Management of Consumers Embraces Drone Delivery
- Miguel Torga Institute of Higher Education, Portugal
- NECE-UBI – Research Unit in Business Sciences, University of Beira Interior, Portugal
- Higher Institute of Accounting and Administration, University of Aveiro, Portugal
- University of Coimbra, Portugal
* Corresponding author
Pages: 1–16
Received: 11 June 2024
Revised: 1 September 2024
Accepted: 15 September 2024
Abstract
Over the past few years, significant progress has been made in the field of drone technology, leading to its implementation and transformation in various industries. As technology has improved, drones have become increasingly adaptable to numerous tasks, such as delivering packages right to customers’ front doors. As the need for swift and reliable delivery options increases, drone delivery is an option that businesses and consumers alike should consider. This paper studies the underlying factors that influence consumers’ intentions to adopt drone delivery services in Portugal. A quantitative methodology was used, and 155 responses were collected from Portuguese citizens. The findings indicate that perceived usefulness, perceived privacy risk, and attitudes serve as key predictors of user behavioural intentions among individuals in Portugal. Moreover, they highlight that perceived usefulness and perceived privacy risks exert an indirect influence on behavioural intentions through attitudes. Consequently, the comprehensive analysis underscores the significant impact of perceived usefulness on behavioural intentions, with attitudes and perceived privacy risks closely behind. The study uncovers new insights into consumer adoption of drone delivery services. This finding suggests that promoting the advantages of drone technology, emphasising its usefulness, efficiency, and convenience in advertising, can positively impact consumer perception and willingness to adopt. Furthermore, this study indicates that addressing potential consumer concerns about drone delivery, such as privacy, safety, and reliability, is essential. Clear communication about the measures taken to ensure these aspects can mitigate apprehensions and enhance acceptance. Governments play a crucial role in regulating drones to increase trust, protect users, and promote a favourable environment for adoption. Public awareness campaigns led by governments can educate citizens about the benefits and safe use of drones. Transparent communication about regulatory measures, safety features, and the positive impacts of drone technology on society can demystify drones and build public trust.
Keywords: service marketing; consumer intention; strategy; drone delivery service; technology acceptance model.
How to Cite: Lopes, J. M., Silva, L.F., & Massano-Cardoso, I., (2024). Management of Consumers Embrace Drone Delivery. Marketing and Management of Innovations, 15(3), 1–16. https://doi.org/10.21272/mmi.2024.4-01
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References
- Abdullah, F., Ward, R., & Ahmed, E. (2016). Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Computers in human behavior, 63, 75-90. [Google Scholar] [CrossRef]
- Al-Jabri, I. M., Eid, M. I., & Abed, A. (2019). The willingness to disclose personal information: Trade-off between privacy concerns and benefits. Information & Computer Security, 28(2), 161-181. [Google Scholar] [CrossRef]
- Barkhi, R., & Wallace, L. (2007). The impact of personality type on purchasing decisions in virtual stores. Information Technology and Management, 8, 313-330. [Google Scholar] [CrossRef]
- Benarbia, T., & Kyamakya, K. (2021). A literature review of drone-based package delivery logistics systems and their implementation feasibility. Sustainability, 14(1), 360. [Google Scholar] [CrossRef]
- Borghetti, F., Caballini, C., Carboni, A., Grossato, G., Maja, R., & Barabino, B. (2022). The use of drones for last-mile delivery: A numerical case study in Milan, Italy. Sustainability, 14(3), 1766. [Google Scholar] [CrossRef]
- Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford publications. [Google Scholar]
- Cai, L., Yuen, K. F., Xie, D., Fang, M., & Wang, X. (2021). Consumer’s usage of logistics technologies: integration of habit into the unified theory of acceptance and use of technology. Technology in Society, 67, 101789. [Google Scholar] [CrossRef]
- Chen, C., Leon, S., & Kaewkitipong, L. (2024). Consumers’ intention to adopt last-mile drone delivery services: A comparison between US and Thai consumers. Thailand and The World Economy, 42(2), 1-19. [Google Scholar]
- Chen, C., Leon, S., & Ractham, P. (2022). Will customers adopt last-mile drone delivery services? An analysis of drone delivery in the emerging market economy. Cogent Business & Management, 9(1), 2074340. [Google Scholar] [CrossRef]
- Chen, C., Nakayama, M., & Ractham, P. (2023). Increasing the intention of Gen Zers to adopt drone delivery services based on a three-step decision-making process. Cogent Business & Management, 10(1), 2188987. [Google Scholar] [CrossRef]
- Chen, H., Hu, Z., & Solak, S. (2021). Improved delivery policies for future drone-based delivery systems. European Journal of Operational Research, 294(3), 1181-1201. [Google Scholar] [CrossRef]
- Chi, N. T. K., & Hanh, N. T. (2023). The drone delivery services: An innovative application in an emerging economy. The Asian Journal of Shipping and Logistics, 39(2), 39-45. [Google scholar] [CrossRef]
- Çıkmak, S., Kırbaç, G., & Kesici, B. (2023). Analysing the Challenges to Adoption of Drones in the Logistics Sector Using the Best‒Worst Method. Business and Economics Research Journal, 14(2), 227-242. [Google Scholar]
- Dancey, C. P. (2007). Statistics without maths for psychology. Prentice Hall. [Google Scholar]
- Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems. Theory and results.Massachusetts Institute of Technology. Cambridge, MA.
- Del-Real, C., & Díaz-Fernández, A. M. (2021). Lifeguards in the sky: Examining the public acceptance of beach-rescue drones. Technology in Society, 64, 101502. [Google Scholar] [CrossRef]
- Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of trust and privacy concerns on technology acceptance in healthcare: an Indian perspective. International journal of medical informatics, 141, 104164. [Google Scholar] [CrossRef]
- Dzwigol, H., Trushkina, N., & Kwilinski, A. (2021). The Organisational and Economic Mechanism of Implementing the Concept of Green Logistics. Virtual Economics, 4(2), 41-75. [Google Scholar]
- Edwards, D., Subramanian, N., Chaudhuri, A., Morlacchi, P., & Zeng, W. (2024). Use of delivery drones for humanitarian operations: analysis of adoption barriers among logistics service providers from the technology acceptance model perspective. Annals of Operations Research, 335(3), 1645-1667. [Google Scholar] [CrossRef]
- Eskandaripour, H., & Boldsaikhan, E. (2023). Last-mile drone delivery: Past, present, and future. Drones, 7(2), 77. [Google Scholar][CrossRef]
- Gajanova, L., & Michulek, J. (2023). Digital Marketing in the Context of Consumer Behaviour in the ICT Industry: The Case Study of the Slovak Republic. Virtual Economics, 6(1), 7-18. [Google Scholar]
- Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS quarterly, 213-236. [Google Scholar][CrossRef]
- Grand View Research. (2023). Delivery Drones Market Size, Share & Trends Analysis Report By Component (Hardware, Services), By Application (Agriculture, Healthcare), By Drone Type, By Range, By Payload, By Duration, By Operation Mode, By Region, And Segment Forecasts, 2023 – 2030. Grand View Research, Inc. [Link]
- Gupta, A., Afrin, T., Scully, E., & Yodo, N. (2021). Advances of UAVs toward future transportation: The state-of-the-art, challenges, and opportunities. Future transportation, 1(2), 326-350. [Google Scholar] [CrossRef]
- Harrington, D. (2009). Confirmatory Factor Analysis. Oxford University Press. [Google Scholar]
- Holzmann, P., Wankmüller, C., Globocnik, D., & Schwarz, E. J. (2021). Drones to the rescue? Exploring rescue workers’ behavioral intention to adopt drones in mountain rescue missions. International Journal of Physical Distribution & Logistics Management, 51(4), 381-402. [Google Scholar] [CrossRef]
- Hu, L. T., & Bentler, P. M. (1999). Cut-off criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural equation modelling: a multidisciplinary journal, 6(1), 1-55. [Google Scholar] [CrossRef]
- Hwang, J., Kim, D., & Kim, J. J. (2020). How to form behavioral intentions in the field of drone food delivery services: The moderating role of the COVID-19 outbreak. International Journal of Environmental Research and Public Health, 17(23), 9117. [Google Scholar] [CrossRef]
- Hwang, J., Kim, H., & Kim, W. (2019). Investigating motivated consumer innovativeness in the context of drone food delivery services. Journal of Hospitality and Tourism Management, 38, 102-110. [Google Scholar] [CrossRef]
- Jazairy, A., Persson, E., Brho, M., von Haartman, R., & Hilletofth, P. (2024). Drones in last-mile delivery: a systematic literature review from a logistics management perspective. The International Journal of Logistics Management. [Google Scholar] [CrossRef]
- Kim, S. H. (2020). Choice model based analysis of consumer preference for drone delivery service. Journal of Air Transport Management, 84, 101785. [Google Scholar] [CrossRef]
- Kita, P., Cvirik, M., Maciejewski, G., & Mazalanova, V. K. (2023). Design of a tool to measure the behavioural aspect of conscious and sustainable consumer attitudes. In Forum Scientiae Oeconomia(Vol. 11, No. 2, pp. 133-146). [Google Scholar]
- Kita, P., Maciejewski, G., Čvirik, M., & Mazalánová, V. K. (2022). New factors of consumer behaviour in the context of business models used in retailing during the COVID-19 era. In Forum Scientiae Oeconomia(Vol. 10, No. 3, pp. 75-92). [Google Scholar]
- Kline, T. J. (2005). Psychological testing: A practical approach to design and evaluation. Sage publications. [Google Scholar]
- Koh, L. Y., Lee, J. Y., Wang, X., & Yuen, K. F. (2023). Urban drone adoption: Addressing technological, privacy and task–technology fit concerns. Technology in Society, 72, 102203. [Google Sholar] [CrossRef]
- Kwilinski, A., Hnatyshyn, L., Prokopyshyn, O., & Trushkina, N. (2022). Managing the logistic activities of agricultural enterprises under conditions of digital economy. Virtual Economics, 5(2), 43-70. [Google Sholar]
- Kwon, D., Son, S., Park, Y., Kim, H., Park, Y., Lee, S., & Jeon, Y. (2022). Design of secure handover authentication scheme for urban air mobility environments. IEEE Access, 10, 42529-42541. [Google Scholar] [CrossRef]
- Lee, D., Hess, D. J., & Heldeweg, M. A. (2022). Safety and privacy regulations for unmanned aerial vehicles: A multiple comparative analysis. Technology in Society, 71, 102079. [Google Scholar] [CrossRef]
- Lee, Y. H., Hsieh, Y. C., & Chen, Y. H. (2013). An investigation of employees’ use of e-learning systems: applying the technology acceptance model. Behaviour & Information Technology, 32(2), 173-189. [Google Scholar] [CrossRef]
- Leon, S., Chen, C., & Ratcliffe, A. (2023). Consumers’ perceptions of last mile drone delivery. International Journal of Logistics Research and Applications, 26(3), 345-364. [Google Scholar] [CrossRef]
- Leong, M. K., & Koay, K. Y. (2023). Towards a unified model of consumers’ intentions to use drone food delivery services. International Journal of Hospitality Management, 113, 103539. [Google Scholar] [CrossRef]
- Li, X., Tupayachi, J., Sharmin, A., & Martinez Ferguson, M. (2023). Drone-aided delivery methods, challenge, and the future: A methodological review. Drones, 7(3), 191. [Google Scholar] [CrossRef]
- Manana, R. W., & Otieno, N. (2022). Drones Operations in Kenya: Perspectives on Privacy Challenges and Prospects. Air and Space Law, 47(1). [Google Scholar] [CrossRef]
- Manis, K. T., & Choi, D. (2019). The virtual reality hardware acceptance model (VR-HAM): Extending and individuating the technology acceptance model (TAM) for virtual reality hardware. Journal of Business Research, 100, 503-513. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Merkert, R., Bliemer, M. C., & Fayyaz, M. (2022). Consumer preferences for innovative and traditional last-mile parcel delivery. International Journal of Physical Distribution & Logistics Management, 52(3), 261-284. [Google Scholar] [CrossRef]
- Mohsan, S. A. H., Othman, N. Q. H., Li, Y., Alsharif, M. H., & Khan, M. A. (2023). Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends. Intelligent Service Robotics, 16(1), 109-137. [Google Scholar] [CrossRef]
- Mordor Intelligence. (2023). Delivery drones market size & share analysis – growth trends & forecasts (2023 – 2028). Mordor Intelligence. [Link]
- Osakwe, C. N., Hudik, M., Říha, D., Stros, M., & Ramayah, T. (2022). Critical factors characterising consumers’ intentions to use drones for last-mile delivery: Does delivery risk matter?. Journal of Retailing and Consumer Services, 65, 102865. [Google Scholar] [CrossRef]
- Poon, W. C., & Tung, S. E. H. (2024). The rise of online food delivery culture during the COVID-19 pandemic: an analysis of intention and its associated risk. European Journal of Management and Business Economics, 33(1), 54-73. [Google Scholar] [CrossRef]
- Raghunatha, A., Thollander, P., & Barthel, S. (2023). Addressing the emergence of drones–A policy development framework for regional drone transportation systems. Transportation Research Interdisciplinary Perspectives, 18, 100795. [Google Scholar] [CrossRef]
- Raivi, A. M., Huda, S. A., Alam, M. M., & Moh, S. (2023). Drone routing for drone-based delivery systems: A review of trajectory planning, charging, and security. Sensors, 23(3), 1463. [Google Scholar] [CrossRef]
- Rath, D. K., & Kumar, A. (2021). Information privacy concern at individual, group, organisation and societal level-a literature review. Vilakshan-XIMB Journal of Management, 18(2), 171-186. [Google Scholar] [CrossRef]
- Shapira, S., & Cauchard, J. R. (2022). Integrating drones in response to public health emergencies: A combined framework to explore technology acceptance. Frontiers in public health, 10, 1019626. [Google Scholar] [CrossRef]
- Smith, A., Dickinson, J. E., Marsden, G., Cherrett, T., Oakey, A., & Grote, M. (2022). Public acceptance of the use of drones for logistics: The state of play and moving towards more informed debate. Technology in Society, 68, 101883. [Google Scholar] [CrossRef]
- Stephan, F., Reinsperger, N., Grünthal, M., Paulicke, D., & Jahn, P. (2022). Human drone interaction in delivery of medical supplies: A scoping review of experimental studies. PLoS One, 17(4), e0267664. [Google Scholar] [CrossRef]
- Tokosh, J., & Chen, X. (2022). Delivery by Drone: Estimating Market Potential and Access to Consumers from Existing Amazon Infrastruture. Papers in Applied Geography, 8(4), 414-433. [Google Scholar] [CrossRef]
- Tu, Y. J., & Piramuthu, S. (2023). Security and privacy risks in drone-based last mile delivery. European Journal of Information Systems, 1-14. [Google Scholar] [CrossRef]
- Valencia-Arias, A., Rodríguez-Correa, P. A., Patiño-Vanegas, J. C., Benjumea-Arias, M., De La Cruz-Vargas, J., & Moreno-López, G. (2022). Factors associated with the adoption of drones for product delivery in the context of the COVID-19 pandemic in Medellin, Colombia. Drones, 6(9), 225. [Google Scholar] [CrossRef]
- Wang, X., Wong, Y. D., Chen, T., & Yuen, K. F. (2021). Adoption of shopper-facing technologies under social distancing: A conceptualisation and an interplay between task-technology fit and technology trust. Computers in Human Behavior, 124, 106900. [Google Scholar] [CrossRef]
- Waris, I., Ali, R., Nayyar, A., Baz, M., Liu, R., & Hameed, I. (2022). An empirical evaluation of customers’ adoption of drone food delivery services: An extended technology acceptance model. Sustainability, 14(5), 2922. [Google Scholar] [CrossRef]
- Xie, W., Chen, C., & Sithipolvanichgul, J. (2022). Understanding e-commerce customer behaviors to use drone delivery services: A privacy calculus view. Cogent Business & Management, 9(1), 2102791. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Yen, C. J., & Abdous, M. H. (2011). A study of the predictive relationships between faculty engagement, learner satisfaction and outcomes in multiple learning delivery modes. International Journal of Distance Education Technologies (IJDET), 9(4), 57-70. [Google Scholar] [CrossRef]
- 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. [Google Scholar] [CrossRef]
- Zhou, T., Lu, Y., & Wang, B. (2010). Integrating TTF and UTAUT to explain mobile banking user adoption. Computers in human behavior, 26(4), 760-767. [Google Scholar] [CrossRef
- Zhu, X., & Bao, Z. (2018). Why people use social networking sites passively: An empirical study integrating impression management concern, privacy concern, and SNS fatigue. Aslib Journal of Information Management, 70(2), 158-175. [Google Scholar] [CrossRef]
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