UXAction-GP: um framework para fornecer diretrizes sobre práticas de UX Research e Long-Term UX

• Ano de defesa: 2025
• Autor(a) principal: Martinelli, Suéllen Rodolfo
• Orientador(a): Zaina, Luciana Aparecida Martinez
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: por
• Instituição de defesa: Universidade Federal de São Carlos; Câmpus São Carlos
• Programa de Pós-Graduação: Programa de Pós-Graduação em Ciência da Computação - PPGCC
• Palavras-chave em Português: Experiência do usuário; Pesquisa sobre a UX; Pesquisa longitudinal da UX; Startups de software; Teoria fundamentada socio-técnica
• Palavras-chave em Inglês: User eXperience; UX Research; Long-Term UX; Software startups; Socio-technical grounded theory
• Área do conhecimento CNPq: CIENCIAS EXATAS E DA TERRA::CIENCIA DA COMPUTACAO::METODOLOGIA E TECNICAS DA COMPUTACAO
• Link de acesso: https://hdl.handle.net/20.500.14289/22221

Software startups face challenges such as a lack of knowledge and limited resources when implementing UX practices. In relation to these practices are UX Research activities, which focus on investigating and evaluating user experience. UX Research practices can be integrated into Long-Term UX to facilitate data collection and evaluation at different stages of product or service usage over time. However, there is a scarcity of solutions that enable the integration of UX Research practices with Long-Term UX in the context of software startups. This doctoral thesis addresses the following research problem: How can professionals in software startups conduct UX Research practices integrated with Long-Term UX, given that they operate in an agile software development environment with limited resources? To answer this question, this doctoral thesis reports on the development and evaluation of the UX Research and Long-term UX in Action: Guidelines for Practice in Software Startups (UXAction-GP Framework), a theory formulated using the Socio-Technical Grounded Theory (STGT). The STGT methodology guided the execution of a systematic literature review and three distinct case studies, whose data were subjected to qualitative analysis. The emerging findings, identified codes, and categories provided the foundation for the UXAction-GP Framework. The framework’s architecture also incorporates technologies such as Generative AI and Machine Learning to train a model based on the results derived from the STGT approach. Two applications, AthenaUX and PALUX Research, were developed to explore the potential applications of the UXAction-GP Framework. Both applications underwent controlled experiments to assess user effort, frustration, and perceived achievement. The UXAction-GP Framework constitutes a solution to the identified research problem and offers substantial recommendations to support software startup professionals in conducting UX Research and Long-Term UX.