A Computational Method for Selecting Service Quality Factors Considering The Representative Models

This paper aims to propose a computational method for comparing different service quality measurements that plays the critical roles in the automobile industry. Finding the critical items to measure service quality led to increase customer satisfaction. Hence different industries intend to find these critical factors to enhance the competitive advantage and profitability. However, there are several service quality models which presented different items, the notable problem is that how a specific industry selects the most appropriate models which presents the all critical factors. This paper collects the all critical factors for measurement service quality from the most representative models in automobile industry. Then the all collected items from these models are rated by experts. The rate of each item is weighted using Lawshe's content validity ratio method. Finally, a multi criteria decision making process is applied to select the most appropriate measurement model in service quality based on the weighted items of these models. The result lead to select the most appropriate service quality model in automobile industry, in addition of identifying a critical measurement items for service quality based on Lawshe's content validity ratio method. This study helps researchers to compare different measurement models and determine essential items. The proposed method also supports industries to increase their customer satisfaction, enhance purchase intention and improve profitability with measuring the quality of their services accurately