Qualitative Model of R&D Management Based on Big Data Analytics

This paper aims to identify and explain the dimensions and components of a qualitative model for research and development management, based on big data analytics.
The current study is a qualitative research design utilizing an exploratory approach. Data collection was conducted through semi-structured interviews with 12 R&D managers and experts who are knowledgeable in data science. Data analysis was performed using open, axial, and selective coding techniques from grounded theory, facilitated by MAXQDA 18 software. Ultimately, a qualitative model was developed and presented.
The research findings identified eight dimensions (code axes) and twenty-four components (categories), which include: systematic management, the ability to utilize big data analysis, the application of data science, higher education, technical and non-technical infrastructure, government support, resource availability, internal factors, and external organizations. These findings are organized within the framework of a paradigm model based on grounded theory, encompassingsix dimensions: causal factors, the central category, strategies, intervening conditions, background conditions, and consequences.
The qualitative model of research and development management was presented based on a process-oriented approach and integrated with the identified core components. In this model, data sources were incorporated into the input phase of the R&D management process. The model processing phase includes the ability to utilize big data analysis, research and development strategies, data science tools, system management, and internal organizational factors. Additionally, external organizational factors, higher education strategies, government support, technical infrastructure, and organizational culture are identified as influential external components of the model.