Consumers’ intention to adopt last-mile drone delivery services: A comparison between US and Thai consumers
Abstract
This research explores factors influencing an intention to adopt last-mile drone delivery services in two groups of culturally different countries: Thailand and the USA. Despite the fact that drone raises privacy concerns to consumers, few research has investigated the interplay between perceived usefulness and perceived privacy risks and their effect on the consumers’ intention to adopt last-mile drone delivery services. In addition, no prior research has compared the effect of privacy risks and usefulness on the adoption intention in different national culture. An online survey was distributed to potential consumers of drone delivery services in both countries. PLS-SEM analysis was then conducted to understand the relationships of seven factors within the context of drone delivery service adoption. The results show that consumers from the two countries share similar perceptions towards last mile drone delivery. Perceived usefulness and trust were found the most influential factors on intention to adopt last-mile drone delivery in both groups.
References
Ayagma, M., Tekinerdogan, B., Kassahun, A., & Rambaldi, G. (2021). Developing a policy framework for adoption and management of drones for agriculture in Africa. Technology Analysis & Strategic Management, 33(8), 970-987.
Barth, S., & de Jong, M. D. T. (2017). The privacy paradox – Investigating discrepancies between expressed privacy concerns and actual online behavior – A systematic literature review. Telematics and Informatics, 34(7), 1038-1058.
Boo, H.C., & Chua, B.-L. (2022). An integrative model of facial recognition check-in technology adoption intention: The perspective of hotel guests in Singapore. International Journal of Contemporary Hospitality Management, 34(11), 4052
https://doi.org/10.1108/IJCHM-12-2021-1471
Cao, J., & Everard, A. (2008). User attitude towards instant messaging: The effect of espoused national cultural values on awareness and privacy. Journal of Global Information Technology Management, 11(2), 30-57.
Chang, V., Chundury, P., & Chetty, M. (2017). “Spiders in the Sky”: User Perceptions of Drones, Privacy, and Security. Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, 6765–6776., Retrieved from
https://dl.acm.org/doi/10.1145/3025453.3025632.
Chin, W. W. (2010). How to write up and report PLS analyses. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications in marketing and related fields (pp. 655-690). Berlin: Springer.
Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research. (pp. 295-336). Mahwah, NJ: Lawrence Erlbaum Associates Publishers.
Chiu, E. M. (2019). The determinants of trade on conflict–some evidence from emerging market economies. Thammasat Review, 22(2), 36–49.
Choi, H., Park, J., & Jung, Y. (2018). The role of privacy fatigue in online privacy behavior. Computers in Human Behavior, 81, 42-51.
Global delivery drones market to reach US$ 5586.5 million by 2028, says coherent market insights (CMI). Retrieved date 23 October 2022, from https://www.globenewswire.com/en/newsrelease/2021/08/24/2285576/0/en/Global-Delivery-Drones-Market-to-Reach-US5586-5-Million-by-2028-Says-Coherent-Market-Insights-CMI.html
Damiani, E., & Ivkovic, M. (2020). Drone usage and privacy infringement: Review. IDOSR JAS CONFERENCE Special Issue 5: Covid-19 the Adverse Effects on Creative Industry in the World , 22-27, 2020 , Retrieved from https://www.idosr.org/wp-content/uploads/2020/12/IDOSR-JAS-SI5-22-272020.pdf
Davis, F. D., Bagozzi, R. P., &Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
Dienlin, T., &Metzger, M. J. (2016). An extended privacy calculus model for SNSs: analyzing self-disclosure and self-withdrawal in a representative U.S. sample. Journal of Computer-Mediated Communication, 21(5), 368-383.
Dinev, T., & Hart, P. (2006). An extended privacy calculus model for e-commerce transactions. Information Systems Research, 17(1), 61-80.
Dinev, T., Xu, H., Smith, J. H., & Hart, P. (2013). Information privacy and correlates: An empirical attempt to bridge and distinguish privacy-related concepts. European Journal of Information Systems, 22(3), 295-316.
Egea, J. M. O., & González, M. V. R. (2011). Explaining physicians’ acceptance of EHCR systems: An extension of TAM with trust and risk factors. Computers in Human Behavior, 27(1), 319-332.
Euchi, J. (2021). Do drones have a realistic place in a pandemic fight for delivering medical supplies in healthcare systems problems? Chinese Journal of Aeronautics, 34(2), 182-190.
Faqih, K. M. (2013). Exploring the influence of perceived risk and internet self-efficacy on consumer online shopping intentions: Perspective of technology acceptance model. International Management Review, 9(1), 67-77.
Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382-388.
Ganjipour, H., & Edrisi, A. (2022). Applying the integrated model to understanding online buyers’ intention to adopt delivery drones in Iran. The International Journal of Transportation Research, 15(2), 98-110. Drone package delivery global market report 2023. Retrieved 23 October 2023, from https://www.globenewswire.com/en/newsrelease/2023/04/19/2650350/28124/en/Drone-Package-Delivery-Global-Market
Report-2023-Major-Players-Include-Amazon-United-Parcel-Service-of-AmericaZipline-FedEx-and-DHL-International.html
Goh, T.-T., & Sun, S. (2014). Exploring gender differences in Islamic mobile banking acceptance. Electronic Commerce Research, 14(4), 435-458.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). Upper Saddle River, NJ: Pearson Prentice Hall.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial least squares structural equation modeling: Rigorous applications, better results and higher acceptance. Long Range Planning, 46(1-2), 1-12.
Henseler, J., Ringle, C.M. & Sinkovics, R.R. (2009). The use of partial least squares path modeling in international marketing, Advances in International Marketing, 20, 277-319.
Hofstede, G. (2011). Dimensionalizing cultures: The Hofstede model in context. Online Readings in Psychology and Culture, 2(1), article 8.
Hofstede, G. (2021). Country Comparison Tool. Retrieved from https://www.hofstedeinsights.com/country-comparison/the-usa/.
Hossain, M. A., & Dwivedi, Y. K. (2014). What improves citizens’ privacy perceptions toward RFID technology? A cross-country investigation using mixed method approach. International Journal of Information Management, 34(6), 711-719.
Huarng, K-H., Yu, T. H-K., & Lee, C.F. (2022). Adoption model of healthcare wearable devices. Technological Forecasting and Social Change, 174.
Hwang, J., & Choe, J.Y.(J). (2019). Exploring perceived risk in building successful drone food delivery services. International Journal of Contemporary Hospitality Management, 31(8), 3249-3269.
Hwang, J., Lee, J-S., &Kim, H. (2019). Perceived innovativeness of drone food delivery services and its impacts on attitude and behavioral intentions: The moderating role of gender and age. International Journal of Hospitality Management, 81, 94-103.
Ibrahim, M., Hinson, R. E., & Stephen, A. (2022). Exploring consumers' intention to adopt mobile paymet systems in Ghana. International Journal of E-Services and Mobile Applications, 14(1).
Iranmanesh, S., Raad, R., Raheel, M. S., Tubbal, F., & Jan, T. (2020). Novel DTN mobility-driven routing in autonomous drone logistics networks. IEEE Access, 8, 13661-13673.
Joerss, M., Schröder, J., Neuhaus, F., Klink, C., & Mann, F. (2016). Parcel delivery: The future of last mile. Retrieved from https://www.supplychain247.com/paper/parcel_delivery_the_future_of_last_mile.
Kehr, F., Kowatsch, T., Wentzel, D., & Fleisch, E. (2015). Blissfully ignorant: The effects of general privacy concerns, general institutional trust, and affect in the privacy calculus. Information Systems Journal, 25(6), 607-635.
Kesharwani, A., & Bisht, S. S. (2012). The impact of trust and perceived risk on internet banking adoption in India: An extension of technology acceptance model. International Journal of Bank Marketing, 30(4), 303-322.
Khan, R., Tausif, S., & Javed Malik, A. (2019). Consumer acceptance of delivery drones in urban areas. International Journal of Consumer Studies, 43(1), 87-101.
Lankton, N. K., McKnight, D. H., & Tripp, J. F. (2017). Facebook privacy management strategies: A cluster analysis of user privacy behaviors. Computers in Human Behavior, 76, 149-163.
Laufer, R. S., & Wolfe, M. (1977). Privacy as a concept and a social issue: A multidimensional developmental theory. Journal of Social Issues, 33(3), 22-42.
Leon, S. (2018). Service mobile apps: A millennial generation perspective. Industrial Management & Data Systems, 118(9), 1837-1860.
Leon, S., Chen, C., & Ratcliffe, A. (2021). Consumers' perceptions of last mile drone delivery. International Journal of Logistics Research and Applications, 26(3), 345-364.
Li, H., Sarathy, R., & Xu, H. (2011). The role of affect and cognition on online consumers’decision to disclose per-sonal information to unfamiliar online vendors. Decision Support Systems, 51, 434–445.
Li, H., Wu, J., Gao, Y., & Shi, Y. (2016). Examining individuals’ adoption of healthcare wearable devices: An empirical study from privacy calculus perspective. International Journal of Medical Informatics, 88, 8-17.
Li, Y., Rho, E. H. R., & Kobsa, A. (2022). Cultural differences in the effects of contextual factors and privacy concerns on users’ privacy decision on social networking sites. Behaviour & Information Technology, 41(3), 655-677.
Li, Y. (2012). Theories in online information privacy research: A critical review and an integrated framework. Decision Support Systems, 54(1), 471-481.
Luppicini, R., & So, A. (2016). A technoethical review of commercial drone use in the context of governance, ethics, and privacy. Technology in Society, 46, 109-119.
Lydinia, C., Philipsen, R., &Ziefle, M. (2017). The sky's (not) the limit - Influence of expertise and privacy disposition on the use of multicopters. Advances in Human Factors in Robots and Unmanned Systems, 595, 270-281
Mathew, A. O., Jha, A. N., Lingappa, A. K., & Sinha, P. (2021). Attitude towards drone food delivery services—Role of innovativeness, perceived risk, and green image. Journal of Open Innovation: Technology, Market, and Complexity, 7(2), 144.
McKnight, D. H., Choudhury, V., & Kacmar, C. (2002). Developing and validating trust measures for e-commerce: An integrative typology. Information Systems Research, 13(3), 334-359.
Min, J., & Kim, B. (2015). How are people enticed to disclose personal information despite privacy concerns in social network sites? The calculus between benefit and cost. Journal of the Association for Information Science and Technology, 66(4), 839-857.
Namahoot, K. S., & Jantasri, V. (2022). Integration of UTAUT model in Thailand cashless payment system adoption: The mediating role of perceived risk and trust. International Journal of Technology, Policy and Management. Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/JSTPM-07-2020-0102
OECD (2021). Business insights on emerging markets 2021. Paris: OECD Emerging Markets Network, OECD Development Centre. Retrieved from http://www.oecd.org/dev/oecdemnet.htm
Osakwe, C. N., Hudik, M., Říha, D., Stros, M., & Ramayah, T. (2022). Critical factors characterizing consumers’ intentions to use drones for last-mile delivery: Does delivery risk matter?. Journal of Retailing and Consumer Services, 65.
Özbilen, P. (2017). The impact of natural culture on new technology adoption by firms: A country level analysis. International Journal of Innovation, Management and Technology, 8(4), 299-305.
Pentina, I., Zhang, L., Bata, H., & Chen, Y. (2016). Exploring privacy paradox in information-sensitive mobile app adoption: A cross-cultural comparison. Computers in Human Behavior, 65, 409-419.
Peter, J. P. (1979). Reliability: A review of psychometric basics and recent marketing practices. Journal of Marketing Research, 16(1), 6-17.
Podsakoff, P. M., Mackenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 5, 879-909.
Ramadan, Z. B., Farah, M. F., & Mrad, M. (2017). An adapted TPB approach to consumers’ acceptance of service-delivery drones. Technology Analysis & Strategic Management, 29(7), 817-828.
Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor's comments: A critical look at the use of PLS-SEM in" MIS Quarterly". MIS quarterly, 36(1), 3-14.
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS3 [computer software]. Retrieved from https://www.smartpls.com
Roca, J. C., García, J. J., & José de la Vega, J. (2009). The importance of perceived trust, security and privacy in online trading systems. Information Management & Computer Security, 17(2), 96-113.
Song, M., Xing, X., Duan, Y., Cohen, J., & Mou, J. (2022). Will artificial intelligence replace human customer service? The impact of communication quality and privacy risks on adoption intention. Journal of Retailing and Consumer Services,
Straub, D., Keil, M., & Brenner, W. (1997). Testing the technology acceptance model across cultures: A three country study. Information & Management, 33(1), 1-11.
Suksawat, G. (2023). Thailand’s first drone delivery service launched in Phuket. Retrieved from https://tpnnational.com/2023/01/10/thailands-first-drone-deliveryservice-launched-in-phuket/
Tan, L. K. L., Lim, B. C., Park, G., Low, K. H., & Yeo, V. C. S. (2021). Public acceptance of drone applications in a highly urbanized environment. Technology in Society, 64,.
Trepte, S., Reinecke, L., Ellison, N. B., Quiring, O., Yao, M. Z., &d Ziegele, M. (2017). A cross-cultural perspective on the privacy calculus. Social media+ Society, 3(1), 2056305116688035.
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision Sciences, 39(2), 273-315.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Wang, Y. Norice, G., & Cranor, L. F. (2011). Who is concerned about what? A study of American, Chinese and Indian users’ privacy concerns on social network sites. In McCune, J.M., Balacheff, B., Perrig, A., Sadeghi, AR., Sasse, A., Beres, Y. (Eds).
Trust and trustworthy computing: 4th International Conference, TRUST 2011, Proceedings 4, (pp.146-153).
Vazhavelil, T., Sonowal, A. (2021). The Future of delivery with drones: Contactless, accurate, and high-Speed. Retrieved from https://www.wipro.com/businessprocess/the-future-of-delivery-with-drones-contactless-accurate-and-high-speed/
Wu, K-W., Huang, S. Y., Yen, D. C., & Popova, I. (2012). The effect of online privacy policy on consumer privacy concern and trust. Computers in Human Behavior, 28(3) 889-897.
Wu, H., & Zhang, W. (2017). Factors affecting customer initial trust in the mobile payment service providers: An empirical study. Proceedings of the Wuhan International Conference on e-Business WHICEB 2017, 30, Retrieved from https://aisel.aisnet.org/whiceb2017/30
Xu, H., Dinev, T., Smith, H. J., & Hart, P. (2008). Examining the formation of individual's privacy concerns: Toward an integrative view. Proceedings of the International Conference on Information https://aisel.aisnet.org/icis2008/6 Systems, Retrieved from https://aisel.aisnet.org/whiceb2017/30
Xu, H., Dinev, T., Smith, J., & Hart, P. (2011). Information privacy concerns: Linking individual perceptions with institutional privacy assurances. Journal of the Association for information systems, 12(12), 798-824.
Xu, H., Teo, H.-H., Tan, B. C. Y., & Agarwal, R. (2012). Research note—Effects of iIndividual self-protection, industry self-regulation, and government regulation on privacy concerns: A study of location-based services. Information Systems
Research, 23(4), 1342-1363.
Yang, Y., Liu, Y., Li, H., & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 115(2), 253 – 269.
Yaprak, Ü., Kılıç, F., & Okumuş, A. (2021). Is the Covid-19 pandemic strong enough to change the online order delivery methods? Changes in the relationship between attitude and behavior towards order delivery by drone. Technological Forecasting and Social Change, 169, 120829.
Yoo, W., Yu, E., & Jung, J. (2018). Drone delivery: Factors affecting the public’s attitude and intention to adopt. Telematics and Informatics, 35(6), 1687-1700.
Zhang, T., Tao, D., Qu, X., Zhang, X., Lin, R., & Zhang, W. (2019). The roles of initial trust and perceived risk in public’s acceptance of automated vehicles. Transportation Research Part C: Emerging Technologies, 98, 207-220.
Zhang, Y., Weng, Q., & Zhu, N. (2018) The relationships between electronic banking adoption and its antecedents: A meta-analytic study of the role of national culture. International Journal of Information Management, 40, 76-87.
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