Modelagem do Observatório de Ciência, Tecnologia e Inovação de Guarapuava-PR – OCT&IG

• Autor(a) principal: Ré, Pedro Henrique Ferst de
• Data de Publicação: 2021
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Biblioteca Digital de Teses e Dissertações do UNICENTRO
• Texto Completo: http://tede.unicentro.br:8080/jspui/handle/jspui/1685
• topic: Observatório; CommonKADS; Inteligência Competitiva; Políticas Públicas; Ciência; Tecnologia e Inovação; Observatory; CommonKADS; Competitive Intelligence; Public Policies; Science; Technology and Innovation; CIENCIAS SOCIAIS APLICADAS::ADMINISTRACAO

Exponential growth in the generation of data has transformed social and organizational relationships. The use of data and information in a precise manner, continues and agile on the part of the organizations is fundamental for its continuity. Equally, the public sector demands these resources for the formulation of fair and adequate public policies. However, this stage is still lacking, with a low use of available data and information, including its misuse and late use. Therefore, this work seeks to propose a design project for the Observatory of Science, Technology and Innovation of Guarapuava-PR. For this, methodologically this resarch was developed through an exploratory investigation, through the case study, with a qualitative approach. The data was collected from semi-structured interviews with specialists and participant observation. For data analysis, the ComomonKADS methodology is used, which is subdivided into six phases: organization model, task model, agent model, knowledge model, communication model and design model. Allied to this methodology, if we use the assumptions of the theory of competitive intelligence to formulate the processes and tasks that the observatory conducts. As a result, one can see the design of the technological infrastructure for the observatory, developing the organizational aspects, processes, tasks and agents involved in the model. In addition, a software is proposed, as a proof of concept, providing greater dynamism to the data, overcoming problems evidenced in other organizations, in addition to offering scalability to the model. Finally, the investigation achieves its objectives, providing the guidelines and strategic planning necessary for the subsequent implementation of the OCT & IG.