This paper proposes to measure the museums performance with amodel that combines the Data Envelopment Analysis (DEA) and Balanced Scorecard (BSC) methodologies with a third method, the analytic hierarchy process (AHP), which is often used to support decision making. Starting from the two-stage DEA–BSC model of Basso et al. (Omega Int J Manag Sci 81:67–84, 2018), which integrates DEA and BSC, we explore the advantages to consider also the AHP methodology, with the aim to include the judgement of some museums’ experts on the relative importance of the BSC perspectives in the performance evaluation model. A first approach uses directly the AHP priorities derived from the judgements expressed by the museums’ experts interviewed to determine the weights to aggregate the four BSC performance scores into an overall performance indicator. A second approach uses the judgments of the museums’ experts indirectly to introduce proper restrictions on the output weights of the second-stage DEA model. With this approach, we overcome the problem arising from the dispersion of the preferences within the group of experts, that may heavily affects the first approach. Both approaches proposed in this contribution are applied to the case study of the municipal museums of Venice.

A three-system approach that integrates DEA, BSC, and AHP for museum evaluation

Antonella, Basso;Stefania, Funari
2020-01-01

Abstract

This paper proposes to measure the museums performance with amodel that combines the Data Envelopment Analysis (DEA) and Balanced Scorecard (BSC) methodologies with a third method, the analytic hierarchy process (AHP), which is often used to support decision making. Starting from the two-stage DEA–BSC model of Basso et al. (Omega Int J Manag Sci 81:67–84, 2018), which integrates DEA and BSC, we explore the advantages to consider also the AHP methodology, with the aim to include the judgement of some museums’ experts on the relative importance of the BSC perspectives in the performance evaluation model. A first approach uses directly the AHP priorities derived from the judgements expressed by the museums’ experts interviewed to determine the weights to aggregate the four BSC performance scores into an overall performance indicator. A second approach uses the judgments of the museums’ experts indirectly to introduce proper restrictions on the output weights of the second-stage DEA model. With this approach, we overcome the problem arising from the dispersion of the preferences within the group of experts, that may heavily affects the first approach. Both approaches proposed in this contribution are applied to the case study of the municipal museums of Venice.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/3729994
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