The Market Before Uber’s Entry
Before introducing the sharing economy in the taxi industry, potential customers had to walk down a busy street and identify an empty cab to take them to their destination. However, the process of hailing a taxi was tiresome, given the unavailability of the cabs during rush hours. During the rainy season, taxis’ demand increases because of the strong desire for individuals to reach their preferred destinations on time (Kim, Baek, & Lee, 2018). On their part, drivers moved around the central business district to locate potential customers, which saw them incur losses due to the massive consumption of fuel and limited returns. From this realization, the process of hailing a cab proved to be tiresome to the customers, while drivers had to consume energy before identifying a potential traveler. Therefore, the taxi market was heavily dependent on individuals’ ability to understand the market and sheer luck that connected them to potential customers.
Taxi stands and airport queues were created to address the inconvenience of hailing a cab and reducing the time taken to reach the specified destination. However, when the demand is high, passengers spend vast chunks of time waiting for cabs while drivers waste a lot of time on the waiting ranks when the demand is low. Given the inability of the two entities to control either outcome, it was unpleasant to hail a taxi on demand because of the unpredictable nature of the cab industry. The emergence of the sharing economy in the business environment provided both drivers and passengers an opportunity to achieve their desired objectives because of their ability to hail cabs on demand. Hence, the taxi-hailing app enabled the industry to overcome challenges that influenced the outcomes of events in the business environment.
Uber’s Surge Pricing in the Contexts of Shifts in Demand and Supply
When using Uber, drivers can enjoy huge profits when the demand for taxis is high because of the willingness of the consumers to pay a higher price to reach their destinations. In this case, Uber controls the pricing strategy to encourage drivers to work for more during rush hours when they cannot cover long distances. For instance, the rush hour period increases the traffic on the road, hindering drivers from accessing various locations within the expected time frame. From this realization, their earnings are likely to decrease because of their inability to cover more shifts than when the roads are clear. In this regard, Uber increases the average price per kilometer to accommodate the average losses incurred by the drivers during their shifts. Notably, extreme weather conditions increase the demand for taxi cabs because of the rush among consumers to reach their destinations within a short duration. Due to the inability of specific consumers to afford the high price ranges, many individuals prefer to use other modes of transport, reducing the demand for the taxi-hailing service.
Alternatively, when the supply for cabs is high, consumers have a wide range of choice to make informed decisions based on their preferred destination. The price surge concept reduces the total cost of hailing a cab to encourage more consumers to request the taxis with the hope of increasing the demand for the service (Guda & Subramanian, 2019). Uber relies heavily on its algorithm, which calibrates the price surge mechanism, regulating the demand and supply for the service. Unlike taxis, which charge a temporary price for the same destination, Uber’s pricing strategy is influenced by the distance covered after requesting the service. In this regard, the laws of supply and demand play an essential role in ensuring that the consumer and the driver receive the best price for the service, a move that has revolutionized the taxi-hailing industry around the world.
Uber’s Surge Price in the Context of Price Discrimination
When Uber was introduced to the world in 2009, the service operated in black cars, which appealed to a specific population group. The private limos that were difficult to use hindered many people from using the service because of the cost associated with the specialized amenity. Based on the sharing economy concept, Uber attracted many individuals who could not afford the private limos previously because of the carpooling feature that allowed customers to travel with their friends. However, the taxi-hailing service failed to accomplish its desired goals because of its specialization that only catered to a specific population group in the community (Kuo, Rice, & Fennell, 2016). In 2012, Uber introduced the Uber X program that allowed individuals to sign up as drivers using their private vehicles. Unlike in 2009, everyone could now access the taxi-hailing service because of the convenience and affordability aspect associated with the Uber X program.
Uber’s ability to create multiple programs within the taxi-hailing app to serve a similar objective is an example of its discriminatory price practices on the broad consumer market. Even though the technology firm believes it has created different solutions for everyone, the price discriminatory practice exploits consumers who can afford the same service by charging them more than other passengers. Notably, Uber insists on creating a personalized experience for its customers through the different Uber programs accessible in its taxi-hailing app. From this realization, price discrimination presents Uber with an opportunity to maximize its profit-generation streams and accomplish different outcomes that equip it with an edge over the traditional taxi service worldwide. With UberPool, consumers can access the taxi-hailing service at a lower cost, a move that has a significant impact on the driver’s cumulative earnings.
The Concepts of Economies of Scale and Economies of Scope to Uber’s Business Model
Although Uber takes pride in its ability to create economies of scale for its drivers, signing up more drivers to the taxi-hailing service increases competition for the available jobs. Uber’s business model and the ability to deploy the economies of scope benefits its shareholders because of the ability to promote and expand the service in different geographical locations. Initially, Uber was only available in the San Francisco Bay Area, where it signed up local drivers through the UberX and UberPool program to attract more customers. Later in 2014, the corporation expanded its service to fifteen other urban areas, including Paris, New York City, and Mumbai. Today, Uber operates in over 900 metropolitan regions worldwide, denoting its rapid growth fuelled by its ability to resolve transport issues in the global business environment (Apte & Davis, 2019). Uber’s ability to utilize its economies of scope has transformed it into a multinational enterprise whose appeal to the global clientele increases by the minute because of the customized experience that addresses problems affecting individuals.
Uber’s business model does not allow drivers to benefit from the economies because of the constant recruitment attempts driven by the corporation to grow its presence in the global market. In this regard, every region where Uber operates increases internal competition by signing up more drivers to meet the ever-increasing demand for the taxi-hailing service. Importantly, when Uber increases its drivers to expand its economies of scale, existing drivers end up scrambling for the available opportunities because of the excess supply of the service. While consumers can enjoy reduced prices because of cabs’ availability, drivers end up earning less because of the price surge concept that increases the cost of hailing a taxi when the supply is low. From this realization, Uber’s argument that it has created economies of scale for its drivers to earn more remains a fallacy because of its inability to cater to the dynamic consumer market around the world.
Applying the Concepts of Game Theory to Uber’s Market
According to the game theory concept, organizations focus on defining the approaches they can use to maximize outcomes that promote their self-interests in the business environment. Notably, self-interest establishes the impact of competition on an organization’s operational performance and ability to dominate the market amidst the emergence of new entrants and existing competitors. In this regard, Uber’s main competitor, Lyft, has strongly influenced its market approaches to remain relevant in the global environment (Pendergrass, 2019). When customers have a viable option other than the traditional taxi service, Uber has been compelled to review its pricing mechanism to generate profits that benefit both its consumers and drivers. Thus, Uber’s application of the game theory concept has enabled the corporation to accomplish a wide range of outcomes-driven by different motives, which define the overall consumer experience.
Uber’s primary focus has been directed to poaching drivers from Lyft and other taxi-hailing apps with offers that allow them to diversify their revenue streams. Despite the ability of the corporation to meet the changing needs of its drivers, Uber increases internal competition by signing up additional cab owners from its competitors. Unlike the traditional cab drivers who are not affected by the wars between Uber and its competitors, Uber drivers suffer the most because of the decreased earnings caused by the inclusion of Lyft drivers into their market range. Looking at the nature of the current market, Uber’s application of the game theory concept has significantly affected its relationship with its employees because of the background under dealings to maximize its profitability.
Uber’s Potential for International Expansion and Potential Trade Policy Issues
Unlike in 2009, when Uber was a fast-rising corporation, the modern times have presented the San-Francisco-based tech corporation with a series of challenges that hinder its global expansion strategy. Significantly, Uber’s ability to connect drivers and consumers in more than 400 cities worldwide has been strongly influenced by its adopted direction that predicts consumer behaviors and develops viable solutions to problems affecting individual experience (Barbour & Luiz, 2019). From this realization, Uber’s capital size equips it with an edge over other new entrants and existing competitors because of its ability to create programs that appeal to both customers and drivers. However, the different laws and regulations adopted in various parts of the world present a challenge that is likely to affect its global expansion strategy.
In 2019, California drafted a law that required Uber to consider its drivers as employees instead of independent contractors. Although the corporation, through its legal department, has expressed its defiance to treat drivers as its employees, the technology firm is likely to encounter similar legal battles across the world where it will be forced to review its business model. Sustaining the millions of drivers in its payroll may present a challenge to its sustainability both locally and globally because of the costs attached to catering to the needs of employees in the U.S. and beyond. However, if the law is not approved, Uber can venture into a foreign market and not worry about the employee regulations that define interactions in the new business environment.
The Incentive Pay Model Uber Uses and How it Affects the Principal-Agent Problem
The principal-agent problem emerges when an organization fails to align the interests of its employees to accomplish specific goals and objectives. In this regard, Uber drivers have a higher-bargaining power over the corporation’s management, a move that has compelled the San Francisco-based tech firm to reward its drivers after completing a specified number of trips. In this regard, the principal (Uber) is expected to create an enabling environment for the agent (driver) to offer a positive experience when ferrying customers to different destinations. From this realization, Uber’s pay model poses a threat because of its inability to cater to employee interests.
Over the years, drivers have compelled Uber to include a payment mode to receive tips from passengers like in the traditional taxi industry. In this regard, individuals have expressed their concerns over the years because of their inability to overcome challenges that hinder them from accomplishing their daily targets (Cassar & Meier, 2018). From this realization, Uber’s pay model has had an adverse impact on its principal-agent problem because of the willingness of the drivers to work for its competitors, a move that negatively affects its vision in the business environment.
Asymmetric Information Issues with Uber’s Business Model
In the shared economy, information asymmetry affects the relations between drivers and Uber’s management because of its impact on the perspectives of individuals towards work. Significantly, information asymmetry influences the nature of the partnership between application providers and the drivers who facilitate the service in the business environment (Rosenblat & Stark, 2015). From this regard, power position inequalities affect the drivers’ ability to negotiate for better terms, which yields to a state of imbalance that influences the quality of service provided to consumers. Given that the service is structured to benefit the customer and Uber, drivers should identify bargaining approaches that enable them to overcome challenges affecting their relationship with the application providers.
Although many scholars have argued against the app’s asymmetrical information approach, Uber’s business model hinders drivers from influencing any change within the taxi-hailing eco-system. Notably, Uber’s decision to block customers from viewing the final trip price compels them to work even less because of their desire to accomplish their objectives in life. While the customer has a higher bargaining power to cancel a trip because of the cost, a driver cannot reject the journey due to their dissatisfaction with the cost of the service offered. From this realization, information asymmetry has significantly affected the relationship between Uber and its drivers because of the power position inequalities that affect employees’ ability to bargain for better terms from the application provider.
