Modeling the Effect of Intellectual Capital on Digital Governance and Its Consequences on Knowledge Management in High-Tech Industries (Case Study: Urmia Science and Technology Park)

Purpose: The acceleration of digital and data-driven technologies in high-tech industries has reshaped competitive structures, value creation, and organizational practices, making many traditional mechanisms insufficient for complex environments. The growth in data volume, process automation, and interactions among intelligent platforms highlights the need for innovative digital governance frameworks. These frameworks, based on algorithms, automated protocols, and intelligent systems, enhance transparency and coordination beyond traditional systems. Achieving effective digital governance requires technological, knowledge, and organizational capacities that many organizations are still developing. Intellectual capital including human, structural, and relational components is a key driver of digital empowerment and innovation. Employees’ digital skills, data-driven infrastructures, organizational standards, and stakeholder trust networks are critical to supporting governance. The influence of intellectual capital on digital governance is often indirect, mediated by digital transformation, which reorganizes processes, infrastructures, and work patterns to enable digital capacities. Digital governance also plays a central role in knowledge management, as the creation, storage, sharing, and utilization of knowledge depend on data transparency, information quality, and integrated knowledge flows. Weak governance can disrupt these flows and limit the effective use of intellectual capital. Human and relational capital particularly drive knowledge flows and innovation capacity, while their interaction with knowledge management strengthens value creation and competitive advantage. At the macro level, regulatory frameworks moderate the relationships among intellectual capital, digital transformation, and governance, acting as enablers or constraints. Organizations must comply with regulations controlling data, privacy, and transparency, which shape digital governance and knowledge management, aligning technology, intellectual capital, and knowledge processes. Despite extensive studies, a fully integrated theoretical model connecting intellectual capital, digital transformation, governance, regulations, and knowledge management has not yet been established. This study aims to provide such a model, offering practical and theoretical insights for managers and policymakers in high-tech industries.
Methodology: The present study is a quantitative, descriptive-analytical research aimed at examining the relationships among intellectual capital, digital transformation, digital governance, and knowledge management in companies located in the Urmia Science and Technology Park. The study population comprised senior IT managers, data managers, human resources managers, digital transformation specialists, and knowledge management experts working in these companies. Initially, a sampling frame was developed based on the official list of eligible employees, with inclusion criteria requiring continuous employment in one of the park''s units, at least two years of relevant experience in IT, data, human resources, or knowledge management, and a managerial or specialist role related to digital transformation. The minimum sample size of 210 participants was determined using Daniel Supr’s (2025) formula for Structural Equation Modeling (SEM/PLS-SEM), considering the number of constructs, indicators, path complexity, and desired statistical power. A simple random sampling method was employed, with unique numeric identifiers assigned to each member and selection performed using R software (version 4.3.2) with seed = 2025. To account for non-responses, 273 questionnaires were distributed, resulting in 210 valid responses after screening. Data were collected using a standardized questionnaire based on a five-point Likert scale. The validity of the instrument was assessed through content validity (via expert evaluation), convergent validity (using Average Variance Extracted, AVE), and discriminant validity (using the Fornell-Larcker criterion). Reliability was confirmed using Cronbach’s alpha and Composite Reliability (CR), both with acceptable thresholds of 0.7. Structural Equation Modeling (SEM) using Partial Least Squares (PLS-SEM) was employed to test the research model, allowing the examination of relationships among multiple variables, measurement errors, multicollinearity, latent constructs, and hypothesized paths. Statistical analyses were performed using SPSS and Smart-PLS. Model evaluation included R² (coefficient of determination), Q² (predictive relevance), T-values (for path significance), and the overall Goodness-of-Fit (GOF) index. Thresholds were applied to interpret model fit, explanatory power, and predictive accuracy. The results indicated that the model provided robust explanatory and predictive capabilities for the relationships among intellectual capital, digital transformation, digital governance, and knowledge management in the context of the park.
Results: The findings of the study indicated that all factor loadings of the questionnaire items were above 0.4 and the T-statistics exceeded 1.96, demonstrating the acceptability of the indicators and the structural validity of the instrument. Cronbach’s alpha for all constructs was above 0.7, and the composite reliability of the research variables was also higher than 0.7, confirming the adequacy and stability of the instrument. Convergent validity of the constructs was assessed using the Average Variance Extracted (AVE), with all values exceeding 0.5, indicating a satisfactory convergence of the variables with their respective indicators. For discriminant validity, the Fornell-Larcker method was applied, showing that the square root of the AVE for each construct was greater than its shared variance with other constructs, confirming acceptable discriminant validity for all constructs. The coefficient of determination (R²) for the knowledge management variable was at a strong level, while it was moderate for digital governance and digital transformation, indicating a good model fit in explaining the variance of the dependent variables. The Q² index, which measures the predictive relevance of the model for endogenous constructs, was strong for knowledge management and moderate for digital governance and digital transformation, reflecting the model’s adequate predictive capability. The overall Goodness-of-Fit (GOF) index was calculated as 0.483, indicating a strong overall fit and proper alignment between observed and predicted values. Additionally, for hypothesis acceptance, the path coefficients needed to be positive and the T-values above 1.96; the results showed that all hypothesized relationships were statistically significant, and thus all research hypotheses were supported. In summary, the results of factor analysis, reliability and validity tests, R² and Q² indices, and the overall GOF confirm the validity, explanatory power, and predictive capability of the research model in examining the relationships among intellectual capital, digital transformation, digital governance, and knowledge management in companies located in the Urmia Science and Technology Park.
Discussion: Digital governance plays a pivotal role in enhancing knowledge management and facilitates the impact of intellectual capital and digital transformation on knowledge management; in such a way that without a coherent digital governance framework, effective exploitation of organizational knowledge is not possible. Digital transformation has a significant impact on digital governance and accelerates the maturation of governance structures. Digital regulations also act as moderators, weakening the impact of human and structural capital on digital governance, which indicates the need for smart and flexible policymaking.
Conclusion: The study findings indicate that human, structural, and relational capital all positively influence digital governance. Human capital, including employees’ digital skills, specialized knowledge, and competencies, especially among IT managers and data specialists, is crucial for implementing effective digital standards and governance mechanisms. Structural capital, such as robust organizational frameworks, integrated information flows, and comprehensive documentation, enhances governance by enabling secure and transparent management of digital information. Relational capital, consisting of trust-based relationships and strategic collaborations, strengthens the organization’s ability to operate effectively within interconnected digital environments. Digital governance, in turn, has a significant positive effect on knowledge management. It improves the accuracy, reliability, and accessibility of organizational data, facilitating knowledge sharing, integration, and optimal use of expertise. Digital transformation positively affects governance by modernizing processes, adopting advanced technologies, and developing data-driven infrastructures. Moreover, digital governance mediates the relationship between intellectual capital and knowledge management, as well as between digital transformation and knowledge management, highlighting that the benefits of intellectual capital and transformation are realized primarily when strong governance is in place. However, digital regulations can negatively moderate these relationships, as overly restrictive frameworks limit organizational flexibility and the effective use of human, structural, and relational resources. Overall, these results emphasize digital governance as a key link connecting intellectual capital, digital transformation, and knowledge management. To enhance knowledge flows and governance while maximizing intellectual assets, organizations must invest simultaneously in human, structural, and relational capacities and implement adaptive regulatory approaches that support innovation and digital effectiveness.