Suporte às micro e pequenas empresas a partir da gestão baseada em evidências: construção de ferramenta computacional baseada em inteligência artificial

Abstract

Small and Medium Enterprises (SMEs) compose 99% of companies in Brazil, 70% of formal jobs and 27% of gross domestic product. Despite this representativeness, the level of education in SMEs is low. This education level difficult decision-making. One alternative to improve SMEs decision making is evidence-based management (EBM). EBM is an approach that helps to acquire and appraise evidence. One organization that helps SMEs find evidence and make decisions is the Brazilian Small and Medium Enterprises Support Service (SEBRAE). SEBRAE has a SMEs call center with limited service capacity. This capacity can be increased with artificial intelligence technologies (AI). A literature review has demonstrated the lack of literature in the use of IA for the application of EBM in SMEs. In this context, what would be a computational tool to support the technical demands in the context of SMEs? To answer this problem, the research goal was create a computational tool that supports the SMEs technical demands from EBM. To create this tool, a working method based on design science research (DSR) was developed. Using the DSR, an artifact with ask-answer module and learning module was created. After four learning rounds, the artifact presented an accuracy of 90,70%. An experiment was carried out to compare the artifact with the SEBRAE call center. In the quality dimension, the artifact presented a performance similar to 53,59% of the call center. In the time dimension, the artifact presented better results than call center. The work contributes to the literature by developing an artifact that applies EBM. SEBRAE benefited from an alternative to increase its service capacity. The artifact can be used to complement and expedite the SMEs call center service.