Abstract
This paper proposes to measure the museums performance with a model 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.
Highlights
The importance of measuring the performance of museums is widely highlighted in the literature and may have different motivations
In order to verify the applicability of the analytic hierarchy process (AHP) methodology to determine the weights to aggregate the Balanced Scorecard (BSC) perspective scores, we interviewed five managers who are currently working in the municipal museums of Venice as field experts
We present a model that takes into account the preference judgements collected from the museums’ experts in a “looser” way and combines the AHP methodology with a further Data Envelopment Analysis (DEA)–BSC model
Summary
The importance of measuring the performance of museums is widely highlighted in the literature and may have different motivations (see for example Basso and Funari 2004). A first approach is to use directly the weights obtained using the pairwise comparison matrices computed from the judgements of the museums’ experts interviewed (the so-called AHP priorities) in order to aggregate the four BSC performance scores computed at the first stage into an overall performance indicator. A second approach utilizes the judgments of the museums’ experts indirectly, to impose proper restrictions on the weights of the outputs of a second-stage DEA model This second approach allows us to overcome the problems arising from the differences in the judgements of the various decision makers, since it can be applied in any case, no matter how big the differences. This model is applied to the municipal museums of Venice in Sect.
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