Addressing Perceived Resistance to Biometric Security Systems in Airports

Ensuring robust security is paramount in the era of global air travel. As airports and other organizations increasingly deploy biometric security systems, end-user resistance poses significant challenges for managers striving to balance security, efficiency, and acceptance. This study develops an interpretable DSS to manage such resistance, utilizing a GBN derived from 339 passenger surveys regarding airport biometric e-gates. The GBN models how perceived risks, compatibility, and trialability shape three distinct resistance outcomes. Building on this model, the authors conduct simulation-based “what-if” analyses across three intervention scenarios to examine how specific design and policy choices can mitigate resistance. An expert evaluation by airport security managers indicates that the DSS's recommendations are realistic and offer improvements over current heuristic strategies. While live field deployment remains a subject for future research, the proposed framework offers a reusable blueprint for DSS design in other biometric and AI-enabled security applications.