Responsibility for opinions expressed in signed articles rests solely with their authors, and publication does not constitute an endorsement by the ILO.
This article is also available in French, in Revue internationale du Travail 164 (4), and Spanish, in Revista Internacional del Trabajo 144 (4).
1. Introduction
For more than a decade, digital labour platforms have operated outside the conventions of legal and normative labour market institutions (Schor 2021; Wood et al. 2019). Even though most workers experience unmistakable subordination and dependence vis-à-vis their hiring platform(s), they are nonetheless treated as independent contractors by default (Berg et al. 2018; Dubal 2017; Prassl 2018). The prolific literature on this subject in recent years has identified an apparent contradiction between, on the one hand, platforms’ control over the labour process and, on the other hand, workers’ lack of access to meaningful protection mechanisms and their piecemeal earning options (Vallas and Schor 2020).
Particular attention has been paid to the socio-economic consequences of this discrepancy. Debates about platform workers’ misclassification and how to address it are no longer confined to trade unions and academic and journalistic circles but have also been taken up by policymakers. Akin to Polanyi’s (1944) “double movement”, the attempt to correct the “evils” derived from the relatively unconstrained action of platforms is gaining unprecedented momentum, with policymakers across several jurisdictions taking action – or vowing to do so soon – to re-embed these labour market actors in the framework of conventional employment relations.
Among the regulatory initiatives already implemented, the Spanish law – commonly known as Ley Rider – stands out for its scope and ambition. In May 2021, the Spanish Parliament passed an amendment to the labour code that (i) established a universal presumption of employment for couriers working through digital labour platforms, and (ii) granted unions unconstrained access to the algorithms that underly the operations of the platform companies employing the workers they represent.1 As part of a broader effort to improve workers’ situation, in subsequent months, the Spanish Parliament passed several other measures that platform couriers – now classified as employees – could benefit from. These included increases in the minimum wage, restrictions on the use of temporary contracts and protections against exposure to extreme weather. Taken together, these initiatives represent a major regulatory shift. For platform couriers, Spain became a true open-air laboratory for future attempts to regulate platform work. This article examines the extent to which the Spanish Government’s initiative to bring platform couriers under the framework of conventional employment relations improved their working and living conditions, and explores the reasons for any shortcomings.
Drawing on 36 semi-structured interviews and non-participant field observation, the findings presented in this article shed light on the ambivalent impact of the Spanish Government’s efforts. As regards the formal side of the employment relationship, platform couriers are now covered by conventional labour regulations – albeit far more successfully in terms of individual than collective rights. However, algorithmic management negatively affects the implicit side of the employment relationship. Work intensification, depersonalized interactions between workers and managers, and substantial constraints on workers’ autonomy – stemming from surveillance and micromanagement – result in occupational health risks and diminished trust in management. The new context thus constitutes an imperfect form of institutional embeddedness or, in Polanyian terms, an incomplete double movement.
The contribution of these findings is threefold. Empirically, they provide new insights into how recently implemented employment regulations shape couriers’ experiences of work in Spain. Theoretically, they advance ongoing debates about the future of the employment relationship in an age of computational algorithms and artificial intelligence, and how these technologies may hinder efforts to correct the pernicious consequences arising from deregulated legal spaces, such as the platform economy. Normatively, the findings offer guidance for stakeholders seeking to design legal mechanisms that protect workers’ rights more effectively – an especially timely consideration as regulators across multiple jurisdictions engage in efforts to regulate platform work.
The remainder of this article is organized as follows. Section 2 defines the employment relationship in its explicit and implicit dimensions and outlines the challenges it faces, both old and new. Section 3 examines the context of initiatives to reclassify platform workers and presents the case of Spain’s pioneering regulation targeting food-delivery platform couriers. Section 4 presents the research design and methods used in this study, while section 5 presents my findings in terms of the explicit and implicit dimensions of the employment relationship. Section 6 discusses the implications of my findings and presents some conclusions.
2. Old and new challenges to the employment relationship
The ILO defines the employment relationship as “the legal link between employers and employees”, which “exists when a person performs work or services under certain conditions in return for remuneration”. Furthermore, “the existence of an employment relationship is […] the key point of reference for determining the nature and extent of employers’ rights and obligations towards their workers.” (ILO 2011 – emphasis added).
This definition echoes the view taken by scholars in law, economics, psychology and sociology: that the employment relationship is composed of two dimensions. One is the explicit contract – the legal link – where tasks, schedules, wages and, where applicable, the duration of employment are established between the employer and the workers, either in writing or orally. The second – an unspoken point of reference – encompasses “the mutual expectations formed between the employee and the employer” (Schein 1978, 112). Owing to its tacit yet critical nature, this dimension of the employment relationship has been termed the “implicit contract” (Hyde 1998; Watson 2003), creating expectations that are seen as decisive in mitigating demotivation, turnover, lack of advancement (Schein 1978), and – ultimately – in legitimizing the employer’s authority (Bolton and Laaser 2013).
Historically, this legitimacy relied on collective bargaining and lifetime employment. The first provided a fundamental vehicle for dialogue and compromise that gave a written and explicit form to otherwise implicit expectations, bridging the two dimensions of the employment relationship and appeasing the tensions between employers and workers inherent in their structurally imbalanced relationship (Watson 2003). The second, although circumscribed mainly to white men in the global North (Neilson and Rossiter 2008), constituted an unspoken promise, converted into aspiration and expectation, central to the Fordist equilibrium (Hyde 1998).
As the vertically integrated firm was gradually replaced by a complex web of direct and indirect – often temporary – employment arrangements (Doellgast, Bidwell and Colvin 2021), these two pillars were significantly eroded, such that the legitimacy of employers’ authority in the post-Fordist age had to be derived from elsewhere (Bray, Budd and Macneil 2020; Doellgast, Bidwell and Colvin 2021). The answer was found in the reformulation of the implicit contract. Over time, mental and physical health protection, access to meaningful information, fair treatment and room for autonomy, creativity and self-expression became part of the new portfolio of workers’ fulfillable expectations (Collins 1986 and 2014; Supiot 2017). However, the post-Fordist equilibrium that led to this somewhat novel form of employment relationship is now itself being challenged by the inception of the business model of digital labour platforms (hereafter, platforms).
Platforms entered the employment relations scene in the aftermath of the 2008 Great Recession (Schor 2021). Before long, names like Uber, Amazon Mechanical Turk and Upwork (to mention but a few) became household names for millions of people worldwide, using them as workers, customers or both (Vallas and Schor 2020).
Leveraging the “diffusion of managerial functions” (Aloisi and Potocka-Sionek 2022) made possible by algorithmic management tools (Lee et al. 2015), platforms have systematically depicted themselves as mere intermediaries in commercial operations (Vieira 2023a; Wood et al. 2019). There is, however, a stark contrast between their narratives and the realities they have created. Most platforms do not act as mere facilitators of commercial transactions – if anything, they are endowed with spectacular social, economic and technological control over workers (Briziarelli 2019; Prassl 2018; Wood et al. 2019).
The platforms’ decision to operate outside the conventions of labour market institutions – both legal and normative (Schor 2021; Wood et al. 2019) – is visibly at odds with the post-Fordist employment relationship equilibrium at both the explicit and implicit contract levels. As regards the explicit contract, the misclassification of workers as independent contractors excludes them from statutory rights, denies them access to welfare mechanisms and effectively prevents collective bargaining (Berg et al. 2018; Dubal 2017; Behrendt, Nguyen and Rani 2019; Prassl 2018). At the implicit contract level, algorithmic management exacerbates information asymmetries, introduces top–down gamification that intensifies work and fosters discrimination, further compounding the negative consequences of worker misclassification (Briziarelli 2019). As a result, platform workers often face earnings and contractual insecurity (Gregory 2021), harsh sanctions (Reid-Musson, MacEachen and Bartel 2020), ultra-pervasive surveillance (Newlands 2022), opaque and unaccountable managerial decision-making (Dubal 2023) and inducements to (self-)exposure to multiple forms of risk and hazardous working conditions (Vieira 2020 and 2023a). These dynamics, as studies have shown, contribute to an atmosphere of suspicion and the erosion of workers’ trust, leading to breaches of the implicit contract (Duggan et al. 2021).
3. Platform workers’ reclassification: A double movement in the making?
3.1. The global landscape
The challenge that platforms pose to the employment relationship and the working conditions they offer workers have not gone unnoticed. Following years of protests, lawsuits, reporting in the media, academic articles, government reports and even artistic interventions, the bad practices of platforms have become too conspicuous to remain unregulated. Albeit in a fragmented and at times contradictory way, initiatives to bring platforms and their workers under the regulatory umbrella of conventional employment relations have been gradually implemented in a number of countries around the world (Bensusán and Santos 2021; Dutta 2023; Potocka-Sionek 2023). Several other initiatives are currently under discussion and are expected to see the light of day soon (e.g. INCP 2023; Larocca 2023; Rainone and Aloisi 2024).
These developments seem to echo Karl Polanyi’s (1944) famous notion of “double movement”, where periods of movement towards a laissez-faire state of affairs – during which markets enjoy some release from societal constraints – are followed by societal counter-movements seeking to resist “the perils inherent in a self-regulating market system” and pursue a new equilibrium that safeguards those most exposed to “the pernicious effects of a market-controlled economy” (Polanyi 1944, 76). Several authors since Polanyi have compared this ever-shifting, iterative balance of forces to that of a pendulum that swings between the dis-embeddedness and re-embeddedness of markets, depicting this movement as quasi-inexorable in the age of neoliberalism (Dale 2012).
Indeed, the ongoing efforts to embed platform workers under conventional employment regulations suggest that a race to regulate has begun. However, it remains to be seen whether the Polanyian proverbial pendulum will ever fully swing back, as it is uncertain whether the ongoing counter-movement will effectively improve workers’ livelihoods and restore not only the explicit contract dimension but also the implicit one. The mixed evidence offered by the sparse literature on salaried platform workers contributes to this uncertainty.
On the one hand, as anticipated by several legal scholars (e.g. Prassl 2018), transitioning to salaried work does not necessarily seem to diminish platforms’ operational efficiency or workers’ access to flexible arrangements (Johnston et al. 2024). Moreover, more effective collective bargaining between platforms and (salaried) workers can enhance access to rights and protections often absent in platform work (Johnston 2020).
On the other hand, the existence of a conventional employment relationship between platforms and their workers does not necessarily guarantee the security and recognition that might be expected. Several studies have documented experiences of precarity and vulnerability that fall short of the standards associated with decent work (Newlands 2022; Niebler et al. 2023; Schreyer 2021; Sun, Chen and Rani 2023). This can be partially attributed to detrimental aspects of the labour market that predate the rise of platforms, such as temporary contracts, involuntary part-time work, low wages and work primarily conducted on the street, which entails exposure to various forms of risk. However, other shortcomings can be directly attributed to the specificities of platforms. As mentioned earlier, platforms’ pervasive use of algorithmic management tools has added a new layer of complexity to these pre-existing challenges. Two lines of reflection are particularly relevant in this context. First, software vendors themselves often frame algorithmic management as a mechanism for treating workers as suspects, thereby inviting heightened surveillance and control by employers, and antagonism in the employment relationship (Williams and Khan 2025). Second, the effectiveness of new provisions for scrutinizing and negotiating algorithmic management tools – particularly in relation to collective bargaining and protecting workers’ rights – has been questioned as potentially insufficient (Molina et al. 2023). If these hypotheses hold true, algorithmic management could constitute yet another obstacle to re-embedding platform workers in the framework of conventional employment relations.
3.2. Spain as open-air regulatory laboratory
Spain stands out among the many countries engaged in regulating platform work. Several of these have created a special, grey employment status for platform workers (e.g. Aloisi 2022; Niebler et al. 2023), controversially tailored to accommodate alleged specificities of platform work. In contrast, in May 2021, following a long battle involving unions, workers, the labour inspectorate and platform companies (Vieira 2023b), the Spanish Parliament passed legislation providing that, from 11 August 2021, all platform couriers – and couriers only – would be classified as standard employees. Additionally, the source code of all algorithms that could potentially impact working conditions was to be made accessible to workers’ representatives, upon request. This double legal provision amending the country’s labour code (Royal Decree Law 9/2021) would come to be known as the Ley Rider (courier law).
Somewhat counter-intuitively, given their well-known precarious working conditions (Vieira 2020 and 2023a), the law’s enactment sparked significant social unrest, with groups of couriers tacitly aligning themselves with most hiring platforms’ vehement opposition to the laws’ presumption of employment provision. Even though their idealized perception of themselves as independent contractors may appear somewhat illusory, at the heart of the couriers’ discontent was the prediction that employment status would bring about low salaries, reduced flexibility and temporary contracts (Vieira 2023b).
As noted above, in light of the literature documenting the working conditions of salaried platform couriers, such concerns were not entirely unfounded. However, the Spanish Government’s regulatory efforts did not end with the Ley Rider. To address long-standing sources of worker insecurity and vulnerability in the country’s labour market, between 2021 and 2023, the Spanish Parliament enacted three measures that, while not specifically targeting platform couriers, could meaningfully impact their working conditions: first, a combined increase of the minimum wage of 13.7 per cent (from €950 to €1,080 per month, paid 14 times per year) (Statista 2023); second, a reform of the labour code that significantly restricted the use of temporary contracts (de la Fuente Lavín and Rey 2022); and third, a prohibition of outdoor work when temperatures reach potentially dangerous levels (37–39°C, depending on the region) unless workers are adequately protected (Olías 2023). In parallel, although not legally binding, in May 2022 the Spanish government published a guide on the use of algorithmic information in the workplace (Spain, Ministry of Labour and Social Economy 2022), aimed at providing all stakeholders with a more comprehensive understanding of the complexities associated with the use of algorithmic tools, and guidelines on how to comply with the newly established legal principle of algorithmic transparency (Lorite 2022).
Importantly, the presumption of employment and the provisions surrounding algorithmic management were aligned ex ante with the direction taken in several other jurisdictions, such as the European Union through the so-called “EU Platform Work Directive” (Rainone and Aloisi 2024).2 As a result, from an international perspective, even if unintentionally, Spain became a critical case of regulatory experimentation, capable of illuminating the expected and unexpected complexities associated with this attempted double movement. In the following sections, I explore this case primarily through the lens of workers’ experiences.
4. Research design and methods
The findings and conclusions presented in this article are based on a qualitative single-case study approach (Yin 2009). The case examined is the platform-based food-delivery sector in Spain. Following the aforementioned reforms to Spain’s labour code, this sector serves as a critical case (Levy 2008) for assessing whether current initiatives can effectively – not just formally – bring platform workers within the full meaning of the employment relationship.
Given its broader theoretical objectives, this article combines positive and reflexive scientific approaches using the extended case method (Burawoy 1998). It goes beyond a standard policy evaluation study, adopting a “theory-driven, politically engaged, macroscopic approach to everyday life” (Eliasoph and Lichterman 1999, 228). The study covers an 18-month research period, from July 2022 to January 2024 (see table A1 in the appendix for details). During this time, I conducted three field visits, totalling 66 hours of ethnographic observation at key gathering points for food-delivery couriers in Barcelona, Madrid and Valencia. These observations were supplemented by 44 conversations held during slower work periods, and by silent participation in seven social media groups.
To support this ethnographic observation, I conducted 36 semi-structured interviews with 21 couriers who were, or had until recently been, working for at least one of two food-delivery platforms that complied with the presumption of employment enshrined in the Ley Rider (Just Eat and Uber Eats), 6 dispatchers working for those same platforms, 7 trade union leaders and 2 officials from Spain’s Labour and Social Security Inspectorate. Twenty-two of these interviews were made up of two rounds, one at the outset of the research period and another close to its conclusion. With a view to preserving the richness of the sample – capturing a broad variation of socio-demographic characteristics (Gupta, Shaheen and Reddy 2019), such as age group, educational background, geographical origin and degree of income dependency on platform work – new couriers were recruited to replace those with whom contact was lost (see table A2 in the appendix for more details).
Given the impossibility of randomly selecting interviewees, I mitigated the potential for “oversampling [the findings] of confirming evidence” (Moravcsik 2014) by using a “modified snowball” (Leech et al. 2013) technique. Couriers participating in this study were recruited through connections established during fieldwork, social media groups and pure snowballing, where one respondent introduced me to another. All participants’ names were pseudonymized. In the first round, interviews were semi-structured, examining aspects of couriers’ working experience following the first year of implementation of the Ley Rider. The second round of interviews were unstructured to capture patterns of continuity and change over the preceding 18 months. On average, interviews lasted 52 minutes.
Leveraging the versatility and comprehensiveness of the extended case method, my sources also included a court ruling, a courier dismissal letter to which I was given access, and the above-mentioned guide (Spain, Ministry of Labour and Social Economy 2022).
Field notes, interview transcripts and other documents underwent thematic analysis (Fereday and Muir-Cochrane 2006). In the initial round of coding, codes were generated through a combination of deduction and induction (Graebner, Martin and Roundy 2012). The subsequent round involved merging these codes into the two overarching categories encompassing the employment relationship: the explicit and implicit contracts. In the case of the implicit contract category, subcodes were not entirely discarded for clarity.
5. Findings
As a result of the above-mentioned transformations in Spain’s labour regulations, delivery platforms operating in the country were legally mandated to adopt the following measures: (i) hire their workers under open-ended contracts; (ii) make their algorithms available to trade unions, if requested; (iii) increase workers’ salaries twice; and (iv) interrupt their operations in the event of extreme weather conditions. The present section explores the implications of these measures for workers’ lives. For clearer readability, aspects related to the explicit contract dimension of the employment relationship (such as tenure, working hours, salaries and collective rights) and those connected with its implicit dimension (related to perceptions of fair treatment, autonomy or trust) are presented in two separate subsections.
5.1. Explicit contract
The statutory rights resulting from workers’ reclassification as platform employees impacted both the individual and collective aspects of the employment relationship. As previously discussed, these rights constitute the explicit contract. They are addressed individually in this subsection.
5.1.1. Impact on individual statutory rights
All Just Eat and Uber Eats couriers interviewed reported holding open-ended contracts that explicitly defined a baseline wage and working hours, typically in the form of rotating shifts.3 Among them, there was a consistent understanding that the employment relationship offered more security than working as an independent contractor under the pre-Ley Rider model.
Nevertheless, the universal open-ended contract binding all couriers did not preclude the adoption of diverse arrangements across and within the two platforms. In 2021, Just Eat began a gradual process of abandoning the use of intermediaries. By the end of 2022 – in the early months of my study – all couriers were directly employed by the company. Throughout the entire research period, Just Eat remained consistent, offering part-time contracts ranging from 12 to 30 hours per week, resulting in a workforce composed of both exclusively dedicated workers and student workers.
Uber Eats differed from Just Eat in two significant ways. First, its transition to a salaried workforce in response to the Ley Rider relied entirely on third-party companies already operating in the parcel-delivery business. These companies, commonly known as “fleets”, handled Uber Eats’ entire operations, typically hiring workers specifically for food-delivery activities.
Second, while at the start of my research period (July 2022) fleets were abundant and employed a large contingent of exclusively dedicated workers – primarily through full-time contracts – this scenario underwent a profound transformation when, by the end of 2022, Uber Eats decided to return partially to the self-employment model.4 As a result, the company terminated its contracts with several fleets and renegotiated its remaining contracts in order to reduce its reliance on salaried work. Consequently, several fleets – primarily smaller ones, according to interviewees – ceased operations in the sector, laying off their workers. Significant adjustments were made among those that remained affiliated with Uber Eats, including workforce reductions, worker relocations and, according to two interviewees, forced reductions in the working hours stipulated in their contracts.
By the end of my research period (January 2024), a significant divergence had emerged in couriers’ perceptions of platforms’ hiring and staffing practices. Just Eat student workers generally perceived the combination of a part-time job with rotating shifts as beneficial, since their workdays were primarily on weekends (to avoid conflicts with classes) and, if required – especially during exam periods – they could negotiate with management to alter shifts or take vacation days to study. In contrast, Just Eat’s exclusively dedicated couriers saw things differently. They perceived the platform’s rotating shift policy as an obstacle to balancing their work as couriers with other jobs in order to supplement their income. This caused resentment among several couriers I talked to throughout the entire research period.
Unlike the consistency observed in the opinions of Just Eat couriers over this period, the perceptions of Uber Eats couriers evolved in tandem with the changes in the company’s operations. Initially, I observed a general acceptance of the requirement to work primarily on weekends and rotating evenings throughout the week, despite the detrimental impact of this arrangement on work–life balance, reported mainly by older workers. However, the reduction in working hours, which inevitably resulted in lower incomes, altered the couriers’ views. Many study participants argued that the benefits of being a salaried employee (fixed salary, paid vacations, access to meaningful social protection mechanisms) were outweighed by the abrupt decline in their incomes as a result of the coercively voluntary change in working time – which even the increases in the minimum wage could not fully compensate. Suddenly, some of the possibilities created by their new status – such as being able to secure a bank loan to purchase a house – became a burden, leading many to wonder whether the independent contractor model might be preferable to salaried work.
5.1.2. Impact on collective statutory rights
Alongside individual aspects, transitioning to salaried employment arrangements marked a departure from the intense atomization often experienced by platform workers (Gregory 2021; Prassl 2018). This transformation was facilitated by access to union representation and collective bargaining. However, here too, relevant nuances and temporal shifts emerge.
In the background of the regulatory changes taking place, in December 2021, Just Eat reached a collective agreement with the two main unions operating at the national level – the Trade Union Confederation of Workers’ Commissions (CCOO) and the General Union of Workers (UGT). By its stipulated termination date (December 2023), the agreement’s relative success in establishing clear standards on aspects such as wages, reimbursement of work-related expenses, responsibility for work tools and schedules emboldened both unions to pursue an industry-wide collective agreement.
This aspiration was partially driven by the challenges to collective bargaining posed by Uber Eats’ reliance on outsourcing companies. By fragmenting its operations across multiple subsidiaries, Uber Eats evaded the responsibility of engaging with trade unions, systematically diverting negotiations to the various fleets. This manoeuvre – though perceived by unions as illegal and illegitimate – was addressed by attempting to achieve collective agreements on a fleet-by-fleet basis. However, by January 2024, not one single collective agreement had been signed.
This outcome can be attributed to the fact that each fleet adhered to the pre-existing sectoral collective agreements of its choosing (the courier services and hospitality agreements being the most common). In this regard, the fleets’ behaviour contradicted some of the advantages associated with the employment relationship and directly impacted workers’ income, for example, by stipulating that couriers were responsible for purchasing and maintaining their work equipment and not applying night or weekend rates.
In parallel to the introduction of standard collective bargaining rights, the newly established right of unions to access companies’ algorithms also encountered complex implementation challenges. In the case of Just Eat, the collective agreement signed with CCOO and UGT went beyond the law’s stipulations, providing for the creation of a bipartite commission to audit all operations run or influenced by algorithms “similar to the occupational health and safety commissions” (Union representative 1 – first round of interviews). While this provision had the potential to pave the way for algorithmic co-determination, by January 2024, the commission had not yet been formed. According to representatives of both of the agreement’s signatory unions, the company had made no steps towards establishing the commission. Furthermore, the unions had chosen not to pursue the matter, deeming other issues to be of greater priority and admitting that they lacked the technical expertise to engage in algorithm assessments.
In the case of Uber Eats, developments regarding the principle of algorithmic transparency were similarly complex. At the time of the interviews, the provision requiring employers to provide workers’ representatives with substantial explanations regarding the content of algorithms embedded in their labour processes had yet to be tested, as none of the unions had requested such information. As in the case of Just Eat, unions attributed their inaction to (i) the need to prioritize more immediate, “concrete issues such as working equipment and flawed payslips” (Union representative 6 – second round of interviews), and (ii) a lack of preparedness to engage in discussions perceived as excessively technical.
The first labour inspectorate official interviewed in 2022 reported not having received any complaints regarding the implementation of the provision on algorithmic transparency. To this, they added that no instructions had been received to pursue this matter, nor had officials received training on how to address complaints in this regard, stating, “we are simply not prepared. I wouldn’t know what to do”. Eighteen months later, the second official interviewed confirmed this state of affairs: “nothing has changed […] when we get any information from a company, we have no way of knowing if we are buying a pig in a poke”. Importantly, although Spain’s platform work sector has traditionally been marked by intense judicial litigation (Hiessl 2023), no lawsuits concerning either Just Eat’s or Uber Eats’ algorithmic management had reached the courts by the end of the research period.
5.2. Implicit contract
In general, there was a clear sense of power asymmetry between couriers and dispatchers at both Just Eat and Uber Eats. Although everyday interactions did not typically give rise to major conflicts, research participant frequently reported unfair treatment and expressed discomfort with dispatchers’ perceived bias, micromanagement and restrictive attitudes. These dynamics often undermined trust and curtailed couriers’ autonomy. Interestingly, however, while these grievances stemmed from a similar source – platform companies’ well-documented emphasis on quantitative performance metrics (Briziarelli 2019) – they related to features of the labour process that varied significantly across the two companies.
Uber Eats focused on three main performance indicators. First, the monthly average number of orders delivered per hour, which was expected to be no fewer than two. Second, the time elapsed between a courier entering the vicinity of the customers’ location and handing over the order (the drop-off time), which should consistently remain below seven minutes. Third, the rejection rate, which was expected to be zero, except in cases duly justified by unavoidable circumstances (e.g. the restaurant being closed).
Falling short of these expectations commonly resulted in the platform issuing formal warnings, or “strikes”. A single strike usually gave rise to an internal fine deducted from couriers’ salaries; however, an accumulation of strikes could lead to dismissal. As one courier explained during a street conversation: “It’s simple. Like baseball: three strikes, you’re out!” (field notes).
While all Uber Eats fleets conveyed to workers that maintaining the minimum average number of deliveries per hour and a low rejection rate were of primary importance, the relevance of drop-off times was not consistently emphasized across the board during the research period. According to the data collected, while some fleets appeared to place limited importance on couriers’ compliance with this indicator, others considered it a central criterion for assessing performance.
The problematic nature of the emphasis on these indicators stems, first, from the fact that they were largely beyond couriers’ control, and second, from their automated and opaque processing by fleets. As several research participants reported, aside from reducing their drop-off times, couriers had no control over how many orders they were assigned each day, and they received no guidance on how to increase that number. To the disbelief of several workers, fleet dispatchers claimed that assignments were determined by Uber Eats’ algorithm, and that they themselves lacked information on the criteria applied. Interviews with several dispatchers in both rounds of interviews did, however, confirm this account.
In parallel, the opacity of algorithms favoured the regular instances of unpaid work when orders were “suspiciously assigned” (Pablo) to couriers in the last minutes of their shift. This extended their workdays – “sometimes 30 to 40 minutes” (Pablo). In their attempt to meet the minimum threshold of average number of deliveries per hour and knowing that rejecting an order would result in a strike, couriers had no choice but to comply with the orders received: “You deliver it, of course. Is it far away? Well, you do it anyway, [although] they do not pay us for it.” (Javier).
In the case of fleets that monitored drop-off times, many couriers reported that the timer was blind to circumstances that prevented compliance with expected delivery times. Impediments included lack of vehicle access or parking, large residential complexes without clear identification of individual units, high-rise buildings without elevators and customers who did not answer their phones or doors. As strikes were automated in several fleets, sanctions were widely perceived as inevitable and irrevocable.
Once you are close [to the customer’s house], the time starts to count. […] You haven’t finished on time? To the company, what matters is that you finish on time. The order was too big and you couldn’t carry it in one single trip? Well, that is your problem. (Pedro)
Lastly, as observed in other cases of platform-based food delivery (Gregory 2021; Vieira 2023a), exposure to automated sanctions generated anxiety and stress among couriers, making them “even afraid of the app!” (Raul). However, unlike most documented cases focusing on non-salaried platform work, human dispatchers wield significant direct control over couriers in food-delivery platforms. During an interview conducted with an Uber Eats fleet dispatcher in the company’s operations room during working hours (Dispatcher 5 – second round of interviews), I observed that couriers were continuously monitored via GPS tracking and received messages or calls whenever the dispatcher detected a delay in the delivery process. All courier movements were thus recorded and, if necessary, could be meticulously reviewed, as evidenced in a dismissal letter I had access to. In line with Williams and Khan’s (2025) predictions, such pervasive surveillance and micromanagement led couriers to describe feeling “like a mere pawn” (Javier) at the mercy of distrustful dispatchers, “[who] are there simply to give us orders and that’s it! Sometimes those orders don’t even make sense; it’s almost harassment because they are very, very pushy!” (José).
Just Eat operated differently but the outcome was not entirely dissimilar. Delivery time was the only performance indicator that was taken into consideration. Unlike Uber Eats, it measured this time as a whole – that is, as the total time taken to complete all the tasks associated with an order, from receiving the notification of an algorithmically attributed job to pressing the dedicated app button that informed the company that the order had been successfully delivered.
Although dispatchers and managers generally analysed performance metrics as averages rather than individual orders (as in Uber Eats fleets), their unsupervised, automated collection often contributed to situations perceived as unfair by workers. One such case related to the impact of rainy weather on driving speed:
With rain, usually, the [delivery] times rise. […] When it rains, I don’t rush because I’m afraid, and my times are terrible. If it goes on like that for a while, you are considered to be cheating the company, so they penalize you. That would be a strike. (Fernando)
This, and similar accounts, contradicted the company’s stated commitment to safe driving. According to interviewees, unlike extreme heat, even very light rain could significantly affect road safety, particularly when couriers were carrying loads on their backs. As a result, whereas the introduction of legal provisions prohibiting outdoor work in very high temperatures protected couriers having to interrupt their work, the absence of a similar provision for rain or snow – combined with the indiscriminate collection of performance metrics – led several research participants to conclude that “[dispatchers] do not care if you drive on the sidewalk, the pressure for having good metrics leads us even to violate the law”. Ultimately, Just Eat was perceived as “reward[ing] the risk rather than [couriers’] safety” (Sergio).
Although the verb “reward” may seem exaggerated, it captures the dynamics at play in the company. Even though it was legally possible, Just Eat did not seem to dismiss workers on the grounds of accumulated strikes. Indeed, in 18 months of research, I did not come across any such cases among its workers. However, the company used other ways of penalizing bad performers and, in some cases, encouraging them to resign. Most interviewees associated poor metrics with low probabilities of being offered extra hours or a promotion to a contract with more working hours – both of which were supposedly available to the company’s best performers. This was no small thing for those working exclusively for the platform: “If you don’t have good metrics, you will always have [a] 16 hours [contract…] and earn €500 per month.” (Luis). As I observed over time, given the above-mentioned incompatibility between working for Just Eat and holding down another job, frustrated promotion expectations often left couriers with no alternative but to resign.5
In addition to the perceived flaws in Just Eat’s purportedly objective, meritocratic labour process design, some of the company’s allegedly algorithmic decision-making remained opaque. One controversial point, particularly in big cities, was the supposedly algorithmic attribution of areas where couriers performed their shifts. In the absence of meaningful and convincing information from dispatchers, several interviewees believed that human hands tampered with the results to favour some couriers over others.6 In a context characterized by low salaries and the paramount importance of performance metrics, this had significant implications.
Who goes to problematic neighbourhoods where you are insulted from the moment you arrive, and who goes to a block of flats where the doorman welcomes you and you are given €5 tips? […] If they [the dispatchers] don’t like you, they’ll put you in areas where restaurants are further away or slower to prepare orders, or simply where buildings have no lifts […] So, you will not only work more but they won’t upgrade your contract. (Luis)
As in Uber Eats fleets, human dispatchers also played an important role at Just Eat. However, once again, they performed this role differently. Just Eat also used live monitoring. Interviewed couriers explained how surveillance enabled different manifestations of micromanagement. For example, William reported that “occasionally, they call and ask what are you doing there … or if you have taken the wrong street: ‘Listen, why are you going that way?’”. Luis explained that live monitoring by management made couriers self-conscious of even the most basic actions during work time: “If you need to go to the toilet […] you can go to a restaurant […], but always with previous authorization and communicating it [to the manager].” However, throughout the research period, I found that live monitoring only partly accounted for how dispatchers played their role.
In addition to real-time monitoring, the data extracted from each courier’s performance metrics were meticulously examined by dispatchers, who regularly applied pressure on workers to enhance their performance. This pressure was primarily exerted by comparing couriers’ delivery times at the city level. According to the data gathered, this practice was more frequent among those with below-average delivery times but was not exclusively confined to them.
As Ana explained, even if a courier was “doing well” (that is, performing above the average), dispatchers’ feedback sometimes included specific guidance on “what you can do to lower your times here and there” (Ana). In fact, during the interview with a dispatcher conducted in a Just Eat operations centre during working hours, I observed that dispatchers did not just care about the overall delivery time; they paid attention to the breakdown of every action performed as part of the delivery process. While analysing these data, dispatchers might identify that, despite performing well overall, a particular courier frequently took too long to arrive at the restaurant to collect orders. In such cases, recordings of couriers’ movements captured by GPS tracking for the past 30 days were available for review, “just like any scene of a movie you want to watch and you find based on the time, playing backwards or forwards” (Dispatcher 5 – second round of interviews).
Lastly, towards the end of my research period, I observed Just Eat experimenting with alternative forms of pressure. In at least one city, couriers were divided into groups of ten to twelve, and dispatchers attempted to motivate each group to improve its delivery times, fostering inter-group competition to achieve the best collective average. However, according to Hugo, in the absence of any rewards for top performers, couriers were largely indifferent to the dispatchers’ motivational messages and carried out their work as usual.
Overall, my observations highlight various nuances. As found by Schor (2021), couriers framed their work experience in terms of their degree of reliance on the hiring platform. Those who exclusively dedicated themselves to working for Just Eat or Uber Eats, with no additional occupations or sources of income, were more prone to developing grievances against management than couriers who studied and worked at the same time. According to the data gathered during fieldwork, this stemmed from two factors. First, student workers tended to view their job as temporary. As a result, they accepted the working conditions without strong criticism, viewing the job as an opportunity to progress to something better in the not-so-distant future. Second, although the dispatchers I interviewed denied this, street conversations and courier interviews indicated that student workers were subject to less intense monitoring and pressure. Perhaps subconsciously, dispatchers were less invested in scrutinizing the work of couriers working fewer hours per week and expected to remain in the company for shorter periods.
6. Discussion and conclusion
For several years, the absence or ineffectiveness of regulation has allowed platforms to operate with minimal constraints, outside or at the margins of frameworks governing conventional employment relations. At the time of writing, in response to increasingly vocal demands to end malpractice and improve working conditions, policymakers – supported by a broad coalition of social actors – have begun to show a willingness to intervene. This article has examined the outcomes of the Spanish Government’s pioneering initiative in this area. To this end, I adopted Karl Polanyi’s (1944) influential “double movement” framework to evaluate the extent and effectiveness of efforts to restore embeddedness by addressing workers’ expectations of both the explicit and implicit dimensions of the employment relationship. The findings reveal a two-sided narrative with both theoretical and practical implications.
At the explicit contract level, the Spanish Government’s efforts have allowed platform couriers to access a guaranteed minimum monthly income in the form of salary, paid vacations and welfare provisions. This success story in formal terms could, nonetheless, be enhanced if the Spanish Government were to take steps to address the detrimental consequences of platforms’ reliance on outsourcing and involuntary part-time work (sometimes, even coercively imposed). These well-known forms of fissuring (Doellgast, Bidwell and Colvin 2021) make it difficult for workers to earn a decent salary every month, and for unions to effectively engage in collective action and social dialogue, resulting in the frustration of workers’ expectations and the development of grievances. Furthermore, the legislation introduced to protect workers from extreme heat is insufficient to tackle couriers’ exposure to weather events beyond the summer months.
Improvements at the implicit contract level appear modest. Compared to previous studies that focus on couriers classified as independent contractors (Briziarelli 2019; Gregory 2021; Vieira 2020 and 2023a), this article finds that platforms’ intensification strategies generate comparable levels of physical and mental fatigue. These conditions are amplified by management practices that, while partially automated, are also based on invasive surveillance practices that allow human dispatchers to micromanage couriers. I have thus illustrated how workers’ autonomy can be curtailed even in trivial actions, such as choosing the most efficient delivery route or accessing a restroom. This leads to a paradoxical situation where managerial authority is simultaneously diffused and exacerbated, fostering mistrust among workers. In contrast to other regulators, the Spanish Government was not oblivious to the detrimental potential of algorithmic tools. However, it merely enshrined algorithmic transparency in the country’s labour code and published a best practice guide that has not yet had any practical effects. Neither unions nor the labour inspectorate are equipped to handle the complexities of algorithmic tools in work settings.
Overall, this article indicates that, while the Spanish Government’s efforts have deterred the most visible form of platform non-compliance with labour law – namely, the misclassification of couriers as independent contractors – it has failed to protect workers from other forms of precarity and insecurity. These vulnerabilities are perpetuated by fragmented contractual arrangements and pervasive algorithmic management techniques, leading to conclusions with both theoretical and normative implications.
From a theoretical standpoint, according to Polanyi’s framework, the changes observed, at best, reflect an incomplete “double movement”. To be sure, the Spanish Government’s initiative counteracted the formal dis-embeddedness of platform workers from the employment relations institutions identified in previous studies (Wood et al. 2019). However, the lack of an effective legal framework and, particularly, the limited capacity of both trade unions and government authorities to address the detrimental potential of algorithmic management have hampered the success of efforts to re-embed platform workers. The expanded powers granted to human managers by computational algorithms remain largely unaddressed and, in some cases, even exacerbate existing power imbalances in platform work (Dubal 2023; Gregory 2021; Vieira 2023a). Workers, reduced to mere data points, are deprived of recognition (Newlands 2022) and subjected to work environments that undermine the benefits of their reclassification as employees.
This state of affairs raises pressing questions about the future of the employment relationship in the context of seemingly inevitable technological change in general, and the reclassification of platform workers in particular. As Williams and Khan (2025) argue – and this article corroborates – algorithmic management fosters mistrust by encouraging managers to treat workers with suspicion. This dynamic stands in direct opposition to the normative foundations of the ideal post-Fordist employment relationship. The foreseeable proliferation of algorithmic management may lead to a new framework in which protective social rights (e.g. salaried work and access to social benefits) coexist with the normalization of invasive surveillance and micromanagement practices. These developments contribute to mistrust and instability in workplace environments, compelling workers to internalize meritocratic ideals and intra-class competition, rather than cooperation and solidarity. While such features are not entirely new, they represent a deepening of the atomization and exploitation of labour that has characterized neoliberalism (Neilson and Rossiter 2008).
Real life is, of course, an ever-moving target and whether the reclassification of platform work results in improvements for workers will depend on the actions of policymakers and the relative strength of the stakeholders involved in the employment relationship. There is clear scope for improvements and achievements on the part of workers that could enhance the quality of the work experience. An optimistic Polanyian perspective – that the pendulum is still swinging and improvements will continue – may find resonance in recent developments, such as the so-called EU Platform Work Directive (Rainone and Aloisi 2024). However, such progress should not be left up to time or optimism alone. Convergence with the findings of earlier research (Newlands 2022; Niebler et al. 2023; Schreyer 2021; Sun, Chen and Rani 2023) suggests that future regulatory initiatives should delve deeper into the effects of fragmented employment relations and algorithmic management tools and how to address them. Furthermore, the known (Molina et al. 2023) and herein confirmed limitations of legal provisions aimed at reducing algorithmic opacity suggest that governments must play a proactive role in developing the capacity of workers and relevant authorities to critically assess algorithmic tools.
Ultimately, the peculiar and increasingly socio-technical compound that platforms represent – where precarity meets and is amplified by new technologies – poses a significant challenge to regulators. This challenge cannot be addressed unless legal provisions are accompanied by practical instruments that empower workers and their representatives to make such laws come to life. As shown by the (unintended) real-life experiment in Spain – which introduced a universal presumption of employment for platform couriers ahead of most other jurisdictions – failure to effectively address such challenges is likely to frustrate the expectations of all those invested in improving platform workers’ working and living conditions.
Notes
- Royal Legislative Decree 9/2021, of 11 May 2021. ⮭
- Directive (EU) 2024/2831 of the European Parliament and of the Council of 23 October 2024 on improving working conditions in platform work. ⮭
- Despite the use of rotating shifts, most interviewees reported that working on weekends was practically mandatory. ⮭
- Uber Eats’ decision was motivated by one of its major competitors’ refusal to comply with Ley Rider, which it perceived as unlawful competition. ⮭
- Although I did not have access to official data from the company, my estimations suggest yearly turnover to be above 50 per cent in big cities. While many of those quitting were students who had finished their studies and were moving on to other jobs – as in the cases of Fernando, Joaquín and William – several interviewees talked of rapid turnover in the company, with people leaving “as soon as they find something better” (Hugo), owing to the combination of high pressure and insufficient income. ⮭
- In my contacts with Just Eat dispatchers, I was not able to confirm this claim. Both interviewees claimed that higher levels of management dealt with this issue without ever clarifying whether it was possible to override the allegedly random attribution of work areas. Should this be the case, it would constitute a salaried work derivation of what Dubal (2023) terms “algorithmic wage discrimination”. ⮭
Acknowledgements
I would like to extend my sincere gratitude to Annamaria Laudini, Anton Hemerijck, Andreea Ferent, Nastazja Potocka-Sionek, Pedro Mendonça and Oscar Molina, members of the European University Institute (EUI)’s Social Investment Working Group, and the Universitat Autònoma de Barcelona’s QUIT group, two anonymous reviewers and Professor Aristea Koukiadaki (Managing Editor of the International Labour Review) for their valuable feedback and suggestions on earlier drafts of this article. A special thanks goes to Daniel Romero and Eva Ramos for their generous hospitality during the initial stages of this research. I am deeply grateful to all participants who generously shared their time and insights, making this research possible. Fieldwork for this article was partially supported by EUI Mission Funding. This research received funding from the Fundação para a Ciência e Tecnologia, I.P., under grant SFRH/BD/151412/2021.
Competing interests
The author declares that they have no competing interests.
References
Aloisi, Antonio. 2022. “Platform Work in Europe: Lessons Learned, Legal Developments and Challenges Ahead”. European Labour Law Journal 13 (1): 4–29. http://doi.org/10.1177/20319525211062557.
Aloisi, Antonio, and Nastazja Potocka-Sionek. 2022. “De-Gigging the Labour Market? An Analysis of the ‘Algorithmic Management’ Provisions in the Proposed Platform Work Directive”. Italian Labour Law e-Journal 15 (1): 29–50. http://doi.org/10.6092/issn.1561-8048/15027.
Behrendt, Christina, Quynh Anh Nguyen, and Uma Rani. 2019. “Social Protection Systems and the Future of Work: Ensuring Social Security for Digital Platform Workers”. International Social Security Review 72 (3): 17–41. http://doi.org/10.1111/issr.12212.
Bensusán, Graciela, and Héctor Santos. 2021. “Digital Platform Work in Latin America: Challenges and Perspectives for its Regulation”. In Work and Labour Relations in Global Platform Capitalism, edited by Julieta Haidar and Maarten Keune, 236–260. Cheltenham: Edward Elgar.
Berg, Janine, Marianne Furrer, Ellie Harmon, Uma Rani, and M. Six Silberman. 2018. Digital Labour Platforms and the Future of Work: Towards Decent Work in the Online World. Geneva: ILO.
Bolton, Sharon C., and Knut Laaser. 2013. “Work, Employment and Society through the Lens of Moral Economy”. Work, Employment and Society 27 (3): 508–525. http://doi.org/10.1177/0950017013479828.
Bray, Mark, John W. Budd, and Johanna Macneil. 2020. “The Many Meanings of Co-operation in the Employment Relationship and Their Implications”. British Journal of Industrial Relations 58 (1): 114–141. http://doi.org/10.1111/bjir.12473.
Briziarelli, Marco. 2019. “Spatial Politics in the Digital Realm: The Logistics/Precarity Dialectics and Deliveroo’s Tertiary Space Struggles”. Cultural Studies 33 (5): 823–840. http://doi.org/10.1080/09502386.2018.1519583.
Burawoy, Michael. 1998. “The Extended Case Method”. Sociological Theory 16 (1): 4–33. http://doi.org/10.1111/0735-2751.00040.
Collins, Hugh. 1986. “Market Power, Bureaucratic Power, and the Contract of Employment”. Industrial Law Journal 15 (1): 1–14. http://doi.org/10.1093/ilj/15.1.1.
Collins, Hugh. 2014. “Implied Terms: The Foundation in Good Faith and Fair Dealing”. Current Legal Problems 67 (1): 297–331. http://doi.org/10.1093/clp/cuu002.
Dale, Gareth. 2012. “Double Movements and Pendular Forces: Polanyian Perspectives on the Neoliberal Age”. Current Sociology 60 (1): 3–27. http://doi.org/10.1177/0011392111426645.
de la Fuente Lavín, Mikel, and Jon Bernat Zubiri Rey. 2022. “La reforma laboral de 2021 en España, cambio de tendencia en la regulación de la contratación temporal”. Revista Crítica de Relaciones de Trabajo, Laborum 3: 199–223.
Doellgast, Virginia, Matthew Bidwell, and Alexander J. S. Colvin. 2021. “New Directions in Employment Relations Theory: Understanding Fragmentation, Identity, and Legitimacy”. ILR Review 74 (3): 555–579. http://doi.org/10.1177/0019793921993445.
Dubal, Veena B. 2017. “Wage Slave or Entrepreneur? Contesting the Dualism of Legal Worker Identities”. California Law Review 105 (1): 65–123. http://doi.org/10.15779/Z38M84X.
Dubal, Veena B. 2023. “On Algorithmic Wage Discrimination”. Columbia Law Review 123 (7): 1929–1992.
Duggan, James, Ultan Sherman, Ronan Carbery, and Anthony McDonnell. 2021. “Multi-party Working Relationships in Gig Work: Towards a New Perspective”. In Platform Economy Puzzles: A Multidisciplinary Perspective on Gig Work, edited by Jeroen Meijerink, Giedo Jansen and Victoria Daskalova, 162–186. Cheltenham: Edward Elgar.
Dutta, Sidhartha. 2023. “Rajasthan Passes Bill for Gig Workers, Touts It as a ‘First’”. The Times of India, 25 July 2023. https://timesofindia.indiatimes.com/city/jaipur/rajasthan-passes-bill-for-gig-workers-touts-it-as-a-first/articleshow/102091693.cms?from=mdr.
Eliasoph, Nina, and Paul Lichterman. 1999. “‘We Begin with Our Favourite Theory…’: Reconstructing the Extended Case Method”. Sociological Theory 17 (2): 228–234. http://doi.org/10.1111/0735-2751.00076.
Fereday, Jennifer, and Eimear Muir-Cochrane. 2006. “Demonstrating Rigor Using Thematic Analysis: A Hybrid Approach of Inductive and Deductive Coding and Theme Development”. International Journal of Qualitative Methods 5 (1): 80–92. http://doi.org/10.1177/160940690600500107.
Graebner, Melissa E., Jeffrey A. Martin, and Philip T. Roundy. 2012. “Qualitative Data: Cooking Without a Recipe”. Strategic Organization 10 (3): 276–284. http://doi.org/10.1177/1476127012452821.
Gregory, Karen. 2021. “‘My Life Is More Valuable than This’: Understanding Risk among On-Demand Food Couriers in Edinburgh”. Work, Employment and Society 35 (2): 316–331. http://doi.org/10.1177/0950017020969593.
Gupta, Manish, Musarrat Shaheen, and K. Prathap Reddy. 2019. Qualitative Techniques for Workplace Data Analysis. Hershey, PA: IGI Global.
Hiessl, Christina. 2023. “Multiparty Relationships in Platform Work: Cross-European Case Law Developments and Points of Departure for (Supranational) Regulation”. European Labour Law Journal 14 (4): 514–540. http://doi.org/10.1177/20319525231208637.
Hyde, Allan. 1998. “Employment Law after the Death of Employment”. University of Pennsylvania Journal of Labor and Employment Law 1 (1): 99–115.
ILO. 2011. “Employment Relationship”. 17 June 2011. https://ilo.org/ifpdial/areas-of-work/labour-law/WCMS_CON_TXT_IFPDIAL_EMPREL_EN/lang--en/index.htm.
INCP (Instituto Nacional de Contadores Públicos Colombia). 2023. “Regulación de las plataformas digitales en Colombia”. 25 January 2023. https://incp.org.co/publicaciones/infoincp-publicaciones/informacion-para-empresas/actualidad/2023/01/regulacion-de-las-plataformas-digitales-en-colombia/.
Johnston, Hannah. 2020. “Labour Geographies of the Platform Economy: Understanding Collective Organizing Strategies in the Context of Digitally Mediated Work”. International Labour Review 159 (1): 25–45. http://doi.org/10.1111/ilr.12154.
Johnston, Hannah, Ozlem Ergun, Juliet Schor, and Lidong Chen. 2024. “Employment Status and the On-Demand Economy: A Natural Experiment on Reclassification”. Socio-Economic Review 22 (1): 169–194. http://doi.org/10.1093/ser/mwad047.
Larocca, Nicolás. 2023. “Chile – Estas son las nuevas reglas que aplicarán a las plataformas de transporte”. DPL News, 1 February 2023. https://dplnews.com/chile-estas-son-las-nuevas-reglas-que-aplicaran-a-las-plataformas-de-transporte/.
Lee, Min Kyung, Daniel Kusbit, Evan Metsky, and Laura Dabbish. 2015. “Working with Machines: The Impact of Algorithmic and Data-Driven Management on Human Workers”. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems, 1603–1612. New York, NY: Association for Computing Machinery.
Leech, Beth L., Frank R. Baumgartner, Jeffrey M. Berry, Marie Hojnacki, and David C. Kimball. 2013. “Lessons from the ‘Lobbying and Policy Change’ Project”. In Interview Research in Political Science, edited by Layna Mosley, 209–224. Ithaca, NY: Cornell University Press.
Levy, Jack S. 2008. “Case Studies: Types, Designs, and Logics of Inference”. Conflict Management and Peace Science 25 (1): 1–18. http://doi.org/10.1080/07388940701860318.
Lorite, Álvaro. 2022. “Trabajo presenta una herramienta para controlar los algoritmos en entornos laborales”. El Salto, 11 June 2022. https://www.elsaltodiario.com/inteligencia-artificial/ministerio-trabajo-herramienta-controlar-los-algoritmos-entornos-laborales-yolanda-diaz.
Molina, Oscar, Florian Butollo, Csaba Makó, Alejandro Godino, Ursula Holtgrewe, Anna Illsoe, Sander Junte, et al. 2023. “It Takes Two to Code: A Comparative Analysis of Collective Bargaining and Artificial Intelligence”. Transfer: European Review of Labour and Research 29 (1): 87–104. http://doi.org/10.1177/10242589231156515.
Moravcsik, Andrew. 2014. “Trust, but Verify: The Transparency Revolution and Qualitative International Relations”. Security Studies 23 (4): 663–688. http://doi.org/10.1080/09636412.2014.970846.
Neilson, Brett, and Ned Rossiter. 2008. “Precarity as a Political Concept, or, Fordism as Exception”. Theory, Culture & Society 25 (7–8): 51–72. http://doi.org/10.1177/0263276408097796.
Newlands, Gemma. 2022. “Anthropotropism: Searching for Recognition in the Scandinavian Gig Economy”. Sociology 56 (5): 821–838. http://doi.org/10.1177/00380385211063362.
Niebler, Valentin, Giorgio Pirina, Michelangelo Secchi, and Franco Tomassoni. 2023. “Towards ‘Bogus Employment?’ The Contradictory Outcomes of Ride-Hailing Regulation in Berlin, Lisbon and Paris”. Cambridge Journal of Regions, Economy and Society 16 (2): 289–301. http://doi.org/10.1093/cjres/rsad007.
Olías, Laura. 2023. “>Se prohíbe trabajar a 39 grados en la calle? Claves sobre la nueva protección laboral ante el calor extremo”. El Diario, 12 May 2023. https://www.eldiario.es/economia/prohibe-trabajar-39-grados-calle-claves-nueva-proteccion-laboral-calor-extremo_1_10196298.html.
Polanyi, Karl. 1944. The Great Transformation: The Political and Economic Origins of Our Time. New York, NY: Farrar & Rinehart.
Potocka-Sionek, Nastazja. 2023. “Platformisation of Work: Challenges beyond Employment Classification”. PhD thesis-dissertation. Florence: European University Institute.
Prassl, Jeremias. 2018. Humans as a Service: The Promise and Perils of Work in the Gig Economy. Oxford: Oxford University Press.
Rainone, Silvia, and Antonio Aloisi. 2024. “The EU Platform Work Directive: What’s New, What’s Missing, What’s Next?” ETUI Policy Brief No. 2024.06. Brussels: European Trade Union Institute.
Reid-Musson, Emily, Ellen MacEachen, and Emma Bartel. 2020. “‘Don’t Take a Poo!’: Worker Misbehaviour in On-Demand Ride-Hail Carpooling”. New Technology, Work and Employment 35 (2): 145–161. http://doi.org/10.1111/ntwe.12159.
Schein, Edgar H. 1978. Career Dynamics: Matching Individual and Organizational Needs. Reading, MA: Addison-Wesley.
Schor, Juliet B. 2021. After the Gig: How the Sharing Economy Got Hijacked and How to Win It Back. Oakland, CA: University of California Press.
Schreyer, Jasmin. 2021. “Algorithmic Work Coordination and Workers’ Voice in the COVID-19 Pandemic: The Case of Foodora/Lieferando”. Work Organisation, Labour & Globalisation 15 (1): 69–84. http://doi.org/10.13169/workorgalaboglob.15.1.0069.
Spain, Ministry of Labour and Social Economy. 2022. Información algorítmica en el ámbito laboral. [Translated into English as Algorithmic Information in the Workplace]. Madrid.
Statista. 2023. “Salario mínimo interprofesional (SMI) al mes en España de 2008 a 2023. https://es.statista.com/estadisticas/475830/espana-salario-minimo-interprofesional-al-mes/ (accessed October 2023).
Sun, Ping, Julie Yujie Chen, and Uma Rani. 2023. “From Flexible Labour to ‘Sticky Labour’: A Tracking Study of Workers in the Food-Delivery Platform Economy of China”. Work, Employment and Society 37 (2): 412–431. http://doi.org/10.1177/09500170211021570.
Supiot, Alain. 2017. Governance by Numbers: The Making of a Legal Model of Allegiance. Oxford: Hart.
Vallas, Steven, and Juliet B. Schor. 2020. “What Do Platforms Do? Understanding the Gig Economy”. Annual Review of Sociology 46: 273–294. http://doi.org/10.1146/annurev-soc-121919-054857.
Vieira, Tiago. 2020. “The Lose–Lose Dilemmas of Barcelona’s Platform Delivery Workers in the Age of COVID-19”. Social Sciences & Humanities Open 2 (1): Article No. 100059. http://doi.org/10.1016/j.ssaho.2020.100059.
Vieira, Tiago. 2023a. “Platform Couriers’ Self-Exploitation: The Case Study of Glovo”. New Technology, Work and Employment 38 (3): 493–512. http://doi.org/10.1111/ntwe.12272.
Vieira, Tiago. 2023b. “The Unbearable Precarity of Pursuing Freedom: A Critical Overview of the Spanish sí soy autónomo Movement”. Sociological Research Online 28 (1): 244–260. http://doi.org/10.1177/13607804211040090.
Watson, Tony J. 2003. Sociology, Work and Industry. 4th ed. London: Routledge.
Williams, Penny, and Maria Hameed Khan. 2025. “Framing Algorithmic Management: Constructed Antagonism on HR Technology Websites”. New Technology, Work and Employment 40 (1): 102–123. http://doi.org/10.1111/ntwe.12305.
Wood, Alex J., Mark Graham, Vili Lehdonvirta, and Isis Hjorth. 2019. “Networked but Commodified: The (Dis)Embeddedness of Digital Labour in the Gig Economy”. Sociology 53 (5): 931–950. http://doi.org/10.1177/0038038519828906.
Yin, Robert K. 2009. Case Study Research: Design and Methods. 4th ed. Thousand Oaks, CA: Sage.
Appendix
Research operations in detail
| Data collection technique employed | Implementation throughout the research period |
| Ethnographic observation | July 2022 9 hours (4 street conversations: 2 Just Eat, 2 Uber Eats) August to October 2022 34 hours (24 street conversations: 10 Just Eat, 14 Uber Eats) October 2023 to January 2024 23 hours (16 street conversations: 7 Just Eat, 9 Uber Eats) July 2022 to January 2024 Observation of interactions in social media groups |
| Interviews | July 2022 Exploratory interviews:
September–October 2022 First round of interviews:
November 2023–January 2024 Second round of interviews:
|
| Documental analysis |
|
Source: Own compilation.
Pseudonymized couriers’ socio-demographic characteristics and trajectory during research period
| Pseudonym | Platform | Gender | Age | Origin | Dependency | Contract | Trajectory |
| Adán | Uber Eats | Male | 51 | Migrant | Total | Full-time | Resigned during research period |
| Ana | Just Eat | Female | 42 | Migrant | Total | Part-time (24h) | No changes |
| Andrés | Uber Eats | Male | 27 | Migrant | Partial (has other informal job) | Part-time (16h) | No changes |
| David | Just Eat | Male | 28 | Native | Total | Part-time (24h) | No changes |
| Fernando | Just Eat | Male | 27 | Native | Partial (student-worker) | Part-time (16h) | Resigned during research period |
| Francisco | Just Eat | Male | 21 | Native | Partial (student-worker) | Part-time (12h) | Joined in the second round only |
| Hugo | Just Eat | Male | 27 | Native | Partial (student-worker) | Part-time (12h) | Joined in the second round only |
| Javier | Uber Eats | Male | 50 | Migrant | Total | Full-time | No changes |
| Joaquín | Just Eat | Male | 24 | Native | Partial (student-worker) | Part-time (20h) | Resigned during research period |
| Jordi | Uber Eats | Male | 29 | Migrant | Total | Part-time (30h) | Dismissed as part of collective dismissal |
| José | Uber Eats | Male | 25 | Migrant | Total | Full-time | Dismissed as part of collective dismissal |
| Juan | Uber Eats | Male | 36 | Migrant | Total | Full-time | Dismissed as part of collective dismissal |
| Luis | Just Eat | Male | 42 | Native | Total | Part-time (16h) | No changes |
| Manuel | Uber Eats | Male | 29 | Native | Total | Part-time (20h) | Contract expired before research period started |
| Marco | Just Eat | Male | 22 | Migrant | Partial (student-worker) | Part-time (20h) | Joined in the second round only |
| Maria | Just Eat | Female | 55 | Migrant | Total | Part-time (20h) | No changes |
| Pablo | Uber Eats | Male | 21 | Native | Total | Part-time (20h) | Contact lost |
| Pedro | Uber Eats | Male | 29 | Native | Total | Full-time | Still working but working hours reduced to 30 hours |
| Raul | Uber Eats | Male | 44 | Migrant | Total | Full-time | Still working but working hours reduced to 30 hours |
| Sergio | Just Eat | Male | 30 | Migrant | Total | Part-time (20h) | No changes |
| William | Just Eat | Male | 20 | Migrant | Partial (student-worker) | Part-time (16h) | Resigned during research period |
Source: Own compilation.