Abstract
Crowdsourced ridesharing companies, such as Uber, have recently made moves to expand into last-mile package delivery, setting up a competitive struggle between them and the national hub-and-spoke companies (NHC). Crowdsourced ridesharing platforms have achieved significant penetration of the passenger service industry. They have done so through constructing a two-sided platform linking consumers seeking rides with independent drivers and have avoided investing in a large physical network. In this paper, we model the interaction between contending suppliers in the same day delivery network, incorporating the scale and scope economies inherent in an NHC local delivery and the two-sided platform characteristics of ridesharing providers. Conditions under which ridesharing providers can successfully complete with NHCs include a high value placed on rapid delivery and significant cost complementarity enjoyed by passenger and delivery services.
UPS can put a driver on every block every day, Uber can put a driver on every block every minute
—Ryan Peterson
… delivery systems are more likely to succeed with top-down optimization, no matter how badly a sharing economy corporation tries to screw its non-employee employees
—Michael Byrne
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Notes
- 1.
- 2.
See Rysman (2009).
- 3.
“Penetration pricing” and similar strategies are discussed in Eisenmann et al. (2006).
- 4.
Such a development is highly dependent on technology, especially mobile technology. On the mobile technologies enabling the expansion of crowdsourcing into goods delivery, see Rouges and Montreuil (2014).
- 5.
See Amazon (2017).
- 6.
See Benzinger (2015).
- 7.
Ibid.
- 8.
We include such items as wear and tear on their vehicles in the drivers’ cost. In terms of the disutility of labor for delivery packages, this includes not only the additional time required for package delivery but also other costs such as dealing with unfriendly dogs, difficulties in getting the package to the proper location at the customer’s address and potential liability issues associated with package damage. These kinds of effects are indexed by lambda and help determine how many drivers enter.
- 9.
Note that \( \underline{\theta} \) represents the utility the consumer receives from having the package delivered by the NHS company.
- 10.
This means that as the value for \( \underline{\theta} \) changes this will influence whether entry by crowdsourcing is likely. If consumers are getting sufficient utility from the NHS company, crowdsourcing will not arise.
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Bradley, M.D., Colvin, J., Perkins, M.K. (2018). Crowdsourcing the Last Mile. In: Parcu, P., Brennan, T., Glass, V. (eds) New Business and Regulatory Strategies in the Postal Sector. Topics in Regulatory Economics and Policy. Springer, Cham. https://doi.org/10.1007/978-3-030-02937-1_5
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