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

In this paper a ranking method using common weights methodology is presented. The goal of the method is enhancing the decision maker (DM)’s influence in the ranking procedure. Although DM’s preference information is an important element in our method, the approach can also be modified to be used in the absence of it. Since we aim to implement the approach on an empirical instance, the model is modified to deal with the properties of the sample, so it is developed in the presence of the interval data and flexible measures. Finally, the results are discussed.

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“…For example see, [59], [22], [52], [32]. For Further works in common weights methodology we refer to [16], [63], [33], [42], [2], [30], and [43].…”

Section: Common Weights Methodologymentioning

confidence: 99%

A novel ranking approach with common weights: An implementation in the presence of interval data and flexible measures

Ramezani-Tarkhorani

Eini

2022

RAIRO-Oper. Res.

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“…Omrani et al [38] presented an integrated (DEA) best-worst method (BWM) for considering DMs' preferences in DEA and reducing flexibility in weights of inputs and outputs. Kazemi et al [23] proposed a common weights model with fuzzy data and non-discretionary inputs. They also ranked homogenous DMUs with considering environmental criteria in the presence of uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

A common weight credibility data envelopment analysis model for evaluating decision making units with an application in airline performance

2022

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Data envelopment analysis (DEA) model has been widely applied for estimating efficiency scores of decision making units (DMUs) and is especially used in many applications in transportation. In this paper, a novel common weight credibility DEA (CWCDEA) model is proposed to evaluate DMUs considering uncertain inputs and outputs. To develop a credibility DEA model, a credibility counterpart constraint is suggested for each constraint of DEA model. Then, the weights generated by the credibility DEA (CDEA) model are considered as ideal solution in a multi-objective DEA model. To solve the multi-objective DEA model, a goal programming model is proposed. The goal programming model minimized deviations from the ideal solutions and found the common weights of inputs and outputs. Using the common weights generated by goal programming model, the final efficiency scores for decision making are calculated. The usefulness and applicability of the proposed approach have been shown using a data set in the airline industry.

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“…In other words, in a research collaborative network, it is expected that a leading company not only has a higher level of influence comparing other collaborative partners but also can demonstrate higher efficiency in using lower levels of inputs and producing higher levels of outputs. Therefore, a research collaborative network leader is defined as an organization that based on its influence on other collaborative partners demonstrates a higher level of efficiency [Li et al, 2018;Mokhtar et al, 2019b;Zenkevich, Kazemi, 2020;Kazemi et al, 2021].…”

Section: Literature Reviewmentioning

confidence: 99%

“…To this aim, this research focuses on the most recent relevant studies (e.g., [Mokhtar et al, 2019b;Zenkevich, Kazemi, 2020]) and defines a research collaborative network leader as an organization that based on its influence on other collaborative partners demonstrates a higher level of efficiency. Secondly, we address a call for research by S. Kazemi with coauthors [Kazemi et al, 2021] to identify a leading organization in a multidimensional way by developing a multidimensional common weight model (MDCW) based on the data envelopment analysis approach for identifying the leader in a more holistic way among a set of collaborative R&D centers in medical universities.…”

Section: Introductionmentioning

confidence: 99%

Leader identification in a research collaborative network

Kazemi¹

2021

Vestnik SPbSU. Management

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There is considerable empirical evidence on the advantages of interorganizational research collaborative networks across societies and research institutes such as research and development (R&D) centers and universities. Identifying a leader in this contexts is important both theoretically for doing leadership studies, and practically for effective governmental funding allocation and private investments. Inconsistent definitions and non-homogeneous attributes with unidimensional measurement approaches such as subjective measuring of power or considering a central company as the leader made the previous efforts inefficient for identifying leaders in an interorganizational setting. This research aims to identify a leading organization among a set of homogenous R&D centers in a research collaborative network context through implementing the main leader’s attributes in different dimensions. The article presents a multidimensional common weight model based on the data envelopment analysis (DEA) approach in a parallel system with several operational dimensions each of which consumes a set of inputs (budget, lecturers, and students) to produce a set of outputs (scientific meetings and conferences, national and international papers). Centrality and visibility are two main leaders’ attributes combined with efficiency influence the contributions and outcomes of each collaborative network partner. It is demonstrated how the proposed model performs its high-efficiency score in the most influential R&D center named the “leader” among 47 R&D centers in medical universities in Iran. The comparative analysis of managerial results showed that reputation has a greater impact on leader identification than centrality. The results based on mathematical calculations showed a robust discriminating power for efficiency measurement of the proposed model.

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

Cited by 6 publications

References 99 publications

A novel ranking approach with common weights: An implementation in the presence of interval data and flexible measures

A novel ranking approach with common weights: An implementation in the presence of interval data and flexible measures

A common weight credibility data envelopment analysis model for evaluating decision making units with an application in airline performance

Leader identification in a research collaborative network

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