A goal-based weighting for composite indicators constructed through Ordered Weighted Averaging (OWA) operator.

The Ordered Weighted Averaging (OWA) operator is a multicriteria method that has conquered space among researchers in the composite indicators field. Typically, OWA operator weights are defined by the decision maker. This type of weighting is highly criticized, as decision-makers are susceptible to errors and bias in judgment. Some methods have been used to define OWA operator weights objectively. However, none of them is concerned about the quality of the composite indicator. This paper introduces a method that defines the weights of the OWA operator based on two quality parameters of the composite indicator: the ability to capture the concept of the multidimensional phenomenon and the informational loss. The method can be implemented in Microsoft Excel Solver and has a high degree of flexibility and applicability in problems of a multidimensional nature and a high degree of appropriation by researchers and practitioners in the area.•Defines weights that maximize the ability of the composite indicator to capture the concept of the multidimensional phenomenon.•Considers restrictions to limit the informational loss of the composite indicator or emphasize positive or negative aspects of the multidimensional phenomenon.•Offers flexibility in setting the objective and constraints of the optimization algorithm.

Multimethod to prioritize projects evaluated in different formats.

The prioritization of Research, Development & Innovation Projects is an essential step in the innovation management process. As a rule, it is carried out applying methods that allow one to process experts' preferences concerning each project according to established criteria. However, there are different preference formats which experts can utilize: Ordering of Alternatives, Utility Values, Multiplicative Preference Relations, Fuzzy Estimates, Fuzzy Preference Relations, etc. Wherein, each prioritization method usually handles only one of these formats. Thus, the following question arises: how do we prioritize projects taken from portfolios evaluated in different formats? The proposed methodology presents a way to overcome this gap by achieving three main objectives. First, develop techniques that make it possible to crossover between preference formats and prioritization methods. Second, merge two portfolios of projects built applying different prioritization methods. Third, prioritize projects evaluated using different formats. The results of this study are universal and can be applied to replace any method of prioritization. In the specific case, the Mapping method is replaced by the Analytic Hierarchy Process and, then, by the Interactive Multicriteria Decision Making method (so called TODIM method). Techniques are also proposed to ensure compatibility between different preference formats and prioritization methods.•Prioritization of projects evaluated in different formats using the Mapping, AHP, and TODIM methods.•Providing fully consistent evaluation matrices.•Application of techniques to make different preference formats and prioritization methods compatible.