Designing an Organizational Factors Model Affecting Tacit Knowledge Management: A Mixed Study

Document Type : Original Research Manuscripts

Authors

1 Department of Educational Sciences, Ardabil Branch, Islamic Azad University, Ardabil, Iran

2 Department of Educational Sciences, Payame Noor University (PNU), Tehran, Iran

Abstract
The current research was conducted to design a model to identify the effective organizational factors for tacit knowledge management in Iran's Social Security Organization. The method and tools of data collection were obtained through field methods such as interviews and questionnaires. The statistical population in the qualitative phase included managers and experts from the general departments of the Deputy of Management Development and Human Resources of the Social Security Organization. The sampling method in the qualitative phase was purposive and snowball. Based on the principle of theoretical saturation, a sample size of 15 interviews was chosen for the semi-structured interviews. To validate qualitative findings, four criteria of validity, generalizability, reliability, and verifiability were used. To assess the validity of the findings, content validity was employed. The process of data analysis was conducted using the open, axial, and selective coding methods in the MAXQDA. The statistical population in the quantitative part of the research was 360 managers and expert experts in 7 specialized vice offices of the organization's headquarters, and according to the table of Karjesi and Morgan (1970), the number of quantitative study samples was 186 people selected by simple random method. In the inferential statistics section, quantitative content analysis of the structural equation modeling method was used. Finally, the model of effective organizational factors on tacit knowledge management in the social security organization was confirmed. It is necessary to pay attention to the institutionalization of effective factors in tacit knowledge management by considering the designed model.

Keywords

Subjects

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Volume 4, Issue 3
Summer 2024
Pages 126-147

  • Receive Date 05 August 2023
  • Revise Date 03 October 2023
  • Accept Date 23 November 2023