Università degli studi di Modena e Reggio Emilia

Gender inequality in research and innovation is well documented (European Commission, 2016) and tools to measure and monitor it have been proposed and tested within EU funded projects as GenderTime (Badaloni & Perini, 2016) or Effective gender equality in research and academia (EGERA) (http://www.egera.eu/). The evaluation proposal at the heart of this contribution has been developed within EQUAL-IST project (Gender Equality Plans for Information Sciences and Technology Research Institutions) funded under the European Union's Horizon 2020 research and innovation programme that aims at introducing structural changes in research organizations to enhance gender equality within Information System and Technology Institutions. The dimensions and indicators used to measure gender equality are consistent to those that the literature on gender equality in research and academic institutions have shown to be significant. Our contribution shows an innovation in the choice on how to measure gender equality by using Fuzzy Multi Criteria Decision Analysis (FMCDA). We propose a Fuzzy Expert System, a cognitive model that, by replicating the expert way of learning and thinking, allows to formalize qualitative concepts and to reach a synthetic measure of the institution's gender equality (ranging from 0 to 1 increasing with gender equality achievements) that can be then disentangled in its different dimensions. The latter characteristic of the model that we propose can be fruitfully used by policy makers and Equal opportunity officers in order to detect and address the critical elements in the organization and carry out changes to improve gender equality. A first application of the model has been experimented within the EQUAL-IST project and is available for other universities and research institutions wishing to obtain an assessment of their organization in terms of gender equality. Further developments of the model, together with its wider implementation, include the assessment by using fuzzy logic of gender equality policies and institutional factors affecting gender equality within the institution.