JSSM  Vol.12 No.3 , April 2019
Artificial Intelligence: A Technological Prototype in Recruitment

Purpose: The study is conducted to evaluate the adaptability of artificial intelligence in recruitment and to assess the effect of this technology on the performance of the employees. Design/Methodology/Approach: Standard Multiple Linear regression model is used to predict the performance of the employees and one-way ANOVA is used to compare the artificial intelligence based recruitment with performance indicating variables namely reliability, productivity, Automation, Gamification & Training using SPSS. Snowball sampling method has been adopted for a sample size of 440 respondents working in leading recruitment consultancies in urban Bangalore. Findings: There is a greater association between the recruitment and performance variables when artificial intelligence is adopted as it is significant at 0.001 per cent level and productivity being the maximum. However, the impact of implementing gamification for recruitment doesn’t have a significant impact on the output due to partial significant effect on the adoption as (p = 0.046 < 0.05). Value of “R” is 0.604 and the coefficient of determination is 0.365. Productivity, Training, Automation & Reliability are the significant predictors of the performance in employees. Originality/Value: Artificial intelligence has emerged as a boon to the recruiters by automating the repetitive tasks, administrative tasks. Intelligent screening helps in automating resume screening, recruiter Chatbots for real-time candidate engagement, and digitization of interviews. This promotes pro-active strategic decision making better by the recruiters.

Cite this paper
Vedapradha, R. , Hariharan, R. and Shivakami, R. (2019) Artificial Intelligence: A Technological Prototype in Recruitment. Journal of Service Science and Management, 12, 382-390. doi: 10.4236/jssm.2019.123026.
[1]   Melder, B. (2018) The Role of Artificial Intelligence (AI) in Recruitment.

[2]   Goyal, M. (2017) How Artificial Intelligence Is Reshaping Recruitment, and What It Means for the Future of Jobs.

[3]   Rafter, R., Bradley, K. and Smyth, B. (2000) Automated Collaborative Filtering Applications for Online Recruitment Services. In International Conference on Adaptive Hypermedia and Adaptive Web-Based Systems, Springer, Berlin, Heidelberg, 363-368.

[4]   Khosla, R. (2003) An Online Multi-Agent E-Sales Recruitment System. Proceedings of 2003 IEEE/WIC International Conference on Web Intelligence (WI 2003), Halifax, NS, 13-17 October 2003, 111-117.

[5]   Färber, F., Weitzel, T. and Keim, T. (2003) An Automated Recommendation Approach to Selection in Personnel Recruitment. AMCIS 2003 Proceedings, North America, 31st December 2003, 302.

[6]   Dreyfus-León, M. and Chen, D.G. (2007) Recruitment Prediction with Genetic Algorithms with Application to the Pacific Herring Fishery. Ecological Modelling, 203, 141-146.

[7]   Loreto, V. and Steels, L. (2007) Social Dynamics: Emergence of Language. Nature Physics, 3, 758-760.

[8]   Faliagka, E., Iliadis, L., Karydis, I., Rigou, M., Sioutas, S., Tsakalidis, A. and Tzimas, G. (2014) On-Line Consistent Ranking on E-Recruitment: Seeking the Truth behind a Well-Formed CV. Artificial Intelligence Review, 42, 515-528.

[9]   Russell, S.J. and Norvig, P. (2016) Artificial Intelligence: A Modern Approach. Pearson Education Limited, Malaysia.