Research on Impact of the Platform Economy on the Employment Relationship

Published: 2021/11/15
Number of words: 16019


The need to understand the uprising digital economy and its social-economic effects is has been on the rise in recent years. The positive effects of the digital economy are significant, but the negative effects do not go unnoticed. Businesses are adopting digital platforms made of complex ecosystems that allow the interaction of different stakeholders. Through analysis of previous literature, this analyses how the digital economy emerged, the features of the digital economy and how it has been capitalised for profit maximisation, thus affecting employment relationships. Qualitative research methods are used for data collection from articles on the topic, media content reviews and case studies. The research findings indicate that employers’ exploitation of the digital platform creates a negative employment relationship through comprehensive textual analysis. Employment laws and do not bind employers to employees in the digital platform. The surveillance of employees in the digital platform indicates lower productivity, and incidences of privacy breaches arise. Incomes from the digital platforms are wrongly concealed, leading to trust issues as workers are underpaid and others earn even below the minimum wage. Research findings also indicate that gender issues underlie the digital economy, with working hours and the job nature being favourable to certain genders. Ultimately, the digital economy needs to be regulated for a positive employment relationship to be established between employees and employers.

Keywords: Digital economy, Gig economy, Capitalism, Uber, Employment relationship, platform,


The platform economy, otherwise defined as the gig economy, refers to infrastructures that utilise online tools to design products, services, and interactive marketplaces for social-economic activities. The digital concept of this platform means that work can be performed anywhere anytime, thus more flexible, temporary, and freelance, unlike formal employment. Uberization involves the provision of goods and services on demand over a platform. The platform economy has imposed power struggles between capital and labour, resting in a vicious cycle where employers chase money at the expense of employees’ wages and working conditions. This research aims to evaluate how the platform economy has influenced uberisation and its impact on the employment relationship. Several negative outcomes have arisen in the gig economy as the workers operate in a highly unregulated market. To understand this, we first examine the features of the platform economy and how its overall operations are undertaken. The research then looks at how employment relationships have been negatively affected by the gig economy, taking a specific focus on Uber. The employment relationship is affected by employee exploitation, gender discrimination, driver surveillance, and devaluation of the economy by the platform.

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Thesis Problem

Whilst the immediate effects of the gig economy has been analysed and documented, the impact of the gig economy on employment relationship is poorly understood. The study aims to identify the exploitative nature of the gig economy and its effect on the employment relationship. The study scope is limited to drawing analysis from secondary data sources and rigorously analysing the data collected. The study scope indicates a limitation to the quantitative analysis of any research. Empirical data analysis will not be analysed, limiting the inferences drawn only to quantitative data. Uber is among the successful gig economy business. However, the company has been under much scrutiny for the constrained relationship between it and its employees. The black is related to its issues on shortchanging drivers,

Research questions

  1. How has worker exploitation in the platform economy affected employment relationship?
  2. How monitoring of drivers affected their employment relationships?
  3. How has the platform economy devalued the local economy of taxi drivers?
  4. Do gender issues arise in the platform economy?

Literature review

Background and Characteristics of Formation of Digital Platforms

This study section develops insights on related literature and past studies on the background and perceived establishment of digital platforms and their roles in network promotion. The digital economic capitalism concepts are also well elaborated throughout the entire section. Digital platforms have significant impacts on how communication is enhanced within various settings. The adoption of these modernised platforms may appear disruptive at first but proves to be useful in yielding the perceived expected benefits. The establishment of digital platforms was fueled by individuals’ need to communicate while nurturing personal communication at substantial levels.

Digital communication platforms’ establishment dates back to the late 1800s. These platforms incorporated numerous electronic dots and dashes that users used to tap on hand on telegraph machines. However, with the digital platforms establishment roots going deep, substantial contemporary accounts of the current and modernised digital platforms point to the emergence in 1969 of the advanced research projects network (ARPANET). The United States defence sector devised and implemented the platform and proved helpful to scientists in sharing hardware, software, and other data sources (Asadullah, Faik and Kankanhalli, 2018, p. 248). However, the technology was followed by launching a countrywide digital network known as the NSFNET, thanks to the National Science Foundation. Following a decade afterwards, a certified social media platform was established in the year 1977.

The digital platform developed further and grew substantially, facilitating other online communication platforms, including America Online, Prodigy and CompuServe. These platforms played crucial roles in introducing customers to digital communication via real-time online conversing, emails and bulletin board messaging. In the successive years, weblogs were introduced that portrayed a rise in utilisation in the late 1990s as publishing platforms. In 2002, the LinkedIn site served as a networking zone for career-oriented individuals, and its growth reached approximately 675 million users by 2020 (Asadullah, Faik and Kankanhalli, 2018, p.248). The site has remained a favourite platform for individuals seeking jobs and human resource managers looking for qualified individuals. Since then, various digital platforms, including Facebook, Twitter, Instagram, Reddit, and WhatsApp, have been developed to enhance efficient and convenient communication between parties.

Business scholars understood Platformarization to be the market that allows transactions between platform holders and platform users. It was understood as a multifaceted market that grew greater with advancement in digital technology and expanded users’ connectivity. The network effects of the digital platform allow the platform holders to place pricing structures on the platform, and these structures were two sides; the money side and the subsidy side. Using Facebook as an example, end users form the subsidy side to access the content freely. The money side is generated through Facebook ads charged to companies that place ads on the platform depending on the number of clicks made on the ad. The platformarisation of digital markets can yield a winner take all effect if the platform holders male timely launch, set optimal pricing and provide the readily accessible infrastructure needed by end-users of the platform Nieborg and Poell, 2018, 4278).

Types of Digital Platforms

Digital platforms are mediums of exchange between different stakeholders through direct interaction. The platform provides a basis on which aggregated services or content from content and service providers are delivered to the end-users. Digital platforms have a global outreach and therefore enjoys the advantages of a wide outreach. The platforms use network technologies that allow information transfers and people connectivity. The network uses communication technologies such as the internet, social networking and the World Wide Web. Three types of digital platforms exist. Data essentially drives the network and connectivity utilised by these firms. First is the transaction platform, which enables online sales and purchases, for example, Amazon, Uber and Airbnb. The platform allows the monetisation of data captured and transferred on the internet and other complex networks. The transactions carried out within the platform depend on each user’s financial needs and operation industries. Second is the Innovation platform who are the technological enabler through the development of digital commentary services, and an example is Microsoft. The innovative platforms provide the technology that allows the platform users to perform the required functions within the platform. The third is the integrated platform which combines the features of innovation and transaction platforms, for example, Apple and Google (Moro Visconti, 2020). These platforms have both the technology and revenue concept operating simultaneously and therefore rely on sophisticated network effects to gain the platform’s optimum potential.

Features of digital platforms

The digital platforms incorporate numerous characteristics that proved them effective for utilisation. First, the majority of these digital platforms correspond to the multi-sided online type platforms. The digital platforms further accomplish the perceived assigned roles as intermediary sites ensuring interaction and exchange of information amongst various users. The functioning of the digital platforms also differs significantly from traditional organisations as they incorporate massive numbers of external users (De Reuver, Sørensen and Basole, 2018, p.125). The extensive user coverage allows for diversity and anonymity within the platform, reducing any chance of bias within the platform. Digital platforms also incorporate distinctive features, including the limitation of authority powers on the platforms’ control side and the lack of a reliable centralised bottom-up management. The platform holders often decide what is visible to the end-users and restrict some administration capabilities to control and better data security.

Additionally, the digital platforms incorporate significant levels of scalability and range. The platforms are entitled to computing capacities that are quicker and flexible to adapt, enabling users to utilise and access such platforms conveniently and efficiently. Another feature associated with the formation of these digital platforms is the bi-directional feedback. The user feedback ensures that digital platform teams easily comprehend the platforms’ technological building blocks that need improvement. The digital platforms further incorporated paved roads that eliminate standard failure modes by automating repetitive tasks. Paved roads also ensure that digital service teams approach problems while contributing enhancements and features to desirable user experiences (De Reuver, Sørensen and Basole, 2018, p.130). Paved roads feature in these digital platforms enhance operability and continuous delivery practices, including constant live traffic monitoring and evading pitfalls. The highlighted perceived features helped improve the effectiveness and convenience associated with utilising these digital platforms.

An effective digital platform also has a revenue model. The digital platform possesses transactional features, and this can offer direct payments, advertisement models, an access model or an acquisition and growth model. The revenue model allows the exchange of goods and services (Van Eijk et al., 2015, 12). Revenue models are also responsible for collecting fees and subscriptions made by the platform clients and workers. The platform holders often control the revenue model, and they assign rates to which good and service delivery will be charged for utilising the platform.

Network effects are essential features for the functioning of digital platforms. Digital platforms utilise both direct and indirect networks, and the greater the growth of the network users, the higher the growth of the Digital Platform. The network brings together the platform participants. The network, therefore, bears the burden of market creation by gathering its dynamic momentum. The platform owner is tasked with a facilitation role where he spreads the platform burden to the participants (Van Eijk et al., 2015, 14). The network effect is used to build a customer base. These customers complete the platform loop of operations, creating a market of willing buyers willing sellers voluntarily using the platform to gain services.

Digital platforms are dependent on the evolution of technologies. Innovations such as cloud computing, AI, automation and mobile internet enable the platform to play functions such as on-demand targeting. By integration into the business processes, they are availed to the end-users. Digital platforms also enjoy economies of scale. Upfront investments are often made to set up infrastructure and software’s that run the platform. An increase in the platform size consequently results in better brand recognition, and therefore the platform is visible to more users. However, economies of scale are not associated with an increased user base. Digital platforms exist in an ecosystem, and the function of one platform depends on the functioning of another. A platform that is relied upon by other platforms acts as a gatekeeper. Therefore, they control the accessibility to end-users, giving them a powerful market position (Van Eijk et al., 2015, 14). However, the use of evolving technologies brings up the dangers of data breaches, and therefore platforms invest in data security to prevent loss of customer information to unsecured users.

Digital platforms are also subject to horizontal integration. Adjacent markets often present opportunities that change or add to the functionality of the digital platforms. Through horizontal integration, data is collected from multiple sources and combined to provide an optimised user experience. Digital platforms also undertake vertical integrations. The platforms may hope to outsource services such as web development and have data centres on other platforms at lower costs. Some digital platforms combine their resources with physical assets for better control of the value chain. Such a platform may control distribution networks and data servers or manufacture products supporting the platform use, such as smartphones and computers.

Another important feature of the digital platform is the geographical dependency. Some digital platforms are functional in limited geographies, while others are global. Digital platforms are designed to offer goods and services that are close to the end-users. Understanding different cultures are necessary to differentiate the types of platforms that work in different markets (Van Eijk et al., 2015, 15).

Massive data and content storage space if offered through digital platforms. The data collected from different sources are collected and combined by big data algorithms to create innovations. One of the major innovations is the Internet of Things, the machine to machine communication used to run the digital platforms. Data is becoming essential for both tech and non-tech users, and organisations are finding ways to use the data.

Digital platform establishment and promotion of the network

The establishment of the digital platform is related to the popularisation of Web 2.0. In the current market, the internet and mobile platforms are highly commercialised. The dot-com boom in 1990 marked a beginning of a greater understanding of the websites and their potential. The Web 2.0 boom occurred with the rise of digital platforms including YouTube, Facebook and related social networking sites where software shares intertextual file exchanges. The technological era moves from a hardware platform to a software platform where the software and content are licensed to create a network platform page (Berry, 2008, 67).

Jared (2016) states that users of the digital platform act as digital housewives. The term defines how consumers do voluntary work on the platform through self-expression, personal opinion, and social solidarity with the digital platforms. The term digital housewife is primarily based on the unpaid, quasi voluntary labour offered at domestic levels illustrated rhetorically. The reference to the housewife draws attention to how capitalist corporations gain free data, and no compensation is offered to the platform users. The concepts of contemporary capitalism are showcased by the value created by workers and consumers of the Platform (Jared 2008, 10).

Technological advancements lead to the shift of media from static monologues to dynamic dialogic spaces that was participatory through customer engagement. The Web became more updated, user experience became richer, and the user base allowed diverse remixing, sharing, and updating ideas. The dynamic nature of the digital platform broke the chains on information holding associated with imbalances, thus creating a participatory platform well distributed within cultures and otherwise content that could be under the control of media corporations.

A digital platform has to be in a performing environment where the user expects continuous quality improvement. The continuous improvement can be achievable by monitoring the quality of content outlaid by the platform, user numbers increase, existing customers remain loyal, and a better revenue stream that facilitates the platform and the participant’s growth get identified (Jared, 2016 16).

Digital platforms ought to be multifaceted for the interaction between external stakeholders to be enhanced. The interaction between the stakeholders is enhanced through participative infrastructure where two distinct users directly communicate and create value. The network effect creates an attraction to more users to the digital platform, which promotes value creation. The matchmaker is an example of a successful multi-sided digital platform that provides an organised environment where users interact and transact. Two kinds of firms often operate using the matchmaking tool. The matchmaking firm reduces its costs through the externalisation of organisational activities. The producing firm using the matchmaking tool, on the other hand, internalises organisational activities to reduce transactional costs within the digital platform. Businesses such as Uber and Airbnb are examples of matchmaking platforms whose organisational activities are purely reliant on asset externalisation. Bonollo and Poopuu, 2019, 18).

Digital platform establishment is meant towards improving employees, customers and perceived partner experiences. The platforms created should thus work effectively towards delivering desirable experiences. Identity is thus one of the crucial factors that should be considered while stabling a digital platform. Understanding the identity of the person or target audience being addressed proves substantial in developing a substantial market intelligence that anticipates a given group of target individuals (Rossotto et al. 2018, p.94). Following the anticipation of the individuals’ needs, the digital platform development team should distinguish between important and helpful aspects to the users towards delivering a delightful experience that proves impactful and helpful.

Additionally, teams developing the digital platforms should have a well-articulated vision and perceived capabilities necessary for delivering substantial value to customers. Sequenced goals should also be devised that incorporate the required numerous metrics for measuring the platform’s success. The metrics include key performance indicators that portray the progress towards the set ambitions meant to be achieved. Additionally, a team should as well be developed to work on the digital platform. The team includes experts such as cloud professionals, data analysts and software developers to ensure the effective functioning of the platforms created (Rossotto et al. 2018, p.98). Service providers should also be engaged in the creation of digital platforms. These providers help in selecting desirable technologies, defining ambition, and ensuring long-term development and substantial maintenance.

Once the platform is established, the team involved should consider scaling and expanding it further. The latter can be accomplished by diversifying the platform capabilities necessary for accomplishing the perceived digital ambitions. The ambitions and related capabilities should be constantly redefined towards ensuring adaptation to the existing market changes (Rossotto et al. 2018, p.99). The perceived relevant technologies are bound to changes and improvement, thus calling for a need to revise the perceived aspects of the original dimensions incorporated into the Digital Platform. The changes are meant to accommodate the new technologies in the digital platform and the changing consumption patterns of the end-users.

The different stakeholders in the digital platform play different roles in the effective performance of the network. Agents within the digital platform often operate autonomously and are responsible for problem-solving, decision making, and job fulfilment. The operations of these agents in the digital ecosystem are defined by structures, behaviours, rules and communication within the digital ecosystem. The will to join the digital ecosystem is often voluntary, and the agents join with their interests at play. They, therefore, give information about their features and resources, which are then uploaded to the platform. Information sharing is associated with being either orchestrators, providers or consumers within the platform. The agents interact using the platform, and information and experience are shared (Bonollo and Poopuu, 2019,18). An orchestrator coordinates all interactions within the ecosystem, the consumers request goods and services, and the producers respond to the request by the consumers. In the case of Uber, the consumers are Uber riders; the Uber drivers are the producers, and Uber is the orchestrator. Another important player in the digital platform is the digital innovator who creates features to ensure constant connection between the different actors, thus enabling valued exchanges. The innovator makes sense of the complex and dynamic data collected from the exchange between the actors.

There are various ways through which digital platforms can be promoted. For instance, social media marketing sites can help create awareness on the perceived digital platform under consideration. The team developing the platforms can use these channels to distribute paid advertisements and sponsored information, shedding light on the platform created. The social media channels also provide means of interacting with users and responding to customer-oriented questions to ensure that constant engagements with the digital platforms developed are enhanced (Rossotto et al. 2018, p. 98). Engaging with the customers may also help in developing positive experiences and winning customer loyalty.

Influencer marketing is also a crucial way of harnessing digital platforms to reach the target customers. The perceived digital teams can partner with sites, celebrities and professionals in their field sharing common values. Email marketing is also a sure way of promoting the digital platforms created. The latter allows institutions to be well connected with their customers and prospects, thus ensuring that the products offered register substantial sales (Rossotto et al. 2018, p.100). Search engine optimisation can also be incorporated in promoting the digital platforms created. In incidences where individuals decide on commodities to acquire, they are prompted with the most viable options to help in decision making.

Likewise, optimising the digital platform contents on google will ensure that search engine characters conveniently and easily access such platforms. Affiliate marketing can also help in promoting the digital platforms set in place. The practice entails working with third parties with the agreement of them promoting your platform in exchange for perceived commissions likely to be made from the sales, which can be attributed to their perceived efforts (Rossotto et al. 2018, p.105). However, this type of promotion should be well monitored and tracked as the digital marketing platform developed depends on third parties.

Digital economic capitalism

Digital economic capitalism is an existing feature established within most political-economic sectors, social and societal relations. Digital capitalism thus involves the centring of economic and social activities on exchanging digital information while utilising data networks. These activities are enhanced by the utilisation of internet platforms within the perceived network economies. The latter can be attributed to the fact that access to internet platforms is equated to individuals’ uniform participation in social life processes (Pace, 2018, p.254). Digital capitalism thus remains an important aspect for both the consumers and producers.

The detailed aspects of digital capitalism, including the ideological, technical and infrastructural considerations, are necessary for enhancing and pushing technological considerations ahead. Digital capitalism should thus be shaped appropriately towards ensuring that the perceived digital growth goes beyond the set ecological units set in place. Incorporating substantial measures into digital capitalism concerns will thus help increase the capabilities of state institutions in improving the future of digitalisation (Pace, 2018, p.258). The perceived international production network and distribution thus depend on the digital platforms’ decline of developed social restraints.

The digital space operates purely on data leading to the emergence of digital capitalism. Companies are hungry for new data sources and even make acquisitions to meet this need. Data is termed as the new oil describing the digital economy novelty. Companies are paying millions, billions and trillions to access the novelty on big data points (Srnicek, 2019, 89). The only way to rein in big tech is to treat them as a public service. An American scholar Dan Schiller inclined that the new digital network was vulnerable to colonisation by the neoliberal market structures. The telecommunication networks created would interlock with existing hyper-capitalism, ensuring optimum reach to all potential markets. Through the internet, a central production and control feature would be transactioned, creating a global capitalist economy.

Digital capitalism would be transnational therefore transcending the social and cultural disparities. It was also argued that telematics would change the views on capitalism. With the telematics innovations, digital capitalism was defined by systematic, market logical exploitation of information technologies that accelerate processes. These outcomes are the emergence of interdependence and communication networking (Maier, 2020, 266). Manuel Castell argued that digital capitalism relies on information production, processing and application. The process takes place on a global scale. Digital capitalism relies on transactional corporations to produce, market and exploits ICT as raw materials for digital platforms. Therefore, the global market structures are defined by the dominance and productivity from digital capitalism as goods and services systematically commoditised. However, commodification on the digital platform exhibits expansive and invasion tendencies and is often associated with privacy invasion, especially in highly privatised and deregulated markets (Maier, 2020, 267).

A dominant form of digital capitalism is surveillance capitalism. It occurs when corporations exploit unregulated cyberspace through internet sources and use the data collected for profit maximisation. Surveillance capitalism observes the behaviour of users and determines those with higher purchasing powers. The platform users provide these information free of charge, and they act as raw materials for those corporations. The firms use highly developed algorithms to convert the data collected to predict the market needs. The surveillance capitalism on the digital platform can therefore be viewed as a threat to human behaviour. The modern liberal order and self-determination principles are threatened. The corporations use plundered, exploited and enslaved forms of profit maximisation (Maier, 2020, 269). Digital capitalism also occurs in the form of platform capitalism. Firms operating on different platforms showcase monopolistic tendencies which affect the society and economy at large. These firms quest for more data and may invade people’s privacy as they try to make higher profits. The need to continuously expand the digital platform arises from the intensified competition in the market, and therefore firms choose aggressive methods to remain competitively relevant and dominant.

Employment relationship

In every organisation, a relationship exists between the employer and employees, defined by the rights and obligations of each player within the organisation. Employment defines how labour laws and social security matters are applied. Employment relationships pertain to collective agreements between the employer and employee on the employment terms. The interpretation of the terms of the engagement of the relationship should exhibit compatibility with the decent work objectives. As such, the regulation deters matters of unequal bargaining power, thus protecting workers from exploitation. Employers often take advantage of inadequacies in the labour laws provisions where uncertainty exists on whether employment relationships exist and abuse this power by not committing to employment relationships, thus depriving its employees of its protection (ILO, 2006).

Digitisation of the employee-employer relationship has led to the emergence of a triangular relation model, creating a worker-platform-customer relationship. Adopting this model creates limitations to the employment relations, thus creating employment relationship constraints to the users. Users are limited to the fact that they can only enter into a relationship through the platform, and they have to accept that their data will be transmitted upstream using the platform for the sole purpose of organisation and management of the platform relationship (Dieuaide, and Azaïs, 2020, 7). Commercial laws often provide regulations on the employer-employee relationship, and the digitisation of this relationship puts the employee in a vulnerable position as they lose institutional visibility. Business models of digital platforms are characterised by no asset ownership and minimum permanent employees. The model makes the classification of these businesses in legal, institutional and fiscal terms difficult. Uber is an example of a digital platform that is hard to categorise. Is the company a transport company, a service enterprise, or a technology enterprise? Through digitalisation, these digital platforms unchain themselves from the legal and regulatory frameworks. They are less bound by laws on competition, labour and taxes. The operations of these platforms are dependent on a strong technological and social base for effective operations. Uber’s platform, for example, requires a data centre, a smartphone, an algorithm and a social base underpinned by the intensive network created by the platform. The players are within diverse geographies but using the digital platform; they are brought together for transactional purposes where they remit commission to the platform from earnings earned. Through the perfection of its algorithms, the platform provides quality matches to the platform users, facilitating smooth transactional flows.

The employment relationship in the digital context could be complex as platform workers do not operate with absolute independence, and neither are they completely subordinated. The digital platform is presented in models that represent are deviant of any kind of employment relationship with no direct link to the production of goods and services in the market. They do not have specified work goals, and no tasks are assigned at particular workstations.

An employment relationship in this platform could be approached in the direction that resources should be mobilised as they operate in public spectrums and could cause social and economic disruptions. The management and resource mobilisation, therefore, requires stakeholder investment. Taking the case of Uber, workers are exposed to externalities such as accidents, illnesses and unemployment. Still, the firm is not involved in managing these externalities as employment relationships do not bind them.

The formation of the employment relationship in the digital platform is also limited because the nature of the tasks being carried out is often temporary. The determination of work duration in the digital platforms is estimated per the time taken to finish a single assignment, for example, a single taxi ride which makes the work duration very short. As a result, classifying a single tenable task within the platform as fixed-term employment could be extremely tedious. Most of the tasks on the digital platforms are carried out autonomously; thus, the platform worker is not limited on when and where they are supposed to operate. The autonomy resembles self-employment, therefore complication the establishment of the employment relationship (Garben, 2017, 18).

The management of externalities will ensure that the digital platforms are made accountable, thus creating an employment relationship. When their social responsibility towards pollution is checked or safe mobility issues are raised against the company, they will be forced to define a working relationship. Professionals and unions can supervise the digital platform’s engagement in driver training, enumeration claims, ethical management, and drivers’ morality, ensuring quality service delivery (Dieuaide and Azaïs, 2020, 10). Therefore, the regulation of a digital platform’s activity is taken as a reaction towards the disruptive nature of the platform, and the regulation is bound to protect the general public.

Workers Exploitation in the gig economy

In the gig economy, major concerns have arisen about the labour relations between employees and employers. The demographics of the people doing the job analysed, issues on outsourcing and precarization of labour are addressed. The uncertainty related to job insecurity in the platform economy will be addressed. Attacks have been launched against workers’ labour rights and protection in the gig economy, and worker exploitation forms part of this fight.

A study by (Bregiannis 2018) states that the platform economy comprises of workers who perform multiple jobs daily or monthly, contrary to the traditional employment relationship. As a result, the workers are seen as independent contractors, and in very few cases, they are recognised as true employees where an employer wants to avoid burdens of subordination. Workers in these economies face challenges in the daily operations of the job nature. The issuance of operating space in the platform comes with the necessity to pay fees and subscriptions. The users, therefore, have to make capital investments every time they are about to use the platform. The platform workers also may go for hours without payments for hours missed on the job. The earnings made are reliant on the working hours on the job, and therefore matters such as waiting time and off days go unpaid. The digital platform workers also lack subordination in their roles. As such, they are expected to play all their roles without assistance, meaning no, in case you don’t show up, no one will cover for you as is the case in formal employments. In the digital platform, the income generation depends entirely on the platform users. The platform users cannot hire people to work for them, so they cannot be regarded as independent contractors. Workers in this economy often face rights infringement but lack a collective means to defend themselves. The employment relationship does not grant workers the right of association, thus exposing them to exploitive terms of engagement (Bregiannis et al., 2017, 8). Workers often face struggles such as unjust suspension where they are accused of wrongdoing and are not granted the opportunity to defend themselves. The employees also face discrimination, and this subjects them to unfair treatment. The platform economy is also associated with long working hours with as most earn lower than the minimum wage and have to put in extra hours to gain sustainable earnings. However, the labour laws do not protect them, leading to a constrained employment relation.

Research by (Rashid 2016) agrees that the gig economy includes multiple people working on multiple jobs. The growth in this market has led to the emergence of an explosive trend that often goes unchecked. The research indicated that the uncontrolled nature of the gig economy results in the establishment of unjust labour standards. The exploitative nature of the job results from regulators being unsure of the kind of laws applicable in the new market and the type of right infringement that the gig workers face. Labour disputes arise in this economy due to unjust law’s obstructed flow of commerce as the gig economy has created a market so different from traditional employment (Rashid, 2016, 6). The regulators are unable to define the kind of employment offered to platform employees. They, therefore, do not clearly state the rights of employees and the obligations of the employers operating in the digital economy.

As Hauben, Lenaerts and Wayaert (2020, 22) explained in their study, there exists unclear employment status among platform employees. Employment status is defined as the classification of an individual as working in an employment relationship framework (employee) or working on their account or behalf (self-employed), showing the dichotomy on which labour and social protection law is based. Being either an employee or self-employed is essential as it determines various rights r obligations, and additional labour and social protection levels of an employee under the existing legislation. Platform work practices have blurred the bounds between traditional “employee” and “self-employed” concepts. Most platform employees lack a clear status and are treated as self-employed people but with lower protection. As the “digital contracting and unilateral enforcement of contractual terms” shows, platform employees do not have any real choice regarding their labour market status. Most platform businesses qualify their relations with platform employees as contracts for services provided and platform employees as independent contractors (self-employed) rather than employees or workers. They commonly deny any employment relationship. Generally, using self-employed employees rather than employees leads to reduced liabilities, lower costs, and reduced responsibilities, and any incurred operational or commercial risks get incurred by individual platform employees.

As a result, platform workers lack real choice regarding labour market status. They often get pushed into disadvantageous and most minor advantageous situations in the labour market, with more risks and costs, without any consultation whatsoever. This scenario poses a high risk for those platform workers contracted as workers involuntary and unlawfully. Their unclear status encourages the platform to push various costs and risks onto them. Platform employees lack a written contract like the employment contract in the traditional economy. Instead, their contracting is mainly carried out online, via simple subscription, in most cases, without personal contact between the involved parties (Hauben, Lenaerts and Wayaert, 2020, 27). Platforms end up changing the terms and conditions on platform employees without any prior consultation and information, primarily through a simple “read and approve” button. Platforms also apply temporary or permanent contract termination via interrupt work allocation, suspension, or accounts closure, giving the platform employees any explanation or review possibilities. Lack of general engagement rules applicable to this contractual relationship between platform and platform employees makes platform workers’ situation more prone to exploitation.

Income gained from platform work is usually meagre, unpredictable, unstable, and insecure. As a result, many platform workers view this work to supplement their incomes, with only a few workers earning enough to support their living expenses. According to ILO, 2018, 6, the platform pays the workers on tasks done and not by the hour, and in some cases, platform workers might end up bearing some costs related to the platform work like vehicle repairs. In other instances, platform workers might not get paid for work already done. Some platforms permit their clients to reject already received work or service as unsatisfactory without enough justification. As most platforms classify their employees as independent contractors, they do not protect workers against risks like unemployment, illnesses, disabilities, or retirement preparations. Due to little pay received, only a tiny share of platform workers pay for pension and social security. A low pay implies that the workers do not maintain decent living standards, therefore, struggling to keep up with the ever-changing inflationary economy.

Surveillance of workers in the gig economy

Literature by (Athreya 2015) assesses how surveillance workers’ control of the gig economy makes them technology slaves. The study indicates that workers are manipulated through the algorithms that work through information asymmetries and predatory business models. The digital platforms often extract data from workers without their consent, and these are used as inputs to optimise the platform’s operations. In the case of Uber, for example, the extraction of these data is seen as a way of bettering the mobility of cars for safety while the gig workers are forced to sign broad agreements when they seek access to the extracted data (Athreya, 2015, 85). Uber has been accused of monitoring its driver’s phones to determine whether they work for competitors such as Lyft. The information gathered is used as a manipulation tool to either offer better rates or threaten the drivers from being denied access to opportunities within the platform. The surveillance of workers is not often consensual and thus making drivers feel that their privacy rights are infringed through constant monitoring.

As Warin and McCann (2018, 7) explained, surveillance in the workplace defines the ability of the business management to track, monitor and record employee personal characteristics, performance, and behaviour in real-time. Improvement in technology has led to increased scope of surveillance in the workplace. Wearables, as well as self-tracking devices, have been introduced to surveillance, offering a new frontier. One category of this wearable technology focuses on more traditional surveillance via recording or measuring the location of platform workers. For example, Uber uses GPS trackers to determine location. It provides an appropriation of employees’ possessions for surveillance use. For platform work, the mobile app black box nature and inner working are highly hidden from workers, meaning that the extent and scale of platform work surveillance are unknown. Surveillance affects platform workers in that it creates a Panopticon-like scenario. Although the platform workers know they are most likely not being directly monitored, the slight chance they are being watched at any given time brings out a psychological response similar to permanent surveillance. It pressures platform workers and brings them more stress as they strive to prevent the surveillance of any negativity in their work.

According to Bajwa, Gastaldo, Ruggiero, and Knorr (2018, 3), gig work has different effects on its platform workers’ health. Gig work has platform-based vulnerabilities with regards to platform surveillance, which is more insidious. Platforms monitor workers using various applications, helping them know when platform employees get logged in, their locations, with others even eavesdropping on their interactions with customers. Even though these platform workers live under digital surveillance, they cannot access any platform-generated data. Their lack of accessibility to acquired data allows the platform to exercise power over platform workers asymmetrically. Also, surveillance makes platform workers feel pressured to undertake emotional labour to satisfy their customers, such as tolerating inappropriate actions and behaviours. It happens because retaliating might cost their jobs as they are watched, leading to stress and mental exhaustion.

Devaluation of traditional economic players by the gig economy

Workers in traditional employment often worked on wages and consisted mainly of older persons likely with families and enjoy social security benefits. However, the gig economy has different demographic features: young, single, self-employed individuals with small earnings and several jobs. The gig economy is considered a fissured workplace. In this case, the workers in this economy do not directly interact with their colleagues, creating room for inequality. Unlike employed workers, they are geographically dispersed, reducing their ability to come together and demand better working conditions (Bregiannis et al., 2017, 9).

Entrance to the gig economy has few barriers, exposing many to the dangers of online enslavement. A study by (Maier 2020) indicates that in the example of the Uber business, the barrier of entry is one’s ability to own a car. However, some people do not have access to this and get themselves into financial obligations, for example, renting a car, which may result in a buildup of debt. Uber drivers often suffer from debt burdens or debt traps when the minimum wages cannot meet the rent payment and simultaneously profit. The drivers work relentlessly with the fear of their cars being repossessed and therefore being rendered unemployed. New drivers in the first years of operation have the energy to work extra hours for the extra pay, but over time, this takes a toll on their social and physical life’s, and they can no longer keep up (Maier, G.E., 2020, 58). Uber does not have established employment relationships with its employees to help finance the vehicles, which shifts the entire burden to the drivers. Therefore, a dependency on the platform is developed, and the driver has to take jobs six to seven days per week, exploiting the workers.

Petropoulos and Bruegel (2016, 1) explained that Uber is among the fastest-growing platforms, but taxi drivers’ rapid growth has caused huge demonstrations. The Uber services engage in unfair competition with taxis. Uber services link drivers providing rides and passengers in need of rides online. Many people prefer Uber to regular taxis because Uber sets ride prices. Each transaction gets carried via the Online platform, allowing people to travel without carrying cash. The Uber platform is made user-friendly, with its rates lower than the rates by regular taxis. Thus many people prefer working in the platform economy to the traditional taxi business. The taxi drivers are required to obtain licenses on areas of operations, yet Ubers have the advantage of operating at any location at any time.

Gender issues in the gig economy

In the gig economy, the issues of gender inequality still arise. Literature (Kasliwal 2020) indicates that platforms in these economies depend on structured algorithms for a job assignment. However, the algorithms may reinforce bias as coders curate them, and their modification involves imitating the user’s actions. At the same time, the platform allows the combination of paid and unpaid work and this often places a burden on women who take the responsibility of household works. Few women are seen enrolling in the gig economy jobs as the job security and employment status in this sector is low. The study indicates that on analysing Uber workers in the US, there existed a gender gap in the performance of similar roles (Kasliwal, 2020 3). Women in the gig market also hold back on raising disputes due to the lengthy processes and a drop in their job ratings (Kasliwal, 2020 5). The avoidance of this process indicates that women are silenced, and therefore, their rights are not heard.

Oyer (2020) adds that the gig economy possesses monopsony power which contributes to the exploitation of gig workers. In this case, a single player has greater control over the market, thus imposing its control on the market demand and supply, which pushes away the small players. As much as the independent workers in the gig economy can earn more per hour, their compensation may not be consistent throughout the day. They earn less than those in traditional employment monthly and annual (Oyer, 2020, 4). The ability to control demand and supply in the market is a powerful weapon that makes the platform owners price-setters, and the end-users can do little to change these circumstances.

According to Hunt and Samman’s (2019, 11) paper on “gender and the gig economy,” figures regarding gender composition within the gig workforce differ widely. In addition, gender breakdown varies significantly by platform sector. For example, in the US, the number of men working in platforms is more significant than that of women, which accounts for their disproportionate activities in the transport sector. In contrast, women mostly prefer participating in non-transport labour and asset-based platform activities. For example, men get readily accepted to work in Uber compared to women. Also, when it comes to paying for work done, women earn little money through gig works compared to men. The study showed that seventy-five per cent of female platform workers received less than 11 500 sterling pounds per annum in the UK compared with sixty-one per cent of all employees in 2017. In 2018, forty-nine per cent of female platform workers received less than 250 sterling pounds compared with thirty-five per cent of men (Hunt and Samman, 2019, 12). Similarly, in 2016, female gig workers earned sixteen per cent in the US while men gig workers earned twenty-three per cent.

The study also showed how earning gaps exist among platform workers carrying out similar work regarding their gender (13). For example, a study conducted in the US on over one million drivers on the Uber platform showed a seven per cent earning difference between male and female drivers. The difference in earning was linked to gender differentials in driving speed (men have a higher propensity of driving faster, without regards to associated risks like getting a speeding ticket and collision), experience length of using the platform, and preferences over when and where to work. Gender discrimination in platform work leads to women earning less and working in only specific sectors.

An article was written by Fletcher (2021, 1) on “gender inequality in the gig economy” showed that thirty-one per cent (approximately 350,000 people) of the UK gig workers in 2017 were women, a number that was expected to increase in a few years. The article discussed the existence of gendered occupational segregation in the platform economy. Fletcher explained that certain jobs are linked to females while others are linked to males due to customer discrimination. In most cases, customers are less likely to choose female gig workers for stereotypically masculinised jobs or roles, and some time and hours needed for gig works make it hard for women to undertake. For instance, Uber rates pay well during the weekends and at night, when most women have childcare and family duties and have concerns regarding personal safety. Most platforms do not address these challenges, making it hard for females to fit in well and earn good pay from gig work.

In addition, the article identified flexibility issues based on gender. Most people, especially women, prefer the gig economy due to flexibility, allowing them to work at their appropriate hours (Fletcher, 2021, 1). However, the classification of gig workers as self-employed leaves them minimal benefits due to employee rights like holiday pay. These reduced benefits are felt more by women who might need time to take care of their children. These self-employed employees in the platform economy also miss other essential benefits like statutory maternity pay, affecting women more. This insecurity caused by increased flexibility and freedom shows the challenges of the platform economy and gets experienced more by women than men.

Similarly, Barzilay and Ben-David (2017, 400) explained that although the platform economy has increased job accessibility to women due to its flexibility, they still face discrimination in the general workforce. Discrimination against females in platform work is highly experienced when anonymity or gender-blindness is perceived in online platforms, which is not experienced in most cases. Still, it is a possible case when the platform does not perceive anonymity. Given the gender differences in opportunities, pay, and promotion, it is evident that the workplace has already exerted. Same as Fletcher, Barzilay and Ben-David (2017, 402) explained that in platforms like Uber, drivers get treated as contractors instead of workers, making it easy to avoid worker protection like family leave, overtime, unemployment insurance, and minimum wage. Also, women face harassment when working for certain platforms like Uber, discouraging them from working during hours when the pay is usually high. Because females still undertake the familial caregiving tasks, they get discriminated against in many platforms works. Most lucrative tasks get constructed for employees free from familial caregiving responsibilities.

Disruption nature of the digital platform

Smart technologies are on the influx, and this affected the operation of contemporary media. Digitalisation has increased media production, moving towards multi-skilled, multitasked and multimedia jobs. Digital systems are examples of disruptive technologies that have changed engagement systems for managers of media platforms. Disruptive technologies emerge from startup firms that utilise upstream research and development to exploit uncertainties and complex processes that multiple actors define. Such technologies evolve to disruptive business models when the firms use the technologies to exploit market opportunities. Digital disruption explains the effects of substantial changes to the perceived basic expectations and behaviours within given cultures, processes and markets resulting from digital channels and capabilities. The disruptive nature of digital platforms can be attributed to the development of new internet business models that pose significant threats to the developed industry structures. The impacts of digital platforms disruption appear severe as the newly established business models and modern technologies negatively affect the value proposition entitled to existing services and goods (Skog, Wimelius and Sandberg, 2018, p.431). The shift to digital platforms appears disruptive due to various factors. For instance, with advancements in technologies majority of societies are working towards digitalising various business aspects. Individuals are thus working constantly towards conforming the existing world into an information world that reflects our own.

The perceived changes in digital technologies thus significantly alter the businesses’ operating dimensions as the digital platforms are not entitled to rules applied within business settings. In the newly developed digital world, services and products offered lack physical substance while eliminating distribution costs. For instance, in our modernised world, single prototypes can produce infinite copies at no perceived costs. Due to the products and services being substantially different, the environmental settings turn out to be unstable. As the digitalisation layer meets the physical world, the resulting products are up for grabs. The aftermath of the interaction is the development of new commodities that replace the existing ones, resulting in them becoming obsolete (Skog, Wimelius and Sandberg, 2018, p.432). In incidences where commodities turn completely digitalised, the development and distribution of such commodities fall to nearly zero. The latter results in most individuals producing such products and distributing them conveniently and cheaply, resulting in them flooding the market, but their perceived prices decline sharply. The disruptive technologies often result in content fragmentation among different media platforms. Content fragmentation presents a challenge to organisations as they must keep the audience interested in their platform.

The disruptive nature of the perceived digital platforms affects employment relationships within the Uber business. A significant number of individuals worldwide gain their fundamental, and supplementary income from platform enabled work. For instance, the digital disruption has resulted in an increased number of rides completed with Ubers significantly. The increment has been accomplished by information technology advancement that has drastically reduced transaction costs, thus increasing the competitiveness of market-based approaches to work organisation compared to the existing traditional hierarchies (Skog, Wimelius and Sandberg, 2018, p.432). For instance, disruption in digital platforms has helped create more Uber business opportunities and income sources for most individuals who would be eliminated within the labour market. The working conditions and work sustainability in Uber business are, however, entitled to controversies.

For instance, despite Uber services offering substantial income for most drivers, many of them struggle a lot to earn minimum income from the business. The latter can be attributed to the digital platforms’ disruption incorporating social and economic implications as they challenge the established business models and the existing structure regarding the employer-employee relationship (Skog, Wimelius and Sandberg, 2018, p.434). The disruption of digital platforms further creates a dilemma as to whether workers entitled to working with the digital platforms should be termed as self-employed or employees, as exemplified by the perceived legal disputes regarding the drivers’ status for perceived drive-hailing procedures. The employee-employer relationship within the Uber business is also affected by the digital platforms’ disruption.

The platforms serve as the fundamental contact points to replace the interactions between Uber drivers and their employers. The platforms thus help in shaping the Uber drivers’ perceptions of their work and significantly affects various aspects of working relationships within the perceived platform-enabled responsibilities. Thanks to digital platforms, the drivers are also entitled to flexibility and freedom in determining the working hours (Skog, Wimelius and Sandberg, 2018, p.437). The platforms enable the Uber drivers to assume a self-employment status, enabling them to accept and decline ride requests and attend to other duties.

Deep fakes on digital platforms

Deep fakes are computer developed videos capable of superimposing images onto others, leading to hyper-realistic videos. Deep flakes result from utilising deep learning software and an images database of individuals whose likeness is utilised. Artificial intelligence, preferably the generative adversarial networks (GANs), plays a crucial role in enhancing the development of deep fakes. GAN technological advancements help in ensuring that deep fakes appear more believable. The perceived challenges associated with deep fakes include the lack of substantial technological solutions to combat the increasing threat. In different scenarios, identification of fake media platforms has been entitled to limited funding, institutional attention and attention than developing it. Two adversarial networks compete, one producing the imagery and the latter spotting the imagery errors until sophisticated artificial images are produced (Kietzmann et al. 2020, p. 136). The operation in the digital platform is subjected to exploits from nefarious actors who use claims of fake news to destroy the reputation of businesses.

The digital platforms are often with algorithms, and this can be manipulated using deep fakes. The data from previous experiences are used to create deep states, and the misinformation created from these can be very dangerous for the digital platforms. Such a feature allows persons without knowledge of computer programing to access the platforms using their mobiles. Different forms of deep fakes exist. First is face swapping or replacement, where one face is switched with that of another person. The second is face reenactment, which involves manipulating certain features that are unique to a certain person. The third is face generation, where a new fake face is entirely generated using Generative Adversarial Networks. Lastly is speech synthesis, which involves the alteration of one’s intonation to generate a completely new voice (Vizoso, Vaz-Álvarez, and López-García, 2020, 263). The advancement of deep fakes is fast, and these digital platforms should find ways to detect them at early stages. When they go unnoticed, they may have serious implications on the firm’s information security.

Claims of misconduct are one of the effects of fake news. Accusations made on the platform subjects the accused to criticism and affected their reputation and performance. Uber, for example, has had a battle with its driver over claims of misconduct. Uber riders may at times take advantage of the platform to get free rides or out of malice and leave bad reviews on Uber drivers. The drivers then get suspended, and others lose their jobs from such incidences. The lack of digital platform structures that can verify the allegations often results in the wrongful dismissal of platform employees. Deep fakes digital media can be used to back up complaints on co-workers or bosses. Such news raises cyber security issues within organisations and people. The technological proliferation associated with deep fakes can adversely affect the relationship between Uber drivers and their employers, undermining their trust.

Uber drivers may constantly utilise deep fake videos to convince their bosses as they support their perceived viewpoints. The other ways Uber drivers can utilise deep fake to gain favour from their employers include but are not limited to; false claims regarding malfeasance that may damage commodities or the Uber company’s reputation (Kietzmann et al. 2020, p. 142). Videos may as well be produced incorporating false news on the Uber business owners, thus resulting in drivers taking advantage of the situation and thus benefiting themselves. The situation’s aftermath might be adverse as the relationship and trust previously entitled to the Uber drivers may go south (Kietzmann et al. 2020, p. 140). The deep faking scenario may also result in the drivers subverting the Ubers’ onboarding processes and creating fraudulent accounts to generate profits for personal improvement. The act may go forth tarnishing Uber’s company name and thus losing clients whose trust was embedded into such companies. Once losses start being incurred, employers can make alternatives that may include sacking the purported drivers involved. The risks of impersonations also make up the headlines of deep fakes. Data breaches are associated with stolen identifies, and these identifications are used for the wrong reasons. Uber has had cases associated with driver’s impersonation. People use the weaknesses of the platform and identify themselves as Uber drivers and therefore take advantage of the Uber riders. Cases of rape cases by alleged Uber drivers are associated with the impersonation persons depicting themselves as Uber drivers.

Additionally, Uber drivers may practice identity theft by utilising videos to convince individuals to change important personal information. The latter may have detrimental outcomes entitling the Uber company involved to theft allegations. The perceived reputation of the business may thus be ruined, and the involved drivers sacked due to disagreements with the employers (Kietzmann et al. 2020, p. 137). Deep fakes may also lead to ordering unnecessary and unwanted materials and fraudulent authorisation of funds transfer. The activities are, in most cases, mean towards benefiting the Uber drivers at personal levels. However, things may go south once the employers get well informed and terminate work contracts between them and Uber drivers.

Additionally, deep fakes may be used for blackmailing purposes on a given threat towards releasing a damaging clip. Such believable rumours may impose real impacts on businesses and individuals in personal settings. The associated business reputation is thus ruined as recovering from such incidences of false narratives may take considerable periods in incidences where the casual observer cannot identify such scenarios as a hoax (Kietzmann et al. 2020, p. 138). Deep fakes generated and shared over numerous social media platforms and accessed by numerous individuals are more likely to generate unnecessary reputational damages and potential loss of revenues in the long run.

The employment relationship between the parties involved may thus be ruined in the long run resulting in loss of jobs to the involved parties. However, individuals within the Uber business may minimise the adverse effects caused by these deep fake videos by educating and creating awareness for their drivers (Kietzmann et al. 2020, p. 136). The awareness may include explanations on how such scenarios happen and vulnerable parties so that analysis of potential scenarios is carried out on a timely basis.

However, quick actions should be taken when such incidences occur within the Uber business to get the fraudulent audios or videos offline as soon as possible to protect the perceived brand. The practice may involve incorporating corporate communications and other Uber business bodies to effectively counter the perceived narratives being presented by the circulating deep fakes (Kietzmann et al. 2020, p. 136). Uber businesses should collaborate with organisations with diverse expertise in cybersecurity and artificial intelligence technologies to counter the detrimental impacts of deep fakes. Additionally, the legal bodies should create substantial legal measures and regulations around artificial intelligence in combating the detrimental impacts resulting from such practices. Most big tech companies make huge investments in research on how to manoeuvre the threats of deep fakes.

At the same time, fake news can be posted concerning the company to draw away confidence in the platform. When trash talk is allegedly made against the company, the company’s reputation is always at stake, whether the allegations are true or false. A bad picture has been painted, which affects how the platform users perceive the organisation.

From the analysis of various literature on the topic, this research identifies a knowledge gap that has to be filled. The gig economy has affected employment relationships, and this topic is not well covered, and our research will analyse this concept. The employment relationship is subject to workers exploitations, gender inequalities, surveillance monitoring, economic devaluation and the emergence of data breach threats on the platform. The literature obtained is aimed at providing policymakers with data that will guide on policies that could be effected to promote the employment relationship.

Research Methodology

The research will adopt a qualitative approach to data collection and analysis. Data will be collected from digital media, podcasts, online reviews, media reviews and secondary data; then, they are rigorously analysed to assess the digital economy effect on the employment relationship. A descriptive research method will be adopted to describe, explain and validate the research findings. Textual analysis is adopted to decode already existing data and research on the topic. Inferences will be drawn from the analysed text a pattern will be established to help conclude the topic. The conclusions reached will fill the literature gap on the topic.


The research findings consist of analysing the experiences of employees working in the gig economy documented in articles, news reviews, and other secondary sources to express how the gig economy has affected their employment relationships. The findings are a collection of experiences from Uber drivers and online media reports on the exploitive nature of Uber on its employees and the effect on the employment relationship.

The surveillance of drivers

The Uber platform is designed to record activities performed by the Uber driver during its rides. The platform records where the driver went, waiting time, how much they make, and the review given by passengers about the ride. It also records all accepted and declined rides, the driver’s techniques on winding in traffic and mapping the trips start and end. The Uber platform is designed so that a 15-second request is sent to a driver who either accepts or rejects passenger pick up. However, the request does not indicate the exact customer’s location and the fare charged, imposing a risk of the drivers accepting unprofitable rides. Uber states that it withholds such information to prevent destination based discrimination. Drivers, however, may cancel any trip deemed unprofitable but Uber uses this against them as they risk suspension or permanent deactivation from the system. Such policies indicate that Uber drivers lack the rights of independent contractors who can freely refuse. Therefore, the Uber drivers are subjected to making a choice, and successive decline on unprofitable trips is considered wrong, which derails the entire concept of operating as independent contractors.

Uber technology utilises telematics which assesses a driver’s performance using the phones accelerometer, GPS and gyroscope. Uber states that the technology is necessary to ensure safe driving by its drivers by monitoring their driving habits. However, drivers see the technology as a privacy concern and belief that the data collected may be used for punishment (Wisniewski, 2019). The drivers believe that their ability to act as independent contractors is compromised when they are being told what they are supposed or not supposed to do. On its end, Uber states that the monitoring system helps in the validation of customer complaints. The platform managers collect the data on the different customers and can profile the drivers as per previous experiences, which is often used to build a case against them.

Uber performs its performance evaluation on an uber driver thorough assessment of customer ratings. Uber drivers, however, believe that constant surveillance lowers their productivity. However, some customers are known to wrongfully use the system to make false accusations about the Uber drivers, thus giving them negative ratings. The consequences of such may be devastating to the drivers. Drivers complain of the company using unconfirmed reports on taking actions against complaints launched on them. Some have lost their jobs due to false accusations and the lack of a mechanism to defend themselves. Some drivers are accused of being drunk at the job, yet timely toxicology and sobriety tests are not conducted to prove such allegations, costing them their livelihoods (Kerr, 2020). Uber drivers have been forced to normalise the platforms surveillance process. The rating system acts as accountability, which incentivises drivers to provide high-quality services and conduct themselves courteously. However, Uber drivers believe that Uber has the responsibility of educating its customers on the importance of good ratings. The passengers cannot understand that issuing enough four-star ratings will ultimately result in a driver’s account deactivation from the platform. Passengers lack the information on the evaluation criteria, and the platform should take responsibility for improving the rating system (Chan, 2019 186). The drivers are subject to customer bias, and others may act irrationally and rate them badly with no justifiable cause. The credibility of the customer reviews as a measuring metric is therefore compromised as it may be used unjustly, putting the drivers at positions of vulnerability. User authentication of customer reviews is therefore questionable, and the clarity of how the drivers are given a chance to appeal such ratings is not clearly defined.

Drivers are often under constant pressure to maintain a high acceptance rate, limiting their ability to work as independent contractors. True independence would mean that Uber does not have control over the driver time. The degree to which they can attend to their activities is as low as that to maintain rates. Blind acceptance of rides often leads to drivers absorbing the risks associated with unknown fares (Eisenbrey and Mishel, 2016). The drivers being subjected to uncertainties in the trips often pushes them to look for ways to beat the system as the full-time adherences takes a toll on them and their vehicles, yet they do not get insurance on their vehicles or even their health subjected to the dangers of the

Uber app surveillance is known to identify fraudulent activities. Cases of Robo-firing have been on the rise relating to Uber. In the Netherlands, the App Drivers & Corirs Union filed a case where four Uber employees state that Robo-firing wrongfully dismissed them. The ride-hailing algorithm indicated that the drivers operated fraudulent activities, and they were not allowed to appeal. However, Uber stated that they were dismissed after manual reviews. However, the Union beliefs that the drivers were dismissed under unfair automated decision making as the driver was told certain activities were detected. One claim was of irregular trips that were linked to fraudulent activities. Another driver was accused of installing software that would manipulate the Drivers App (Keane, 2020). The type of fraudulent activity is not defined in exact terms, and therefore a connection between dismissal and the fraudulent activity is not made.

In London, it is required that any private operator firing a driver report the incident to Transport of London (TfL). The drivers are then given a 14-day allowance where they defend themselves and get to retain their licenses. However, was not compliant in giving full disclosures on their end, claiming that it would compromise their security. The transparency and fairness of the Uber algorithm are in question, and automated decisions greatly impact people’s lives.

Drivers’ exploitation through the platform economy

The platform economy has created massive global connectivity attracting people from diverse locations to interact through a single platform. The outcome of this is the oversupply of labour through the different geographical segments creating a market where digital jobs are fewer than workers willing to perform the tasks. The excess supply often creates unhealthy competition between players as each aims to gain maximum output from engagement within the digital platform. Uber drivers remit a certain percentage from their rides for compensation. In this case, the. Under the gig economy, drivers are considered independent contractors rather than employees, which places them outside the employment law protection. However, drivers argue that Uber’s algorithm denies them the opportunity of acting as independent entrepreneurs as it controls most elements of their work. Research by (Rosenblat and Stark, 2016, 3764) indicates that although Uber states that its drivers are well paid, they claim that they earn lower than minimum wage and are excluded from basic benefits and protection that comes with employment, for example, unemployment insurance and workers compensation. Uber’s business model is said to be incompatible with switching the drivers to employee’s status. The complexity of the business model affects the earning made by Uber drivers. The hours worked by drivers are calculated when an Uber driver picks up a passenger and travels to pick up a passenger after an accepted ride. By applying this approach, 35% of their work time is omitted as the drop point’s return time is not taken into account. Therefore, the model does not account for drivers’ waiting time, yet compensation is made regardless of waiting time in normal employment.

Uber is also known to be negligent of accruing expenses for Uber rides. They only cover incremental expenses that are used up during passenger rides this including gas and cleaning. However, the Uber drivers meet the costs associated with vehicles ownership, including insurance, depreciation, repair and maintenance, registration, and licensing. Uber drivers also complain of the dehumanisation effect of the platform. Drivers are often lonely and isolated as they lack the team to socialise and be part of (Möhlmann and Henfridsson, 2019, 3). Employees build working relationships on different hierarchies in normal working conditions, while such privileges do not exist in the gig economy.

In 2017, Uber admitted to underpaying drivers in tens of millions as they took the bigger cut on the driver’s fares. The collection indicates fraudulent transactions by the company as they home more than their justified amount. Uber’s spokesperson indicated that refunds would be made, and moving forward, the rates will be calculated correctly using a new pricing scheme. Refunds made to New York and Philadelphia drivers were done after lawsuits were filled. Uber’s behaviour of shortchanging drivers is a clear indication of corporate exploitation. Drivers complained that Uber was charging customers more, yet they did not receive any cut from the price increases. Uber also admitted to a plan to decrease driver bonuses in its IPO filling in an attempt to save money and pay its debts. The company’s move towards settling its debts came at the expense of the Uber drivers. A driver shared their experience with the Guardian in 2019. Price surges exited for rides between minute maid park to Houston Astros baseball game from $20 to $90. The rider’s app indicated that a crazy price was being charged for the ride on the way back. However, on the driver’s app, no surge in prices. (Sanaito, 2019). Such incidences depict the unfair remittance and lack of full disclosures from Uber, thus exploiting the drivers for their benefits. The lack of transparency depicts the organisation as having things to hide.

Uber’s exploitative nature was greatly highlighted by the lawsuits launched by drivers on Uber’s claims hiding their earnings. The FTC conducted investigations that indicated that prospective drivers were misled by the company’s exaggerated earnings and the Vehicle Solution Program. Uber was directed to pay $20 million in refunds to the affected drivers. Uber used the platform to indicate that an Uber drive can earn higher than $90,000 in New York, while in San Francisco, earnings would amount to approximately $74 000. However, investigations indicated that earnings would amount to $61,000 and $53 000 in New York and San Francisco. Advertisements made on Craigslist gave false indicates of hourly pay by Uber, and drivers did not make as much as they expected. Uber’s financing option also indicated that a driver’s credit history did not matter. They could obtain car financing facilities for as low as $20 per day (140 weekly) or make lease payments as low as $17 per day ($119 weakly). However, evaluations were done, and the results indicated that weekly payments and lease amounts would exceed $160 and $200, respectively (FTC, 2017). Drivers were also misled that leases came with unlimited mileage while, in reality, the terms and conditions indicated that lease agreements would include mileage limits.

Devaluation of the local economy of taxi drivers

The traditional taxi industry taxi medallions authorised limited automobile owners to offer taxi services in a given area. The adoption of such a policy was aimed at reducing traffic congestion and profit maximisation. However, in 2011, the supply of vehicles increased, leading to the entrance of Uber into the gig economy (Gabel, 2016). No restrictions are made on how many qualified drivers are allowed in the system at certain working hours in the local economy. The degree of freedom creates greater supply than demand, leading to many drivers being at one location than the number of customers who need rides. Uber drivers, therefore, resort to lowering their prices that may not be highly remunerative to them. Drivers are also forced to queue and therefore take turns to pick up passengers. The prices imposed by Uber act as price floors and ceilings, leaving drivers with less power to manipulate prices (Reich, 2020, 5). When earnings by uber drivers are benchmarked against the average wage scale, it is evident that they have a lower earning value. Most of the Uber drivers come from disadvantaged groups in society, putting them in low-income jobs. The Taxi drivers operated under a highly regulated environment where their roles involved expensive commercial jobs, and their cars had to undergo comprehensive safety inspections (Gabel, D., 2016, 529).

Research by Adams and Coyle 2021 indicates that taxi markets previously closed out the new competition to safeguard regulation. The results were always an increase in prices, and customers had longer waiting times if prices were capped. However, the gig economy operates in a highly regulated market, and the pro-competitive principle acts as an entry barrier. The introduction of Uber kicked out taxi drivers, and the gig economy has resulted in weaker safety standards. Taxi drivers experienced income shocks as many went to work for the incumbent employer (Adams and Coyle, 2021, 6).

Uber drivers are subject to self-employment tax which covers their social security and Medicare taxes. Each driver pays 15.3% self-employment tax, yet W-2 workers paid half the amount as their employers cover the other half (Mishel, 2018). Uber deducts commission and fees, and these should therefore be termed as fares generated rather than earnings. A third of the customer payments are made to Uber as commission and trip booking fees. When this deduction is made, the average pay received by the Uber driver per hour is at $11.77, which is comparably lower than the average wage received by workers in the private sector, which amounts to $32.06 while service workers in the lowest-paying occupation make up to $14.99 hourly Mishel, 2018).

Gender discrimination

Sexual harassment and gender discrimination are also part of the issues affecting employment relationships in the gig economy. Uber has faced a lot of scrutiny on sexual harassment and gender discrimination cases against its female employees.

Assessment of new reports indicates that in February 2014, the company CEO Kalanick stated that his company enabled him to score women and called it the “Boob-er”. Another incident of gender discrimination occurred in France, where the regional managers took a new approach to reach new markets. Through its Avions de chase campaign, which translates for “Lucky you, The world most beautiful are awaiting in the app”. However, the ad was immediately deleted when a reporter made enquiries on the same but screenshots had been saved by the media. The event’s occurrence was followed by several lawsuits against the company on issues of gender discrimination. The leaders are the company’s face, and when they make such remarks, they may be mistaken as company opinion, therefore, tainting the overall picture about the organisation.

A rape case on a female passenger was reported in 2014, where an Uber driver in New Delhi was accused of the rape incident. The company previously bragged of a fingerprint less background check on its drivers, and a check analysis indicated that the driver had a previous record of a rape case accusation. Following the incident, Uber was banned from conducting operations in the region for several months.

Following investigations by the Equal Employment Opportunity Commission, Uber made a $4.4 million compensation to settle victims of sexual harassment and issues on gender discrimination (Rosenblat and Stark, 2016). Uber took responsibility and voluntarily paid the settlement and additionally setting up a compensation fund.

The study finding also indicates that in large gig economy platforms such as Uber, gender differences exists. Results show that men earn 7% more than females on the Uber platform, attributed to several reasons. However, the earning rates are defined by the drivers’ experience in the industry, their driving speed, and the locations where they pick and drop passengers. The study adds that in traditional employment and gig economies, men tend to earn more as they take up more working hours and are more consistent on the job, therefore, sustaining better payouts. When on the platform economy are also seen to demand less pay for similar tasks carried out by men at a higher rate (Oyer, 2020, 8 ). Data breach concerns are often on the rise on digital platforms. The platform holders are therefore obligated to raise awareness of data breaches as soon as they arise. The access of private data by unknown individuals is likely to lease to fraudulent use of these credentials and therefore expose the affected persons to litigation issues. In 2014, Uber had a data breach where Uber drivers’ names and license numbers were leaked. However, Uber did not disclose this breach until five months later. A similar event occurred in 2016 within the organisation, and personal details, including emails, phone numbers and driving licenses, were breached. The personal data collected was used to gain unauthorised access to different platforms. The lack of immediate disclosure of such matters erodes the employment relationship as the organisation is depicted to be untrustworthy. When an organisation is aware of the vulnerability of the information breached on their hands, immediate disclosure is prudent to prevent further losses. However, Uber strained its relationship with its employees as they decided to act without transparency, putting the personal details of its users at the hands of hackers. The firm is therefore liable to lawsuits indicating its inability to control data breaches within its platform. When data security is compromised, the company’s may suffer from distrust and may even lose their customers as people are less willing to risk their data on platforms whose security is not guaranteed.


The study findings indicate that digital platforms are associated with negative effects on the employment relationship. The platform performance and continued improvement depend on the accumulation of data collected from the platform user. As a result of this, most platforms monitoring the use of the platform by different stakeholders. Even though platforms managers believe that employee monitoring is often necessary to keep employees in check, employees feel differently about the constant surveillance. They consider constant surveillance an intrusive form of monitoring that is likely to invade their privacy, resulting in mistrust between employees and management. Trust issues affect employee’s commitment, job satisfaction, turnover rates and job performance. Granting employees a certain degree of control allows them to direct, regulate, and decide on what’s best for the organisation, which is a sign of trust (Bernstrøm, and Svare, 2017, 32). A positive employment relationship exists between employees and management in situations where employees operate with autonomy and interference by management on their jobs is minimal. In the case of Uber drivers, monitoring their movements is considered a safety requirement, but the driver feels mistrusted. The drivers opt to stay offline and may operate with other competitors to beat the system. As a result, a driver shows up on Uber for a week, then shifts to Lyft or any other platform for another week and at times operate offline to reduce surveillance from the Uber algorithm and increase their chances of better earnings.

Electronic monitoring measures productivity levels in the form of the highest outcomes in the gig economy and instils organisational policies. The output is often measured in terms of working hours, which puts employees under stress as the longer they are offline, the lower the pay (Moussa, 2015, 5). In the case of Uber, employees earn an average of 9 dollars per hour and the rate changes considerably with peak hours, traffic issues and distance covered. The monitoring of employees working hours by Uber is believed to be a limitation to their ability to work as independent contractors. Time surveillance affects one’s productivity, which means Uber drivers do not fully control their workflow. The Uber drivers often file suits against Uber as they feel that the management should treat them as employees rather than contract workers to be fully productive.

The employment relationship is supposed to be two-way where both parties, the employer and employee, have obligations to fulfil. In this regard, the employer expects loyalty, productivity and may even have unvoiced expectations. On the other hand, employees expect fair compensation for the work done, employment benefits and job security. However, constant surveillance is likely to breach this relationship as employees perceive it as a breach of expectations, attracting employees’ resistance (McParland, and Connolly, 2019, 550). The use of telematics by Uber to determine location, acceleration and braking by Uber drivers is often taken as a breach of expectations as drivers believe they know their jobs and need not constant monitoring. The effect of constant surveillance is a negative attitude by the employees and increased stress levels. Driver believes that the data collected is intended to leverage when drivers call out the employers on legal matters. The use of surveillance technology is, therefore, a limitation to optimum productivity in the gig economy. The knowledge of being watched creates uneasiness, and people are always cautious and take less risk, reducing their work productivity.

The gig economy is associated with the rise in innovation and job creation. However, this has led to misallocation of resources resulting in distributive injustice within the organisation. Within the organisation, fairness does not exist between the employer and employee. The gig economy pays the employer more than the employee. Employees enjoy zero benefits and workplace protections. The employer massively cuts its costs, and the benefits are not shared with the employees. The gig employee invests their capital and meets depreciation costs, yet remitting profits to the employer. Society also suffers from having persons working around without health insurance. It is, therefore, justifiable to claim that the gig employers do very little to meet their corporate social responsibilities Rashid, 2016, 7). Uber’s is one of Silicon Valleys high valued companies and has immersed a lot of wealth over the years. However, the company insists if maintaining its drivers as independent contractors over the years and collects so much from them. The drivers operate even lower than the daily minimum wage, and therefore, the company benefits from independent contractors by misclassification. The exploitation of workers is in unjust value extraction through the voluntary transaction, otherwise legal—drivers who sign up voluntarily due to the need to earn a living desperately. Uber takes advantage of the driver job urgency need and irrationally making profits for themselves (Rashid, 2016, 8). Regardless of the flexibility that the job offers, Uber does not compensate the drivers enough to be comparable to the traditional job.

In 2020, the lockdowns led to a severe decline in travelling. Uber drivers were forced to look for alternative ways to earn a living. Following the previous exploitive nature of the job, many drivers moved on and are not looking on going back. Uber and Lyft offer cash incentives to the drivers, but this has not returned to full speed. The Uber prices are seen to soar, and customers now experience longer waiting times. Some drivers have transitioned from the gig economy, but others have switched to food and grocery deliveries. The transition to other forms of delivery puts less wear and tear on their cars, and with rising gas prices, they are more in control of what to charge customers as they work independently. Drivers state that the company’s early life payouts were good and sustainable, but this has been washed down over time. Some drivers are still enjoying the unemployment benefits, but they will be coerced back to the Uber business once these are over (Bursztynsky 2021). The slowdown has forced Uber to reconsider its strategies, and it is looking at sponsoring education and building programs for its riders.

The gig economy significantly affects the taxi business. Taxi lobbies in many cities are pressing the local government to block Uber operations. They argue that Uber drivers have unfair advantages as they are not exposed to restrictions and licensing. Cities are working on legalisation paths where mobile riding apps operations will be regulated (Daniels Fund Ethics Initiative, 2015, 6).

Gender issues in the gig economy are often overlooked as the major focus is on the misclassification of workers. However, women in the gig economy thrive on jobs such as writing and transcription while men take up more masculine tasks. Taking a look at Uber, their paying rates are higher during the weekends and at night. Women are disadvantaged since they take up most domestic roles, which increase at night and during weekends. Women are also vulnerable to personal security issues in this market, putting them at a disadvantage over their male counterparts. The gig economy is purported to be flexible, yet the realities of the job are not material. The low wage and demanding work schedules create unpredictable work patterns. However, the constant changes do not work well will women who arranged for child care within such shifts. Their earnings may be compromised, which ultimately cause financial distress to the women operating in the sector (Fletcher, 2021). Women in the sector do not also enjoy benefits such as maternity leaves. Their job security is therefore compromised, exposing the harsh realities of working in the gig economy. The absence of sick days, holiday pay often comes with traditional employment, and thus the exposure of the female drivers to this compromises their job securities (Fletcher, 2021).

The research findings show that the employees operating in the digital platform are often misclassified. The fixation on the fact that they are independent workers rather than employees creates unfairness in the labour markets. The lack of protection from the Fair Labor standards indicates the need for better protection of platform employees. The lack of protection subjects them to low earnings that may go below the minimum wage requirements. Benefits such as social security, insurance, maternity leaves and paid to seek leaves are not enjoyed by the digital worker’s (Artecona and Chau, 2017, 11). The relationship between management and employees is therefore not personalised, and this put employees in vulnerable positions, and they may not enjoy even the basic rights of an ordinary employee.

The research finding indicates that the digital workers treated as independent employees pay to cover their entire social security payments, unlike employees whose employers pay half the contribution. The earnings made by the platform corporates are high enough, and these should be transferable to employees to better their work attitude and the feeling that their employer cares for their wellbeing.

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The digital platform allows the interaction of different stakeholders, and for the attainment of platform efficiency, the needs of each stakeholder must be met. The inclusion of the multifaceted platform in labour regulation is the only way to achieve an optimal employment relationship within the platform. The issues on labour relations have to be balanced with platform owners being willing to compromise and consider and develop a system that manages its diverse workers. The different stakeholders are responsible for taking charge and placing the digital platforms responsible for their businesses to ensure positive integration between public and private services in the community. The digital platform managers should also recognise that the emergence of digital media threats such as deep fakes and digital disruptions are likely to compromise employment relationships. Greater security and authentication measures have to be put in place to control the degree to which anonymity within the platform will lead to quality and delivery compromises. The state and municipalities must also regulate the monopoly power of the digital platform. The degree of control these platforms have over the end-users is higher, and if not well managed, it leads to end-user exploitation. As such, provisions within the state should be made to ensure the firms correctly disclose their earning and workers are granted collective bargaining rights so that the platform benefits become transferable to them.


The employment relationship in the digital platforms can need to be solved to improve these platforms’ efficiency. This is achievable through the labour regulation of the digital platform. Issues such as permanent deactivation of one’s account could be addressed by introducing a system in which a deactivation does not affect one’s rating until fair judgment is carried out. As such, employee’s grievances on unfair retrenchment or deactivation will be addressed. Another important measure that could be adopted is the extension of collective agreements that provide a wider employee category to include platform workers. The platforms could use their monitoring techniques to assess who should be termed as self-employed and part-time employees. Workers in the digital platform should be given collaborative rights, which will allow the platform employees to voice their grievances. High tech data security measures should be adopted to ensure that the private data of the platform users are safe from unauthorised access.


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