An application of selective coding to create a theoretical framework on the factors that influence success or failure in adopting the SIG-UFRN software ecosystem

Introduction We call Software Ecosystems (SECOs) the development of software around a common technology platform that results in a set of solutions or services for a consumer market. The Integrated Management System (SIG-UFRN) of the Federal University of Rio Grande do Norte (UFRN) is a technological platform that forms part of the UFRN SECO and is used by a considerable number of Federal Educational Institutions (FEIs) throughout Brazil. The scenarios of use of the technology platform involve a complex mix of technical, social, and business aspects that can lead to the adoption or rejection of the SIG-UFRN. If these aspects are not observed, they can harm the business activities of the FEI, waste resources, and result in penalties for public managers who make poor decisions. This research aimed to construct and validate a theoretical framework that represents the adoption of the SIG-UFRN based on technical, business, and social factors. Methods To achieve this objective, Selective Coding, as defined in the Grounded Theory (GT) method, was used, as well as Focus Groups for validation. Selective coding is the third stage of the GT. This refines and integrates concepts to construct a theoretical framework for a phenomenon. This coding process enabled the refinement and interrelation of the concepts inherent in the adoption scenarios of the SIG-UFRN. These concepts were identified in the previous open and axial coding stages of GT. Using techniques such as Storyline Memo, Core Category, and Paradigm Model, a theoretical framework could be constructed representing the adoption of SIG-UFRN. Results We identified a theoretical framework that explained the adoption process of the SIG-UFRN, which was then validated with stakeholders. Discussion The results of this study could influence the success or failure of the adoption process and guide stakeholders in adopting SIG-UFRN best practices.