An Intellectual Capital Risk Measurement Model Using Bayesian Network

This research introduces a hybrid problem-solving method to enhance the understanding and management of intellectual capital risks in organizations. It aims to assess the current situation, identify the underlying causes, and propose suitable strategies. The study offers a fresh perspective on measuring and exploring intellectual capital risks using the Bayesian network methodology, consisting of four comprehensive steps. First risks are identified and classified based on relevant literature, and hypothesizing that person, organization, and environment are key root causes, using a validated questionnaire. Second, a Bayesian network model is constructed to evaluate the probability of risks occurring across different situations. Third, data analysis techniques are used to prioritize risks and identify internal strengths and weaknesses, as well as external opportunities and threats. Finally, a strategic planning method is proposed, including three stages of input, matching, and decision making. Through the application of this methodology to a case study, all risk factors within the organization are identified and the probability of intellectual capital risk in various conditions is determined. The results highlighted the significant impact of human risks in the case studied. Based on the strategic formulation method, the study provides effective strategies for mitigating intellectual capital risks, prioritizing and proposing the best approach, which includes enhancing the company's knowledge through training programs, attracting educated individuals, and establishing connections with successful organizations. This method enables organizations to proactively monitor and manage intellectual capital risks, enhancing their competitive advantage in the market.