Determinants of key facets of job satisfaction in the banking sector: applying SMART PLS and artificial neural networks Online publication date: Wed, 17-Jul-2019
by Sahil Raj; Shivinder Nijjer; Viput Ongsakul; Harpreet Singh
J. for Global Business Advancement (JGBA), Vol. 12, No. 2, 2019
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.
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