Presenting a Model for Predicting the Risk of Technology Transfer Using the Backpropagation Algorithm Case Study: The Pharmaceutical-Nutritional Supplement Industry

The dietary and pharmaceutical supplements industry is of particular importance due to its key role in improving human and animal health, as well as its impact on economic and agricultural development. Technology transfer in this industry requires attention to factors such as tacit knowledge, the use of appropriate technology transfer methods, and the ability of recipient organizations to absorb and recreate the technology. It is a complex process that necessitates identifying and assessing the associated risks. Moreover, one of the main challenges in technology transfer is the inherently non-transferable nature of tacit knowledge, which must be recreated within recipient organizations. This increases the risks, complexity, and costs of technology transfer, potentially resulting in negative effects on individuals' health, environmental sustainability, and the economic success of producers. In this study, the backpropagation algorithm is used for modeling the technology transfer risk assessment. Initially, the components of technology transfer risk assessment are identified. Then, these components are categorized using the Delphi method and expert panels, and finally, a model is built and estimated, with the data for this stage being generated using the backpropagation algorithm. The required information is collected through a structured questionnaire. The results show that, based on the proposed model, it is possible to predict technology transfer risks in the pharmaceutical-nutritional supplement industry with an accuracy of 0.68.