Uber’s Revolutionary Technology: Transforming or Destroying ‘Gig’ Work?

How Uber has transformed the taxi industry, turning the way 'gig work' is performed on its head through their revolutionary technology.

"IMG_6421" by {Futuretester | Jason Tester} is licensed under CC BY-ND 2.0

Since its emergence, Uber has played both a significant part in the development of the ‘gig economy’, with its revolutionary technology transforming the taxi industry today. Uber has produced significant positive impacts on the ease and efficiency of catching on-demand transport. However, it will become clear that the ‘gig economy’ has in fact taken away the highest incentive for people to participate in ‘gig’ work – autonomy.

Uber and its position in the Sharing Economy

Uber is a predominantly smart phone-based app, with its primary business being a platform to connect drivers and passengers, transporting passengers from one location to another, similar to that of a traditional taxi service (Rogers, 2015). However, unlike traditional taxi services, all capital transactions are paid through Uber, which stores customer credit cards, taking a percentage of the payment for providing this service. Uber does not own the vehicles used, and does not directly employ drivers, instead acting as a liaising sharer of its network of drivers and customers (Lee, Chan, Balaji & Chong, 2018).

The umbrella term of a ‘sharing economy’ is used for both services that ‘share’ property for a financial compensation (i.e. Airbnb and Uber) and media platforms where people ‘share’ experiences and content (i.e. Facebook, snapchat) (Schor, 2016). Hence, in this sense, Uber can be argued as a major player in the “sharing economy”. However, Belk (2007, as cited in Rogers, 2015) defines sharing as the process of exchanging goods or services with no monetary payment or renumeration. With this definition, Uber can be seen as merely a set of capital acts occurring between all parties (Rogers, 2015), and should be excluded from the “sharing economy” (Dwyer & Martin, 2015). Thus, whilst Uber definitely has a role in the “sharing economy”, due to its difficult to define and controversial nature (John, 2016), a more definite analytical term, the ‘gig economy’, should be used to assess Uber’s position as a transformative business in the internet ecology.

The ‘gig economy’ is the emergence of workers using digital platforms to sell their ‘gig’ labour, with the platforms providing a supply of consumers (Gandini, 2018). ‘Gig work’ or ‘gig labour’ is the demand for a professional service or product that is temporary or not lasting (Gandini, 2018). Thus, Uber is most definitely a participant in this emerging economy and has been a key player in shaping the way people access and perform ‘gig work’. As will be discussed later on, Uber has been able to take advantage of being an intermediary in the ‘gig economy’, reducing autonomy in both the worker’s charging payment and communication with their consumers.

Features for passengers include seeing nearby drivers, how far their destination is and the predicted cost, which changes with demand. “Uber in Beijing” by bfishadow is licensed under CC BY 2.0

How Uber became what it is today

In 2009, ‘Uber Cab’ was founded by Garrett Camp and Travis Kalanick, in San Francisco, as an app to hail black luxury cars (Bashir, Yousaf & Verma, 2016). For just one and a half times the price of a normal taxi, this service became extremely popular as a cost effective but luxurious transport. Camp and Kalanick were able to charge such a low price due to their revolutionary technology that calculated when demand would be highest, reducing wait times and travel times for drivers between jobs and becoming much more reliable than services from regular cab companies (Lee et. Al., 2018). Changing their name to ‘Uber’, after a series of complaints from San Francisco cab operators, Uber introduced the cheaper ‘UberX’, a service allowing people to drive for Uber, using their own cars (Uber, 2020). Using their predictive technology, the company has since grown rapidly, expanding to include services such as food delivery (UberEats) and car rental, amongst others (Uber, 2020).

Google maps con Uber“Google maps con Uber” by DownloadsourceES is licensed under CC BY-NC-SA 2.0

 

Uber’s Ecosystem

Disrupting the traditional Taxi ecology with revolutionary technology

Traditionally, a passenger would either call up a taxi company and request transport or wait on the side of the street for one to appear (Rogers, 2015). Taxis would congregate in high demand areas, such as hotels and airports to reduce searching for customers, however, this led to higher competition between drivers, who had to increase rates to stay afloat due to less jobs available (Rogers, 2015).

Additionally, had a customer called for a cab that took a while to arrive, they might have hailed a new one on the street in the wait time (Rogers, 2015). Similarly, drivers might have accepted another closer street hailing customer en-route to their original passenger. This results in high search costs – i.e. longer wait time due to increased scarcity and decreased dependability outside of these high demand areas (Rogers, 2015). However, Uber developed revolutionary technology to calculate surge pricing and real-time demand, benefiting both drivers and passengers and completely transforming the taxi industry.

Employing mathematicians and scientists, Uber created prediction algorithms and heat maps to forecast demand in fifteen-minute intervals (Bashir et. Al., 2016). Using user data, including GPS signals, Uber can direct drivers in rush hours to clusters of passengers requesting rides (Bashir et. Al., 2016). This significantly reduces search costs for both parties, lowering wait time for passengers, and allowing drivers to accept more trips due to shorter travelling between dropping one passenger off and picking up the next (McClaine, 2012). This increased efficiency has allowed Uber drivers to possibly earn the same amount in one day as a normal taxi driver would earn in a week (Bashir et. Al., 2016).

Through predicting high-demand areas and times, Uber instigates their second technology of surge pricing; raising the rate of a ride up to eight times when hot spots arise on their heat maps (Bashir et. Al., 2016). Paying drivers more for taking rides in these areas at these times brings an influx of available rides to the hotspot, thus keeping up with the increased demand (Hall, Kendrick and Nosko, 2015). Secondly, due to their valuing of Uber as a cost-effective service in low-demand times, loyal passengers are still likely to accept the surged prices, thus no or little negative impact is made on the number of rides. The surged prices counter-act slightly less ride numbers, with Uber making just as much, if not more capital during these high-demand times (Hall et. Al., 2015).

Unnecessary high rates were further combatted by Uber through their reduced costs in infrastructure and physical assets (Lee et. Al.,2018). Uber wants to be referred to as a ‘communications platform’ rather than a transport company (Cannon and Summers, 2014), and confirms this with their simple provision of a platform so that drivers can connect with passengers. They don’t supply cars, driver’s license costs or car cleaning, and by contracting drivers rather than employing them, overhead costs are significantly reduced (Lee et. Al., 2018). Drivers do not have to be given minimum wage, annual and sick leave, and other employee benefits (Chau, 2019). Whilst significantly disadvantaging drivers, this method of reducing costs allows Uber to benefit customers with transport prices significantly less than a regular taxi (Smith, 2015).

The Supreme Court in Canada rules in favour of Uber drivers, opening a lawsuit which was initiated due to the unfair disadvantage in work rights drivers have due to being contractors rather than employees.

The Gig Economy – Disrupting the very nature of ‘Gig Work’

Work in the ‘gig economy’ such as Uber driving, has drastically transformed the nature of how people earn money in today’s society and the nature of gig work. Such work is traditionally shaped by the individual, with them often choosing when and for how long they work and is predominantly self-regulated. ‘Gig’ work allows autonomy over one’s work, and many Uber drivers argue that if intelligible, drivers are able to make substantially more money through being a contractor rather than an employee with an hourly wage (Wells, Attoh and Cullen, 2020). Autonomous perceptions in the workplace are found to increase worker motivation, satisfaction, and performance, thus leading to both increased earnings for workers and the company (Wells et. Al., 2020). As defined earlier, the ‘gig economy’ introduces an intermediary platform to ‘gig work’, hence reducing the autonomy held by both workers and consumers. Whilst there is still an illusion of autonomy and control in the ‘gig economy’, as it will become clear, this is not the case.

As a dominating company participating in the ‘gig economy’, Uber has significantly disrupted the traditional nature of ‘gig’ work by taking control over worker income, turning workers into consumers themselves. Traditionally in ‘gig work’, a worker or entrepreneur would decide on their charging price for a service or product, however Uber as the intermediary makes this decision (Griswold, 2017). In 2016, Uber introduced ‘upfront pricing’, where a predicted price was charged at the beginning of a ride based on factors such as distance, estimated time and traffic conditions (Griswold, 2017). Whilst this may seem like a fairer method of pricing, as customers are not left with unforeseen surged rates that occur during their trip, it allows Uber to take extra money from both passengers and drivers (Rosenblat, 2019). Though customers pay upfront with a predicted cost of the trip, drivers are still paid a percentage of what the actual trip cost (Perea, 2016). Therefore, if the trip cost amounts to less that what was predicted, the customer overpays, and the driver is paid less than the promised percentage of the customer’s payment (Rosenblat, 2019). Whilst Uber argues this is a result of its neutral technological algorithms, and thus should not be a concern, it disrupts the traditional control workers have over their income within the gig economy, blurring the line between worker and consumer.

This lack of control and consumer identity is not only projected in a worker’s income, but in how they communicate with the mediating body of Uber. Traditionally in the gig economy, the worker and consumer would communicate and negotiate issues between themselves, keeping the control and autonomy over the outcome between both parties (Gandini, 2019). However, until recently, should an issue have arisen for Uber drivers or customers, the main form of communication was not with the other party, but via a customer service email (Rosenblat, 2019). The fact that drivers did not have a human manager, but were communicated to through an email service, places them further into the category as a consumer rather than entrepreneur or employee. Due to significant driver complaints, regulation changes have seen the introduction of call centres and in-app communication services, however drivers are still considered end-users of Uber’s software (Rosenblat, 2019). This clever use of language ensures drivers are not considered employees but consumers. Evidently, the introduction of an intermediary reduces autonomy, thus destructing the main incentive and beneficiary of participating in gig work.

Clearly, whilst Uber’s technology of on-demand and surge pricing have revolutionised the taxi industry, it is not perfect. Its transformative effect on the gig economy and ability to avoid worker rights through contracting drivers rather than employing them has negatively impacted a key stakeholder in their business – drivers. Should they wish to continue shaping and transforming the ‘sharing economy’ and how we share services and products, further action is needed to ensure workers are not severely disadvantaged in the process. The emergence of the ‘gig economy’, whilst making gig work more widely available, has damaged the very purpose of ‘gig work’, which fosters autonomy and control over an individual’s work style and income. Government bodies and other organisations should continue to push and keep these sharing economy giants in check as they grow in the digital landscape.

References

Bashir, M., Yousaf, A., & Verma, R. (2016). Disruptive Business Model Innovation: How a Tech Firm is Changing the Traditional Taxi Service Industry. Indian Journal of Marketing46(4), 49. https://doi.org/10.17010/ijom/2016/v46/i4/90530

Cannon, S., & Summers, L. H. (2014, October 13). How Uber and the Sharing Economy Can Win Over Regulators. Retrieved from Harvard Business Review website: https://hbr.org/2014/10/how-uber-and-the-sharing-economy-can-win-over-regulators

CBC News: The National. (2020). Supreme Court rules in favour of Uber drivers, opening door to lawsuit [YouTube Video]. Retrieved from https://www.youtube.com/watch?v=eji7CBGSfUM

Chau, D. (2019, June 7). Uber drivers are not employees, won’t get the minimum wage, finds FWO. ABC News. Retrieved from https://www.abc.net.au/news/2019-06-07/uber-fair-work-ombudsman-investigation-contractor-employee/11189828

Dwyer, T., & Martin, F. (2017). Sharing News Online. Digital Journalism5(8), 1080–1100. https://doi.org/10.1080/21670811.2017.1338527

Gandini, A. (2018). Labour process theory and the gig economy. Human Relations72(6), 001872671879000. https://doi.org/10.1177/0018726718790002

Griswold, A. (2017, April 6). How to tell when Uber is overcharging you. Retrieved November 23, 2020, from Quartz website: https://qz.com/948785/how-to-tell-when-ubers-upfront-pricing-is-overcharging-you/

Hall, J., Kendrick, C., & Nosko, C. (2015, September). The Effects of Uber’s Surge Pricing: A Case Study. Retrieved from Uber Engineering Blog website: https://eng.uber.com/research/the-effects-of-ubers-surge-pricing-a-case-study/

John, N. A. (2018). Sharing Economies. In The age of sharing (pp. 69–97). Polity.

Lee, Z. W. Y., Chan, T. K. H., Balaji, M. S., & Chong, A. Y.-L. (2018). Why people participate in the sharing economy: an empirical investigation of Uber. Internet Research28(3), 829–850. https://doi.org/10.1108/intr-01-2017-0037

Longley, R. (2019). Gig Economy: Definition and Pros and Cons. Retrieved from ThoughtCo website: https://www.thoughtco.com/gig-economy-4588490

Martin, F. (2019). Sharing News Online: Commendary Cultures and Social Media News Ecologies. Springer International Publishing. https://doi.org/10.1007/978-3-030-17906-9

McClaine, D. (2020). Rider seeking taxi driver: Taxi matchmaker app gains traction in San Francisco. Retrieved November 23, 2020, from Bizjournals.com website: https://www.bizjournals.com/sanfrancisco/print-edition/2012/02/03/rider-seeking-taxi-driver.html

Perea, C. (2016, September 26). Uber’s Upfront Pricing is Secretly Overcharging Passengers Without Paying Drivers. Retrieved November 23, 2020, from The Rideshare Guy Blog and Podcast website: https://therideshareguy.com/ubers-upfront-pricing-is-secretly-overcharging-passengers-without-paying-drivers/

Rogers, B. (2015). The Social Costs of Uber. SSRN Electronic Journal, 18. doi: 10.2139/ssrn.2608017

Rosenblat, A. (2019, March 15). Uber Treats Its Drivers the Way Cellphone Companies Treat Customers. Retrieved November 23, 2020, from Slate Magazine website: https://slate.com/technology/2019/03/uber-gig-workers-customers.html

Schor, J. (2016). DEBATING THE SHARING ECONOMY. Journal of Self-Governance and Management Economics4(3), 7. https://doi.org/10.22381/jsme4320161

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Wells, K. J., Attoh, K., & Cullen, D. (2020). “Just-in-Place” labor: Driver organizing in the Uber workplace. Environment and Planning A: Economy and Space, 0308518X2094926. https://doi.org/10.1177/0308518×20949266

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About Julia Cole 1 Article
Usyd student studying Media & Communications and Psychology. I currently work for a company that edits corporate videos, working in graphics, video editing and content creating for their social platforms. This semester, I have become increasingly interested in the numerous issues we are facing with the rapid and constantly changing digital landscape, particularly privacy and sustainability.