Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance
The banking industry is a market with great competition and dynamism where organizational performance becomes paramount. Different indicators can be used to measure organizational performance and sustain competitive advantage in a global marketplace. The execution of the performance indicators is us...
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Applied Computational Intelligence and Soft Computing |
| Online Access: | http://dx.doi.org/10.1155/2021/7747907 |
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| author | Sulaiman O. Atiku Ibidun C. Obagbuwa |
| author_facet | Sulaiman O. Atiku Ibidun C. Obagbuwa |
| author_sort | Sulaiman O. Atiku |
| collection | DOAJ |
| description | The banking industry is a market with great competition and dynamism where organizational performance becomes paramount. Different indicators can be used to measure organizational performance and sustain competitive advantage in a global marketplace. The execution of the performance indicators is usually achieved through human resources, which stand as the core element in sustaining the organization in the highly competitive marketplace. It becomes essential to effectively manage human resources strategically and align its strategies with organizational strategies. We adopted a survey research design using a quantitative approach, distributing a structured questionnaire to 305 respondents utilizing efficient sampling techniques. The prediction of bank performance is very crucial since bad performance can result in serious problems for the bank and society, such as bankruptcy and negative influence on the country’s economy. Most researchers in the past adopted traditional statistics to build prediction models; however, due to the efficiency of machine learning algorithms, a lot of researchers now apply various machine learning algorithms to various fields, including performance prediction systems. In this study, eight different machine learning algorithms were employed to build performance models to predict the prospective performance of commercial banks in Nigeria based on human resources outcomes (employee skills, attitude, and behavior) through the Python software tool with machine learning libraries and packages. The results of the analysis clearly show that human resources outcomes are crucial in achieving organizational performance, and the models built from the eight machine learning classifier algorithms in this study predict the bank performance as superior with the accuracies of 74–81%. The feature importance was computed with the package in Scikit-learn to show comparative importance or contribution of each feature in the prediction, and employee attitude is rated far more than other features. Nigeria’s bank industry should focus more on employee attitude so that the performance can be improved to outstanding class from the current superior class. |
| format | Article |
| id | doaj-art-95de4ed320e54ea6bfc12a7022545ef5 |
| institution | Kabale University |
| issn | 1687-9724 1687-9732 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Applied Computational Intelligence and Soft Computing |
| spelling | doaj-art-95de4ed320e54ea6bfc12a7022545ef52025-02-03T01:24:48ZengWileyApplied Computational Intelligence and Soft Computing1687-97241687-97322021-01-01202110.1155/2021/77479077747907Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks PerformanceSulaiman O. Atiku0Ibidun C. Obagbuwa1Harold Pupkewitz Graduate School of Business, Namibia University of Science and Technology, Windhoek, NamibiaDepartment of Computer Science and Information Technology, Sol Plaatje University, Kimberley, South AfricaThe banking industry is a market with great competition and dynamism where organizational performance becomes paramount. Different indicators can be used to measure organizational performance and sustain competitive advantage in a global marketplace. The execution of the performance indicators is usually achieved through human resources, which stand as the core element in sustaining the organization in the highly competitive marketplace. It becomes essential to effectively manage human resources strategically and align its strategies with organizational strategies. We adopted a survey research design using a quantitative approach, distributing a structured questionnaire to 305 respondents utilizing efficient sampling techniques. The prediction of bank performance is very crucial since bad performance can result in serious problems for the bank and society, such as bankruptcy and negative influence on the country’s economy. Most researchers in the past adopted traditional statistics to build prediction models; however, due to the efficiency of machine learning algorithms, a lot of researchers now apply various machine learning algorithms to various fields, including performance prediction systems. In this study, eight different machine learning algorithms were employed to build performance models to predict the prospective performance of commercial banks in Nigeria based on human resources outcomes (employee skills, attitude, and behavior) through the Python software tool with machine learning libraries and packages. The results of the analysis clearly show that human resources outcomes are crucial in achieving organizational performance, and the models built from the eight machine learning classifier algorithms in this study predict the bank performance as superior with the accuracies of 74–81%. The feature importance was computed with the package in Scikit-learn to show comparative importance or contribution of each feature in the prediction, and employee attitude is rated far more than other features. Nigeria’s bank industry should focus more on employee attitude so that the performance can be improved to outstanding class from the current superior class.http://dx.doi.org/10.1155/2021/7747907 |
| spellingShingle | Sulaiman O. Atiku Ibidun C. Obagbuwa Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance Applied Computational Intelligence and Soft Computing |
| title | Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance |
| title_full | Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance |
| title_fullStr | Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance |
| title_full_unstemmed | Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance |
| title_short | Machine Learning Classification Techniques for Detecting the Impact of Human Resources Outcomes on Commercial Banks Performance |
| title_sort | machine learning classification techniques for detecting the impact of human resources outcomes on commercial banks performance |
| url | http://dx.doi.org/10.1155/2021/7747907 |
| work_keys_str_mv | AT sulaimanoatiku machinelearningclassificationtechniquesfordetectingtheimpactofhumanresourcesoutcomesoncommercialbanksperformance AT ibiduncobagbuwa machinelearningclassificationtechniquesfordetectingtheimpactofhumanresourcesoutcomesoncommercialbanksperformance |