Machine Learning Based Method for Deciding Internal Value of Talent
This paper presents a machine-learning-based method for evaluating the internal value of talent in any organization and for evaluating the salary criteria. The study assumes the design and development of a salary predictor, based on artificial intelligence technologies, to help determine the interna...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2022-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2022.2151160 |
| _version_ | 1797641116882829312 |
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| author | Edurne Loyarte-López Igor García-Olaizola |
| author_facet | Edurne Loyarte-López Igor García-Olaizola |
| author_sort | Edurne Loyarte-López |
| collection | DOAJ |
| description | This paper presents a machine-learning-based method for evaluating the internal value of talent in any organization and for evaluating the salary criteria. The study assumes the design and development of a salary predictor, based on artificial intelligence technologies, to help determine the internal value of employees and guarantee internal equity in the organization. The aim of the study is to achieve internal equity, which is a critical element a that directly affects employees’ motivation. We implemented and validated the method with 130 employees and more than 70 talent acquisition cases with a Basque technology research organization during the years 2021 and 2022. The proposed method is based on statistical data assessment and machine-learning-based regression. We found that while most organizations have established variables for job evaluation as well as salary increments for staff according to their contribution to the organization, only a few employ tools to support equitable internal compensation. This study presents a successful real case of artificial intelligence applications where machine learning techniques help managers make the most equitable and least biased salary decisions possible, based on data. |
| first_indexed | 2024-03-11T13:40:57Z |
| format | Article |
| id | doaj.art-f894ca5c010e467c8fe3d9114613e3c3 |
| institution | Directory Open Access Journal |
| issn | 0883-9514 1087-6545 |
| language | English |
| last_indexed | 2024-03-11T13:40:57Z |
| publishDate | 2022-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Applied Artificial Intelligence |
| spelling | doaj.art-f894ca5c010e467c8fe3d9114613e3c32023-11-02T13:36:39ZengTaylor & Francis GroupApplied Artificial Intelligence0883-95141087-65452022-12-0136110.1080/08839514.2022.21511602151160Machine Learning Based Method for Deciding Internal Value of TalentEdurne Loyarte-López0Igor García-Olaizola1Vicomtech Foundation, Basque Research and Technology Alliance (BRTA)Vicomtech Foundation, Basque Research and Technology Alliance (BRTA)This paper presents a machine-learning-based method for evaluating the internal value of talent in any organization and for evaluating the salary criteria. The study assumes the design and development of a salary predictor, based on artificial intelligence technologies, to help determine the internal value of employees and guarantee internal equity in the organization. The aim of the study is to achieve internal equity, which is a critical element a that directly affects employees’ motivation. We implemented and validated the method with 130 employees and more than 70 talent acquisition cases with a Basque technology research organization during the years 2021 and 2022. The proposed method is based on statistical data assessment and machine-learning-based regression. We found that while most organizations have established variables for job evaluation as well as salary increments for staff according to their contribution to the organization, only a few employ tools to support equitable internal compensation. This study presents a successful real case of artificial intelligence applications where machine learning techniques help managers make the most equitable and least biased salary decisions possible, based on data.http://dx.doi.org/10.1080/08839514.2022.2151160 |
| spellingShingle | Edurne Loyarte-López Igor García-Olaizola Machine Learning Based Method for Deciding Internal Value of Talent Applied Artificial Intelligence |
| title | Machine Learning Based Method for Deciding Internal Value of Talent |
| title_full | Machine Learning Based Method for Deciding Internal Value of Talent |
| title_fullStr | Machine Learning Based Method for Deciding Internal Value of Talent |
| title_full_unstemmed | Machine Learning Based Method for Deciding Internal Value of Talent |
| title_short | Machine Learning Based Method for Deciding Internal Value of Talent |
| title_sort | machine learning based method for deciding internal value of talent |
| url | http://dx.doi.org/10.1080/08839514.2022.2151160 |
| work_keys_str_mv | AT edurneloyartelopez machinelearningbasedmethodfordecidinginternalvalueoftalent AT igorgarciaolaizola machinelearningbasedmethodfordecidinginternalvalueoftalent |