Purpose: The purpose of this paper is to present the innovation management program (IMP) (FAZ Program) and analyze its results according to the public policy goals that support it (Pró-Inova) suggesting improvements.Design/methodology/approach: Intensive-direct-observation method in 43 companies; systematic data gathering and analysis (172 meeting documents); and innovation maturity diagnostics in 30 companies between August 2013 and May 2016.Findings: The FAZ Program success rate according to the Pró-Inova goals achieved 81 percent. The percentage of completion of FAZ activities decreases during its implementation from 100 percent (strategic module) to 74 percent (management module) and ending at 46 percent (project module). The maturity for innovation of these committees/teams is decisive for those percentages. Companies whose maturity for innovation of the strategic committee and the organizational team are above average or excellent have, respectively, 1.8 and 1.7 times greater probability of implementing the program successfully.Research limitations/implications: The FAZ Program represents only 4 percent of the programs supported by Pró-Inova. The innovative products, processes and businesses produced by the FAZ Program implementation are not measured. These innovations usually happen several years after an innovative management models implementation.Practical implications: The maturity for innovation diagnosis is useful both to evaluate the company’s innovation capacity and to predict its chances of implementing the program successfully. Adjusting the structure of the model (e.g. PDCA cycle for the organizational module) and improving the program’s implementation (e.g. ensure management module resources and maturity for innovation capacity) can increase the program’s success rate.Originality/value: Previous research works on IMPs supported by Pro-Inova focus on describing their methodology or benefits. The results allow answering what and how one of these programs offers in a return to the public innovation support received.

DINÂMICA ESPAÇO TEMPO DA DEMANDA POR INTERNET: AS CIDADES DO FUTURO NO CENÁRIO BRASILEIRO DE TELECOMUNICAÇÕES

Composite indicators are mathematical tools that assist sociologists, geographers, environmentalists, and urban planners in understanding and analyzing complex phenomena. Composite indicators also attract significant interest among researchers in operations research, who seek to improve and develop new methods for their construction. This study aligns with this latter line of research by proposing a new method for constructing composite indicators. The so-called Subjective Benefit-of-the-Doubt (S-BoD) was developed to address the uncertainty about the conceptual importance of sub-indicators in the data-driven definition of their respective weights. This logic ensures the weights adapt to the maturity of the theoretical-conceptual framework for the multidimensional phenomenon. On the one hand, absolute consensus among experts signals a mature theoretical-conceptual framework that prevents the BoD from modifying the weights. On the other hand, absolute disagreement among experts signals the presence of incipient theoretical-conceptual frameworks, which allow for full weight fluctuations, corresponding to the operationalization of the classic BoD algorithm. The results of applying the method show that the S-BoD outperforms BoD in multidimensional governance by better distinguishing units, extracting information, explaining GDP per capita, and generating scores without outliers, resulting in a composite governance indicator with greater discriminatory, informational, and explanatory power. Simulations confirm this superiority, except for the proportion of outliers, which is specific to governance data.

Interest in child health and well-being is growing, and both are multidimensional. Composite indicators (CIs) are useful tools for analyzing their relationships. This study examines the correlation between the CI of child well-being and that of public health system performance and proposes a weighting scheme for constructing CIs. Among schemes, entropy weighting yielded the strongest correlation. Uncertainty analysis revealed that country rankings with entropy weighting are the most stable, demonstrating robust CI. Strong explanatory power indicates that the CIs correlate with GDP, validating their compatibility with a key reference variable. High discriminant power confirms that CIs provide informational diversity, helping decision-makers distinguish between countries. The analysis shows that better health system performance is associated with higher child well-being, reinforcing the role of public health systems in promoting sustainable development and child well-being.

Companies face multi-criteria problems every day, such as prioritizing projects, investments, and suppliers. In this respect, the literature offers countless methods, some of which provide partial and flawed solutions. Therefore, identifying and replacing a flawed method with a more efficient one is fundamental. However, this replacement is not straightforward because each method has a different evaluation structure. More specifically, in this study, the Mapping method uses scoring evaluations, whereas the Analytic Hierarchy Process (AHP) uses pairwise comparisons. In other words, scoring evaluations are incompatible with pairwise comparison evaluations. This incompatibility prevents one method from being replaced by another without loss of information. This is a significant gap because the re-evaluation process is expensive, time-consuming, and may even be impossible if the experts are no longer available. This study presents a novel approach to automatically substitute prioritization methods without loss of information. The approach was applied to a real-world case involving forty-four Brazilian companies. The specific case shows the prioritization of four projects evaluated by scores, combined with three new projects evaluated by pairwise comparisons. The application of the approach offers specific and general contributions. For example, substituting prioritization methods without loss of information, such as the Mapping method with AHP. Flexibility in choosing the evaluation method that offers greater psychological comfort to the experts. Obtaining transitive pairwise comparison matrices independently of the number of new projects evaluated.