Blockchain Initiatives Literature Review

Introduction

Blockchain systems have the potential to facilitate economic coordination in ways that have not been previously possible (Berg et al., 2019). This paper will discuss how blockchain solutions are being implemented to solve real-world economic coordination issues. More specifically, it will cover how blockchain systems are causing a paradigm shift in the ridesharing industry. The current ridesharing paradigm came as a result of advancements in technology that addressed critical issues in the previous model; however, there are still issues that need to be addressed with the model currently used by the industry. One potential alternative is the TRIP (2024) ridesharing protocol.

Background

Knight (1921) argued that, under perfect circumstances and in a completely free market, individuals would be able to manage their own economic activities and outputs without the need for a modern organization. In reality, layers of management emerge to coordinate productive resources in response to stimuli, such as price changes (Coase, 1937). These management layers also often take on the task of forecasting (Coase, 1937). This illustrates the discrepancy between the theoretical self-regulating free market economy and the reality of economic activity that is often “directed by management” (Berg et al., 2019, p. 2).

These management structures theoretically emerge as the most efficient solutions for dealing with less-than-perfect market conditions (Coase, 1937). However, new technologies often cause these market conditions to shift. For example, researchers have been investigating how networked computers would increase economic coordination and the subsequent effects that would have on market participants for decades (Malone & Rockart, 1991).

One example of how these networks are actually increasing coordination is the establishment of electronic marketplaces and digital platforms on our global networks, giving rise to a new economy often referred to as the “platform economy” (Kollmann et al., 2020). These digital platforms are characterized by both technology and social processes that “manage an ecosystem of independent complementors who complete the platform’s value proposition by co-creating its value” (Saadatmand et al., 2019, p. 1).

Malone et al. (1987) predicted that “by reducing the costs of coordination, information technology will lead to an overall shift toward proportionately more use of markets–rather than hierarchies–to coordinate economic activity” (p. 484). This has been referred to as the “Electronic Markets Hypothesis” (Berg et al., 2019, p. 1). Malone et al. (1987) argued that electronic communications networks would lead to the following three effects: “electronic communication effect,” the “electronic brokerage effect,” and the “electronic integration effect” (p. 488). The electronic communications effect refers to the idea that communications costs and speed should fall when enabled by these networks (Malone et al., 1987). The electronic brokerage effect refers to the increased number of options, improved quality of options, and decreased costs when computers take the place of traditional brokers (Malone et al., 1987). Finally, the electronic integration effect refers to the idea that improved communications networks should allow for new and improved processes as a result of faster feedback loops (Malone et al., 1987).

Berg et al. (2019) argue that the electronic communications effect was only observable in the market until the early twenty-first century. The electronic communications effect allowed major corporations such as Google, Facebook, and Amazon to take advantage of the electronic brokerage effect (Berg et al., 2019). However, Berg et al. (2019) claim that the electronic integration effect has not yet been operationalized at scale.

Blockchain applications may allow these platforms and marketplaces to be taken to the next level through “instant, automated labor contracts” (Anagnostakis et al., 2021, p. 7) and “infrastructure for electronic integration” (Berg et al., 2019, Conclusion). The remainder of this paper examines how one specific use case, ridesharing, is using blockchain to fully leverage electronic networks for increased economic coordination.

Conventional Ridesharing

The rise of so-called “ridesharing” services represented a disruptive change in the traditional taxi business (Cramer & Krueger, 2016). The adoption of services like Uber and Lyft increased economic coordination and led to better outcomes for both drivers and riders (Cramer & Krueger, 2016). In many cases, these new drivers had less time on the road without a passenger when compared to to traditional taxi drivers (Cramer & Krueger, 2016). Consumers also experienced lower prices for many of their rides (Cramer & Krueger, 2016). This phenomenon demonstrated another example of the electronic brokerage effect (Malone et al., 1987) being applied to real-world problems.

However, these transactions are not completely being brokered by autonomous computer networks. Though computer networks are being used to improve efficiency, the fact of the matter is that there are still relatively large firms (Uber, Lyft, etc.) brokering these transactions. These firms have introduced several coercive practices into the market, including Resale Price Maintenance (RPM), nonlinear pay structures, and the withholding of information (Peterson & Steinbaum, 2023).

In the case of ridesharing services, RPM is the practice of the platform setting the price that the rider pays on behalf of the driver (Peterson & Steinbaum, 2023). In some cases, the driver is not even aware of the price until after the service is completed (Peterson & Steinbaum, 2023). This practice is antithetical to the benefits that would otherwise be provided by the electronic brokerage effect.

Nonlinear pay structures often come in the form of bonuses for completing a predetermined number of rides on a single platform (Peterson & Steinbaum, 2023). This practice leads to drivers being compelled to accept rides that they would not otherwise choose to accept and sometimes being coerced into using a single platform (Peterson & Steinbaum, 2023).

In many instances, conventional rideshare platforms do not share details about pay, origin, or destination with the driver before the trip is accepted (Peterson & Steinbaum, 2023). This leads to reduced elasticity on the supply side of the labor market, ultimately resulting in lower wages for drivers (Peterson & Steinbaum, 2023).

This all amounts to intermediaries, on average, taking over 44% of fares (TRIP, 2024). Clearly, the full benefits anticipated by the electronic markets hypothesis have not yet been realized by this industry (Malone et al., 1987). It’s been estimated that, with blockchain rideshare applications, the 44% taken by intermediaries and facilitators could be reduced to 15% (TRIP, 2024).

The Rideshare Protocol

The TRIP rideshare protocol is a decentralized ridesharing ecosystem being developed by the Decentralized Engineering Corporation using blockchain (TRIP, 2024). The protocol mainly consists of drivers, riders, operators, verifiers, and an application layer (though supporting roles such as developers, auditors, and ambassadors also participate) (TRIP, 2024). Drivers and riders are not new stakeholders in the ridesharing ecosystem; however, operators and verifiers are new participants in this market (TRIP, 2024). Additionally, the application layer looks different than it does in traditional ridesharing marketplaces (TRIP, 2024).

At the core of the TRIP rideshare protocol is a set of smart contracts working to increase economic coordination amongst network participants (TRIP, 2024). According to TRIP (2024), “these smart contracts are governed by a Decentralized Autonomous Organization (DAO) and help the network remain neutral and fair without having to rely on centralized authority” (Section 2. Design).

Operators

According to TRIP (2024), “operators validate that Drivers and Riders have passed all necessary checks, while Drivers and Riders validate that the Operator has been approved for operation in the region” (Section 2.1 Ordering a Ride). This process ensures that relevant rules and regulations are properly being applied while making sure drivers and riders are properly vetted (TRIP, 2024).

When a rider submits a request, it will be received by operators with active drivers in the rider's area (TRIP, 2024). Crucially, operators use the protocol’s pricing engine to deliver quotes to both the rider and the driver (TRIP, 2024). This solves the information-withholding issue seen in conventional ridesharing marketplaces (Peterson & Steinbaum, 2023). Another important detail is that all of the relevant pricing information, including information about the take rate of all network participants, is fully transparent (TRIP, 2024). Both of these features move the industry closer to the fully realized electronic brokerage effect previously discussed by Malone et al. (1987).

Riders will receive “offers from all validated and licensed Operators in the geographic area” (TRIP, 2024, Section 2.3 Matching & Pricing); however, drivers will only receive offers from operators that they are explicitly connected to (TRIP, 2024). Drivers are able to be connected to more than one operator at any given moment, can instantly terminate their relationship with operators, or instantly switch to another operator (TRIP, 2024). This addresses some of the anti-competitive practices seen in conventional marketplaces (Peterson & Steinbaum, 2023).

Verifiers

Verifiers work to “inspect driver licenses, perform car inspections, conduct background checks and verify phone numbers” (TRIP, 2024, Section 2. Design). Both verifiers and operators are certified by auditors (TRIP, 2024). Verifiers work to validate assertions made by network participants and, once validated, provide cryptographic attestation that the assertions are true (TRIP, 2024). This attestation process allows stakeholders to participate in the network with confidence (TRIP, 2024).

Application Layer

Developers on the TRIP network work to create application layers that allow users to interact with the underlying protocol (TRIP, 2024). There is currently only one mobile application, Teleport, available for the TRIP protocol (TRIP, 2024). TRIP (2024) claims that developers will play a crucial role in enhancing the user experience for the protocol. It’s expected that these developers will act in a decentralized manner, building their applications on top of the protocol (TRIP, 2024).

Conclusion

While networked computers have progressed many of the world’s business models, it has been argued that the full potential of electronic networks and marketplaces (Malone et al., 1987) has not yet been realized (Berg et al., 2019). This can be plainly seen in the ridesharing industry. Ridesharing as it is today revolutionized how consumers access on-demand transportation; however, there are still major issues that need to be addressed. The TRIP (2024) rideshare protocol offers a potential solution to these drawbacks and a way to progress the industry toward the full potential of electronic markets.

Literature Gaps

The literature includes a lot of discussion on the benefits and drawbacks of the current ridesharing paradigm. However, more data needs to be collected on proposed alternatives, such as the TRIP (2024) ridesharing protocol. TRIP (2024) represents a real-world case study that is still being proven in the market. This means that there is still progress that needs to be made before the claims can be validated.

Opportunities for Future Study

Researchers have the opportunity to empirically study whether blockchain-based ridesharing protocols truly address the issues the current industry is facing. As the TRIP (2024) ridesharing protocol continues to be built out there will be a lot of research required to study this topic as well as to test whether these models introduce new issues to the marketplace.

References

Anagnostakis, A. G., Pappa, P., & Kypriotelis, E. (2021). Blockchainification in the 4ir gig labor market. 2021 6th South-East Europe Design Automation, Computer Engineering, Computer Networks and Social Media Conference (SEEDA-CECNSM). https://doi.org/10.1109/seeda-cecnsm53056.2021.9566249

Berg, C., Davidson, S., & Potts, J. (2019). Blockchain Technology as Economic Infrastructure: Revisiting the Electronic Markets Hypothesis. Frontiers in Blockchain, 2. https://doi.org/10.3389/fbloc.2019.00022

Coase, R. H. (1937). The nature of the firm. Economica, 4(16), 386–405. https://doi.org/10.1111/j.1468-0335.1937.tb00002.x

Cramer, J., & Krueger, A. B. (2016). Disruptive Change in the Taxi Business: The Case of Uber. The American Economic Review, 106(5), 177–182. https://doi.org/10.1257/aer.p20161002

Knight, F. H., Ph. D. (1921). Risk, uncertainty and profit. Houghton Mifflin Company. https://fraser.stlouisfed.org/files/docs/publications/books/risk/riskuncertaintyprofit.pdf

Kollmann, T., Hensellek, S., de Cruppe, K., & Sirges, A. (2020). Toward a renaissance of cooperatives fostered by Blockchain on electronic marketplaces: a theory-driven case study approach. Electronic Markets, 30(2), 273–284. https://doi.org/10.1007/s12525-019-00369-4

Malone, T. W., Yates, J., & Benjamin, R. I. (1987). Electronic markets and electronic hierarchies. Communications of the ACM, 30(6), 484–497. https://doi.org/10.1145/214762.214766

Malone, T. W., & Rockart, J. F. (1991). Computers, networks and the corporation. Scientific American, 265(3), 128–136. https://doi.org/10.1038/scientificamerican0991-128

Peterson, C. L., & Steinbaum, M. (2023). Coercive Rideshare Practices: At the Intersection of Antitrust and Consumer Protection Law in the Gig Economy. The University of Chicago Law Review, 90(2), 623–658. https://www.jstor.org/stable/27222252

Saadatmand, F., Lindgren, R., & Schultze, U. (2019). Configurations of platform organizations: Implications for complementor engagement. Research Policy, 48(8), 103770. https://doi.org/10.1016/j.respol.2019.03.015

TRIP. (2024). LitePaper. TRIP Guides. Retrieved December 1, 2024, from https://guides.trip.dev/start-here/litepaper