sirajuddin mohebi

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 13 axis codes based on the similarity and distinction between the extracted codes. The interview sample was 10 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.

The purpose of the current research was to provide a model of effective educational strategies on the renewable energy business model with an Islamic-Iranian approach (by examining the upstream documents and laws of Iran). Quantitative research method and the number of samples using Cochran's formula, 337 people were selected. The research tool was a researcher-made questionnaire. The analysis of the collected data was done in two descriptive and inferential ways using SPSS 16 and Smart PLS software. The results showed that fourteen components play a role in explaining the model of providing effective educational strategies on the renewable energy business model with an Islamic-Iranian approach (by examining the upstream documents and laws of the country of Iran), which are: key partners, key activities, Income flow, key resources - physical, key resources - intellectual, key resources - human resources, value - brand, value - movement at the edge of science and technology, value - price, value - timely delivery and easy access, customer relations, Customer segment, distribution channels, and finally its costs and structure.

The purpose of this study is to investigate the diagnosis of employees' mental absence and to provide a suitable model for the Iranian Tobacco Company.

This research was conducted with a qualitative-inductive approach and the method of Strauss-Corbin's grounded theory. The research tool (data collection) is a semi-structured interview that analyzes the data obtained from interviews with 10 qualified elites and experts of the Iranian Tobacco Company in three stages of open, axial, and selective coding in ATLAS TI software using the grounded theory method.

13 general categories in the form of a paradigm model in which these factors include causal conditions (individual factors; factors of group behavior; organizational factors; environmental factors), axial phenomenon (diagnosis of the mental absence of employees), contextual conditions (environmental and physical context), intervening conditions (individual factors ), and strategies (organizational strategy; job-related strategy; career development strategy), and consequences (individual outcomes; effectiveness; group outcomes) were identified.

In a business environment with digital disruption from competitors and customers who have more choices through the availability of digital applications and information, it is important to ensure that organizations can quickly understand and adapt to new information. The need for mindfulness and control of mental absence is a prerequisite for digital transformation.