Technologically mediated human resource management in the gig economy

Abstract

Gig work accessed through the medium of digital platforms has become an increasingly researched and debated topic owing to several features which distinguish it from other variants of temporary work. It represents a form of working that typically falls outside the standard boundaries of the organisation and employment relationships and where technology has a most pervasive role. This paper, alongside five special issue contributions, explores the enactment of technologically mediated HRM in the gig economy. We make the case for enhanced research efforts on HRM without employment contexts. More specifically, there is a need for greater appreciation of the diversity within the digital platform classification, and what this may mean for the role and value of HRM. While gig workers tend to fall outside the HRM field’s remit, we expose how this is problematic given the presence of several activities and practices that one traditionally associates with the HR function.

Keywords:

• Gig economy
• platform work
• on-demand economy
• algorithmic management
• technology
• HRM

Introduction

The gig economy, which most commonly refers to an economic system that uses digital platforms to connect workers with consumers and clients (Harris, 2017), is increasingly omnipresent in discussions and debates around the future of work. This relatively new form of work pervades many spheres of our lives in line with trends of increasing consumer desire for convenience (Mehmood & Najmi, 2017; Pasquale, 2016). The gig economy, also known as the online platform, on-demand or digital platform economy, is in many respects the most distinctive and extreme side of the increasingly digitalised and fragmented nature of work (Kaine & Josserand, 2019). At the heart of the gig economy is the easy and cheap exchange of considerable swathes of data and technological innovations. These features have been pivotal to the development of such new ways of working that purportedly offer flexibility for workers, businesses, and individual consumers. However, the extent to which such innovation and flexibility is positive and mutually beneficial amongst all parties is coming under greater scrutiny.

Gig work represents another type of contingent labour which bears some similarities to other temporary forms but also brings key and acute differences; notably in the use of technological solutions or digital architectures, and the hyper-flexibility involved (Duggan et al., 2020). Rather than representing one homogenous entity, the gig economy takes on several heterogeneous forms which is only beginning to gain critical evaluation (e.g. Duggan et al., 2020; Howcroft & Bergvall-Kåreborn, 2019). The spread of gig work appears to be increasing across industries and countries, and as such it is becoming more pervasive within economies. Its rise, and the associated implications of this for individuals and the social fabric of work, means that interest in it is has stretched far beyond academic researchers to now include policy makers, and the wider public. While in its infancy as a research domain, scholarly interest has exploded as evident through cross-disciplinary publication patterns in labour law, consumer studies, marketing, employment relations, sociology, strategy and so forth.

What we now see is typically commentary from two opposing and dominant perspectives in relation to the merits or shortcomings of gig work for individuals and wider society. On the positive side, we have those that speak to the flexibility and autonomy that gig work offers to individuals in respect to how much or little, and when they wish to work (Duggan et al., 2021b; Kirven, 2018). In other words, this standpoint places emphasis on the entrepreneurial opportunity afforded to individuals by gig work and the benefits that this also offers consumers. Counter to this perspective, is the argument that this type of work places too great a risk on individuals in terms of financial and social insecurities (Friedman, 2014; Wood et al., 2019). In so doing, it ultimately creates an extremely individualised and detached working life (Kaine & Josserand, 2019). The lack of widespread social protection across countries for gig workers has been a magnified concern in some countries in the context of the Covid-19 pandemic (Apouey et al., 2020).

Gig work is fundamentally comprised of hyper, short-term jobs (gigs) where those that perform it are typically classified as independent contractors rather than employees. As such the gig economy involves organisations not hiring employees but mediating an exchange between worker and customers to undertake requested tasks, with all the work allocation and associated processes managed by algorithm. The classification, or misclassification, of these workers (i.e. whether gig workers are employees or independent contractors) is, however, one that continues to attract legal challenge across several jurisdictions (Collier et al., 2017; Fabo et al., 2017; Halliday, 2021). These cases have led to conflicting outcomes with some courts finding that gig workers should in fact be classified as employees, and others dismissive of them being in a formal employment relationship. For example, in 2021, the Supreme Court of the United Kingdom, determined that Uber drivers were employees, on grounds that drivers cannot negotiate with customers on price (this being set by the platform’s algorithm), while in 2020, the Fair Work Ombudsman in Australia drew the opposite conclusion, concluding that Uber drivers were self-employed given they could choose their hours of work. Ultimately, most organisations in the gig economy look to deny the existence of a formal employment relationship, which, in theory should mean full independence for workers. The reality, however, appears somewhat different with emerging evidence identifying that there are significant levels of organisational control and power exercised by these platform firms (e.g. Meijerink, Keegan et al., 2021; Norlander et al., 2021; Shanahan & Smith, 2021).

In this paper, we delineate what the gig economy is and highlight its key features and characteristics. In so doing, we consider whether the reality of gig working is more nuanced and complex than is often presented. We note the need for greater appreciation of the idiosyncrasies between different forms of gig work rather than treating this ‘new’ economy as a uniform entity (Duggan et al., 2020). Given the transactional nature of gig work and the lack of formal employment relationships, one may determine on first inspection that HRM holds little relevance in the context of gig work. However, we argue in this paper that this is not necessarily the case. We highlight the increasing role being played by technologically mediated HRM practices in gig work where many of the broad activities traditionally associated with HR functions are increasingly enacted and enabled by algorithms. This goes to the heart of how platform organisations exercise control and power over seemingly independent workers.

The gig economy: a review of what it is and its relevance to the world of work

The gig economy can be described as a representation of a free and global market where independent workers and customers come together on an on-demand, short-term professional working relationship basis that enables much flexibility for all parties involved with little commitment. In other words, it is a labour market that is entirely made up of short-term jobs conducted by independently contracted or freelance workers. As such the term ‘gig’ stems from the music industry where a job lasts for a short and defined period. These gigs or jobs tend to be mediated through digital platforms that match the requester or customer with an individual directly who undertakes this work as an independent, service provider rather than an employee. Payment then tends to be for the completion of each task by the gig worker with the intermediary platform firm normally receiving a fixed-rate percentage from each gig. However, as we elucidate later there are key distinctions in some forms of gig work that mean payment systems and work assignment are heterogeneous rather than uniform.

Evaluating the size and scale of the gig economy is difficult owing to labour statistics tending to encapsulate all self-employed people which, while likely to include many gig workers, also includes more traditional freelance or contractor roles. In addition, many individuals undertake gig work as a side-line or additional income source rather than it being their primary or sole method of income generation. As such existing labour market measurement tools appear problematic to capture the real size of the gig economy and the scope of various work-based activities in this context. We are however beginning to gain a greater appreciation through focussed survey work. For example, a 2017 RSA/ISPOS survey in the UK estimated 1.1 million people being involved which puts it on a similar scale to the National Health Service in England (Balaram et al., 2017). The European Commission (2018) found 10% of survey respondents had indicated offering services through online labour platforms on at least one occasion. Looking to the US, data from 2016 indicated that 8% of all Americans had worked through an online labour platform (Smith, 2016). Thus, while the numbers involved do not indicate the mass takeover of gig work and end of more traditional employment its scale is undoubtedly noteworthy, especially given that all predictions point to further growth.

This expansion is highlighted by Berg et al. (2018), who suggest that digital labour platforms are attracting workers across multiple sectors and countries as they provide flexibility in work schedules, the option to undertake work from any place and at any time, and the ability to choose the tasks to be performed. According to Rani et al. (2021) there has been a fivefold increase in the number of digital labour platforms in the past decade. A large proportion of these platforms are concentrated in just a few locations, including the United States of America (29%), India (8%) and the United Kingdom of Great Britain and Northern Ireland (5%). The number of platforms rose from 142 in 2010 to over 777 in 2020 with the number of taxi and food delivery platforms growing almost tenfold in this period.

Gig work is an increasingly significant influence on the rise of an on-demand societal approach (Newlands, 2021). This is evident through different forms including locally specific platform-mediated jobs which is broadly classified as gig work (Schmidt, 2017). However, others call for more specific terminology to acknowledge the distinctions within the gig economy, such as app-work (see Duggan et al., 2020). There is also crowdwork which is a form that holds no locational boundaries, but still involves digital platform mediation. This has, in turn, been broken down into different constituent types by Howcroft and Bergvall-Kåreborn (2019). First, online requester initiated paid work is one of the most prominent forms which includes platforms such as Fiverr and Mechanical Turk. This work sees individuals perform very simple micro-tasks to more complicated projects in an online setting. Second, profession-based freelancers represent a type of worker-initiated work which can be unpaid or highly speculative in nature. For example, an individual may propose the development of a new App for an Android or Apple. Third, playbour crowdwork is requester initiated but has similarities to the previous form in that it tends to fall into the speculative and often non-paid form. For example, a software company may seek solutions to problems they have identified, and individuals may put forward solutions which can be unpaid or it may be some sort of cash prize that is offered rather than a more formalised pay arrangement. The final type – asset-based services – has received some critical comment and disagreement in terms of whether it represents crowd work or not (Duggan et al., 2020, 2022). Howcroft and Bergvall-Kåreborn (2019) identify this as worker initiated localised transaction work and include organisations such as Airbnb, Uber and TaskRabbit. The critique of this is that crowdwork should only represent work that can be completed remotely which this latter category does not align to (see Duggan et al., 2020, 2022). It is also argued that the nature of organisations like Airbnb is best described as capital platform work given its focus on leasing assets rather than necessarily the completion of discrete forms of labour (see Duggan et al., 2021a for wider debate).

Duggan et al. (2020) further emphasise that cloudwork describes tasks that can be completed remotely via the Internet and are not bound to a specific location. They view crowdwork as where a task is not given to a specific individual but to an undefined group of people online. If the task is further subdivided into smaller units for piecemeal work, with each individual remunerated with an equally small amount of money, it is micro-tasking crowdwork; and where tasks cannot be subdivided but work is carried out simultaneously, by a large group of individuals, but in the end only one result is used and paid for, is contest-based crowdwork. Cloudwork and all types of crowdwork allow for the completion of tasks remotely, thereby lacking a discernible employer. It is argued that compared to app-workers in particular, these individuals are less likely to develop a transparent working relationship with the gig or platform organisation (Duggan et al., 2020) which is likely to mean a differential role for HRM depending on the form of gig work.

Table 1 presents a summary of the characteristics of these different forms of gig working arrangements through providing examples of digital platforms and differentiating the defining features from the perspective of the worker, customer/requester, supplier, platform, location of labour and payment for labour. We also provide examples of technologically mediated HRM practices which will be expanded on further in the next section.

Table 1. Characteristics of gig working arrangements.

Type of gig work	Example of platform	Worker	Customer/Requester	Supplier	Platform	Location	Payment	Examples of technologically-mediated HR practices
App-work	Uber, Deliveroo, Lyft, UberEats, Taskrabbit, Helpling	Independent contractor who works flexibly on a piece-rate basis.	End-user of the service; generates demand on platforms by initially requesting services via apps.	Exists in certain app-work arrangements as an additional partner of the online platform/app e.g. restaurants in the case of food delivery	Platforms assign tasks to workers via apps based on demand, setting minimum quality standards of service and in the selection and management of those who perform the work	Tasks performed locally in person	Workers paid by platform. Platform specifies the payment level for the task.	Compensation e.g. Uber’s surge price algorithm that adjusts workers’ pay on the basis of labour supply and demand dynamics. Work allocation e.g. Deliveroo’s algorithm dispatches orders to riders without human involvement.
Capital platform work	Airbnb, etsy	Workers use a digital platform to sell goods peer-to-peer or to lease assets	Customers are connected with a form of capital owned by an individual	N/A	Platform intermediates an e-commerce arrangement	Local (e.g. in the case of accommodation) or remote (e.g. in the case of the sale of goods)	Workers set compensation rates and are paid via platform	Compensation e.g. Airbnbs ‘Smart Pricing’ feature dynamically adjusts the cost of a night’s stay based on current demand and allows hosts to set a minimum price
Cloud work	Upwork, Freelancer,	Work done by a specific individual online	Organisation or individual requests a task or project to be completed via a platform	N/A	Digital labour platforms provide services that are not bound to a specific location	Tasks completed remotely online	Platforms extract value from the service transaction e.g. by taking a commission, or charging workers a fee	Selection e.g. an algorithm grants or denies workers access to the Upwork platform
Crowdwork	Crowdflower, Fiverr, Workana, Appen	Work carried out by undefined group of people	Organisation or individual requests a task or project to be completed via a platform	N/A	Platforms connect workers with customers to sell their services by performing tasks	Tasks completed remotely online	Certain crowdwork platforms set minimum compensation for certain tasks, while others let the compensation be set by their requester. Requesters can pay workers directly plus a fee to the platform	Work allocation e.g. CrowdFlower distributes tasks to workers based on data collected information about workers’ abilities and qualifications. Quality of the completed work is measured and recorded, and used in allocation of future tasks.
Micro-tasking crowdwork	Amazon Mechanical Turk, Prolific, Clickworker	Tasks are subdivided into smaller units for piecemeal work.	Organisation or individual requests a task or project to be completed via a platform	N/A	Platform distributes repetitive tasks across a large and unspecified group of workers. Workers self-assign to the tasks.	Tasks completed remotely online	Each individual remunerated with an equally small amount of money	Talent management e.g. Amazon Mechanical Turk identifies talent through a ‘Master Worker’ qualification; a designation awarded to individuals that consistently exceed expectations. Workers cannot apply for the designation and it is determined by a proprietary algorithm.
Contest-based crowdwork	TopCoder, 99designs, Jovoto	Work is carried out simultaneously, by a large group of individuals	Organisation or individual requests a task or project to be completed via a platform	N/A	Customer selects and pays for only one result	Tasks completed remotely online	No guarantee of payment; customer pays for only one result	Performance management e.g. at the end of each contest on TopCoder, their algorithm rates each contestant, based on past performance and ranking compared to other contestants

Human resource management in gig work

It is evident that there are significant challenges to our knowledge and understanding of people management in the context of gig work and the role HRM plays. Table 1 intimates that referring to gig work as a monolithic concept is problematic, most notably in a research context where such an approach may create confusion about what and who exactly is being studied, leading to inconclusive results (Gerwe & Silva, 2020). Gig work can therefore, we argue, be regarded as a new archetype for HRM, which allows for the expression and understanding of a new range of atypical working conditions, the nature of which may vary across different types of roles within the overall gig economy.

The short-term temporal nature of gig work means individuals complete tasks or provide labour for a defined time with no long-lasting connection to their ‘employing’ organisation (Todolí-Signes, 2017). The role of HRM may appear limited given that most gig-workers are viewed as independent contractors rather than employees. Notwithstanding the widespread legal ambiguity surrounding the employment classification of gig workers, the assumption that independent workers are not relevant to or within the remit of HRM practice and theory is questionable (see Cross & Swart, 2021). While digital platforms deny the existence of a traditional employment relationship, they nevertheless impose various measures of control on workers to ensure proper work assignment and performance management (Duggan et al., 2020). As a result, workers regularly compare themselves to employees (Petriglieri et al., 2019). Workers are often guided and controlled by technologically mediated HR practices, or algorithmic management.

Duggan et al. (2020) define algorithmic management as ‘a system of control where self-learning algorithms are given the responsibility for making and executing decisions affecting labour, thereby limiting human involvement and oversight of the labour process’ (p. 119). Kellogg et al. (2020) argue that algorithmic management goes beyond traditional forms of organisational control in several ways. It is generally used to direct workers through restricting and recommending behaviour, to evaluate workers through recording and rating behaviour, and to discipline workers through threatening replacement or promising reward (Kellogg et al., 2020). Consequently, algorithmic management is ‘more comprehensive, instantaneous, interactive and opaque’ than traditional means of control (Kellogg et al., 2020, p. 396). Meijerink, Boons et al. (2021) suggest the adoption of the term ‘algorithmic HRM’ to describe the ‘use of software algorithms that operate on the basis of digital data to augment HR-related decisions and/or to automate HRM activities’ (p. 2547).

We argue that the way in which technologically mediated HRM practices bind both digital platforms and workers to a working agreement, and the means through which they manage workers and bypass many of the regular responsibilities of employment has significant implications for HRM. Indeed, Duggan et al. (2020) note the potential redundancy of the traditional HR function in gig working because designers and developers of algorithms produce the technologically mediated HR practices that, in part, mimic traditional HR activities. Permeable organisational boundaries and increasingly fragmented forms of work combined with concerns that HR is a single actor unable to keep pace in such a complex environment network or ecosystem of stakeholders (Harney & Collings, 2021), means that there is a need to move away from the dominant ‘organisational’ or ‘employee’ conceptual lens when considering HRM in gig work. Cross and Swart (2021) similarly espouse the need to adopt a much broader focus to studying HR and individuals within an ecosystem in order to generate a more nuanced discussion and analysis of how different types of workers deploy their labour, knowledge, skills and abilities.

HR processes in the gig economy, including recruitment and selection, work allocation, performance management, reward management and employee termination, can be opaque, and not readily communicated to workers. In instances where algorithmic management is used, a lack of transparency about platform decision-making can limit workers’ agency and autonomy. In terms of pay, platforms are generally not obligated to comply with local minimum wage legislation, and consequently workers are often in danger of earning below their local minimum wages, especially when time spent searching for work and overtime are considered (Halliday, 2021).

The performance of gig workers is generally controlled through algorithms which are purportedly unbiased and efficient in work allocation. The theoretical underpinning of performance management as a HR process generally focuses on performance identification and pay for performance. From the performance identification viewpoint, algorithms use harder platform control to capture the exact log of working hours, number of working assignments accepted or cancelled, punctuality, etc. (Shapiro, 2018). Newlands (2021) highlights how the allocation of work by algorithms lacks awareness of certain spatial conditions that workers encounter, which are generally beyond their control (e.g. weather or traffic conditions). This can negatively impact performance metrics and associated rewards, since the surveillance mechanisms fail to capture and account for these conditions, many of which may directly impact the worker’s health and safety. From the pay for performance viewpoint, algorithms are designed around ‘choice architecture’ that uses certain gamic elements and institutionalised nudges as a method of exercising ‘soft control’ on the worker’s motivation and behaviour that impacts their decisions and ultimately their performance outcomes (Rosenblat & Stark, 2016). More often, these outcomes fail to favour the worker or are not in alignment with their expectations, as the worker ends up receiving pre-determined incentives for achieving the targets that were curated by these digital platforms in the first place. In addition, workers choice appears to be severely compromised due in no small part to asymmetries of information whereby the platform organisation owns the knowledge and controls what is released (Cheng & Foley, 2019; Veen et al., 2020).

Heiland (2021) argues that control mechanisms deployed by algorithms on behalf of platforms are not solely technologically driven. In the case of food delivery, while platforms try to limit worker power through technological control, some loopholes remain, allowing workers some capacity to act autonomously. Therefore, additional forms of control are used in the form of working time regimes. Through working time regimes, platforms must fill unattractive and unprofitable shifts with workers, and thus limit the temporal flexibility of workers. Working time regimes do not only coordinate the labour process but control it by means of restricting both the worker’s effort and mobility power. Working time regimes exist as a form of temporal (and thus organisational) control and are not just merely an efficient allocative instrument of labour, as workers tend to compete for shifts through their performance, which combines the issues of flexibility and control of platform labour.

The above practices demonstrate that, from a HRM perspective, the gig economy creates a new dynamic where technologies do not just mediate economic and social relations, but also help to co-constitute them (Wood & Lehdonvirta, 2019). Digital platforms deploy technologically mediated HR practices at scale, allowing them to manage large, invisible workforces. Meijerink and Keegan (2019) suggest that the increasing prevalence of gig work redefines the role of HRM away from upholding employment relationships towards the governance of exchanges on platforms that serve to allow the co-creation of value for all parties involved. However, the long-term viability of the role of HRM in the gig economy has been questioned, particularly within the strategic context of motivating workers, ensuring high-quality performance, and providing social support (Jabagi et al., 2019). While Crawford (2021) suggests that algorithmic management is ‘simply the latest (technology) in the long historical development of factories timepieces, and surveillance architectures’ (p. 85), HRM activities now tend to be outsourced to system designers and coders that develop and deploy technologically mediated practices. This removes many of the costs of HRM and passes employment risks to the individual (Snyder, 2016), further eradicating the more interpersonal and empathetic aspects of people management (Angrave et al., 2016).

Special issue papers

The first paper by Meijerink, Keegan et al. (2021) unpacks the HRM activities that are used by online labour platforms under the theoretical lens of institutional complexity. As such, the paper rails against conceptions that HRM is of no relevance within the gig economy. Specifically, the paper reports on how these platforms create tensions between the competing market logic and corporation logic. The market logic is evident by how platform organisations see themselves as merely creating an online marketplace to enable independent contractors and customers to transact. The offer of considerable or full autonomy to gig workers is viewed as indicative of HRM activities that support this institutional logic. Counter to this is the competing corporation logic which accentuates the legitimisation of coordinating and controlling workers to support organisational profitability and success. HRM activities that fall under this logic, and which creates the tension include the utilisation of performance evaluations and algorithmic driven and managed compensation. Through an innovative qualitative methodology that incorporates ethnographic accounts of life as a gig worker this paper unpacks the interrelationships among several HRM activities that gig workers are subject to, the tensions from competing institutional logics at play, and the response strategies of these organisations.

The second paper by Norlander et al. (2021) focuses on perceptions of control and motivation held by three types of workers involved in transportation services namely, Uber, taxi and limousine drivers. Drawing from both a between-subjects survey and within-subjects field experiment leads the authors to identify how workers experience technological supervision. Noteworthy is that this study evaluates perceptions of motivation and control under algorithmic management conditions and more traditional human centred management, while holding the tasks being performed constant. The results show Uber drivers to report greater levels of organisational control than taxi drivers but not more intrinsic motivation, enjoyment or needs satisfaction. There were some divergent results between the two studies undertaken here but overall platforms are linked to moderately increased perceptions of control and work enjoyment amongst Uber drivers. The use of additional controls does not however appear to decrease motivation. The authors importantly report the existence of selection effects in understanding the differences between the gig workers and taxi and limousine drivers.

Our third paper in this special issue sees Shanahan and Smith (2021) draw on semi-structured interviews and participant observation methods, and Lukes’ (2005) three key facets of power (i.e. decision-making; non-decision-making, and ideological power) to illustrate how food delivery platforms to promote the idea of gig work being fair. Moreover, the paper examines what the authors view as coping approaches to psychological contract violations by gig workers which both reinforce and resist the power possessed by platform firms. The findings offer some fascinating insights into how several ideological, explicit, and implicit mechanisms of power influence gig worker perceptions regarding their work. The role of principled dissent (Krause & Moore, 2018) as a coping approach is particularly prominent in that it enables individuals to continue with the exchange, thus holding the (limited) autonomy available but without supporting the platform firm view that the terms provided are fair.

The fourth contribution by Myhill et al. (2021) concentrates on the underpinning nature and quality of gig work; an area that has received scant attention to date in the literature. The authors seek to apply an approach that focuses on objective aspects that encompass a quality job but also incorporate the subjective, lived experiences of gig workers. The limitations surrounding existing models of job quality are highlighted through the research, in particular the limitations that surround objective styled characteristics. The lived experience of gig workers emerges as more complex and nuanced than existing commentary. For example, while there is critical discourse around the negative side to the tight, technological control that is endemic in gig work, workers appeared to be less concerned on much of this, instead perceiving themselves as holding greater control over when to work. Also, there was appreciation for the opportunities that existed to generate additional income despite the substantial job insecurity that is evident. Of note is that the key findings illustrate gig worker experiences vary depending on the platform and whether they undertake this work as their core income versus it being a supplementary source. This gives further credence for the need for wider empirical research into contrasting variants of gig work to be able to better contextualise and appreciate the different forms that encompass this new economy.

Our final paper by Williams et al. (2021) centres on exploring how different platform organisations attract and select independent gig workers. The data consists of an analysis of multiple websites and terms and conditions documents of online platform organisations in Australia. They reveal evidence of traditional recruitment practice alongside new algorithmic enabled aspects. The research indicates a layer of invisibility around some selection practices used which the authors argue raises concerns over the equitability of access to work on these online labour platforms. The title of the Meijerink, Keegan et al. paper (2021), in this issue, which refers to platform organisations as having their cake and eating it too also has resonance here in that they, ‘simultaneously abdicate responsibility for selection decisions to clients, while maintaining control over attraction of workers and key selection processes, such as algorithmic shortlisting or “matching”’ (Williams et al., 2021, p. 22).

Where to next? A look to future research avenues

The five papers published in this Special Issue illuminate a future research agenda on which to build upon and critique. For example, Williams et al. (2021) provides a model, developed through documentary and website analysis, that could serve as a starting point to further research and understanding on gig worker attraction, selection and turnover. Meijerink, Keegan et al. (2021) offer a useful framework that can serve to further unpack the inter-relationships among HRM activities for gig workers and the competing logics and response strategies underpinning the inherent complexity involved in this form of work. Given the urgency of change permeating work arrangements in the gig economy over the last decade, it is challenging for scholars to imagine what the future holds for this dynamic form of labour. Indeed, there is a danger that the speed with which existing technologies develop, and new technologies emerge may render a lot of the assumptions made about technologically mediated HRM and employment relationships in this space obsolete by the time they get through the publication cycle. However, if ‘human work’ is to remain a feature of gig working either within or beyond the boundaries of the gig economy, there are significant theoretical and practical implications for HRM (Cross & Swart, 2021). We argue that inter-disciplinary research perspectives which broadly encompasses the topic – work in the gig economy – has never been more important and necessary to provide critical perspectives that can be used to positively influence practice and inform policy decisions.

As evident from the papers included in this Special Issue, one of the most pressing issues requiring greater attention from HRM scholars is the need to better understand the role of the algorithm in the different variants of gig working. Algorithms at work, by design, are self-learning and dynamic and exist in ever-shifting employment contexts (Kellogg et al., 2020). This, of course, presents an obstacle to those seeking to explore how they function. Yet, given that algorithmic management has replaced several of the more traditional HRM activities such as selection and rewards, particularly in many app-work and crowdwork variants, there is a clear need to deepen our knowledge on the implications of algorithms for people management. There are many unanswered questions in this domain requiring a sharper focus from researchers. First, should there be a role for the HR function within the platform organisation in the design and development of the algorithm? Might algorithms function more effectively when it comes to people management issues if HR specialists have had an input in their construction (rather than this being the preserve of, for example, software developers)? If this is the case, in what ways can algorithms be adapted so that HR has more of a say in how gig-workers should be managed?

There is an increasing body of research arguing that algorithms are transitioning from employment ‘tool’ to employment ‘partner’ (Bankins & Formosa, 2020; Duggan et al., 2021a; Sherman & Morley, 2020), assuming the responsibilities once held by supervisors or line managers. Indeed, if algorithms are to be seen as the manager by proxy in the context of employment relationships, the implications for HR are significant. For instance, can gig workers have an employment relationship with an algorithm whereby they create a mutual exchange agreement? In this special issue, Shanahan and Smith (2021) consider the implications of how violated agreements influence perceptions of fairness in this type of arrangement. However, the idea of an algorithm being viewed by workers as the ‘employer’ requires further scholarly attention. If there is, for example, the potential for a more co-determined working relationship, how can HR assist workers in their interactions with the algorithm? Answering such questions, may help to bring the disparate literatures of HRM and algorithmic management closer together.

Several papers in this issue either directly or indirectly consider aspects of control and motivation in online platform work. For example, Norlander et al. (2021) found some causal effects of platforms in their second experimental study, that stood in contrast to their first study, raising the issue of possible selection effects at play. The issue of selection effects is worthy of further recognition, such as whether they are related to those that opt into gig work or if it is through attrition. This is something that has not gained much, if any, consideration prior to the publication of this paper by Norlander et al. (2021). Overall, greater research on how the types of technological control and monitoring that is made possible through these platforms impact on gig workers are required.

While untangling the complexities of algorithms at work is critical to gain deeper insights into the technologization of HRM, a growing number of researchers are focussing their attention on gig workers’ experiences of being managed by an algorithm (see for example in this special issue, Myhill et al., 2021). We argue that foregrounding the lived experiences of the ‘human’ in the grip of an algorithmic mediated employment relationship should remain a central tenet of future exploration in the HRM field, a particular feature of papers in this Special Issue. Of course, it can be argued that the lack of clarity surrounding the precarious employment status of some gig-workers across the globe means that having a duty of care over these workers is not a universal concern for the gig organisation. Yet, a growing argument within HR scholarship contends that HRM should have responsibility for the independent workers with whom they work in a wider network (Connelly et al., 2021; Cross & Swart, 2021). There is a need, therefore, for broadening the parameters on where HRM is relevant. This view also fits with calls for a greater focus on wider societal matters as exemplified in the UN Sustainable Development Goals where, for example, HR could, and we argue should, promote moral policies and practices that are sustainable for humans and the environment. Accordingly, the employment status of gig workers may be somewhat immaterial in the context of considering the responsibilities of the HR function in terms of managing this cohort of workers. The potential implications of this line of enquiry for research is significant as it raises several important questions. In what way is the management of gig workers different to employees in more standard employment arrangements? If the nature of the working relationship grows and moves beyond its transactional and short-term focus what are the implications for the HR function in terms of reward, development, retention, etc.? Does ‘best practice’ within these processes neatly extend to a gig-work context or is novel thinking required in these areas to ensure these workers are managed effectively? Again, answering questions like these are crucial in comprehending how HR is to remain a valuable and necessary party in the ecosystem (Meijerink, Keegan et al., 2021).

There is also a need to consider the wider transmissibility of gig economy HR processes and practices into the more traditional employment model (Huws et al., 2018). If algorithmic management can capture and duplicate many of the traditional HR functions it raises the question as to how much of the gig economy experience can be replicated elsewhere? Can organisations outside the gig economy substantially increase their use of HR software systems that in turn sees the need for less HR professionals? The forced move for millions of workers into home working arrangements arising from the Covid-19 has seen increasing calls for greater flexibility in where and when people work once this global health crisis has abated (Wheatley et al., 2021). This leads us to question whether there could be a greater role for algorithmic style management within ‘standard’ employment to remotely manage staff.

As explained, gig work typically involves multiple parties (Duggan et al., 2020) and the successful functioning of the work system is dependent on each party contributing effectively. Yet, there exists concern about the implications of the gig economy for the future of work but also about the long-term sustainability of gig work as a viable form of labour (Wood et al., 2019). There is also a need to consider whether the potential implications of gig work are equal across the world. For example, gig work may provide opportunities to improve job quality, voice, and the generation of jobs in a developing economy context like Africa where over 80% of employment is informal (Heeks, 2017). Through greater platform regulation and certification, decent work conditions could be transplanted into the informal economy (Heeks et al., 2020). There is emerging evidence of several international platforms progressively introducing better work conditions into contracts (Fairwork, 2019), and thus there does exist some potential for embedding decent work conditions through digital platforms.

To effectively address the challenges presented by gig work it seems likely that HR will be required to play a pivotal role in ensuring employment opportunities in this space remain worthwhile, durable and meaningful for workers. Indeed, the idea of developing a ‘career’ within gig-work has received only minor attention from researchers to date (see for example, Duggan et al., 2021b). What are the responsibilities of the HR function in digital platforms regarding the career development of gig workers? How can the HR function help gig workers with the transition into and out of this form of labour? Relatedly, how do employers value work experience in the gig economy when assessing a prospective candidate for a new role? How can the HR function in digital platforms help to develop career competencies for workers unlikely to stay within the industry on a long-term basis? These, and other questions, require much consideration by researchers trying to assess how HRM can contribute to the long-term sustainability of this form of labour.

Finally, we wish to briefly touch upon methodological matters. The innovative nature of gig work brings with it the potential for more creative methodologies being applied to best address many of the future research questions set out here. In this issue we see several innovative approaches adopted (e.g. Meijerink, Keegan et al., 2021; Norlander et al., 2021) which we welcome and encourage more of. Moreover, there is much scope to strengthen the data sources used in gig economy research; many papers in the gig economy and work context tend to rely on very small samples of participants. Greater diversity of perspectives from the different actors involved in this type of work is required to really advance understanding and knowledge. The perspectives of clients and the digital platforms themselves would be especially welcome inputs into future research, though we are appreciative of the access challenges. While challenging to undertake, longitudinal research designs will be especially welcome in that they may assist in charting the evolution of gig work overtime. Given the dynamism of the legal and regulatory environment of gig work, along with the ways in which algorithms are deployed this is likely to be a highly evolving research domain, making it ripe for a more long-term focussed research designs.

Conclusion

The papers in this special issue provide empirical insight on how technologically mediated HRM is enacted in the gig economy. A variety of HRM activities deployed by digital platform organisations that gig workers are subject to including attraction, recruitment and selection, job quality, motivation and control provide an insight into how technology mediates the management of labour in gig work. Considering work and working conditions are hugely individualised across digital platform organisations, the implications for HRM along with policy and union responses will vary and will need to be tailored accordingly. More pertinently, the role and value of organisational HR functions and traditionally internal HR activities such as selection, training and development etc. may be eroded in the context of gig work. These issues notwithstanding, as long as organisations operating in the gig economy remain reliant on humans to complete the gigs, it is hard to argue against an important role for HRM in this new form of labour.

Disclosure statement

No potential conflict of interest was reported by the author(s).