Analysis of the Optimal Customization Degree of Different Service Industries by Integrating the Neural Network and the Genetic Algorithm

Customers have different emotions towards service industries of different natures, leading to inconsistent service quality characteristic items and levels of demands. Different emotions would affect customer perceptions of the customization degree of a hospital (the emotion is relatively sad) and a theme park (the emotion is relatively joyful); while different service quality characteristic-related contents of the budget-limited DOH (Department of Health) hospitals and theme parks also affect the customization degree. Therefore, this study established the PZB (Parasuraman, Zeithaml and Berry) service quality characteristic scale for different DOH hospitals and theme parks, conducted a questionnaire survey (qualitative) and integrated the neural network and genetic algorithm in order to analyze the service quality gap item rankings of different services. Next, this study incorporated the quantitative contents of the top five service quality gap items into a quantitative customized mathematical model. The model considers the occurrence of demand in unit time as a Poisson distribution, the demand in normal distribution and the uncertain parameter subject to the effect of the boom countermeasure signals. This study then established and verified the correct method for a customization degree profit model. Decision-makers can determine improvement items according to the optimal customization degree. The research findings can serve as the basis for DOH hospital and theme park operational improvements. In addition, this study analyzed the managerial implications of the research findings in different service industries.