Factor-bridging algorithm for the prediction of job satisfaction: Developing country perspective
Job satisfaction is crucial for both job seekers and employers. To ensure positive job satisfaction, companies must implement policies that consider employees’ perceptions of their work. This study utilizes machine learning and factor analysis to predict job satisfaction. Although, factor analysis h...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | Journal of King Saud University: Computer and Information Sciences |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1319157823002975 |
| _version_ | 1797629712052256768 |
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| author | Mohammad Aktaruzzaman Khan Sayed Allamah Iqbal Maliha Sanjida Khan Md. Golam Hafez |
| author_facet | Mohammad Aktaruzzaman Khan Sayed Allamah Iqbal Maliha Sanjida Khan Md. Golam Hafez |
| author_sort | Mohammad Aktaruzzaman Khan |
| collection | DOAJ |
| description | Job satisfaction is crucial for both job seekers and employers. To ensure positive job satisfaction, companies must implement policies that consider employees’ perceptions of their work. This study utilizes machine learning and factor analysis to predict job satisfaction. Although, factor analysis has limitations, such as data quality, small sample size, and difficulty interpreting factors, machine learning algorithms can overcome these challenges. This study predicts job satisfaction using field data by combining factor analysis with machine learning algorithms. Factor loading values significantly impact classification algorithms such as Logistic Regression, Decision Trees, Support Vector Machines, and Random Forest. Especially, the Management Support, Equity, Non-Financial Compensation, and Financial Compensation feature variables are highly effective. They are used to predict job satisfaction with factor-1, factor-2, and factor-3 values. The Random Forest and Support Vector Machines algorithms have shown the importance of these values. The enhanced precision has been demonstrated visually to highlight the contrast when compared to the factor loading analysis and their corresponding eigenvalues. |
| first_indexed | 2024-03-11T10:57:56Z |
| format | Article |
| id | doaj.art-1ee3da2ff0ea42089eefb5986547176d |
| institution | Directory Open Access Journal |
| issn | 1319-1578 |
| language | English |
| last_indexed | 2024-03-11T10:57:56Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of King Saud University: Computer and Information Sciences |
| spelling | doaj.art-1ee3da2ff0ea42089eefb5986547176d2023-11-13T04:08:56ZengElsevierJournal of King Saud University: Computer and Information Sciences1319-15782023-10-01359101743Factor-bridging algorithm for the prediction of job satisfaction: Developing country perspectiveMohammad Aktaruzzaman Khan0Sayed Allamah Iqbal1Maliha Sanjida Khan2Md. Golam Hafez3Department of Business Administration, International Islamic University Chittagong, Chittagong 4318, BangladeshDepartment of Electrical and Electronic Engineering, International Islamic University Chittagong, Chittagong 4318, Bangladesh; Department of Mathematics, Chittagong University of Engineering & Technology, Chittagong 4349, Bangladesh; Corresponding author at: Department of Electrical and Electronic Engineering, International Islamic University Chittagong, Chittagong 4318, Bangladesh.Department of Finance, Chittagong University, Chittagong 4331, BangladeshDepartment of Mathematics, Chittagong University of Engineering & Technology, Chittagong 4349, BangladeshJob satisfaction is crucial for both job seekers and employers. To ensure positive job satisfaction, companies must implement policies that consider employees’ perceptions of their work. This study utilizes machine learning and factor analysis to predict job satisfaction. Although, factor analysis has limitations, such as data quality, small sample size, and difficulty interpreting factors, machine learning algorithms can overcome these challenges. This study predicts job satisfaction using field data by combining factor analysis with machine learning algorithms. Factor loading values significantly impact classification algorithms such as Logistic Regression, Decision Trees, Support Vector Machines, and Random Forest. Especially, the Management Support, Equity, Non-Financial Compensation, and Financial Compensation feature variables are highly effective. They are used to predict job satisfaction with factor-1, factor-2, and factor-3 values. The Random Forest and Support Vector Machines algorithms have shown the importance of these values. The enhanced precision has been demonstrated visually to highlight the contrast when compared to the factor loading analysis and their corresponding eigenvalues.http://www.sciencedirect.com/science/article/pii/S1319157823002975Factor analysisLogistic regressionDecision tree classifierRandom forestSupport vector machineFeature importance |
| spellingShingle | Mohammad Aktaruzzaman Khan Sayed Allamah Iqbal Maliha Sanjida Khan Md. Golam Hafez Factor-bridging algorithm for the prediction of job satisfaction: Developing country perspective Journal of King Saud University: Computer and Information Sciences Factor analysis Logistic regression Decision tree classifier Random forest Support vector machine Feature importance |
| title | Factor-bridging algorithm for the prediction of job satisfaction: Developing country perspective |
| title_full | Factor-bridging algorithm for the prediction of job satisfaction: Developing country perspective |
| title_fullStr | Factor-bridging algorithm for the prediction of job satisfaction: Developing country perspective |
| title_full_unstemmed | Factor-bridging algorithm for the prediction of job satisfaction: Developing country perspective |
| title_short | Factor-bridging algorithm for the prediction of job satisfaction: Developing country perspective |
| title_sort | factor bridging algorithm for the prediction of job satisfaction developing country perspective |
| topic | Factor analysis Logistic regression Decision tree classifier Random forest Support vector machine Feature importance |
| url | http://www.sciencedirect.com/science/article/pii/S1319157823002975 |
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