Designing and Validating an Intelligent Predictive Model for the Risk of Human Capital and Organizational Knowledge Erosion in Industrial Organizations

Ali khorramdoust¹

1) PostDBA Student, Center for Open Education and Skill Development, Allameh Tabataba’i University

IOI Code : AAHC-ACZBKF
Publication : National Conference on Management, Commercialization, Knowledge-Based Economy and Investment for Production(impeb.ir)
Abstract :
    In the complex and knowledge-intensive environment of industrial organizations, a significant portion of an organization’s competitive advantage and operational capabilities is embedded in the knowledge, experience, skills, and interpersonal networks of its employees. The departure of key employees, retirement, job transfers, migration, knowledge decay, ineffective experience transfer, and inadequate documentation can lead to the erosion of human capital and the loss of critical organizational knowledge. Although the knowledge management literature has addressed knowledge retention and transfer, many organizations still predominantly adopt a reactive approach, taking action after employee departures and knowledge loss have occurred, while predictive approaches for identifying high-risk employees, positions, and knowledge domains remain relatively underdeveloped. The present study aims to design and propose an intelligent model for predicting the risk of human capital and organizational knowledge erosion in industrial organizations. The proposed model integrates the Resource-Based View (RBV), the Knowledge-Based View of the Firm (KBV), Human Capital Theory, and the literature on organizational forgetting and knowledge management to incorporate a range of individual, knowledge-related, job-related, structural, and organizational factors into a composite Human Capital and Organizational Knowledge Erosion Risk Index. In the proposed model, key variables include the probability of employee turnover or transfer, knowledge criticality, knowledge uniqueness, level of documentation, knowledge transferability, depth of experience, number of available successors, process dependence on individuals, and the extent of knowledge interactions. The study proposes that, by employing data-driven methods and predictive modeling techniques, organizations can identify critical risk points before employee departure or knowledge loss occurs and activate preventive measures such as succession planning, job shadowing, knowledge transfer, knowledge documentation, and the development of knowledge networks. The proposed model can serve as a foundation for designing an Organizational Knowledge Early Warning System and developing a proactive approach to knowledge management in industrial organizations. By shifting the focus from responding to knowledge loss after it occurs toward anticipating and preventing knowledge erosion, the model has the potential to strengthen organizational resilience, preserve critical knowledge, and reduce dependency on individual knowledge holders.

Keywords : human capital organizational knowledge knowledge erosion knowledge loss organizational forgetting knowledge management knowledge risk knowledge retention artificial intelligence industrial organizations