A modified slacks-based measure of efficiency in data envelopment analysis

Tone, Kaoru; Toloo, Mehdi; Izadikhah, Mohammad

doi:10.1016/j.ejor.2020.04.019

Cited by 111 publications

(36 citation statements)

References 28 publications

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“…Data envelopment analysis (DEA) is a commonly used method to calculate GTFP because it can calculate the decision-making units (DMU) with more input and output. In this paper, a non-radial super-efficiency SBM model [ 31 , 32 ], including undesirable output, is established to measure GTFP. Assuming that each province is a decision-making unit (DMU), there are n DMUs in total.…”

Section: Measurement Of Gtfpmentioning

confidence: 99%

Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt

Sun

Tang

Kong

et al. 2022

IJERPH

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The Yangtze River economic belt is an inland river economic belt with international influence composed of 11 provinces and municipalities in the Yangtze River Basin. This paper uses the super-efficiency model to calculate the green total factor productivity of 11 provinces and municipalities in the Yangtze River economic belt (YREB). Then we establish a model to study the impact of industrial structure upgrading, industrial structure rationalization, and environmental regulation on green total factor productivity (GTFP). Empirical analysis shows that the industrial structure upgrading and environmental regulation have a significant impact on GTFP and show regional characteristics. The more developed the economy and the higher the industrial structure, the greater the impact of upgrading and environmental regulation on GTFP. Compared with other control variables, the urbanization rate impacts GTFP, followed by regional economic development.

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Section: Measurement Of Gtfpmentioning

confidence: 99%

Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt

Sun

Tang

Kong

et al. 2022

IJERPH

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“…On the basis of the SBM-GML model to measure GTFP and by referring to the research methods of Tone [32], Zhou [33] and Sun [34] have defined the SBM directional distance function containing the undesirable output, which is specifically expressed as follows:…”

Section: Explained Variablementioning

confidence: 99%

Resource Endowment, Industrial Structure, and Green Development of the Yellow River Basin

et al. 2021

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The Yellow River Basin is an important energy base of China, and its green development is crucial to Chinese economic transformation. In this paper, we calculate the green total factor productivity (GTFP) to measure the green development level of the Yellow River Basin by using an Slack Based Model- Global Malmquist-Luenberger (SBM-GML) index model. On this basis, we use a Generalized Method of Moments (GMM) model to further analyze the impact of resource endowment and industrial structure on the green development of cities. The results show that resource endowment inhibits the green development of cities and that the resource curse is observed in the Yellow River Basin. The industrial structure advancement significantly promotes the green development of cities. The impact of industrial structure rationalization on green development varies significantly on the type of city. Specifically, it has an inhibiting effect on key environmental protection cities but a promoting effect on non-key environmental protection cities.

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“…Therefore, future studies may apply a field study approach (Roslender and Fincham, 2004) or even develop new propositions on how IC accounting works (Mouritsen, 2006). Moreover, researchers may have interests in ranking strong efficient companies by applying the modified SBM approach by Tone et al (2020) while measuring efficiency. Despite the ongoing debate between utilizing either OLS or truncated regression when involving efficiency scores as the dependent variable, this study suggests that future research may conduct a test to verify their respective validity.…”

Section: Discussionmentioning

confidence: 99%

Risk-adjusted banks' resource-utilization and investment efficiencies: does intellectual capital matter?

Kweh

Tone

et al. 2021

JIC

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PurposeThe purpose of this study is twofold. First, this research estimates banks' efficiencies from the perspectives of resource utilization and investment after incorporating risk measures as an exogenous input in the investment-efficiency stage. Second, the current study examines the relationship between intellectual capital (IC) and banks' efficiencies.Design/methodology/approachFirst, this study uses a dynamic network data envelopment analysis approach in investigating the efficiencies of 24 Taiwanese banks in 2007–2018 from two perspectives. Second, this research utilizes various regression techniques, namely, ordinary least squares (OLS), robust least squares and truncated regression, to gauge the impact of IC on banks' efficiencies. Typically, IC is determined based on a monetary value-based measure and value-added intellectual coefficient (VAICTM).FindingsResource-utilization (investment) efficiencies were observed as 0.941 (0.964), thereby contributing to the mean overall efficiency of the sample banks at 0.952. However, the related efficiency changes decline over the sample period, thereby suggesting that the average banks' efficiencies hardly increase. Regression analyses show a significantly positive relationship between IC and banks' overall resource-utilization and investment efficiencies.Research limitations/implicationsOverall, this study suggests that researchers should consider risks when estimating banks' efficiencies owing to their connection to banks' investment performance. From banks' dynamic two-stage efficiencies, this study demonstrated that investments in IC will bring improved future economic benefits.Originality/valueDifferent from prior studies, this study improves banks' efficiency evaluation models by incorporating risk measures and assuming weighted periods for the 2007–2008 global financial crisis. Moreover, the use of monetary value-based measure of IC provides consistent results as the commonly-used VAICTM does.

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A modified slacks-based measure of efficiency in data envelopment analysis

Cited by 111 publications

References 28 publications

Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt

Impact of Industrial Structure Upgrading on Green Total Factor Productivity in the Yangtze River Economic Belt

Resource Endowment, Industrial Structure, and Green Development of the Yellow River Basin

Risk-adjusted banks' resource-utilization and investment efficiencies: does intellectual capital matter?

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