Future of Work and Digital Management Journal In Press 2027 09 01 Design and Validation of a Technical Knowledge Valuation Model for Agricultural Knowledge-Based Companies Using Structural Equation Modeling Design and Validation of a Technical Knowledge Valuation Model for Agricultural Knowledge-Based Companies Using Structural Equation Modeling 1 24 EN Journal Article 2026 07 08 <table> <tbody> <tr> <td> <p>This study aimed to design and validate a structural equation model for the valuation of technical knowledge in agricultural knowledge-based companies by examining the relationships among causal conditions, contextual conditions, intervening factors, the central phenomenon, strategies, and outcomes. This applied, quantitative, cross-sectional study was conducted on 150 university academics, managers, and specialists affiliated with agricultural knowledge-based companies, science and technology parks, and relevant research institutions. Participants were selected through simple random sampling from the eligible population. Data were collected using a researcher-developed questionnaire scored on a five-point Likert scale. The measurement model was evaluated using Cronbach’s alpha, composite reliability, average variance extracted, and discriminant validity criteria. Partial least squares structural equation modeling was performed using SmartPLS to assess the measurement and structural models, including standardized path coefficients, bootstrapped t statistics, R², Q², SRMR, and NFI. All principal structural relationships were statistically significant. Causal Conditions had a positive effect on the Central Phenomenon (β = .681, t = 20.40, p &lt; .001). Contextual Conditions significantly predicted Strategies (β = .350, t = 4.95, p &lt; .001), as did Intervening Factors (β = .429, t = 6.73, p &lt; .001). The Central Phenomenon also had a positive but weaker effect on Strategies (β = .134, t = 2.35, p = .019). Strategies exerted the strongest direct effect on Outcomes (β = .827, t = 44.30, p &lt; .001). The model explained 72.7% of the variance in Strategies and 68.4% of the variance in Outcomes. All Q² values were positive, and overall model fit was acceptable (SRMR = .069; NFI = .850). The validated model demonstrated that technical knowledge valuation in agricultural knowledge-based companies is a multidimensional process shaped by economic feasibility, scalability, contextual support, internal capabilities, and strategic mechanisms. Strategic actions played the most prominent role in translating technical knowledge into economic, organizational, environmental, developmental, and market-oriented outcomes.</p> </td> </tr> </tbody> </table> https://journalfwdmj.com/index.php/fwdmj/article/download/389/399