Towards a Framework for Improving Quality of User-Centered Services in Socio-technical Systems: A Case Study of Airport System

This paper proposes a theoretical framework to analyze and improve airport services as a socio-technical system (STS) using Natural Language Processing (NLP). We apply BIRCH clustering and Association rule mining algorithms to evaluate passengers’ experiences and expectations of airport services, and identify hidden and open drawbacks based on defined rules. We then provide an optimal service structure to address these drawbacks and enhance travelers’ experience in the airport. Our framework leverages the technological innovation of NLP to analyze the complex socio-technical structures of airport services. Our findings will have significant implications for STS research and practice, especially for airport management that seeks to gain a competitive edge by delivering optimal airport services.