Abstract
Background: Knowledge resources and documentation mechanisms are essential for managers' productivity. Thus, identifying and categorizing knowledge resources and developing documentation mechanisms are crucial organizational tasks. Objectives: This research aimed to analyze, categorize, and rank knowledge resources in Iranian medical science libraries. Methods: This exploratory and correlational study was conducted on 209 managers, heads, and deputies of central and hospital libraries in Iranian medical universities. Sampling was performed using a proportional stratified random sampling method, and a researcher-made questionnaire was used to collect the data. Partial least squares structural equation modeling was utilized for data analysis. Results: Knowledge resources in medical science libraries were categorized into implicit, explicit, and web-based knowledge. Regarding the stages of knowledge documentation, the results indicated that the distribution stage and its related mechanisms received the highest scores. In contrast, the storage stage and its mechanisms received the lowest scores. The path coefficient test showed that the highest path coefficient was related to implicit knowledge (0.68), while the lowest was related to web-based knowledge (0.13). Regression coefficients and Cronbach's alpha were higher than 0.7, and the average extracted variance (AVE) was higher than 0.5, indicating the adequacy of the measurement and structural model evaluation. Conclusions: Based on the results, Iranian medical science libraries lack specific mechanisms for documentation and identifying and categorizing knowledge resources. Therefore, this research provided a suitable foundation for these libraries to employ knowledge documentation mechanisms and discover knowledge resources. These libraries' sources of implicit and explicit knowledge and necessary mechanisms for recording, capturing, and documenting knowledge are not clearly defined. Consequently, organizational knowledge documentation is not conducted formally based on a knowledge management model. Therefore, the present research enables the identification of important knowledge acquisition resources and the methods and mechanisms for extracting and documenting knowledge.