Meta-choices in Ranking knowledge-based organizations

Purpose: This paper addresses the issue of knowledge visualization and its connection with performance measurement from an epistemological point of view, considering quantification and measurement not just as technical questions but showing their relevant implications on the management decision making of knowledge-based organizations. Study design/methodology/approach: We propose a theoretical contribution that combines two lines of research for identifying the three main meta-choices problems that arise in the multidimensional benchmarking of knowledge-based organizations. The first is the meta-choice problem related to the choice of the algorithm used (Iazzolino et al., 2012; Laise et al., 2015; Daraio, 2017a). The second refers to the choice of the variables to be included in the model (Daraio, 2017a). The third concerns the choice of the data on which the analyses are carried out (Daraio, 2017a). Findings: We show the interplay existing among the three meta-choices in multidimensional benchmarking, considering as KPIs IC, including Human Capital, Structural Capital and Relational Capital, and performances, evaluated in financial and non-financial terms. We provide an empirical analysis on Italian Universities, comparing the ranking distributions obtained by several efficiency and multi-criteria methods. Originality/value: The paper demonstrates the difficulties of the “implementation problem” in performance measurement, related to the subjectivity of results of the evaluation process when there are many evaluation criteria, and proposes the adoption of the technologies of humility related to the awareness that we can only achieve “satisficing” results.