Modeling the Role of Organizational Ethics Atmospheres and Work-family Conflict in Cyber Loafing of Academic Staff with Artificial Neural Network (ANN)

Document Type : Original Article

Authors

1 PhD of educational administration. Alborz General Directorate of Education - District 2 of Karaj, Iran

2 M.A in educational sciences, cultural working in Kermanshah education. Kermanshah, Iran

Abstract

Objective: The purpose of this study was to modeling the role of organizational ethics atmospheres and work-family conflict in cyber loafing.
Methods: Methodology of the research in terms of the strategy was quantitative-field and in terms of analytical, was descriptive. The statistical population of this study included all staff of Universities affiliated with the Ministry of Science at four levels of international, national, regional and local levels of performance across the country. The research data were collected from 430 staff members who were selected by multi-stage cluster sampling method. The sample size according to the Kregci-Morgan model and with error α = 0/05, was considered 430 persons. To collect data Cyber Loafing Questionnaire of Bella et al (2006), Organizational Ethical Atmospheres Questionnaire of Victor and Cullen (1988) and work-family Conflict Questionnaire of DuBrin (1985) was used. Validity of the tools was confirmed by the professors of education and psychology. Data were analyzed by artificial neural network with multilayer perceptron (MPL) method.
Results: The results showed modeling the organizational factors affecting on cyber loafing has an input layer with seven units and a hidden layer with one unit and the artificial neural network is well able to predict the jumps and the process of cyber loafing of academic staff based on organizational factors of organizational ethics atmospheres and work-family conflict.
Conclusion: It is necessary to pay attention to these components and their effects on the organization human resource behavior and avoiding deviant behavior.

Keywords

References

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  • Receive Date: 20 June 2023
  • Revise Date: 19 July 2023
  • Accept Date: 22 July 2023

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