Application of Artificial Intelligence for Better Investment in Human Capital

Mohammed Abdullah Ammer, Zeyad A. T. Ahmed (), Saleh Nagi Alsubari, Theyazn H. H. Aldhyani () and Shahab Ahmad Almaaytah
Additional contact information
Mohammed Abdullah Ammer: Department of Finance, School of Business, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Zeyad A. T. Ahmed: Department of Computer Science, Dr Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India
Saleh Nagi Alsubari: Department of Computer Science, Dr Babasaheb Ambedkar Marathwada University, Aurangabad 431004, India
Theyazn H. H. Aldhyani: Applied College in Abqaiq, King Faisal University, Al-Ahsa 31982, Saudi Arabia
Shahab Ahmad Almaaytah: Applied College in Abqaiq, King Faisal University, Al-Ahsa 31982, Saudi Arabia

Mathematics, 2023, vol. 11, issue 3, 1-17

Abstract: 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.

Keywords: Big Five personality test; artificial intelligence; human resources; employee selection; teamwork; machine learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/3/612/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/3/612/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:3:p:612-:d:1047043

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().