Volume 10, Issue 4 (Winter 2024)
johe 2024, 10(4): 251-264 |
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Research code: 3055
Ethics code: IR.UMSU.REC.1401.191
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Allahyari T, Nasiri H. Presenting an Explanatory Model of the Relationship between Ergonomic Climate and Job Stress using the Adaptive Neuro-fuzzy Inference System. johe 2024; 10 (4) : 3
URL: http://johe.umsha.ac.ir/article-1-895-en.html
URL: http://johe.umsha.ac.ir/article-1-895-en.html
1- Urmia University of Medical Sciences, Occupational Health Engineering Department, Faculty of Health, Urmia University of Medical Sciences, Urmia, Iran , allahyarit@yahoo.com
2- Urmia University of Medical Sciences, Urmia, Iran
2- Urmia University of Medical Sciences, Urmia, Iran
Abstract: (1331 Views)
Background and Objective: Ergonomic climate reflects employees' understanding of organizational emphasis on job design and modification in order to improve "operational performance" and "employee well-being. Job stress is a growing problem worldwide, affecting employee well-being and organizational productivity". The present study aimed to provide a model to explain job stress centered on ergonomic climate using the adaptive neuro-fuzzy inference system approach.
Materials and Methods: This research was conducted based on an applied design. The statistical population of the current research is the healthcare workers of one of the hospitals in Urmia. To this end, 376 questionnaires were collected from this population. Cronbach's alpha was used to determine reliability. Adaptive Neuro-Fuzzy Inference System (ANFIS) is a suitable method for solving nonlinear problems, ambiguities, and uncertainties. ANFIS was used to model the present research. The model was validated through Root Mean Squared Error (RMSE), mean absolute error, and R2 evaluation criteria.
Results: The mean scores of hospital ergonomic climate and job stress were obtained at 122.69 ±34.41 and 96.13 ±18.35, respectively. the correlation coefficient between ergonomic climate and job stress for 376 data was calculated at -0.63. The RMSE for the training data in the fuzzy c-means clustering method was 0.02.
Conclusion: Ergonomic climate and its dimensions, including operational performance and employees' well-being, have an inverse relationship with employees' job stress. Compared to the evaluation criteria of the model, the presented model can predict employees' job stress using ergonomic climate and its related dimensions with a lower mean error, indicating the accuracy and reliability of the model.
We would like to express our gratitude to all the dear ones who helped us in carrying out this research.
Materials and Methods: This research was conducted based on an applied design. The statistical population of the current research is the healthcare workers of one of the hospitals in Urmia. To this end, 376 questionnaires were collected from this population. Cronbach's alpha was used to determine reliability. Adaptive Neuro-Fuzzy Inference System (ANFIS) is a suitable method for solving nonlinear problems, ambiguities, and uncertainties. ANFIS was used to model the present research. The model was validated through Root Mean Squared Error (RMSE), mean absolute error, and R2 evaluation criteria.
Results: The mean scores of hospital ergonomic climate and job stress were obtained at 122.69 ±34.41 and 96.13 ±18.35, respectively. the correlation coefficient between ergonomic climate and job stress for 376 data was calculated at -0.63. The RMSE for the training data in the fuzzy c-means clustering method was 0.02.
Conclusion: Ergonomic climate and its dimensions, including operational performance and employees' well-being, have an inverse relationship with employees' job stress. Compared to the evaluation criteria of the model, the presented model can predict employees' job stress using ergonomic climate and its related dimensions with a lower mean error, indicating the accuracy and reliability of the model.
We would like to express our gratitude to all the dear ones who helped us in carrying out this research.
Article number: 3
Type of Study: Research Article |
Subject:
Ergonomics
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