IDEAS home Printed from https://ideas.repec.org/a/dbk/datame/v3y2024ip.387id1056294dm2024387.html
   My bibliography  Save this article

Enhancing the hiring process: A predictive system for soft skills assessment

Author

Listed:
  • Asmaa Lamjid
  • Ariss Anass
  • Imane Ennejjai
  • Jamal Mabrouki
  • Ziti Soumia

Abstract

Human Resource Management faces the ongoing challenge of identifying top-performing candidates to enhance organizational success. Traditional recruitment methods heavily rely on assessing hard skills alone, overlooking the importance of soft skills in identifying individuals who excel in their roles. To address this, our paper introduces a novel predictive model that leverages Artificial Intelligence in the hiring process. By analyzing soft skills extracted from CVs, cover letters, websites, professional social media, and psychometric tests, the model accurately predicts potential candidates suitable for specific job roles. This system effectively eliminates poor hiring decisions, reduces time and effort, minimizes recruitment costs, and mitigates turnover risks. The implementation of our proposed model employs various predictive machine learning classifiers, with key input soft skills including creativity, collaboration, empathy, curiosity, and critical thinking. Notably, the Support Vector Machine classifier emerges as the top-performing model in terms of predictive accuracy

Suggested Citation

Handle: RePEc:dbk:datame:v:3:y:2024:i::p:.387:id:1056294dm2024387
DOI: 10.56294/dm2024.387
as

Download full text from publisher

To our knowledge, this item is not available for download. To find whether it is available, there are three options:
1. Check below whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a
for a similarly titled item that would be available.

More about this item

Statistics

Access and download statistics

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:dbk:datame:v:3:y:2024:i::p:.387:id:1056294dm2024387. See general information about how to correct material in RePEc.

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

We have no bibliographic references for this item. You can help adding them by using this form .

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Javier Gonzalez-Argote (email available below). General contact details of provider: https://dm.ageditor.ar/ .

Please note that corrections may take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.