Academic journal article Business: Theory and Practice

The Viability of Neural Network for Modeling the Impact of Individual Job Satisfiers on Work Commitment in Indian Manufacturing Unit

Academic journal article Business: Theory and Practice

The Viability of Neural Network for Modeling the Impact of Individual Job Satisfiers on Work Commitment in Indian Manufacturing Unit

Article excerpt

Introduction

Job satisfaction is one of the core constructs in management and is the most extensively studied variable in organizational behavior and industrial psychology and it contributes more to the success of any organization.

According to Lease (1998), investigating job satisfaction is very essential because the employee who have higher job satisfaction tend to be more productive, extremely committed and loyal towards their organization and are highly satisfied towards their life.The success of organization depends on the appropriate use of manpower which will be an auxiliary to all other assets. The satisfied employees have greater morale, oneness and promote cohesiveness among the members of the organization which leads to enhanced organizational performance.

1. Statement of the problem

The organizational performance is the outcome of work commitment rooted through job satisfiers. Thus the current study scrutinizes important factors of individual job satisfiers towards work commitment of the employees using neural network which is vital in the globalized competitive scenario. The majority of previous studies used logistic regression analysis and several statistical tools for analyzing the data and some study, compared neural networks with conventional statistical tools to evaluate the job satisfaction attributes, but this is a study which uses neural network to find out the normalized importance of individual job satisfiers of employees towards work commitment and more specifically this study uses Multilayer Perceptron neural network model which is more advantageous than some of the other statistical tools like logistic regression analysis (Huang 2012). The application of neural networks in those days was mostly in finance and operations research such as detecting fraudulent customers and bankruptcy prediction (cf. Vellido et al. 1999). Although the usage of neural network in the field of organizational behavior was at the infancy stage, some studies used artificial neural networks to model employee turnover (Sexton et al. 2005). A research using computer simulation by (Seitz et al. 1997) analyzed the relationship between job tenure and turnover, and (Somers 2001) investigated relationship amid work attitude and job performance. Artificial Neural Networkis useful in projecting the ceiling point of explained variance when non-linearity is present. Since the relation exists in the study is a non-linear relationship the use of neural network in this study is not able to forgo. A non-linear association between two entities states that modification in one entity does not have any correspondence with the constant change in the other entity. In this study even if the pay, supervision satisfaction, promotion etc. exits for an employee, he may not get satisfied. In the field of organizational behavior Somers (1999) suggested that primary relationships amid variables might not be linear and called for the greater usage of nonlinear methods.

Multilayer Perceptron Networks (MLP Networks) and Radial Basis Function Networks (RBF networks) are the two popular methods of feed forward neural network. The MLP model is a supervised learning technique and it uses feed forward architecture because the data moves from the input layer to hidden layer and finally to the output layer. The reverse is not possible. It analyses the data set in three stages. The first stage is the "training process". It tries to perceive the association between the variables in the dataset. Based on the learning's of the first stage, it will attempt to discern a model and it is done in a hidden layer and is called hidden/Perceptron process. In this process, the optimal functions in the model are produced and dependent variables are assigned weights (Wi). In the third stage new model is estimated and it is called as the output process (Manel et al. 1999).

The distinguishing feature of neural network is to classify the dataset based on which strategic conclusions can be made by the organization. …

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