Developing a model for the success of research and development projects in knowledge-based companies

The article proposes a model to estimate the success rate of research and development (R&D) projects in knowledge-based companies before implementing projects and attracting investments or credit. The model evaluates projects based on known variables to combine projects based on available evidence. Many knowledge-based companies must choose the best plan from numerous R&D options. For these companies operating at the intersection of knowledge and industry, choice is doubly important. Therefore, this research collected variables affecting project success rates using the grounded theory method. Variables were ranked using a combined compromise solution to simplify the model. The most impactful variables were selected for inclusion. Interpretive structural modeling was used to extract the desired model, which was then analyzed with the Micmac method. The population studied was knowledge-based companies in various technology fields. The target sample came from companies in science and technology parks. Based on this, eight main variable groups impacting project success rates in knowledge-based companies were identified and compiled. The model shows these eight indexes and their 32 subcategories classified into four general levels. "Rules and approvals" and "project user specifications" and "company financial coordinates" are placed in the fourth level. Proposed R&D projects are measured against this model, and investment for each project is also determined according to the model.