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Developing a methodology to predict the adoption rate of Connected Autonomous Trucks in transportation organizations using peer effects

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  • Simpson, Jesse R.
  • Mishra, Sabyasachee

Abstract

This paper presents a methodology for predicting the adoption rate of Connected Autonomous Trucks (CATs) in transportation organizations using peer effects. There are a number of different factors that must be considered when developing innovation adoption models for organizations. This paper briefly describes each of the relevant variables and combines them into a discrete choice model for predicting the adoption rate of CATs by a hypothetical sample of transportation organizations. The model incorporates new peer effect modeling techniques to simulate the competition and informal communication network. Preliminary results suggest that organizations which are larger are less likely to change their decisions due to the decisions of other, competing organizations, whereas smaller organizations are more easily influenced by the decisions of larger organizations. The methodology developed in this paper produces reasonable results using a hypothetical dataset, and the methodology has been designed to be transferrable to any number of organizational innovations.

Suggested Citation

  • Simpson, Jesse R. & Mishra, Sabyasachee, 2021. "Developing a methodology to predict the adoption rate of Connected Autonomous Trucks in transportation organizations using peer effects," Research in Transportation Economics, Elsevier, vol. 90(C).
    • Handle: RePEc:eee:retrec:v:90:y:2021:i:c:s0739885920300640
    DOI: 10.1016/j.retrec.2020.100866
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    as
    1. Stephen Ryan & Catherine Tucker, 2012. "Heterogeneity and the dynamics of technology adoption," Quantitative Marketing and Economics (QME), Springer, vol. 10(1), pages 63-109, March.
    2. Escobar-Rodríguez, T. & Carvajal-Trujillo, E., 2014. "Online purchasing tickets for low cost carriers: An application of the unified theory of acceptance and use of technology (UTAUT) model," Tourism Management, Elsevier, vol. 43(C), pages 70-88.
    3. Paul Goldsmith-Pinkham & Guido W. Imbens, 2013. "Social Networks and the Identification of Peer Effects," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(3), pages 253-264, July.
    4. Coralio Ballester & Antoni Calvó-Armengol & Yves Zenou, 2006. "Who's Who in Networks. Wanted: The Key Player," Econometrica, Econometric Society, vol. 74(5), pages 1403-1417, September.
    5. Bramoullé, Yann & Djebbari, Habiba & Fortin, Bernard, 2009. "Identification of peer effects through social networks," Journal of Econometrics, Elsevier, vol. 150(1), pages 41-55, May.
    6. Marcati, Alberto & Guido, Gianluigi & Peluso, Alessandro M., 2008. "The role of SME entrepreneurs' innovativeness and personality in the adoption of innovations," Research Policy, Elsevier, vol. 37(9), pages 1579-1590, October.
    7. Liu, Xiaodong & Patacchini, Eleonora & Zenou, Yves & Lee, Lung-Fei, 2011. "Criminal Networks: Who is the Key Player?," Research Papers in Economics 2011:7, Stockholm University, Department of Economics.
    8. Vijay Mahajan & Eitan Muller & Frank M. Bass, 1995. "Diffusion of New Products: Empirical Generalizations and Managerial Uses," Marketing Science, INFORMS, vol. 14(3_supplem), pages 79-88.
    9. Simpson, Jesse R. & Mishra, Sabyasachee & Talebian, Ahmadreza & Golias, Mihalis M., 2019. "An estimation of the future adoption rate of autonomous trucks by freight organizations," Research in Transportation Economics, Elsevier, vol. 76(C).
    10. Richard L. Daft & Robert H. Lengel, 1986. "Organizational Information Requirements, Media Richness and Structural Design," Management Science, INFORMS, vol. 32(5), pages 554-571, May.
    11. Clark P. Bishop & C. Richard Shumway & Philip R. Wandschneider, 2010. "Agent Heterogeneity in Adoption of Anaerobic Digestion Technology: Integrating Economic, Diffusion, and Behavioral Innovation Theories," Land Economics, University of Wisconsin Press, vol. 86(3).
    12. Le Pira, Michela & Inturri, Giuseppe & Ignaccolo, Matteo & Pluchino, Alessandro & Rapisarda, Andrea, 2017. "Finding shared decisions in stakeholder networks: An agent-based approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 466(C), pages 277-287.
    13. Dorothy Leonard-Barton & Isabelle Deschamps, 1988. "Managerial Influence in the Implementation of New Technology," Management Science, INFORMS, vol. 34(10), pages 1252-1265, October.
    14. Mary M. Crossan & Marina Apaydin, 2010. "A Multi‐Dimensional Framework of Organizational Innovation: A Systematic Review of the Literature," Journal of Management Studies, Wiley Blackwell, vol. 47(6), pages 1154-1191, September.
    15. Ehsan Shafiei & Hlynur Stefansson & Eyjólfur Ingi Ásgeirsson & Brynhildur Davidsdottir, 2014. "Market Penetration of Alternative Fuel Vehicles in Iceland: A Hybrid Modeling Approach," Operations Research Proceedings, in: Stefan Helber & Michael Breitner & Daniel Rösch & Cornelia Schön & Johann-Matthias Graf von der Schu (ed.), Operations Research Proceedings 2012, edition 127, pages 437-442, Springer.
    16. Wolf, Ingo & Schröder, Tobias & Neumann, Jochen & de Haan, Gerhard, 2015. "Changing minds about electric cars: An empirically grounded agent-based modeling approach," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 269-285.
    17. Bansal, Prateek & Kockelman, Kara M., 2017. "Forecasting Americans’ long-term adoption of connected and autonomous vehicle technologies," Transportation Research Part A: Policy and Practice, Elsevier, vol. 95(C), pages 49-63.
    18. Jack A. Nickerson & Brian S. Silverman, 2003. "Why aren't all Truck Drivers Owner‐Operators? Asset Ownership and the Employment Relation in Interstate for‐hire Trucking," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 12(1), pages 91-118, March.
    19. Massiani, Jérôme & Gohs, Andreas, 2015. "The choice of Bass model coefficients to forecast diffusion for innovative products: An empirical investigation for new automotive technologies," Research in Transportation Economics, Elsevier, vol. 50(C), pages 17-28.
    20. Hall, Bronwyn & Van Reenen, John, 2000. "How effective are fiscal incentives for R&D? A review of the evidence," Research Policy, Elsevier, vol. 29(4-5), pages 449-469, April.
    21. Jean Hartley, 2005. "Innovation in Governance and Public Services: Past and Present," Public Money & Management, Taylor & Francis Journals, vol. 25(1), pages 27-34, January.
    22. Marcucci, Edoardo & Le Pira, Michela & Gatta, Valerio & Inturri, Giuseppe & Ignaccolo, Matteo & Pluchino, Alessandro, 2017. "Simulating participatory urban freight transport policy-making: Accounting for heterogeneous stakeholders’ preferences and interaction effects," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 103(C), pages 69-86.
    23. Martin Zsifkovits & Markus Günther, 2015. "Simulating resistances in innovation diffusion over multiple generations: an agent-based approach for fuel-cell vehicles," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 23(2), pages 501-522, June.
    24. Gopalakrishnan, S. & Damanpour, F., 1997. "A review of innovation research in economics, sociology and technology management," Omega, Elsevier, vol. 25(1), pages 15-28, February.
    25. Antoni Calvó-Armengol & Eleonora Patacchini & Yves Zenou, 2009. "Peer Effects and Social Networks in Education," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 76(4), pages 1239-1267.
    26. Valeri, Eva & Gatta, Valerio & Teobaldelli, Désirée & Polidori, Paolo & Barratt, Benjamin & Fuzzi, Sandro & Kazepov, Yuri & Sergi, Vittorio & Williams, Martin & Maione, Michela, 2016. "Modelling individual preferences for environmental policy drivers: Empirical evidence of Italian lifestyle changes using a latent class approach," Environmental Science & Policy, Elsevier, vol. 65(C), pages 65-74.
    27. Fagnant, Daniel J. & Kockelman, Kara, 2015. "Preparing a nation for autonomous vehicles: opportunities, barriers and policy recommendations," Transportation Research Part A: Policy and Practice, Elsevier, vol. 77(C), pages 167-181.
    28. Le Pira, Michela & Marcucci, Edoardo & Gatta, Valerio & Inturri, Giuseppe & Ignaccolo, Matteo & Pluchino, Alessandro, 2017. "Integrating discrete choice models and agent-based models for ex-ante evaluation of stakeholder policy acceptability in urban freight transport," Research in Transportation Economics, Elsevier, vol. 64(C), pages 13-25.
    29. Aubert, Benoit A. & Hamel, Geneviève, 2001. "Adoption of smart cards in the medical sector: : the Canadian experience," Social Science & Medicine, Elsevier, vol. 53(7), pages 879-894, October.
    30. Mahler, Alwin & Rogers, Everett M., 1999. "The diffusion of interactive communication innovations and the critical mass: the adoption of telecommunications services by German banks," Telecommunications Policy, Elsevier, vol. 23(10-11), pages 719-740, November.
    31. Frank M. Bass, 2004. "Comments on "A New Product Growth for Model Consumer Durables The Bass Model"," Management Science, INFORMS, vol. 50(12_supple), pages 1833-1840, December.
    32. Subramanian, A. & Nilakanta, S., 1996. "Organizational innovativeness: Exploring the relationship between organizational determinants of innovation, types of innovations, and measures of organizational performance," Omega, Elsevier, vol. 24(6), pages 631-647, December.
    33. Frambach, Ruud T. & Schillewaert, Niels, 2002. "Organizational innovation adoption: a multi-level framework of determinants and opportunities for future research," Journal of Business Research, Elsevier, vol. 55(2), pages 163-176, February.
    34. Alessio Emanuele Biondo & Alessandro Pluchino & Andrea Rapisarda, 2017. "Informative Contagion Dynamics in a Multilayer Network Model of Financial Markets," Italian Economic Journal: A Continuation of Rivista Italiana degli Economisti and Giornale degli Economisti, Springer;Società Italiana degli Economisti (Italian Economic Association), vol. 3(3), pages 343-366, November.
    35. Noll, Daniel & Dawes, Colleen & Rai, Varun, 2014. "Solar Community Organizations and active peer effects in the adoption of residential PV," Energy Policy, Elsevier, vol. 67(C), pages 330-343.
    36. Boahene, Kwasi & Snijders, Tom A. B. & Folmer, Henk, 1999. "An Integrated Socioeconomic Analysis of Innovation Adoption: The Case of Hybrid Cocoa in Ghana," Journal of Policy Modeling, Elsevier, vol. 21(2), pages 167-184, March.
    37. Hudson, John & Orviska, Marta & Hunady, Jan, 2019. "People’s attitudes to autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 121(C), pages 164-176.
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    1. Sharma, Ishant & Mishra, Sabyasachee, 2022. "Quantifying the consumer’s dependence on different information sources on acceptance of autonomous vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 160(C), pages 179-203.

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    More about this item

    Keywords

    Organizational innovation adoption; Peer effects; Connected autonomous vehicles;
    All these keywords.

    JEL classification:

    • R42 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Transportation Economics - - - Government and Private Investment Analysis; Road Maintenance; Transportation Planning

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