Determining the Knowledge Components Affecting Intelligent Knowledge Extraction: A Meta-Synthesis

Considering the importance of knowledge extraction as one of the stages of knowledge acquisition in the organization, this article aims to identify the effective components and dimensions of intelligent knowledge extraction and determine the importance of each one. Using the seven-step method of Wilson and Lipsey (2001), 280 research articles retrieved from databases were examined, and finally, 32 articles were evaluated. The critical Assessment Skills Program was used to score the articles. To extract codes and concepts, the articles were entered into the Atlas.ti software. Shannon's entropy was used to determine the importance of each component. In this research, 51 codes were categorized into 6 main dimensions (individual factors, education and learning, technology agents and smart technology, knowledge, dynamism and agility, and organizational factors). The results show that the component of "empowerment" has the most frequency and importance in extracting knowledge. This result shows the importance of training human resources in strengthening the organization. Also, in the spider diagram, the teaching and learning dimension has the highest weight among the identified dimensions. In this field, there is no organized study of the factors affecting intelligent knowledge extraction. Therefore, as a pioneer, this research has achieved an organized framework for extracting knowledge and determining the importance of each identified dimension by categorizing themes. With the increasing importance of knowledge management in recent years, the attention of organizations and companies' managers has been attracted to processes leading to achieving, controlling, and making available their knowledge as an organizational asset. Knowledge as an organizational asset plays an extremely vital role today. Organizations necessitate knowledge related to their business field to achieve a competitive advantage.