Analyzing customer satisfaction in self-service technology adopted in airports

Customer satisfaction level is one of key performance indicators in the service industry. The various factors affecting this are studied to maintain an excellent relationship with customers. Self-service technology (SST) is widely implemented by companies in service sector. This paper proposes to apply the customer satisfaction survey to investigate factors influencing the customer satisfaction. The relationships among the factors are discovered using PC-algorithm. The critical factors are identified to be inputs in regression tree and ANN to estimate the customer satisfaction level. By means of comparison of models, importance of selected inputs is quantified and discussed. The results show that customer satisfaction has strong connectivity relationship with personal service attributes as well as affective and temporal commitment by running PC-algorithm. ANN validated by 10-fold cross validation is the best among the models. The most important factor influencing the satisfaction level to the companies is the customer’s desire of continuing a relationship. The key benefit of the proposed approach is to avoid making subjective decisions, for instance, building a plausible initial path models in the analysis. The analytical results facilitate the decision-making process and better resource allocation in the airline and state its future development of self-service technology.