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Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context

Author

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  • Caroline Lancelot Miltgen

    (GRANEM - Groupe de Recherche Angevin en Economie et Management - UA - Université d'Angers - AGROCAMPUS OUEST - Institut National de l'Horticulture et du Paysage)

  • Ales Popovic

    (ULISBOA - Universidade de Lisboa = University of Lisbon)

  • Tiago Oliveira

    (ULISBOA - Universidade de Lisboa = University of Lisbon)

Abstract

The information systems (IS) literature has long emphasized the importance of user acceptance of computer-based IS. Evaluating the determinants of acceptance of information technology (IT) is vital to address the problem of underutilization and leverage the benefits of IT investments, especially for more radical technologies. This study examines individual acceptance of biometric identification techniques in a voluntary environment, measuring the intention to accept and further recommend the technology resulting from a carefully selected set of variables. Drawing on elements of technology acceptance model (TAM), diffusion of innovations (DOI) and unified theory of acceptance and use of technology (UTAUT) along with the trust-privacy research field, we propose an integrated approach that is both theoretically and empirically grounded. By testing some of the most relevant and well-tested elements from previous models along with new antecedents to biometric system adoption, this study produces results which are both sturdy and innovative. We first confirm the influence of renowned technology acceptance variables such as compatibility, perceived usefulness, facilitating conditions on biometrics systems acceptance and further recommendation. Second, prior factors such as concern for privacy, trust in the technology, and innovativeness also prove to have an influence. Third, unless innovativeness, the most important drivers to explain biometrics acceptance and recommendation are not from the traditional adoption models (TAM, DOI, and UTAUT) but from the trust and privacy literature (trust in technology and perceived risk).

Suggested Citation

  • Caroline Lancelot Miltgen & Ales Popovic & Tiago Oliveira, 2013. "Determinants of end-user acceptance of biometrics: Integrating the “Big 3” of technology acceptance with privacy context," Post-Print hal-01116141, HAL.
    • Handle: RePEc:hal:journl:hal-01116141
    DOI: 10.1016/j.dss.2013.05.010
    Note: View the original document on HAL open archive server: https://audencia.hal.science/hal-01116141
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    Cited by:

    1. Mohapatra, Malaya Ranjan & Moirangthem, Nirmalkumar Singh & Vishwakarma, Pankaj, 2020. "Mobile Banking Adoption Among Rural Consumers: Evidence from India," American Business Review, Pompea College of Business, University of New Haven, vol. 23(2), pages 300-315, November.
    2. Amel Attour & Marco Baudino & Jackie Krafft & Nathalie Lazaric, 2020. "Determinants of smart energy tracking application use at the city level: Evidence from France," Post-Print hal-02942483, HAL.
    3. Michael Breward & Khaled Hassanein & Milena Head, 2017. "Understanding Consumers’ Attitudes Toward Controversial Information Technologies: A Contextualization Approach," Information Systems Research, INFORMS, vol. 28(4), pages 760-774, December.
    4. Matemba, Elizabeth D. & Li, Guoxin, 2018. "Consumers' willingness to adopt and use WeChat wallet: An empirical study in South Africa," Technology in Society, Elsevier, vol. 53(C), pages 55-68.
    5. Normalini M.K., T. Ramayah, 2017. "Trust in Internet Banking in Malaysia and the Moderating Influence of Perceived Effectiveness of Biometrics Technology on Perceived Privacy and Security," Journal of Management Sciences, Geist Science, Iqra University, Faculty of Business Administration, vol. 4(1), pages 3-26, March.
    6. Yan Mandy Dang & Yulei Gavin Zhang & James Morgan, 2017. "Integrating switching costs to information systems adoption: An empirical study on learning management systems," Information Systems Frontiers, Springer, vol. 19(3), pages 625-644, June.
    7. Dlodlo N, 2017. "Re-Thinking a Structural Model for M-Phone Paying among South African Consumers," Journal of Economics and Behavioral Studies, AMH International, vol. 9(2), pages 114-130.
    8. Sameena Naaz & Sarah Ali Khan & Farheen Siddiqui & Shahab Saquib Sohail & Dag Øivind Madsen & Asad Ahmad, 2022. "OdorTAM: Technology Acceptance Model for Biometric Authentication System Using Human Body Odor," IJERPH, MDPI, vol. 19(24), pages 1-17, December.
    9. Pizzi, Gabriele & Scarpi, Daniele, 2020. "Privacy threats with retail technologies: A consumer perspective," Journal of Retailing and Consumer Services, Elsevier, vol. 56(C).
    10. Kasim, Kabir O. & Winter, Scott R. & Liu, Dahai & Keebler, Joseph R. & Spence, Tyler B., 2021. "Passengers’ perceptions on the use of biometrics at airports: A statistical model of the extended theory of planned behavior," Technology in Society, Elsevier, vol. 67(C).
    11. Dassel, Katharina Sophie & Klein, Stefan, 2023. "To Zoom or not: Diverging responses to privacy and security risks," Journal of Business Research, Elsevier, vol. 161(C).
    12. Merhi, Mohamed & Hone, Kate & Tarhini, Ali, 2019. "A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust," Technology in Society, Elsevier, vol. 59(C).
    13. Yan Mandy Dang & Yulei Gavin Zhang & James Morgan, 0. "Integrating switching costs to information systems adoption: An empirical study on learning management systems," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    14. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    15. Oliveira, Tiago & Faria, Miguel & Thomas, Manoj Abraham & Popovič, Aleš, 2014. "Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM," International Journal of Information Management, Elsevier, vol. 34(5), pages 689-703.
    16. Jeeyeon Jeong & Yaeri Kim & Taewoo Roh, 2021. "Do Consumers Care About Aesthetics and Compatibility? The Intention to Use Wearable Devices in Health Care," SAGE Open, , vol. 11(3), pages 21582440211, August.
    17. Chong Li & Yingqi Li, 2023. "Factors Influencing Public Risk Perception of Emerging Technologies: A Meta-Analysis," Sustainability, MDPI, vol. 15(5), pages 1-37, February.
    18. Franziska Schlichte & Sebastian Junge & Jan Mammen, 2019. "Being at the right place at the right time: does the timing within technology waves determine new venture success?," Journal of Business Economics, Springer, vol. 89(8), pages 995-1021, December.
    19. Fatemeh Maleki & Seyed Mohsen Hosseini, 2020. "Charity donation intention via m-payment apps: donor-related, m-payment system-related, or charity brand-related factors, which one is overkill?," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 17(4), pages 409-443, December.
    20. Mousa Albashrawi & Hasan Kartal & Asil Oztekin & Luvai Motiwalla, 2019. "Self-Reported and Computer-Recorded Experience in Mobile Banking: a Multi-Phase Path Analytic Approach," Information Systems Frontiers, Springer, vol. 21(4), pages 773-790, August.
    21. Hsieh, Pi-Jung, 2021. "Understanding medical consumers’ intentions to switch from cash payment to medical mobile payment: A perspective of technology migration," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    22. Amit Shankar & Biplab Datta, 2018. "Factors Affecting Mobile Payment Adoption Intention: An Indian Perspective," Global Business Review, International Management Institute, vol. 19(3_suppl), pages 72-89, June.

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