Title: Determinants of key facets of job satisfaction in the banking sector: applying SMART PLS and artificial neural networks

Authors: Sahil Raj; Shivinder Nijjer; Viput Ongsakul; Harpreet Singh

Addresses: School of Management Studies, Punjabi University, Patiala – 147001, India ' Independent Researcher, Patiala – 147001, India ' NIDA Business School, National Institute of Development Administration, Bangkok– 10240, Thailand ' School of Management Studies, Punjabi University, Patiala – 147001, India

Abstract: Job satisfaction is closely associated with life satisfaction and important workplace behaviours (Judge and Kammeyer-Mueller, 2012). Minnesota satisfaction questionnaire (MSQ) or job description ındex (JDI) (Castanheira, 2014; Saane et al., 2003) are widely used to measure job satisfaction. However, since these instruments cannot be generalised to all industrial sectors (Khalilzadeh et al., 2013), this two-part study attempted to manifest the facets of job satisfaction of public sector bank employees in India through factor identification using systematic literature review, exploratory factor analysis and statistical mode, followed by partial least squares (PLS) modelling of the problem using SmartPLS and comparing the outcome to artificial neural networks (ANN) output. The study has important implications for policy recommendations to banking sector to promote satisfaction among employees. Secondly, it is able to demonstrate which of the two techniques is better - PLS or ANN.

Keywords: ANNs; artificial neural networks; partial least squares modelling; job satisfaction; banking sector; exploratory factor analysis; SMART PLS; SQL Server 2012.

DOI: 10.1504/JGBA.2019.100767

Journal for Global Business Advancement, 2019 Vol.12 No.2, pp.298 - 323

Received: 27 Apr 2019
Accepted: 27 Apr 2019
Published online: 17 Jul 2019 *

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