Developing HRIS for Predictive Attrition and Retention management of Indian IT Engineers-using ANN, ANOVA and Smart PLS
Shivinder Nijjer1, Jaskirat Singh2, Sahil Raj3

1Dr. Shivinder Nijjer, School of Management Studies, Punjabi University, Patiala, India.

2Jaskirat Singh, Chandigarh Business School of Administration, Chandigarh Group of Colleges, Landran, India.

3Dr. Sahil Raj, School of Management Studies, Punjabi University, Patiala, India.

Manuscript received on 05 August 2019 | Revised Manuscript received on 12 August 2019 | Manuscript Published on 26 August 2019 | PP: 710-715 | Volume-8 Issue-9S August 2019 | Retrieval Number: I11140789S19/19©BEIESP | DOI: 10.35940/ijitee.I1114.0789S19

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Abstract: Growth of IT sector in India (Heeks, 2015) is phenomenal, however, employee turnover has been a persistent issue in IT sector (Yiu & Saner, 2008). Voluntarily turnover among employees has been attributed to dissatisfaction with organizational factors and individual characteristics (Elkjaer & Filmer, 2015). Therefore, this research examines how to retain employees in IT firms, by focusing on the Job attitudes, theory of individual differences and theory of planned behaviour. It also explores which individual characteristics contribute to employee turnover intent, as a consequence of their negative job attitudes. The techniques of Artificial Neural Networks, Two-way ANOVA and PLS testing have been utilised. The analysis confirms the proposition that individual differences have an effect on job attitudes, which ultimately affect the turnover intention.

Keywords: Predictive Analytics, ANN, HRM, Attrition, Retention, IT industry, Software Engineers.
Scope of the Article: Information Ecology and Knowledge Management