The Uber Taxi-Hailing Company’s Analysis

Problem Background

The taxi-hailing industry had undergone a significant transformation since the inception of Uber in 2009 when it began operations in San Francisco. The founders of Uber, Garret Camp and Travis Kalanick, revolutionized the taxi industry by introducing an app-based substitute, which became a game-changer in how cabs operate today. Because of their low cost and elegance, Uber’s services have become a global phenomenon. They have been utilized worldwide in several countries and have been valued at more than $ 70 billion since their inception (Rao, 2019). The firm’s goal is to make the public transport system as reliable as water. Because the company wants to help everyone, it has expanded its services globally. The firm can utilize mobile apps to provide consumers with swift, efficient, affordable transportation. Competitors do not share the company’s brand or infrastructure. A wide range of services is available to people with diverse tastes and preferences. With the arrival of Uber, the taxi industry was revolutionized drastically, beginning a trend toward digitalization and the ride-hailing economy.

Market Prior to Entry

Conventional taxi services were widely used before the arrival of Uber on the scene. In the past, traditional modes of transportation included cabs, chauffeur-driven, car hire, and other forms of public transportation, such as buses and trains. Being among the most accessible standard service in metropolitan areas in the United States, people relied heavily on ride-hailing services. Taxi is highly demanded in parts of the United States, such as New York, one of the busiest cities because they are the most common mode of transportation (Joshi et al., 2019). The taxi services were available in the industry to pay back the medallion purchased by the company they worked for to operate in the city. The market lacked a digitalized transportation platform prior to Uber’s entry.

Emergence of Uber

There has been an incredible shake-up within the sector since the emergence of Uber in the industry. It began offering services similar to those provided by taxis almost as soon as they were introduced to the market. However, they did not have the additional expenses since they were essentially a software company, placing the cost of the vehicles and their maintenance on the drivers, while digital automatization covered the majority of the administrative load. As a result, competition has increased to the point where cab companies and drivers must work harder than ever before and be more innovative to stay competitive. Taxi services have grown in popularity in the United Kingdom over the last decade. The main issue that has significantly affected the entire industry is that Uber has connected drivers and customers reasonably. It primarily took place as the number of vehicles providing similar services increased. According to Rao (2019), the total number of registered taxi cabs increased by 54 percent between 2006 and 2018. The figure includes Uber-registered cars and other vehicles that have penetrated the market, thus transforming the sector.

Surge Pricing

Uber services are becoming increasingly popular in most cities around the world, and the number of people who use them is increasing at a rapid pace. Since its inception in 2009, people have begun to move away from traditional modes of acquiring transportation services and toward more advanced modes of acquiring the service, such as ridesharing services. As a result of the high demand from customers, Uber has discovered a pricing method that is both convenient and efficient for the company. The company can remunerate its drivers based on the distance traveled in addition to the operating fee market value at the time of the trip, depending on the circumstances. As the industry refers to it, the firm employs “surge pricing” to ascertain the charges for a particular trip. It is dependent on the number of consumers who solicit the service, time of day, season, and multiple other factors during which the business is currently in operation that the fees charged are determined. Surge pricing is a data-driven algorithmic approach meant to provide useful information to consumers, drivers, and Uber in order to maintain a balance of supply and demand for its services (Battifarano & Qian, 2019). To summarize, surge pricing refers to a set of quantities demanded and supplied that fluctuate in response to a variety of numerous perspectives.

The organization maintains a consistent fare for all customers during average demand periods, regardless of their geographic location. In situations where demand surpasses normal levels, prices are expected to rise in tandem. During the price appraisal process, the firm employs a surge pricing algorithm to attain equilibrium and stabilize the level of demand with the available supply based on the resources the firm has at its disposal. As a result, the degree to which prices rise is perceived to depend on how demand and supply are misaligned. In the case of Uber, the implementation of the surge pricing strategy has several ramifications. To begin, customers can choose another mode of transportation if the Uber charge is prohibitively expensive. In addition, drivers in low-demand areas may decide to relocate to high-demand areas to bring the demand-supply graph back into equilibrium.

Generally speaking, the challenge of price discrimination occurs when a company or business purchases goods and services at significantly higher or lower prices depending on some pre-existing factors that may be discriminatory, such as race or social status. Customers worldwide benefit from differential pricing for Uber services because the pricing is determined based on where the client resides. Each customer has a unique reservation price, which is determined based on utility maximization and the desire and ability to pay for the services provided by the company, among other factors. Uber employs dynamic pricing to protect itself from being sued for price-inflating activities. Dynamic pricing is defined as pricing that shifts in response to shifts in demand and availability of goods and services. Its pricing system is based on the likelihood that a client will pay, determined by Uber’s forecast of the client’s financial situation.

Economies of Scale and Scope

The economy of scope is focused on the average cost of products utilized to produce several different goods. When it comes to economies of scale, it is primarily concerned with the cost-benefit that results from producing large quantities of goods. In addition to concentrating on its core competencies, the cost advantage is created through economies of scale achieved by producing comparable goods and services. Although Uber is a global business and service provider, it also offers various value-added services such as delivering food to its clients upon request, ride-hailing, and a mini transport system such as bicycles and motorcycles. Uber’s operations constitute substantially 29 percent of the total share of the taxi industry, which helps to strengthen the already thriving ridesharing Uber economy even further. An institution can achieve a cost advantage by expanding the number of products produced. This phenomenon is known as economies of scale since the total cost of production falls as the number of commodities produced grows.

Game Theory

The ride-hailing business was a relative newcomer to the market. Uber has burst in popularity in most major urban locations throughout the world, and the company is expanding rapidly. In turn, this has enabled Uber to provide a more positive ridesharing experience for their clients. Even though, as previously said, there were significant entry barriers prior to the development of Uber, the ride-hailing sector was able to push its way into the market despite these constraints successfully. The application of game theory to the Uber market was made possible due to game theory research. It is possible to argue that the entrance of Uber into the market is an example of game theory in action (Chen et al., 2019). The introduction of game theories, such as the low price and convenience while also incorporating flexibility, time minimization, and effectiveness, were all utilized in order to disrupt the industry by the ridesharing company Uber. As a result of game theory, Uber has gained a higher percentage of the market than the taxi service industry, which is a significant development.

Incentive Pay Model

Uber’s reward compensation scheme is the most straightforward in the transportation sector. Clients who wish to utilize their services must pay a surcharge, after which the rate is calculated based on the total number of minutes and miles traveled. The driver driving the client receives a significant part of the cost for the entire trip, with Uber taking a small portion of the charged amount, known as a service fee. It is well known that Uber gets a 25 percent fee from each journey, with the remaining funds going to the driver and other expenses. The pay model introduced by Uber impacts the principal-agent problem since drivers have a great deal of influence over where and when they drive, even though they own the car. The driver’s incentives are not linked to the firm as a result, and this is something that Uber is determined to address through the application of behavioral economic principles.

Potential for International Expansion

According to Slowik et al. (2019), in the last decade, Uber has successfully expanded its services to almost 500 markets worldwide, and the company is continuously developing at a rapid pace. The most recent figures from 2018 indicate the company has an approximate net worth of $42 billion across all its locations. Since it charges less than conventional competitors, more clients opt for their services, which are also more convenient and flexible than their competitors. It has contributed to the company’s international expansion and growth. Among the most significant trade policies that Uber experienced shortly after entering the market is imposed regulation, including regulations for compulsory licensing and other forms of government intervention (Slowik et al., 2019). Next, drivers assert that they are not employees of Uber, thereby claiming that the company is not liable in the event of an accident or incident. As a result, several firms worldwide have prohibited the use of Uber services since the industry is perceived to present an unfair trading environment. As a result of this development, numerous governments implement trade laws to control the industry.

Asymmetric Information Issues

The concept of asymmetric information can be defined as ‘information failure’ or when one party to an economic transaction holds more information than the other. It can be intentional, necessary, or a byproduct; in some cases, it is suitable for a market economy, while in others, it may seem like a near-fraudulent outcome (Bloomenthal, 2021). Naturally, Uber as the digital company that collects a tremendous amount of data, has a significant informational advantage over its other stakeholder participants, the drivers, and the consumers. Therefore, asymmetric information is not a byproduct of Uber’s business model, but it is a fundamental foundation meant to drive supply and demand as well as generate greater profit for the company.

One of the major issues for Uber’s business model is the information asymmetry between the company and its drivers. The drivers are not considered salaried or employees of Uber; they are gig workers, contractors essentially. There is already an inherent disbalance of power, given that drivers have little to no negotiating power. Information asymmetry exacerbates this further, weakening the driver’s position with the provider. The driver voluntarily and blindly accepts the provider’s protocol without having the ability to negotiate on behalf of themselves or even question the advantages and disadvantages of the system (Dermawan et al., 2019). The more prominent Uber grew and became the only digital taxi-hailing transportation choice essentially in many regions of the world, the greater power they held.

Uber utilizes its algorithmic capabilities and tenuous data collection as a manner of surveillance used as a means of soft control and management over the drivers. A significant example of information asymmetry includes the process by which drivers are provided orders. The system enforces blind acceptance as the driver has no knowledge of the pick-up and drop-off locations and distance needed to be driven until the order is accepted, thus not knowing their fare either. Meanwhile, not accepting orders risks fines and eventual suspension from the company. Thus, the drivers absorb the primary risk of unknown fares (Rosenblat & Stark, 2015). Despite common marketing positioning drivers as independent entrepreneurs and decision-makers, Uber holds tremendous power over the drivers and their earnings because of the information asymmetry.

Similarly, Uber holds power in information asymmetry when interacting with consumers. First, because of the business structure, Uber is technically classified as a software developer, not a taxi provider; thus, it does not take responsibility or risk on itself, placing it all on the drivers and consumers. Second, the surge pricing discussed earlier is applied algorithmically, but the user has no information regarding the actual status quo. The user has no awareness of the true number of drivers nearby or other people hailing the service, as well as even an approximate understanding of how the algorithm works. In fact, Uber utilizes a range of manipulative behaviors to drive consumer demand, such as showing ‘phantom’ cars when opening the app, making it seem like drivers are readily available when that is not the case, or displaying the price just slightly under the nearest dollar value to make it be perceived as less. There have been reports that Uber charges more for passengers with low phone batteries as they are willing to pay the premium (Calo & Rosenblat, 2017).

Uber collects such a significant amount of information about the user, then uses it to its advantage without the latter not realizing it, blaming it on the ‘algorithm’, which is the legal and public justification the company has utilized for years. Overall, as revealed in recent years, Uber has been leveraging access to information about drivers, and users, and having full control over the ride-hailing experience to coerce and mislead the economy participants to their disadvantage.


Battifarano, M., & Qian, Z. (Sean). (2019). Predicting real-time surge pricing of ride-sourcing companies. Transportation Research Part C: Emerging Technologies, 107, 444–462. Web.

Bloomenthal, A. (2021). Asymmetric information. Web.

Calo, R., & Rosenblat, A. (2017). The taking economy: Uber information, and power. Columbia Law Review, 117(6). Web.

Chen, M. K., Rossi, P. E., Chevalier, J. A., & Oehlsen, E. (2019). The value of flexible work: Evidence from uber drivers. Journal of Political Economy, 127(6), 2735-2794.

Dermawan, D., Ashar, K., Noor, I., & Manzilati, A. (2019). Asymmetric information of sharing economy. Advances in Economics, Business and Management Research, 144, 29-33. Web.

Dubal, V. B. (2019). An Uber ambivalence: Employee status, worker perspectives, & regulation in the gig economy. UC Hastings Research Paper, (381). Web.

Joshi, M., Cowan, N., Limone, O., McGuinness, K., & Rao, R. (2019). E-hail regulation in global cities. New York, US: NYU Rudin Center for Transportation.

Rao, A. (2019). Uber was designed to exploit drivers. Web.

Rosenblat, A., & Stark, L. (2015). Uber’s drivers: Information asymmetries and control in dynamic work. Web.

Slowik, P., Wappelhorst, S., & Lutsey, N. (2019). How can taxes and fees on ride-hailing fleets steer them to electrify? Web.

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