Towards a digital enterprise: the impact of Artificial Intelligence on the hiring process

Authors

  • Karim AMZILE FSJES-Agdal-UM5-Rabat
  • Mohamed BERAICH
  • Imane AMOURI
  • Cheklekbire MALAININE

DOI:

https://doi.org/10.37380/jisib.v12i3.894

Keywords:

Artificial Neural Network , Human resources, Artificial Intelligence, Digital Enterprise, Recruitment

Abstract

In this paper, we proposed a decision support tool for recruiters to improve their hiring decisions of suitable candidates for such a vacancy post. For this purpose, we proposed the use of the Artificial Neural Network (ANN) method from Artificial Intelligence (AI), thus we used real data from a semi-public recruitment agency in Morocco. However, for the adopted methodology, we used the process opted by the methods and techniques related to Data Mining. As a result, after completing the modelling process, we were able to obtain a model capable of predicting the decision to accept or reject such a candidate for such a vacancy. However, we obtained a model with an accuracy of 99% as well as with a very low error rate. However, our results show that Artificial Intelligence techniques can provide a better decision support tool for recruiters while minimising the cost and time of processing applications and maximising the accuracy of the decisions made.

References

C. E. A. Pah and D. N. Utama, "Decision support model for employee recruitment using data mining classification," International Journal, vol. 8, no. 5, 2020.

D. Alao and A. Adeyemo, "Employee attrition analysis using decision tree algorithms", Computing, Information Systems, Development Informatics and Allied Research Journal, vol. 4, no. 1, pp. 17-28, 2013.

Dana Pessach, Gonen Singer, Dan Avrahami, Hila Chalutz Ben-Gal, Erez Shmueli, Irad Ben-Gal, "Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming", Decision Support Systems 134, 2020.

Josh Tullier, "The Use of Artificial Intelligence to Recruit Employees", Citations Journal of Undergraduate Research, Volume 18, 2021.

Linkai Qi a, Kai Yao, "Artificial Intelligence enterprise human resource management system based on FPGA high performance computer hardware", Microprocessors and Microsystems 82, 2021.

S. Singh and V. Kumar, "Analysis of engineering students' performance for recruitment using classification data mining techniques", International Journal of Computer Science, Engineering and Technology, vol. 3, no. 2, p. 31, 2013.

Samrat Singh, Dr. Vikesh Kumar, "Performance Analysis of Engineering Students for Recruitment Using Classification Data Mining Techniques", Samrat Singh et al | IJCSET |February 2013 | Vol 3, Issue 2, 31-37.

Downloads

Published

2023-03-09

Issue

Vol. 12 No. 3 (2022): Journal of Intelligence Studies in Business

Section

Article

License

Copyright (c) 2022 Journal of Intelligence Studies in Business

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Authors who publish with this journal agree to the following terms:

    1. Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.

    1. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.

  1. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).