PurposeThe purpose of this paper is to address 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.Design/methodology/approachThis study proposes 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).FindingsThe authors show the interplay existing among the three meta-choices in multidimensional benchmarking, considering as key performance indicators intellectual capital, including Human Capital, Structural Capital and Relational Capital, and performances, evaluated in financial and non-financial terms. This study provides an empirical analysis on Italian Universities, comparing the ranking distributions obtained by several efficiency and multi-criteria methods.Originality/valueThis study 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.

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Ensuring the quality of integrated data is undoubtedly one of the main problems of integrated data systems. When focusing on multi-national and historical data integration systems, where the “space” and “time” dimensions play a relevant role, it is very much important to build the integration layer in such a way that the final user accesses a layer that is “by design” as much complete as possible. In this paper, we propose a method for accessing data in multipurpose data infrastructures, like data integration systems, which has the properties of (i) relieving the final user from the need to access single data sources while, at the same time, (ii) ensuring to maximize the amount of the information available for the user at the integration layer. Our approach is based on a completeness-aware integration approach which allows the user to have ready available all the maximum information that can get out of the integrated data system without having to carry out the preliminary data quality analysis on each of the databases included in the system. Our proposal of providing data quality information at the integrated level extends then the functions of the individual data sources, opening the data infrastructure to additional uses. This may be a first step to move from data infrastructures towards knowledge infrastructures. A case study on the research infrastructure for the science and innovation studies shows the usefulness of the proposed approach.