Identifying Knowledge Management Infrastructures to Reduce Employee Mental Absenteeism Based on Data Mining Techniques

This research aims to identify the knowledge management infrastructure due to reducing employee absenteeism based on data mining. Examining the status and reports of employees using data recording systems, creating information dashboards, and applying data mining techniques is important for the transparency of the mental state of employees. The mixed research method (qualitative-quantitative) has been done in two phases. The first phase was conducted with a qualitative-inductive approach using the Delphi method and a semi-structured interview tool. In the second step, codes were grouped in a common axis and ۱۳ axis codes based on the similarity and distinction between the extracted codes. The interview sample was ۱۰ people selected using the purposeful sampling method. In the second phase, the quantitative research method was data mining; Then, according to the research literature and experts' opinion, the researcher-made questionnaire was designed with a five-point Likert scale. The data mining technique is based on neural networks and decision trees in Rosseta and Weka software. The results showed that knowledge management can increase the quality of organizational processes based on data, increase the empowerment of employees, and reduce absenteeism. The knowledge obtained from the data mining of organizational information dashboards is important for strengthening the mental health systems of employees and increasing productivity.

Keywords:

knowledge management infrastructure , Absenteeism , employee , Data mining