Enrique Herrera Viedma

Analysing the scientific evolution of e-Government using a science mapping approach

Abstract This study presents a science mapping approach to analysing the thematic evolution of the e-Government field. We combine different bibliometric tools to analyse the evolution of the cognitive structure of this research topic, allowing us to discover the dynamics over different years and detecting the most prominent, productive, and highest-impact subfields. Science mapping provides a novel perspective to reveal the scientific frontiers and dynamic structure with visualization methods. Findings indicate symptoms of a research field in constant evolution that has not yet reached a stage of maturity, and specially, in the following areas of study: smart cities (provision of public services), e-Participation (political area) and technologies used and citizens acceptance (technological tools).

Designing a Decision Support System for Recommending Smartphones Using Fuzzy Ontologies

Nowadays, smartphones have become indispensable items for everybody. Thanks to them, people can communicate and access Internet at any time regardless of where they are located. New smartphones belonging to a high amount of labels and with different features and prices keep appearing constantly in the market. This way, there is a need of tools that help buyers to select and buy the smartphone that better fits their necessities. In this article, a decision support system build over a fuzzy ontology has been designed in order to help people to select the perfect smartphone for them. Linguistic labels are used in order to provide the buyer with a comfortable way of expressing himself/herself.

Measuring inconsistency and deriving priorities from fuzzy pairwise comparison matrices using the knowledge-based consistency index

Abstract The fuzzy analytic hierarchy process (AHP) is a widely applied multiple-criteria decision-making (MCDM) technique, making it possible to tackle vagueness and uncertainty arising from decision makers, especially in a pairwise comparison process. Indeed, as the human brain reasons with uncertain rather than precise information, pairwise comparisons may involve some degree of inconsistency, which must be correctly managed to guarantee a coherent result/ranking. Several consistency indexes for fuzzy pairwise comparison matrices (FPCMs) have been proposed in the literature. However, some scholars argue that most of these fail to be axiomatically grounded, which may lead to misleading results. To overcome this lack of an axiomatically grounded index, a new index is proposed in this paper, referred to as the knowledge-based consistency index (KCI). A comparative study of the proposed index with an existing one is carried out, and the results show that KCI contributes to substantially reducing the computation time. In addition, the different fuzzy weights derived from the initial FPCM (for KCI computation purposes) can also be employed to find a crisp set of weights that corresponds to an optimal solution to the MCDM problem according to the decision maker’s viewpoint and expertise.