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The purpose of this paper is to propose a Meta modeling based on regression, neural network, and clustering to analyze the job satisfaction factors and improvement policy making.

Since any job satisfaction evaluation supposes to improve the status by prescribing specific strategies to be performed in the organization, proposing applicable strategies is decisively important. Task demand, social structure and leader-member exchange (LMX) are general applications easily conceptualized while proposing job satisfaction improvement strategies.

On the basis of these empirical findings, the authors first aim to identify relationships between LMX, task demand, social structure and individual factors, organizational factors, job properties, which are easier to be employed in strategy formulation for job satisfaction, and then determine the sub-factors and subsequently cluster them. The effectiveness of the proposed model is verified by a case study.

Here, a Meta modeling based on regression, neural network, and clustering is proposed to analyze the job satisfaction factors and improvement policy making.