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Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations

Author

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  • Cheong Kim

    (SKK Business School, Sungkyunkwan University, Seoul 03063, Korea
    Korea Airports Corporation (KAC), Seoul 07505, Korea)

  • Francis Joseph Costello

    (SKK Business School, Sungkyunkwan University, Seoul 03063, Korea)

  • Kun Chang Lee

    (SKK Business School, Sungkyunkwan University, Seoul 03063, Korea
    Samsung Advanced Institute for Health Sciences & Technology (SAIHST), Sungkyunkwan University, Seoul 03063, Korea
    Creativity Science Research Institute (CSRI), Sungkyunkwan University, Seoul 03063, Korea)

Abstract

For the sake of maintaining sustainable airport operations, biometric e-gates security systems started receiving significant attention from managers of airports around the world. Therefore, how to reduce flight passengers’ perceived resistance to the biometric e-gates security system became much more important than ever. In this sense, the purpose of this study is to analyze the factors which contribute to passenger’s resistance to adopt biometric e-gate technology within the airport security setting. Our focus lies on exploring the effects that perceived risks and benefits as well as user characteristics and propagation mechanisms had on causing such resistance. With survey data from 339 airport users, a support vector machine (SVM) model was implemented to provide a tool for classifying resistance causes correctly, and csQCA (crisp set Qualitative Comparative Analysis) was implemented in order to understand the complex underlying causes. The results showed that the presence of perceived risks and the absence of perceived benefits were the main contributing factors, with propagation mechanisms also showing a significant effect on weak and strong resistance. This study is distinct in that it has attempted to explore innovation adoption through the lens of resistance and in doing so has uncovered important complex causation conditions that need to be considered before service quality can be enhanced within airports. This study’s implications should therefore help steer airport managers in the right direction towards maintaining service quality while implementing sustainable new technologies within their current airport security ecosystem.

Suggested Citation

  • Cheong Kim & Francis Joseph Costello & Kun Chang Lee, 2019. "Integrating Qualitative Comparative Analysis and Support Vector Machine Methods to Reduce Passengers’ Resistance to Biometric E-Gates for Sustainable Airport Operations," Sustainability, MDPI, vol. 11(19), pages 1-22, September.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:19:p:5349-:d:271461
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    References listed on IDEAS

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    Cited by:

    1. Huang, Dan & Jin, Xin & Coghlan, Alexandra, 2021. "Advances in consumer innovation resistance research: A review and research agenda," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
    2. Chang Hee Yoon & Francis Joseph Costello & Cheong Kim, 2019. "Assisting Sustainable Entrepreneurial Activities Through the Analysis of Mobile IT Services’ Success and Failure Factors," Sustainability, MDPI, vol. 11(20), pages 1-22, October.
    3. Jingjing Hao & Ling Zhang & Xiaofeng Ji & Xiaolong Wu & Lan Liu, 2020. "Investigating the Accessibility between Civil Airports and Tourist Locations in Tourist Cities in Yunnan Province, China," Sustainability, MDPI, vol. 12(10), pages 1-22, May.
    4. Cheong Kim & Kun Chang Lee & Francis Joseph Costello, 2020. "The Intention of Passengers towards Repeat Use of Biometric Security for Sustainable Airport Management," Sustainability, MDPI, vol. 12(11), pages 1-18, June.
    5. Cheng-Hong Yang & Borcy Lee & Pey-Huah Jou & Yu-Fang Chung & Yu-Da Lin, 2023. "Analysis and Forecasting of International Airport Traffic Volume," Mathematics, MDPI, vol. 11(6), pages 1-19, March.
    6. da Silva, Francisco Gildemir Ferreira & Falcão, Viviane Adriano & Domingos, Maria Cecília de Farias & Negri, Nathane Ana Rosa, 2022. "Analysis of the generalized use of logit models in simulating changes in decision attributes at the airport check-in," Research in Transportation Economics, Elsevier, vol. 92(C).
    7. Cheong Kim & Francis Joseph Costello & Kun Chang Lee & Yuan Li & Chenyao Li, 2019. "Predicting Factors Affecting Adolescent Obesity Using General Bayesian Network and What-If Analysis," IJERPH, MDPI, vol. 16(23), pages 1-18, November.

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