The growing, aging, and urbanizing traveling population of the United States requires different and more personal designed mobility options. The private sector, in association with public firms, has started to test and deploy innovative solutions to address these shifting needs. Unfortunately, the build-first-and-users-will-come theory does not hold much weight. Overnight success stories are often the result of years of hard work behind the scenes. Simply put, start-up marketing is often a unique challenge because of limited resources, whether it is time, money, or talent. This study, therefore, introduced an agent-based model that could be used to simulate the reaction of commuters and travelers on the introduction of an innovative mobility option and business model. The model was backed up with empirical data from an Austin, Texas–based innovative mobility solution, the Metropia app. The results of this study show the importance of how and when promotional activities are conducted, with respect to the characteristics of the concept involved, to achieve appropriate public engagement. Thus, decision makers are provided with a quantitative projection of the effect of different strategies and the associated public reaction. For instance, in the case of introducing innovative mobility options that require high public engagement, such as carpooling or crowdsourcing solutions, it is suggested that strategists focus their attention and marketing budget to make an initial splash.
BassF. M.The Relationship Between Diffusion Rates, Experience Curves, and Demand Elasticities for Consumer Durable Technological Innovations. Journal of Business, Vol. 53, S3, 1980, pp. S51–S67. https://doi.org/10.1086/296099.
5.
KieslingE., GüntherM., StummerC., and WakolbingerL. M.. Agent-Based Simulation of Innovation Diffusion: A Review. Central European Journal of Operations Research, Vol. 20, No. 2, 2012, pp. 183–230. https://doi.org/10.1007/s10100-011-0210-y.
6.
SquazzoniF.The Impact of Agent-Based Models in the Social Sciences After 15 Years of Incursions. History of Economic Ideas, Vol. 17, 2010, pp. 197–233.
7.
MahajanV., MullerE., and WindY. (eds.). New-Product Diffusion Models, Vol. 11. Springer Science and Business Media, Berlin, 2000.
8.
BeardC., and EasingwoodC.. New Product Launch: Marketing Action and Launch Tactics for High-Technology Products. Industrial Marketing Management, Vol. 25, No. 2, 1996, pp. 87–103. https://doi.org/10.1016/0019-8501(95)00037-2.
9.
BagozziR. P., and LeeK.-H.. Consumer Resistance to, and Acceptance of, Innovations. Advances in Consumer Research, Vol. 26, 1999, pp. 218–225.
ErdösP., and RényiA.. On the Evolution of Random Graphs. Publication of the Mathematical Institute of the Hungarian Academy of Science Series A5, 1960, pp. 7–61.
12.
TraversJ., and MilgramS.. An Experimental Study of the Small World Problem. Sociometry, Vol. 32, No. 4, 1969, pp. 425–443. https://doi.org/10.2307/2786545.
MyersS. A., ZhuC., and LeskovecJ.. Information Diffusion and External Influence in Networks. In Proceedings of 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Association for Computing Machinery, 2012. https://doi.org/10.1145/2339530.2339540.
15.
WattsD. J., and StrogatzS. H.. Collective Dynamics of “Small-World” Networks. Nature, 1998, pp. 440–442.
BarabásiA.-L., AlbertR., and JeongH.. Mean-Field Theory for Scale-Free Random Networks. Physica A, Vol. 272, No. 1, 1999, pp. 173–187. https://doi.org/10.1016/S0378-4371(99)00291-5.
18.
NusairK., BilgihanA., OkumusF., and CobanogluC.. Generation Y Travelers’ Commitment to Online Social Network Websites. Tourism Management, Vol. 35, 2013, pp. 13–22. https://doi.org/10.1016/j.tourman.2012.05.005.
19.
StummerC., KieslingE., GüntherM., and VetscheraR.. Innovation Diffusion of Repeat Purchase Products in a Competitive Market: An Agent-Based Simulation Approach. European Journal of Operational Research, Vol. 245, No. 1, 2015, pp. 157–167. https://doi.org/10.1016/j.ejor.2015.03.008.
20.
RogersE. M.Diffusion of Innovativeness. Free Press of Glencoe, New York, 1962.
21.
AbrahamsonE., ChangS., ChoiY., and KaticI.. Clashing Fashions and Institutions: Mid-Life Uncertainty in Innovation Diffusion. Columbia Business School Research Paper, 2015.
22.
DelreS. A., JagerW., BijmoltT. H. A., and JanssenM. A.. Targeting and Timing Promotional Activities: An Agent-Based Model for the Takeoff of New Products. Journal of Business Research, Vol. 60, No. 8, 2007, pp. 826–835. https://doi.org/10.1016/j.jbusres.2007.02.002.
23.
JahanmirS. F., and LagesL. F.. The Late-Adopter Scale: A Measure of Late Adopters of Technological Innovations. Journal of Business Research, Vol. 69, No. 5, 2016, pp. 1701–1706. https://doi.org/10.1016/j.jbusres.2015.10.041.
24.
RogersE. M., and ShoemakerF. F.. Communication of Innovations: A Cross-Cultural Approach. Free Press, New York, 1971.
25.
HensherD. A., and ManneringF. L.. Hazard-Based Duration Models and Their Application to Transport Analysis. Transport Reviews, Vol. 14, No. 1, 1994, pp. 63–82. https://doi.org/10.1080/01441649408716866.
26.
CoxD. R.The Analysis of Exponentially Distributed Life-Times with Two Types of Failure. Journal of the Royal Statistical Society. Series B. Methodological, Vol. 21, No. 2, 1959, pp. 411–421.
27.
CoxD. R.Models and Life-Tables Regression. Journal of the Royal Statistical Society. Series B. Methodological, Vol. 34, 1972, pp. 187–220.
28.
ChungY.Development of an Accident Duration Prediction Model on the Korean Freeway Systems. Accident Analysis and Prevention, Vol. 42, No. 1, 2010, pp. 282–289. https://doi.org/10.1016/j.aap.2009.08.005.
PendyalaR. M., and BhatC. R.. An Exploration of the Relationship Between Timing and Duration of Maintenance Activities. Transportation, Vol. 31, No. 4, 2004, pp. 429–456. https://doi.org/10.1023/B:PORT.0000037060.42921.35.
31.
TiwariG., BangdiwalaS., SaraswatA., and GauravS.. Survival Analysis: Pedestrian Risk Exposure at Signalized Intersections. Transportation Research Part F: Traffic Psychology and Behaviour, Vol. 10, No. 2, 2007, pp. 77–89. https://doi.org/10.1016/j.trf.2006.06.002.
32.
Hossein RashidiT., and MohammadianK.. Application of a Nested Trivariate Copula Structure in a Competing Duration Hazard-Based Vehicle Transaction Decision Model. Transportmetrica A: Transport Science, 2016, pp. 1–18.
33.
van den BergP., ArentzeT., and TimmermansH.. A Latent Class Accelerated Hazard Model of Social Activity Duration. Transportation Research Part A: Policy and Practice, Vol. 46, No. 1, 2012, pp. 12–21. https://doi.org/10.1016/j.tra.2011.09.015.
34.
VlahogianniE. I., KepaptsoglouK., TsetsosV., and KarlaftisM. G.. A Real-Time Parking Prediction System for Smart Cities. Journal of Intelligent Transport Systems, Vol. 20, No. 2, 2016, pp. 192–204. https://doi.org/10.1080/15472450.2015.1037955.
