This paper provides an exposition about application of neural networks in the context of research to find out the contribution of individual job satisfiers towards work commitment. The purpose of the current study is to build a predictive model to estimate the normalized importance of individual job satisfiers towards work commitment of employees working in TVS Group, an Indian automobile company. The study is based on the tool developed by Spector (1985) and Sue Hayday (2003).The input variable of the study consists of nine independent individual job satisfiers which includes Pay, Promotion, Supervision, Benefits, Rewards, Operating procedures, Co-workers, Work-itself and Communication of Spector (1985) and dependent variable as work commitment of Sue Hayday (2003).The primary data has been collected using a closed-ended questionnaire based on simple random sampling approach. This study employed the multilayer Perceptron neural network model to envisage the level of job satisfiers towards work commitment. The result from the multilayer Perceptron neural network model displayed with four hidden layer with correct classification rate of 70% and 30% for training and testing data set. The normalized importance shows high value for coworkers, superior satisfaction and communication and which acts as most significant attributes of job satisfiers that predicts the overall work commitment of employees.
Chandrasekar, T., Chidambaram, V., Venkatraman, S., & Venugopal, V. (2015). The viability of neural network for modeling the impact of individual job satisfiers on work commitment in Indian manufacturing unit. Business: Theory and Practice, 16(3), 326-333. https://doi.org/10.3846/btp.2015.522
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