One of the most significant problems in the development of quantitative models to assess the performance of decision-making units (DMUs) is the availability of information coded into variables and indicators that are proxies for the relevant dimensions of the models: resources or inputs, products or services or outputs, and heterogeneous contextual factors including environmental factors. Data envelopment analysis (DEA) and balanced scorecard (BSC) are two of the best-known and applied tools to model and measure the performance of DMUs. Within the information set requested to model the performance, the most critical and sensitive variables and indicators related to the intangible capital of organizations, which includes with a primary role the intellectual capital (IC). Although they are very different, DEA (based on linear programming) and BSC (including a set of indicators along four dimensions) have recently been combined to try to address the problem indicated earlier, namely, having a set of variables and indicators available to better measure the performance of DMUs. We apply a three-level methodology combining (i) a series of systematic reviews, (ii) a bibliometric analysis of all the published works found, and (iii) an analysis of the so-called grey literature contained in the reports of knowledge-based organizations. The main results obtained are (1) a comprehensive survey and mapping of all scientific works combining DEA, BSC, and IC including the gender dimension; (2) an integral and inclusive list integrating all indicators found in both published works and reports, reclassified according to the main dimensions of the IC.

DEA, balanced scorecard and intellectual capital including the gender dimension: A comprehensive list of indicators / Daraio, C.; Di Leo, S.; Iazzolino, G.; Laise, D.. - In: INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH. - ISSN 0969-6016. - 30:6(2023), pp. 2972-3012. [10.1111/itor.13293]

DEA, balanced scorecard and intellectual capital including the gender dimension: A comprehensive list of indicators

Daraio C.
;
Di Leo S.;Laise D.
2023

Abstract

One of the most significant problems in the development of quantitative models to assess the performance of decision-making units (DMUs) is the availability of information coded into variables and indicators that are proxies for the relevant dimensions of the models: resources or inputs, products or services or outputs, and heterogeneous contextual factors including environmental factors. Data envelopment analysis (DEA) and balanced scorecard (BSC) are two of the best-known and applied tools to model and measure the performance of DMUs. Within the information set requested to model the performance, the most critical and sensitive variables and indicators related to the intangible capital of organizations, which includes with a primary role the intellectual capital (IC). Although they are very different, DEA (based on linear programming) and BSC (including a set of indicators along four dimensions) have recently been combined to try to address the problem indicated earlier, namely, having a set of variables and indicators available to better measure the performance of DMUs. We apply a three-level methodology combining (i) a series of systematic reviews, (ii) a bibliometric analysis of all the published works found, and (iii) an analysis of the so-called grey literature contained in the reports of knowledge-based organizations. The main results obtained are (1) a comprehensive survey and mapping of all scientific works combining DEA, BSC, and IC including the gender dimension; (2) an integral and inclusive list integrating all indicators found in both published works and reports, reclassified according to the main dimensions of the IC.
2023
balanced scorecard; data envelopment analysis; gender; indicators; intellectual capital; quantitative models; systematic review
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DEA, balanced scorecard and intellectual capital including the gender dimension: A comprehensive list of indicators / Daraio, C.; Di Leo, S.; Iazzolino, G.; Laise, D.. - In: INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH. - ISSN 0969-6016. - 30:6(2023), pp. 2972-3012. [10.1111/itor.13293]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1685473
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