IDEAS home Printed from https://ideas.repec.org/a/eee/teinso/v82y2025ics0160791x25000934.html

Cross-country differences in willingness to use conditionally automated driving systems: Impact of technology affinity, driving skills, and perceived traffic climate

Author

Listed:
  • Öztürk, İbrahim
  • Lehtonen, Esko
  • Madigan, Ruth
  • Lee, Yee Mun
  • Aittoniemi, Elina
  • Merat, Natasha

Abstract

The importance of public acceptance of automated driving systems (ADS) has grown as these systems have advanced. Previous research has acknowledged the existence of individual and cross-cultural differences in drivers’ willingness to use these systems. This study aimed to further investigate cross-country differences in willingness to use conditionally automated driving systems, and the factors that influence it, such as technology affinity, driving skills, and traffic climate, across eight countries with varying road safety profiles. A large-scale survey was conducted as part of the Hi-Drive project, involving 7896 participants from eight countries (UK, USA, Germany, Sweden, Poland, Greece, China, and Japan). The findings revealed significant cross-country differences in willingness to use ADS, with China having the highest and the United Kingdom having the lowest scores. A mixed-effects model showed that willingness to use ADS was positively associated with technology affinity, driving skills, and external affective demands, and functionality dimensions of Traffic Climate Scale, and negatively associated with internal requirements factor of traffic climate. The results indicate that technology affinity plays a crucial role in influencing willingness to use ADS across countries, while perceptions of driving skills and traffic climate may provide insights into some country and individual-level differences in acceptance of these systems. These findings contribute to our understanding of the acceptance of ADS and the role of individual differences and transport-specific factors.

Suggested Citation

  • Öztürk, İbrahim & Lehtonen, Esko & Madigan, Ruth & Lee, Yee Mun & Aittoniemi, Elina & Merat, Natasha, 2025. "Cross-country differences in willingness to use conditionally automated driving systems: Impact of technology affinity, driving skills, and perceived traffic climate," Technology in Society, Elsevier, vol. 82(C).
    • Handle: RePEc:eee:teinso:v:82:y:2025:i:c:s0160791x25000934
    DOI: 10.1016/j.techsoc.2025.102903
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0160791X25000934
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techsoc.2025.102903?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Shelly Etzioni & Jamil Hamadneh & Arnór B. Elvarsson & Domokos Esztergár-Kiss & Milena Djukanovic & Stelios N. Neophytou & Jaka Sodnik & Amalia Polydoropoulou & Ioannis Tsouros & Cristina Pronello & N, 2020. "Modeling Cross-National Differences in Automated Vehicle Acceptance," Sustainability, MDPI, vol. 12(22), pages 1-22, November.
    2. Ajzen, Icek, 1991. "The theory of planned behavior," Organizational Behavior and Human Decision Processes, Elsevier, vol. 50(2), pages 179-211, December.
    3. Viswanath Venkatesh & Fred D. Davis, 2000. "A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies," Management Science, INFORMS, vol. 46(2), pages 186-204, February.
    4. Anania, Emily C. & Rice, Stephen & Walters, Nathan W. & Pierce, Matthew & Winter, Scott R. & Milner, Mattie N., 2018. "The effects of positive and negative information on consumers’ willingness to ride in a driverless vehicle," Transport Policy, Elsevier, vol. 72(C), pages 218-224.
    5. Wang, Song & Li, Zhixia & Wang, Yi & Wyatt, Daniel Aaron, 2024. "How effective is automated vehicle education? – A Kentucky case study revealing the dynamic nature of education effectiveness," Transport Policy, Elsevier, vol. 147(C), pages 140-157.
    6. Tengilimoglu, Oguz & Carsten, Oliver & Wadud, Zia, 2023. "Implications of automated vehicles for physical road environment: A comprehensive review," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 169(C).
    7. Tyron Louw & Ruth Madigan & Yee Mun Lee & Sina Nordhoff & Esko Lehtonen & Satu Innamaa & Fanny Malin & Afsane Bjorvatn & Natasha Merat, 2021. "Drivers’ Intentions to Use Different Functionalities of Conditionally Automated Cars: A Survey Study of 18,631 Drivers from 17 Countries," IJERPH, MDPI, vol. 18(22), pages 1-19, November.
    8. Manivasakan, Hesavar & Kalra, Riddhi & O'Hern, Steve & Fang, Yihai & Xi, Yinfei & Zheng, Nan, 2021. "Infrastructure requirement for autonomous vehicle integration for future urban and suburban roads – Current practice and a case study of Melbourne, Australia," Transportation Research Part A: Policy and Practice, Elsevier, vol. 152(C), pages 36-53.
    9. Zhu, Ge & Chen, Yuche & Zheng, Jiali, 2024. "Partially autonomous vehicles (PAVs) vs. fully autonomous vehicles (FAVs): A comparative study with adoption models," Technology in Society, Elsevier, vol. 79(C).
    10. Peng Jing & Gang Xu & Yuexia Chen & Yuji Shi & Fengping Zhan, 2020. "The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review," Sustainability, MDPI, vol. 12(5), pages 1-26, February.
    11. Maghrour Zefreh, Mohammad & Edries, Belal & Esztergár-Kiss, Domokos, 2023. "Understanding the antecedents of hedonic motivation in autonomous vehicle technology acceptance domain: A cross-country analysis," Technology in Society, Elsevier, vol. 74(C).
    12. Benjamin D Douglas & Patrick J Ewell & Markus Brauer, 2023. "Data quality in online human-subjects research: Comparisons between MTurk, Prolific, CloudResearch, Qualtrics, and SONA," PLOS ONE, Public Library of Science, vol. 18(3), pages 1-17, March.
    13. Swain, Ritwik & Truelove, Verity & Rakotonirainy, Andry & Kaye, Sherrie-Anne, 2023. "A comparison of the views of experts and the public on automated vehicles technologies and societal implications," Technology in Society, Elsevier, vol. 74(C).
    14. Hardman, Scott & Lee, Jae Hyun & Tal, Gil, 2019. "How do drivers use automation? Insights from a survey of partially automated vehicle owners in the United States," Transportation Research Part A: Policy and Practice, Elsevier, vol. 129(C), pages 246-256.
    15. Thurner, Thomas & Fursov, Konstantin & Nefedova, Alena, 2022. "Early adopters of new transportation technologies: Attitudes of Russia’s population towards car sharing, the electric car and autonomous driving," Transportation Research Part A: Policy and Practice, Elsevier, vol. 155(C), pages 403-417.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Winter, Scott R. & Rice, Stephen & Lamb, Tracy L., 2020. "A prediction model of Consumer's willingness to fly in autonomous air taxis," Journal of Air Transport Management, Elsevier, vol. 89(C).
    2. Nastjuk, Ilja & Herrenkind, Bernd & Marrone, Mauricio & Brendel, Alfred Benedikt & Kolbe, Lutz M., 2020. "What drives the acceptance of autonomous driving? An investigation of acceptance factors from an end-user's perspective," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
    3. Zhang, Kai & Cheng, Xiaoting, 2025. "Determinants of consumers’ intentions to use smart home devices from the perspective of perceived value: A mixed SEM, NCA, and fsQCA study," Journal of Retailing and Consumer Services, Elsevier, vol. 87(C).
    4. Debora Bettiga & Lucio Lamberti & Emanuele Lettieri, 2020. "Individuals’ adoption of smart technologies for preventive health care: a structural equation modeling approach," Health Care Management Science, Springer, vol. 23(2), pages 203-214, June.
    5. Yu Wang & Shanyong Wang & Jing Wang & Jiuchang Wei & Chenglin Wang, 2020. "An empirical study of consumers’ intention to use ride-sharing services: using an extended technology acceptance model," Transportation, Springer, vol. 47(1), pages 397-415, February.
    6. Paul Juinn Bing Tan, 2013. "Applying the UTAUT to Understand Factors Affecting the Use of English E-Learning Websites in Taiwan," SAGE Open, , vol. 3(4), pages 21582440135, October.
    7. Peter Mantello & Manh-Tung Ho & Minh-Hoang Nguyen & Quan-Hoang Vuong, 2023. "Machines that feel: behavioral determinants of attitude towards affect recognition technology—upgrading technology acceptance theory with the mindsponge model," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 10(1), pages 1-16, December.
    8. Mahajan, Advitya Indu & Taggar, Rashi & Gupta, Suruchi, 2025. "Augmented reality in E-Commerce: A dual value and barrier-based perspective on Gen Z's satisfaction and AR-Integrated shopping behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 87(C).
    9. Fox, Stephen & Groesser, Stefan N., 2016. "Reframing the relevance of research to practice," European Management Journal, Elsevier, vol. 34(5), pages 457-465.
    10. Mäntymäki, Matti & Salo, Jari, 2013. "Purchasing behavior in social virtual worlds: An examination of Habbo Hotel," International Journal of Information Management, Elsevier, vol. 33(2), pages 282-290.
    11. Fatima Zahra Barrane & Gahima Egide Karuranga & Diane Poulin, 2018. "Technology Adoption and Diffusion: A New Application of the UTAUT Model," International Journal of Innovation and Technology Management (IJITM), World Scientific Publishing Co. Pte. Ltd., vol. 15(06), pages 1-19, December.
    12. Wang, Song & Li, Zhixia & Wang, Yi & Wyatt, Daniel Aaron, 2024. "How effective is automated vehicle education? – A Kentucky case study revealing the dynamic nature of education effectiveness," Transport Policy, Elsevier, vol. 147(C), pages 140-157.
    13. Joan Torrent-Sellens & Cristian Salazar-Concha & Pilar Ficapal-Cusí & Francesc Saigí-Rubió, 2021. "Using Digital Platforms to Promote Blood Donation: Motivational and Preliminary Evidence from Latin America and Spain," IJERPH, MDPI, vol. 18(8), pages 1-17, April.
    14. Garima Malik & A. Sajeevan Rao, 2019. "Extended expectation-confirmation model to predict continued usage of ODR/ride hailing apps: role of perceived value and self-efficacy," Information Technology & Tourism, Springer, vol. 21(4), pages 461-482, December.
    15. Wang, Guoqiang & Tan, Garry Wei-Han & Yuan, Yunpeng & Ooi, Keng-Boon & Dwivedi, Yogesh K., 2022. "Revisiting TAM2 in behavioral targeting advertising: A deep learning-based dual-stage SEM-ANN analysis," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    16. Riffat Ara Zannat Tama & Md Mahmudul Hoque & Ying Liu & Mohammad Jahangir Alam & Mark Yu, 2023. "An Application of Partial Least Squares Structural Equation Modeling (PLS-SEM) to Examining Farmers’ Behavioral Attitude and Intention towards Conservation Agriculture in Bangladesh," Agriculture, MDPI, vol. 13(2), pages 1-22, February.
    17. Scott, Stephanie & Hughes, Paul & Hodgkinson, Ian & Kraus, Sascha, 2019. "Technology adoption factors in the digitization of popular culture: Analyzing the online gambling market," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
    18. Pan Gong & Ningshuang Zeng & Kunhui Ye & Markus König, 2019. "An Empirical Study on the Acceptance of 4D BIM in EPC Projects in China," Sustainability, MDPI, vol. 11(5), pages 1-19, March.
    19. Cabrera-Sánchez, Juan-Pedro & Villarejo-Ramos, à ngel F., 2020. "Acceptance and use of big data techniques in services companies," Journal of Retailing and Consumer Services, Elsevier, vol. 52(C).
    20. Alfiero, Simona & Battisti, Enrico & Ηadjielias, Elias, 2022. "Black box technology, usage-based insurance, and prediction of purchase behavior: Evidence from the auto insurance sector," Technological Forecasting and Social Change, Elsevier, vol. 183(C).

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:teinso:v:82:y:2025:i:c:s0160791x25000934. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/technology-in-society .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.