2017
DOI: 10.1016/j.omega.2016.10.005
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Graph productivity change measure using the least distance to the pareto-efficient frontier in data envelopment analysis

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Cited by 20 publications
(4 citation statements)
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“…Kaur and Kaur (2016) analysed the efficiency and productivity changes in 50 wine companies from 1988 to 2011 using a non-parametric DEA to calculate the changes in the Malmquist total factor productivity (TFP) and then decomposed these into efficiency changes and technological changes. Aparicio et al (2017) collected the latest data on the Spanish high-quality wine industry, decomposed the productivity changes into efficiency changes and technological changes, and then empirically analysed the productivity changes in the decision-making units (DMUs) in the full input-output space. Lekic et al (2018) used a non-parametric linear programming DEA (Data Envelopment Analysis) model to evaluate the activity development and financial efficiencies of representative small wineries in the Republic of Serbia, the results from which allowed for the development of specific strategic recommendations.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Kaur and Kaur (2016) analysed the efficiency and productivity changes in 50 wine companies from 1988 to 2011 using a non-parametric DEA to calculate the changes in the Malmquist total factor productivity (TFP) and then decomposed these into efficiency changes and technological changes. Aparicio et al (2017) collected the latest data on the Spanish high-quality wine industry, decomposed the productivity changes into efficiency changes and technological changes, and then empirically analysed the productivity changes in the decision-making units (DMUs) in the full input-output space. Lekic et al (2018) used a non-parametric linear programming DEA (Data Envelopment Analysis) model to evaluate the activity development and financial efficiencies of representative small wineries in the Republic of Serbia, the results from which allowed for the development of specific strategic recommendations.…”
Section: Literature Reviewmentioning
confidence: 99%
“…Non-radial measures such as the slacks based measure (SBM) proposed by Tone (2001), the enhanced Russell graph measure (ERGM) proposed in Pastor et al (1999) and the rangeadjusted measure developed by Cooper et al (1999) belong to this sort of models. In the second approach, the closest targets are computed for a given DMU according to a previously specified criterion of similarity, that is, considering the closeness between the values of the inputs and/or outputs of the DMU under assessment and the corresponding targets (see, e.g., Aparicio et al, 2007Aparicio et al, , 2017Fukuyama et al, 2014;Ruiz and Sirvent, 2020;Vakili et al, 2020). Although the least distance approach has the merit of potentially identifying the closest or easily acceptable efficient target for the DMs, we start by utilizing the greatest distance framework that has traditionally been employed because of its computational easiness (Fukuyama et al, 2014), then progressing towards the most preferred efficient target according to the DM's preferences.…”
Section: Introductionmentioning
confidence: 99%
“…Based on the least distance to the Pareto-efficient frontier (Min DS) model, Aparicio J. and Ruiz J. proposed a new method to measure productivity change of DMUs in the full input-output space [22]. Then Aparicio J., Garcia N. and Kapelko M. applied the Min DS model to identify different levels of inefficiency [23]. To achieve the further division of effective units, Piao S. and Li J. estimated the environmental efficiency of China based on the super-efficiency DEA model [24].…”
Section: Introductionmentioning
confidence: 99%