enterprises’ efficiency of technological innovation in the internet industry: Evidence from China

Piao

Yueqin

2020

PLoS ONE

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“…The results in Tables 2 and 3 show that the value operation and the value creation stages have different weights and the value creation stage has a greater impact on the comprehensive efficiency. In fact, in the application research of two-stage network DEA models, many scholars assign the same weight to the two stages [41,42]. Therefore, considering the game and cooperation between the two stages, referring to the treatment methods of other scholars, the weights of the two stages are uniformly set to 0.5 and 0.5, which means the contribution of the two stages to the whole is the same.…”

Section: Discussionmentioning

confidence: 99%

Operational Efficiency Evaluation of Chinese Internet Banks: Two-Stage Network DEA Approach

Zhu

et al. 2022

Sustainability

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An in-depth study of the operational efficiency of Internet banks is essential to enhance banks’ competitiveness and sustainable development. We use the two-stage network data envelopment analysis (DEA) model to divide the operational process of Internet banks into the value operation stage (stage 1) and the value creation stage (stage 2). This paper adopts the R&D investment that reflects the characteristics of Internet banks as the input of the value operation stage in the two-stage DEA model. It examines the operating efficiency of China’s Internet banks from 2018 to 2019, including stage efficiency and comprehensive efficiency. The empirical analysis results indicate that the contribution of stage 2 to the comprehensive efficiency is higher than that of stage 1. Similarly, it can also be shown that the average values of the stage efficiency and comprehensive efficiency of Internet banking in 2019 are higher than those in 2018. In addition, the Kruskal–Wallis test shows no significant difference in the average comprehensive efficiency of Internet banks in the three major economic belts. These results have significant strategic implications for managers, regulators, and policymakers who share a common interest in boosting financial sustainability and performance.

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“…Appropriate inputs are those variables that managers would like to minimize, and appropriate outputs are those with the maximizing purpose [59]. We found employees, fixed assets, and interest expenses are regularly defined as input variables (e.g., [34,58]) while loans and incomes are regularly defined as output variables (e.g., [24,40]). We performed a comprehensive review of the recent DEA applications in the banking industry presented in Table 1 to select the most suitable input and output variables for our study.…”

Section: Measurement Inputs and Outputsmentioning

confidence: 91%

A common weights model for investigating efficiency-based leadership in the russian banking industry

et al. 2021

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In this race for productivity, the most successful leaders in the banking industry are those with high-efficiency and a competitive edge. Data envelopment analysis is one of the most widely used methods for measuring efficiency in organizations. In this study, we use the ideal point concept and propose a common weights model with fuzzy data and non-discretionary inputs. The proposed model considers environmental criteria with uncertain data to produce a full ranking of homogenous decision-making units. We use the proposed model to investigate the efficiency-based leaders in the Russian banking industry. The results show that the unidimensional and unilateral assessment of leading organizations solely according to corporate size is insufficient to characterize industry leaders effectively. In response, we recommend a multilevel, multicomponent, and multidisciplinary evaluation framework for a more reliable and realistic investigation of leadership at the network level of analysis.

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An extension from network DEA to copula-based network SFA: Evidence from the U.S. commercial banks in 2009

Cited by 19 publications

References 52 publications

Financing innovation and enterprises’ efficiency of technological innovation in the internet industry: Evidence from China

Financing innovation and enterprises’ efficiency of technological innovation in the internet industry: Evidence from China

Operational Efficiency Evaluation of Chinese Internet Banks: Two-Stage Network DEA Approach

A common weights model for investigating efficiency-based leadership in the russian banking industry

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