Employment Issues at Uber

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Design and justify research methodologyUber Technologies

Executive Summary

AIM: This report aims to design and justify research methodology in order to investigate a real business problem. This research methodology investigates the issue of employment within the company Uber Technologies from the point of view of its drivers, focussing specifically on issues regarding work and employment in a collaborative economy.

ANALYSIS & DISCUSSION: The research methodology will be defined by, the identification of the business problem and research questions, finding a theorical framework, searching the literature, collecting and analysing the research data.

CONCLUSION:  The potential findings will be drawn following the analyse of the data sets, then it will be possible to organise an appropriate strategy in order to prevent any future costs related to the labour standards regarding the definition of the Uber drivers’status.

2. Introduction

2.1 Uber Technologies (Uber)

Founded in 2009 by Travis Kalanick and Garett Camp, Uber Technologies (Uber) is a private-held company headquartered in San Francisco in the United States. The companies key service is a mobile application (smartphone app), known simply as ‘Uber’.

This application connects drivers of privately held vehicles with customers who pay a fare set by the company in 260 cities in North America and 78 countries worldwide (Uber Technologies Inc 2017).

The application connects riders with drivers by tracking the arrival of the vehicle and managing the payments through credit cards, PayPal accounts and Google Wallet at the end of the trip (MarketLine 2016).

The original concept of the company, previously called, ‘Ubercab’,  was to provide full size luxury vehicles by using the service Uber Black via Android and IOS platforms. Due to its fast and growing popularity, Uber expanded rapidly its product range from private hire black cars to a variety of budgets and created its own Taxi service (Uber Taxi). (MarketLine 2014)


Considered a disruptive as well as innovative company, Uber Technologies has presented a business model which has founded the concept of a new sharing and collaborative economy.

By delivering a taxi service smartphone based application via the internet, Uber Technology has created an innovative, efficient and popular form of intermediation between customers and ride-share services. It has created a standardised service where customers can rely on a specific level of service with standard operating procedure irrespective of the partner company providing the taxi service.

In addition to providing the app software and booking system Uber also dictates certain stipulations to the affiliated drivers who use their app. Indeed, Uber is setting the fares and the drivers’ activities are strongly monitored by them on the platform, for example the Uber’s drivers never know in advance where their customers would like to go before picking them up (O’Connor, Croft and Murgia 2016).

Given these further stipulations which have helped to further the idea of Uber as a brand this business model has also led to complex work relationships between Uber and its ‘partners’ drivers, and the nature of this relationship has recently been under much public scrutiny (Leighton 2016).

There is confusion regarding whether Uber should act as the employer for their partners and as such provide state recognised standards as a result, including minum wage and sick pay. This has the potential to be very expensive for Uber and could lead to many unforeseen costs.

The current employment model of Uber, decribed as a ‘Gig economy’, operates via its technology (smartphone-app) as an intermediate to connect workers and hirers (Everett 2016). This employment on-demand changed the working conditions into two categories; highly-skilled people, relatively well paid and are expecting to work flexibility and workers who are working temporarly, fixed-term, zero hour contracts and have a number of low paid and insecure jobs (Everett 2016).

With its large and disaggregated growing workforce of drivers, Uber Technologies counts around 1.1 million drivers on its global platform today (Rosenblat 2016) and ‘aiming for 42,000 drivers in London’ (Leighton 2016).

Those drivers are considered by the company as independent contractors or self-employees rather than actual employees, which was firmly criticised by media, polititics and Uber drivers themselves (MarketLine 2014).

Recently, Uber has faced some legal issues due to the classification of its drivers as being self-employed. Indeed, two Uber drivers have won their case at an employment tribunal court in London where they petitioned to be recognised by Uber as workers, with all the statutory rites that entails. This potentially gives 40,000 UK Uber drivers the right to access the mimimum wage and paid holiday leave (Osborne 2016).

Similar tribunals might cost the company considerably and increase their liabilities if more courts consider their drivers as employees (Leighton 2016).

According to Jo Betram, the regional general manager of Uber in the UK, many Uber drivers do not wish to be classified as workers and a large majority of them would like to keep the freedom and flexibility of driving anytime and anywhere at will (Osborne 2016). Despite the ruling, the position of Uber concerning the classification of their drivers as workers is firmly denied and the firm will take the case to the employment appeal tribunal or further to the supreme court depending of the decision in the court of appeal (Osborne 2016).


The aim of this research is to explore the extent of Uber drivers wanting to be considered as employees and ultimatley prevent a significant change in company policy and profit.   

The objectives of the study are threefold:

  1. To determine the number of affiliated Uber drivers who would like to be formally employed by Uber;
  2. To determine the reasons why they wish to be formally employed by Uber;
  3. To inform a strategy on how to prevent this from happening.


The main research questions to be addressed are:

  1. What is the opinion of Uber drivers with regard to the work relationships and the employment model at Uber?
  2. In what ways does the current employment model at Uber bring loyalty and satisfaction amongst their affiliated drivers?
  3. Why do Uber’s drivers would like to be considered as employees by Uber?

2.5 research paradigm

In this research, the choosen method will be quantitative. Indeed, the research paradigm will be focus on positivism with the use of surveys in order to answer to our research questions based on the research area of job satisfaction and loyalty.

2.5.1 Positivism

Based on the philosophical conclusions of the French philosopher August Comte from the nineteenth-century, the positivist paradigm endorses the fact that true knowledge is ‘based on experience of senses and can be obtained by observation and experiment’ (Dash 2005). The positivism acknowledges the fact that our social world (the reality) exists externally and its features are measured through objective methods based on deduction instead of subjective methods based on induction such as intuition or sensation (Easterby-Smith, Thorpe and Jackson 2012). Some of its features are cited on the below table (Table 2.5.1):

Table 2.5.1  Features of the main paradigm (Collis and Hussey 2014)

The main assumption of positivism is the measurement of the social phenomena ‘by using quantitative methods of analysis based on the statistical analysis of quantitative research data’ (Collis and Hussey 2014).

2.5.2 Methodology associated with positivism

In our positivism study, we will undertake a survey methodology in order to collect primary and secondary data from a sample issued from Uber’s Drivers based in the UK. This methodology (descriptive survey) is choosen in order to investigate the views of Uber drivers regarding their work relationships and the current employment model at Uber (Collis and Hussey 2014).



3.1.1 Identifying the litterature review

In order to identify which sources we will use to conduct the research, it is necessary to understand where will research our information as it is shown in the Figure 1:

Figure 1 : Mind-map of a database search using Mindup

According to Collis and Hussey (2014), the literature is a reference to all existing sources that we will use as secondary data. The research started with keywords such as ‘Uber’, ‘drivers’ and ‘employment’ in order to obtain some basic information about the company in online newspapers after our prior-research about the company on MarketLine Advantage. Then, keywords such as ‘sharing economy’, ‘self-employment’, ‘Uberization’ enabled to find more interested sources as shown in the table below:

Table 3.1.1 Examples of sources (references) using for this literature review


3.2.1 Online Survey Design

Online surveys such as SurveyMonkey or Qualtrics are the web-based tools, which may be used in this study in order to create our main questionnaire. The questionnaire has many advantages such as it allows to reach a large numbers of individuals at the same time with few costs in comparison to interviews and it consumes less time as well (Sekaran and Bougie 2013).

3.2.2 Questionnaire Design

Our questionnaire, designed on SurveyMonkey, consistes mainly of closed questions amenable to quantitative analysis. The following are the three main parts of a questionnaire featuring nineteen questions;

The first part of the survey questionnaire, the questions (Q1 to Q9) will be focus on the situation of the driver before becoming a Uber driver. The second part, questions (Q10 to Q17) will be designed according to current work conditions and job satisfaction at Uber. The third and last part, the questions (Q18 to Q19) will be focus on loyalty in order to understand if the drivers wish to continue working with Uber or not in the future.

Some of the questions will be issued of an existing survey (secondary data) conducted by Research Interactive in 2016. Around 551 Uber Drivers in London answered to the poll which shows that 61% of this amount of Uber drivers do not have any other job than Uber (Research Interactive 2016).

Anonymity and confidentiality would be offered to all participants in this research in order to increase honesty and obtain higher response rate (Collis and Hussey 2014). Moreover the purpose of the study will be explained at the beginning of the questionnaire to allow the participants to understand the context of the research and how the questions are being posed (Collis and Hussey 2014). It will take around between 20 and 40 minutes for a participant to complete the following questionnaire.

Box 3.2.2 : Uber Drivers Questionnaire by using SurveyMonkey

Questions 9-10,11,13-14 (Research Interactive 2016), Question 17 (Spector 1994)


According to the research paradigm (positivism) adopted in this study, the research data would be analysed and presented in a numerical form. The questions from the questionnaire above would be pre-coded for statistical analysis and a record of the codes used for each questions and their significance would be kept in a Excel spreadsheet (Collis and Hussey 2014) and in a data file by using SPSS, a statstitics software. After collecting the results from SurveyMonkey, the data will be analysed with SPSS by using descriptive statistics to summarize the data from individual variables. The explored data from the questionnaire will be presented in a bar chart and histogram (Collis and Hussey 2014) in order to compare in a easier way the data sets and highlighted the relationships between them.



The conclusion will be drawn according to the results’ analysis of the data collected through the questionnaire. The focus of the conclusion will be accentuated regarding the answers of the Uber drivers about their wish to pursue the driving with the Uber platform regardless their status as ‘employee’ or ‘partner’. The potential findings will allow to determine the satisfaction and the loyalty amongst the Uber drivers in the UK and figure out if the company Uber should continue to appeal at the employment tribunal and potentially loose the case at high costs or accept the regulation of the drivers who are driving under their brand.


Collis, J. and Hussey, R. 2014. Business Research: A practical guide for undergraduate and postgraduate students. 4th ed. Basingstoke: Palgrave Macmillan. In text: (Collis and Hussey 2014)

Dash, N. K. 2005. Module: Selection of the research paradigm and methodology. Online Research Methods for Teachers and Trainers. [online]. Available from: http://www.celt.mmu.ac.uk/researchmethods/Modules/Selection_of_methodology/ [Accessed 9 March 2017]. In text: (Dash 2005)

Easterby-Smith, M., Thorpe, R. and Jackson, P. 2012. Management Research. 4th ed. London: SAGE Publications. In text: (Easterby-Smith, Thorpe and Jackson 2012)

Everett, C. 2016. What does the gig economy means for HR? Personnel Today. Available from: http://www.personneltoday.com/hr/gig-economy-what-it-means-for-hr/ [Accessed 3 March 2017]. In text: (Everett 2016)

Leighton, P. 2016. Professional self-employment, new power and the sharing economy: some cautionary tales from Uber. Journal of Management & Organization. 22:6, pp. 859-874. Available from: doi:10.1017/jmo/.2016.30 [Accessed 15 March 2017]. In text: (Leighton 2016)

MarketLine. 2014. Uber Technologies Inc. Calling a cab for the taxi Industry? Avalaible from: http://advantage.marketline.com.libproxy.abertay.ac.uk/Product?ptype=Case+Studies&pid=ML00017-062 [Accessed 3 March 2017].

MarketLine. 2016. Uber Technologies Inc. Available from: http://advantage.marketline.com.libproxy.abertay.ac.uk/Product?ptype=Companies&pid=7095D6A2-7CF9-4A69-8642-C371C11220ED [Accessed 3 March 2017]. In text: (MarketLine 2016)

Nerinckx, S. 2016. The ‘Uberization’ of the labour market: some thoughts from an unemployment law perspective on the collaborative economy. ERA Forum. 17(2). Available from: doi:10.1007/s12027-016-0439-y [Accessed 6 March 2017]. In text: (Nerinckx 2016)

O’Connor, S., Croft, J. and Murgia, M. 2016. Uber drivers win legal battle for workers’ rights. The Financial Time. 28 October. [online]. Available from: https://www.ft.com/content/a0bb02b2-9d0a-11e6-a6e4-8b8e77dd083a [Accessed 3 March 2017].  In text: (O’Connor, Croft and Murgia 2016)

Osborne, H. 2016. Uber loses right to classify UK drivers as self-employed. The Guardian. 28 October. Available from:  https://www.theguardian.com/technology/2016/oct/28/uber-uk-tribunal-self-employed-status [Accessed 9 March 2017]. In text: (Osborne 2016)

Spector, P.E., 1994. Job satisfaction survey. Tampa, Florida: Department of Psychology, University of South Florida. In text: (Spector 1994)

Research Interactive. 2016. Uber Partner-Driver Survey. 2 June. Available from: http://www.s342414334.websitehome.co.uk/research-interactive/surveyContent/Uber_Results_Spring_2016/20160603_RI1769_Uber_Drivers_D1v2.pdf [ Accessed 2 April 2017]. In text: (Research Interactive 2016)

Rosenblat, A. 2016. The truth about how Uber’s app manages drivers. Harvard Business Review. Available from: https://hbr.org/2016/04/the-truth-about-how-ubers-app-manages-drivers [Accessed 13 march 2017]. In text: (Rosenblat 2016)

Rørvik Nilsen, S. 2015. Business Insider. I drove for Uber for a week, and here’s what it was like. [online image]. Available from: http://uk.businessinsider.com/i-drove-for-uber-for-a-week-heres-what-its-really-like-2015-2?r=US&IR=T [Accessed 3 March 2017].

Sekaran, U. and Bougie, R. 2013. Research methods for business : a skill-building approach, 6th ed. Chichester, West Sussex: Wiley. In text: (Sekaran and Bougie 2013)

Uber Technologies Inc. 2017. Available from: https://www.uber.com/en-GB/cities/ [Accessed 3 March 2017].

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