Enhancing Job Satisfaction Using an Adaptive Neuro-Fuzzy Inference System by Considering HSEE Factors
Job satisfaction plays a crucial role in enhancing productivity and reveals intriguing insights that impact the operational effectiveness of organizations. Due to the importance of maintenance units, special attention should be paid to their employees. This study employs a machine learning approach...
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| Format: | Article |
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University of Qom
2024-08-01
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| Series: | مدیریت مهندسی و رایانش نرم |
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| Online Access: | https://jemsc.qom.ac.ir/article_3091_3d5eb6d063fc32902c3df2a34947f8b2.pdf |
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| _version_ | 1832577555353305088 |
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| author | Mehrab Tanhaeean Fatemeh Raeisi Hamid Saffari |
| author_facet | Mehrab Tanhaeean Fatemeh Raeisi Hamid Saffari |
| author_sort | Mehrab Tanhaeean |
| collection | DOAJ |
| description | Job satisfaction plays a crucial role in enhancing productivity and reveals intriguing insights that impact the operational effectiveness of organizations. Due to the importance of maintenance units, special attention should be paid to their employees. This study employs a machine learning approach to enhance the performance and job satisfaction of maintenance units through the focus on health, safety, environment, and ergonomics (HSEE). A standardized questionnaire is developed for on HSEE data. Within the neural-fuzzy inference network, inputs such as health and safety protocols, environmental data collection, and its reliability is assessed using Cronbach's alpha coefficient. Subsequently, various adaptive neuro fuzzy inference system (ANFIS) models are utilized to predict job satisfaction based factors, and ergonomics are considered, while job satisfaction serves as the output. Following the selection of the optimal model, individual efficiency levels are assessed and scrutinized based on the calculated error. The findings suggest that enhancing employee job satisfaction relies on prioritizing the enhancement of ergonomics and the work environment. |
| format | Article |
| id | doaj-art-beef0b1cef71479d8d89d63a25767590 |
| institution | Kabale University |
| issn | 2538-6239 2538-2675 |
| language | fas |
| publishDate | 2024-08-01 |
| publisher | University of Qom |
| record_format | Article |
| series | مدیریت مهندسی و رایانش نرم |
| spelling | doaj-art-beef0b1cef71479d8d89d63a257675902025-01-30T20:19:19ZfasUniversity of Qomمدیریت مهندسی و رایانش نرم2538-62392538-26752024-08-01101506610.22091/jemsc.2024.11008.11833091Enhancing Job Satisfaction Using an Adaptive Neuro-Fuzzy Inference System by Considering HSEE FactorsMehrab Tanhaeean0Fatemeh Raeisi1Hamid Saffari2Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranDepartment of Industrial Engineering and Management, Shahrood University of Technology, Shahrood, IranIndustrial Engineering Department, Iran University of Science and Technology, Tehran, IranJob satisfaction plays a crucial role in enhancing productivity and reveals intriguing insights that impact the operational effectiveness of organizations. Due to the importance of maintenance units, special attention should be paid to their employees. This study employs a machine learning approach to enhance the performance and job satisfaction of maintenance units through the focus on health, safety, environment, and ergonomics (HSEE). A standardized questionnaire is developed for on HSEE data. Within the neural-fuzzy inference network, inputs such as health and safety protocols, environmental data collection, and its reliability is assessed using Cronbach's alpha coefficient. Subsequently, various adaptive neuro fuzzy inference system (ANFIS) models are utilized to predict job satisfaction based factors, and ergonomics are considered, while job satisfaction serves as the output. Following the selection of the optimal model, individual efficiency levels are assessed and scrutinized based on the calculated error. The findings suggest that enhancing employee job satisfaction relies on prioritizing the enhancement of ergonomics and the work environment.https://jemsc.qom.ac.ir/article_3091_3d5eb6d063fc32902c3df2a34947f8b2.pdfsafetyjob satisfactionmachine learningadaptive neuro fuzzy inference system |
| spellingShingle | Mehrab Tanhaeean Fatemeh Raeisi Hamid Saffari Enhancing Job Satisfaction Using an Adaptive Neuro-Fuzzy Inference System by Considering HSEE Factors مدیریت مهندسی و رایانش نرم safety job satisfaction machine learning adaptive neuro fuzzy inference system |
| title | Enhancing Job Satisfaction Using an Adaptive Neuro-Fuzzy Inference System by Considering HSEE Factors |
| title_full | Enhancing Job Satisfaction Using an Adaptive Neuro-Fuzzy Inference System by Considering HSEE Factors |
| title_fullStr | Enhancing Job Satisfaction Using an Adaptive Neuro-Fuzzy Inference System by Considering HSEE Factors |
| title_full_unstemmed | Enhancing Job Satisfaction Using an Adaptive Neuro-Fuzzy Inference System by Considering HSEE Factors |
| title_short | Enhancing Job Satisfaction Using an Adaptive Neuro-Fuzzy Inference System by Considering HSEE Factors |
| title_sort | enhancing job satisfaction using an adaptive neuro fuzzy inference system by considering hsee factors |
| topic | safety job satisfaction machine learning adaptive neuro fuzzy inference system |
| url | https://jemsc.qom.ac.ir/article_3091_3d5eb6d063fc32902c3df2a34947f8b2.pdf |
| work_keys_str_mv | AT mehrabtanhaeean enhancingjobsatisfactionusinganadaptiveneurofuzzyinferencesystembyconsideringhseefactors AT fatemehraeisi enhancingjobsatisfactionusinganadaptiveneurofuzzyinferencesystembyconsideringhseefactors AT hamidsaffari enhancingjobsatisfactionusinganadaptiveneurofuzzyinferencesystembyconsideringhseefactors |