Meta-Choices in Ranking Knowledge-Based Organizations

This paper deals with the main meta-choices problems that arise in the multidimensional benchmarking of knowledge-based organizations. The first is the metachoice problem related to the choice of the algorithm used to compute the ranking (Iazzolino et al., 2012; Laise et al., 2015; Daraio, 2017a). The second is the metachoice problem related to the choice of the variables to be included in the model (Daraio, 2017a). Lastly, a third metachoice problem is related to the choice of the data on which the analyses are carried out (Daraio, 2017a). The authors focus on multidimensional benchmarking analysis applied to KPIs related on one side to the IC (divided in the three dimensions of Human Capital, Structural Capital and Relational Capital) and on the other side to performances, evaluated in both financial and non-financial terms. In particular, this paper analyzes the problem of ranking a sample of 65 Italian Universities based on performance-related criteria. The methods used to carry out the comparative analysis are (i) Data Envelopment Analysis (DEA) and (ii) Analytic Hierarchy Process (AHP). 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, as in a benchmark context.