Knowledge management
Shahram Bakhshi hajikhajeloo; Yousef Namvar; Nasibeh Pourasghar
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 ...
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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.
Data mining
Taimour Jafarian Dehkordi; Mohammadreza Dalvi Esfahan; Saeed Aghasi
Abstract
Purpose: The current research was conducted by designing the knowledge-based organizational satisfaction modeling with a data-driven approach using a qualitative and quantitative method of grounded theory and data mining techniques.methods: The data was taken from in-depth and semi-structured interviews ...
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Purpose: The current research was conducted by designing the knowledge-based organizational satisfaction modeling with a data-driven approach using a qualitative and quantitative method of grounded theory and data mining techniques.methods: The data was taken from in-depth and semi-structured interviews with 25 general managers of social security insurance departments in the provinces of the country, based on purposeful sampling. The validity of the research data was checked and confirmed by going back to the participants and external auditors. In the data mining section, registered data and the organization's database were used. Using the data recorded in the Clementine software, the happiness and unhappiness of the employees in the organization were categorized.Findings: The results showed that the model of organizational happiness in the social security organization was identified at three levels, group, individual and organization, including causal factors, intervenors, platforms, strategies and finally consequences. Also, the status of employees was determined based on the proposed model of happiness according to the collected data. Finally, the data mining model showed classification with 66% accuracy for happy and unhappy employees.Conclusion: The human resource management approach based on organizational data leads to correct decision making in organizational performance. The more transparent the collected data is, the more accurately the state of the organization can be predicted. Also, based on the proposed model and implementation in the form of data mining, it is possible to estimate the number of happy employees.
