Exploring the acceptance of delivery robots by online buyers using diffusion of innovation theory and structural equation modeling

Document Type : Research Article

Authors

Civil Engineering Faculty, K. N. Toosi University of Technology, Tehran, Iran

Abstract

Last-mile delivery is always one of the key issues in the field of transportation and the environment. Considering the rise in the online shopping rate, last-mile delivery has become one of the most important challenges for logistics service providers. In contrast to traditional delivery methods, new methods have been proposed, including sidewalk autonomous delivery robots (SADRs). These robots are a new generation of emerging technologies for the fast delivery of goods. The present study aims to investigate the factors affecting the adoption of SADRs by online shoppers in Iran. To this end, a model was proposed based on the diffusion of innovation theory (DOI). A total of 287 respondents were surveyed using an online questionnaire, and the partial least squares structural equation modeling (PLS-SEM) was employed for modeling. The results indicated that relative advantage, compatibility, and observability had a positive and complexity had a negative impact on consumers’ intention to use the delivery robot. However, no significant relationship was found between trialability and intention. Also, The findings of the present study provide significant theoretical and practical contributions to logistics service providers and marketers.

Keywords

Main Subjects


[1] D. Eqtesad, Thirty percent growth in Iran's e-commerce, in, 2019.
[2] P. Lebeau, C. Macharis, J. Van Mierlo, Exploring the choice of battery electric vehicles in city logistics: A conjoint-based choice analysis, Transportation Research Part E: Logistics and Transportation Review, 91 (2016) 245-258.
[3] H.L. Lee, Y. Chen, B. Gillai, S. Rammohan, Technological disruption and innovation in last-mile delivery, Value Chain Innovation Initiative,  (2016).
[4] L. Ranieri, S. Digiesi, B. Silvestri, M. Roccotelli, A review of last mile logistics innovations in an externalities cost reduction vision, Sustainability, 10(3) (2018) 782.
[5] R. Mangiaracina, A. Perego, A. Seghezzi, A. Tumino, Innovative solutions to increase last-mile delivery efficiency in B2C e-commerce: a literature review, International Journal of Physical Distribution & Logistics Management, 49(9) (2019) 901-920.
[6] T. Hoffmann, G. Prause, On the regulatory framework for last-mile delivery robots, Machines, 6(3) (2018) 33.
[7] Starship, The Self-Driving Delivery Robot, in, 2014.
[8] O. Kunze, Replicators, ground drones and crowd logistics a vision of urban logistics in the year 2030, Transportation Research Procedia, 19 (2016) 286-299.
[9] E.M. Rogers, Diffusion of Innovations, 3rd ed., Free Press, New York, 1983.
[10] P. Deng, G. Amirjamshidi, M. Roorda, A vehicle routing problem with movement synchronization of drones, sidewalk robots, or foot-walkers, Transportation Research Procedia, 46 (2020) 29-36.
[11] N. Boysen, S. Schwerdfeger, F. Weidinger, Scheduling last-mile deliveries with truck-based autonomous robots, European Journal of Operational Research, 271(3) (2018) 1085-1099.
[12] D. Jennings, M. Figliozzi, Study of sidewalk autonomous delivery robots and their potential impacts on freight efficiency and travel, Transportation Research Record, 2673(6) (2019) 317-326.
[13] M. Figliozzi, D. Jennings, Autonomous delivery robots and their potential impacts on urban freight energy consumption and emissions, Transportation Research Procedia, 46 (2020) 21-28.
[14] M.A. Figliozzi, Carbon emissions reductions in last mile and grocery deliveries utilizing air and ground autonomous vehicles, Transportation Research Part D: Transport and Environment, 85 (2020) 102443.
[15] M. Joerss, J. Schröder, F. Neuhaus, C. Klink, F. Mann, Parcel delivery: The future of last mile, McKinsey & Company,  (2016).
[16] S. Kapser, M. Abdelrahman, Acceptance of autonomous delivery vehicles for last-mile delivery in Germany–Extending UTAUT2 with risk perceptions, Transportation Research Part C: Emerging Technologies, 111 (2020) 210-225.
[17] X. Wang, K.F. Yuen, Y.D. Wong, C.C. Teo, An innovation diffusion perspective of e-consumers’ initial adoption of self-collection service via automated parcel station, The International Journal of Logistics Management, 29(1) (2018) 237-260.
[18] Y. Chen, J. Yu, S. Yang, J. Wei, Consumer’s intention to use self-service parcel delivery service in online retailing: An empirical study, Internet Research, 28(2) (2018) 500-519.
[19] W. Yoo, E. Yu, J. Jung, Drone delivery: Factors affecting the public’s attitude and intention to adopt, Telematics and Informatics, 35(6) (2018) 1687-1700.
[20] J. Hwang, J.-S. Lee, H. Kim, 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 (2019) 94-103.
[21] A. Pani, S. Mishra, M. Golias, M. Figliozzi, Evaluating Public Acceptance of Autonomous Delivery Robots During COVID-19 Pandemic, Transportation Research Part D: Transport and Environment,  (2020) 102600.
[22] K.F. Yuen, X. Wang, L.T.W. Ng, Y.D. Wong, An investigation of customers’ intention to use self-collection services for last-mile delivery, Transport Policy, 66 (2018) 1-8.
[23] K.F. Yuen, Y.D. Wong, F. Ma, X. Wang, The determinants of public acceptance of autonomous vehicles: An innovation diffusion perspective, Journal of Cleaner Production,  (2020) 121904.
[24] C. Gkartzonikas, K. Gkritza, What have we learned? A review of stated preference and choice studies on autonomous vehicles, Transportation Research Part C: Emerging Technologies, 98 (2019) 323-337.
[25] A. Talebian, S. Mishra, Predicting the adoption of connected autonomous vehicles: A new approach based on the theory of diffusion of innovations, Transportation Research Part C: Emerging Technologies, 95 (2018) 363-380.
[26] I.M. Al-Jabri, M.S. Sohail, Mobile banking adoption: Application of diffusion of innovation theory, Journal of Electronic Commerce Research, 13(4) (2012) 379-391.
[27] Y.-H. Lee, Y.-C. Hsieh, C.-N. Hsu, Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems, Journal of Educational Technology & Society, 14(4) (2011) 124-137.
[28] E.M. Rogers, Diffusion of innovations, 4th ed., Free Press, New York, 1995.
[29] M. Tan, T.S.H. Teo, Factors influencing the adoption of Internet banking, Journal of the Association for information Systems, 1(1) (2000) 5.
[30] H. Strömberg, O. Rexfelt, I.C.M. Karlsson, J. Sochor, Trying on change–Trialability as a change moderator for sustainable travel behaviour, Travel Behaviour and Society, 4 (2016) 60-68.
[31] R.W. Brislin, Back-translation for cross-cultural research, Journal of cross-cultural psychology, 1(3) (1970) 185-216.
[32] G.C. Moore, I. Benbasat, Development of an instrument to measure the perceptions of adopting an information technology innovation, Information systems research, 2(3) (1991) 192-222.
[33] M.L. Meuter, M.J. Bitner, A.L. Ostrom, S.W. Brown, Choosing among alternative service delivery modes: An investigation of customer trial of self-service technologies, Journal of marketing, 69(2) (2005) 61-83.
[34] J.E. Collier, D.L. Sherrell, E. Babakus, A.B. Horky, Understanding the differences of public and private self-service technology, Journal of Services Marketing, 28(1) (2014) 60-70.
[35] S. Zakeri, Digikala Interesting Statistics on Sales in 2019 in, 2019.
[36] ChinaGoAbroad, E-commerce A key emerging theme in Iran, in, 2019.
[37] J.F. Hair, W.C. Black, B.J. Babin, R.E. Anderson, Multivariate data analysis, Pearson Prentice Hall, New Jersey, 2010.
[38] W.W. Chin, The partial least squares approach to structural equation modeling, Modern methods for business research, 295(2) (1998) 295-336.
[39] J.F. Hair, M. Sarstedt, C.M. Ringle, J.A. Mena, An assessment of the use of partial least squares structural equation modeling in marketing research, Journal of the academy of marketing science, 40(3) (2012) 414-433.
[40] J.F. Hair, C.M. Ringle, M. Sarstedt, PLS-SEM: Indeed a silver bullet, Journal of Marketing theory and Practice, 19(2) (2011) 139-152.
[41] J.C. Anderson, D.W. Gerbing, Structural equation modeling in practice: A review and recommended two-step approach, Psychological bulletin, 103(3) (1988) 411.
[42] C.M. Ringle, S. Wende, J.-M. Becker, SmartPLS 3, Boenningstedt: SmartPLS GmbH,  (2015).
[43] K.S. Taber, The use of Cronbach’s alpha when developing and reporting research instruments in science education, Research in Science Education, 48(6) (2018) 1273-1296.
[44] C. Fornell, D.F. Larcker, Evaluating structural equation models with unobservable variables and measurement error, Journal of marketing research, 18(1) (1981) 39-50.
[45] N. Jimenez, S. San-Martin, N. Puente, The path to mobile shopping compatibility, The Journal of High Technology Management Research, 30(1) (2019) 15-26.
[46] J. Henseler, C.M. Ringle, M. Sarstedt, A new criterion for assessing discriminant validity in variance-based structural equation modeling, Journal of the academy of marketing science, 43(1) (2015) 115-135.
[47] P.M. Podsakoff, S.B. MacKenzie, J.-Y. Lee, N.P. Podsakoff, Common method biases in behavioral research: a critical review of the literature and recommended remedies, Journal of applied psychology, 88(5) (2003) 879.
[48] H.H. Harman, Modern factor analysis, 3rd ed., University of Chicago press, Chicago, 1976.
[49] N. Kock, Common method bias in PLS-SEM: A full collinearity assessment approach, International Journal of e-Collaboration (ijec), 11(4) (2015) 1-10.
[50] J.S. Armstrong, T.S. Overton, Estimating nonresponse bias in mail surveys, Journal of marketing research, 14(3) (1977) 396-402.
[51] J.K. Choi, Y.G. Ji, Investigating the importance of trust on adopting an autonomous vehicle, International Journal of Human-Computer Interaction, 31(10) (2015) 692-702.