Uber is a ride-sharing platform that connects drivers and passengers through the use of their online mobile application. Uber has implemented an incentive pay model that allows drivers to receive payments based on certain criteria, such as the number of trips taken, total distance driven and customer ratings. This system is designed to reward hard working and quality drivers with higher earnings by incentivizing them for providing excellent service. However, this model also creates a principal-agent problem due to the structure of the relationship between Uber (the “principal”) and its driver partners (the “agent”).
The principal-agent problem arises when there is an asymmetry in information or interests between two parties involved in a contract – in this case, Uber and its driver partners. Traditionally, agents are responsible for representing their principals’ best interests; however, if agents have different interests than those of their principals – such as maximizing profit over customer satisfaction – then this can create an agency problem. While Uber does not directly employ its driver partners, it still has ultimate control over how much they earn since it sets fares and other incentives which affect how much money they make from each trip. Furthermore, Uber collects data about every trip made by its driver partners which may be used to assess performance levels without allowing them any input into how that data is used or interpreted (Sung et al., 2019).
Explain the incentive pay model Uber uses and how it affects the principal-agent problem.
In addition to creating a principal-agent problem due to the power imbalance inherent in its business model, the incentive pay model employed by Uber also carries some potential risks associated with rewarding short term gains over long term loyalty among its driver partners (Cheng & Yildirimcioglu 2020). For example, while paying more money per mile may encourage some drivers to take longer trips which generate greater revenue for both parties; it could also lead others who are looking out primarily for themselves to focus on only short distance rides even if these do not offer as high returns overall. This could potentially reduce customer satisfaction if drivers attempt rides they cannot handle properly or neglect vehicles that require regular maintenance thereby resulting in lower ratings; thus reducing revenue opportunities over time despite any immediate gains from higher fares per mile (Ganapati & O’Keeffe 2015).
An additional concern with respect to incentivizing performance through monetary rewards relates back again to information asymmetries between Ubere’s principals and agents (Buhalis et al., 2017). Namely; since customers provide ratings based on subjective judgments rather than objective criteria it is difficult for both sides of the equation determine exactly what behaviors will produce better ratings consistently across multiple customers – leaving room for misalignment between what drives success versus what gets rewarded financially at times depending on individual preferences (Vuori et al., 2018). Therefore while long term loyalty towards achieving collective goals should be encouraged at least tacitly beyond just immediate outcomes; ultimately organizational objectives must remain aligned with individual ones if businesses wish avoid problems related agent motivation within their incentive structures going forward(Selvarajan & Chen 2017 ) .
In conclusion , Ubers incentive pay model offers many benefits such as encouraging excellence among its driving partners but unfortunately carries certain risks associated with principal – agent dynamics arising from information asymmetry between all stakeholders involved . As long as organizations ensure fairness when establishing objectives along side recognition of specific contributions amongst employees; then proper incentives can still be applied effectively while avoiding potential negative outcomes created by misaligned motivations .