Application of Artificial Intelligence for Better Investment in Human Capital
Selecting candidates for a specific job or nominating a person for a specific position takes time and effort due to the need to search for the individual’s file. Ultimately, the hiring decision may not be successful. However, artificial intelligence helps organizations or companies choose the right...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/3/612 |
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| author | Mohammed Abdullah Ammer Zeyad A. T. Ahmed Saleh Nagi Alsubari Theyazn H. H. Aldhyani Shahab Ahmad Almaaytah |
| author_facet | Mohammed Abdullah Ammer Zeyad A. T. Ahmed Saleh Nagi Alsubari Theyazn H. H. Aldhyani Shahab Ahmad Almaaytah |
| author_sort | Mohammed Abdullah Ammer |
| collection | DOAJ |
| description | Selecting candidates for a specific job or nominating a person for a specific position takes time and effort due to the need to search for the individual’s file. Ultimately, the hiring decision may not be successful. However, artificial intelligence helps organizations or companies choose the right person for the right job. In addition, artificial intelligence contributes to the selection of harmonious working teams capable of achieving an organization’s strategy and goals. This study aimed to contribute to the development of machine-learning models to analyze and cluster personality traits and classify applicants to conduct correct hiring decisions for particular jobs and identify their weaknesses and strengths. Helping applicants to succeed while managing work and training employees with weaknesses is necessary to achieving an organization’s goals. Applying the proposed methodology, we used a publicly available Big-Five-personality-traits-test dataset to conduct the analyses. Preprocessing techniques were adopted to clean the dataset. Moreover, hypothesis testing was performed using Pearson’s correlation approach. Based on the testing results, we concluded that a positive relationship exists between four personality traits (agreeableness, conscientiousness, extraversion, and openness), and a negative correlation occurred between neuroticism traits and the four traits. This dataset was unlabeled. However, we applied the K-mean clustering algorithm to the data-labeling task. Furthermore, various supervised machine-learning models, such as random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and AdaBoost, were used for classification purposes. The experimental results revealed that the SVM attained the highest results, with an accuracy of 98%, outperforming the other classification models. This study adds to the current literature and body of knowledge through examining the extent of the application of artificial intelligence in the present and, potentially, the future of human-resource management. Our results may be of significance to companies, organizations and their leaders and human-resource executives, in addition to human-resource professionals. |
| first_indexed | 2024-03-11T09:34:56Z |
| format | Article |
| id | doaj.art-fb0e9ed4652b4dafb7c4865123a605e7 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-11T09:34:56Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-fb0e9ed4652b4dafb7c4865123a605e72023-11-16T17:22:06ZengMDPI AGMathematics2227-73902023-01-0111361210.3390/math11030612Application of Artificial Intelligence for Better Investment in Human CapitalMohammed Abdullah Ammer0Zeyad A. T. Ahmed1Saleh Nagi Alsubari2Theyazn H. H. Aldhyani3Shahab Ahmad Almaaytah4Department of Finance, School of Business, King Faisal University, Al-Ahsa 31982, Saudi ArabiaDepartment of Computer Science, Dr Babasaheb Ambedkar Marathwada University, Aurangabad 431004, IndiaDepartment of Computer Science, Dr Babasaheb Ambedkar Marathwada University, Aurangabad 431004, IndiaApplied College in Abqaiq, King Faisal University, Al-Ahsa 31982, Saudi ArabiaApplied College in Abqaiq, King Faisal University, Al-Ahsa 31982, Saudi ArabiaSelecting candidates for a specific job or nominating a person for a specific position takes time and effort due to the need to search for the individual’s file. Ultimately, the hiring decision may not be successful. However, artificial intelligence helps organizations or companies choose the right person for the right job. In addition, artificial intelligence contributes to the selection of harmonious working teams capable of achieving an organization’s strategy and goals. This study aimed to contribute to the development of machine-learning models to analyze and cluster personality traits and classify applicants to conduct correct hiring decisions for particular jobs and identify their weaknesses and strengths. Helping applicants to succeed while managing work and training employees with weaknesses is necessary to achieving an organization’s goals. Applying the proposed methodology, we used a publicly available Big-Five-personality-traits-test dataset to conduct the analyses. Preprocessing techniques were adopted to clean the dataset. Moreover, hypothesis testing was performed using Pearson’s correlation approach. Based on the testing results, we concluded that a positive relationship exists between four personality traits (agreeableness, conscientiousness, extraversion, and openness), and a negative correlation occurred between neuroticism traits and the four traits. This dataset was unlabeled. However, we applied the K-mean clustering algorithm to the data-labeling task. Furthermore, various supervised machine-learning models, such as random forest (RF), support vector machine (SVM), K-nearest neighbor (KNN), and AdaBoost, were used for classification purposes. The experimental results revealed that the SVM attained the highest results, with an accuracy of 98%, outperforming the other classification models. This study adds to the current literature and body of knowledge through examining the extent of the application of artificial intelligence in the present and, potentially, the future of human-resource management. Our results may be of significance to companies, organizations and their leaders and human-resource executives, in addition to human-resource professionals.https://www.mdpi.com/2227-7390/11/3/612Big Five personality testartificial intelligencehuman resourcesemployee selectionteamworkmachine learning |
| spellingShingle | Mohammed Abdullah Ammer Zeyad A. T. Ahmed Saleh Nagi Alsubari Theyazn H. H. Aldhyani Shahab Ahmad Almaaytah Application of Artificial Intelligence for Better Investment in Human Capital Mathematics Big Five personality test artificial intelligence human resources employee selection teamwork machine learning |
| title | Application of Artificial Intelligence for Better Investment in Human Capital |
| title_full | Application of Artificial Intelligence for Better Investment in Human Capital |
| title_fullStr | Application of Artificial Intelligence for Better Investment in Human Capital |
| title_full_unstemmed | Application of Artificial Intelligence for Better Investment in Human Capital |
| title_short | Application of Artificial Intelligence for Better Investment in Human Capital |
| title_sort | application of artificial intelligence for better investment in human capital |
| topic | Big Five personality test artificial intelligence human resources employee selection teamwork machine learning |
| url | https://www.mdpi.com/2227-7390/11/3/612 |
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