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We advance the concept of platformic management, and the ways in which platforms help to structure project-based or “gig” work. We do so knowing that the popular press and a substantial number of the scholarly publications characterize the “rise of the gig economy” as advancing worker autonomy and flexibility, focusing attention to online digital labor platforms such as Uber and Amazon’s Mechanical Turk. Scholars have conceptualized the procedures of control exercised by these platforms as exerting “algorithmic management,” reflecting the use of extensive data collection to feed algorithms that structure work. In this paper, we broaden the attention to algorithmic management and gig-working control in two ways. First, we characterize the managerial functions of Upwork, an online platform that facilitates knowledge-intensive freelance labor - to advance discourse beyond ride-sharing and room-renting labor. Second, we advance the concept of platformic management as a means to convey a broader and sociotechnical premise of these platforms’ functions in structuring work. We draw on data collected from Upwork forum discussions, interviews with gig workers who use Upwork, and a walkthrough analysis of the Upwork platform to develop our analysis. Our findings lead us to articulate platformic management -- extending beyond algorithms -- and to present the platform as a ‘‘boundary resource” to illustrate the paradoxical affordances of Upwork and similar labor platforms. That is, the platform (1) enables the autonomy desired by gig workers, while (2) also serving as a means of control that helps maintain the viability of transactions and protects the platform from disintermediation.
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Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Platformic Management, Boundary Resources for Gig
Work, and Worker Autonomy
Mohammad Hossein Jarrahi, University of North Carolina at Chapel Hill
Will Sutherland, University of Washington
Sarah Beth Nelson, University of Wisconsin-Whitewater
Steve Sawyer, Syracuse University
We advance the concept of platformic management, and the ways in which platforms help to
structure project-based or “gig” work. We do so knowing that the popular press and a substantial
number of the scholarly publications characterize the “rise of the gig economy” as advancing worker
autonomy and flexibility, focusing attention to online digital labor platforms such as Uber and
Amazon’s Mechanical Turk. Scholars have conceptualized the procedures of control exercised by
these platforms as exerting “algorithmic management,” reflecting the use of extensive data collection
to feed algorithms that structure work. In this paper, we broaden the attention to algorithmic
management and gig-working control in two ways. First, we characterize the managerial functions of
Upwork, an online platform that facilitates knowledge-intensive freelance labor - to advance
discourse beyond ride-sharing and room-renting labor. Second, we advance the concept of
platformic management as a means to convey a broader and sociotechnical premise of these
platforms’ functions in structuring work. We draw on data collected from Upwork forum discussions,
interviews with gig workers who use Upwork, and a walkthrough analysis of the Upwork platform to
develop our analysis. Our findings lead us to articulate platformic management -- extending beyond
algorithms -- and to present the platform as a ‘‘boundary resource” to illustrate the paradoxical
affordances of Upwork and similar labor platforms. That is, the platform (1) enables the autonomy
desired by gig workers, while (2) also serving as a means of control that helps maintain the viability
of transactions and protects the platform from disintermediation.
Keywords: Gig work, knowledge work, Upwork, platformic management, algorithmic management,
autonomy paradox, boundary resources, sociotechnical systems.
We advance the concept of platformic management, focusing on the algorithmic features and related
functions that together help to structure gig work and shape gig-workers’ control over their work. To
do this we use data from a study of the online freelance platform,, one of the most
popular of the many online job-posting/job-seeking platforms that are helping to reshape how part-
time, gig and freelance workers find work (Chapman 2018).
Findings from this study make two contributions to the fast-growing body of research on gig-work
platforms, platform-based work, and worker autonomy in online labor markets. First, we advance the
concept of platformic management as distinct from algorithmic management. We do this by outlining
how the platform’s features, policies and norms of use are structured in ways that locate the
algorithms being used to be one part of an overall managerial structure. Second, we focus explicitly
on knowledge work, distinguishing the work of programmers, editors, architects, designers and other
forms of work that rely on formal education, abstraction and conceptual knowledge and complex
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
tasks (that often rely on both collaboration and extensive interaction with the client). In doing this, we
seek to distinguish freelance and knowledge-based gig work from the fogginess that comes from so
much attention to gig work as ride-sharing and home-sharing.
We are motivated to explore platforms and platformic management given the growth of gig work and
the expanding roles that online or digital platforms play in this work. To the first, on-demand or “gig”
work, data show the number of American gig workers is expected to nearly double in the next few
years and to reach 9.2 million in 2021. Many of these workers pursue the knowledge-based
freelance work that serves as our focal interest (Molla 2017). Many see this shift to gig work as
“liberating” workers from traditional work environments, providing them with opportunities to work
independently and flexibly (Hannák et al. 2017). Flexible work is thus seen as one of the primary
attractions of the gig economy, since it is argued that gig workers enjoy higher autonomy in deciding
where and when to work (Friedman 2014; Kuhn and Maleki 2017). In fact, the “rhetorical markers” of
the on-demand economy are “freedom, flexibility, and entrepreneurship” (Rosenblat and Stark 2016,
p. 3761).
The rise in their central role and impressive functionality suggest to us that a deeper understanding
of a platform’s management strategies requires consideration of the digital features and resources
that mediate knowledge-intensive gig work. Pursuing this goal, we examine Upwork’s managerial
functions relative to how gig workers navigate and interact with the management and administrative
functions, features, and algorithms of the platform. We begin with the research question: What are
the managerial functions of Upwork that enable the platform to manage gig workers?
To respond to this question, the paper continues in seven sections. First, we outline why we choose
to focus on Upwork for this study. In the next section, we review related literature, focusing on the
sociotechnical basis of gig-working and the rise of the digital platforms as a form of managing.
Following this, we conceptualize these digital platforms as boundary resources to focus attention on
the ways in which Upwork serves as a boundary and a resource between those seeking work and
those seeking workers. In the fourth section, we provide an overview of the research approach, data
collection and analysis. In section five, we present the findings, and then discuss these and their
implications in the sixth section. In the final section, we summarize the work and highlight future
Focusing on Upwork
We focus this study on Upwork.com1 as a proxy for what these online platforms provide to freelance
and gig-based work and workers. We selected Upwork because it is the world’s largest online
freelancer platform (both in terms of revenue generated and number of workers, with three million
jobs posted annually) (World Economic Forum 2016). Upwork serves as a market-making platform,
providing a means to connect those offering work to potential workers (Kuhn and Maleki 2017).
Market-making platforms provide mechanisms to ‘make a market,’ such that the independent worker
performs a job with the platform seemingly replacing the boss (Spreitzer et al. 2017). In this
simplified view, platforms like Upwork are seen as providing workers both flexibility and
independence. Furthermore, and returning to oft-used examples, gig-enabling digital platforms like and Uber tout these attributes as a central part of their service (Kuhn and Maleki 2017;
Ticona et al. 2018).
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Upwork allows employers to post a range of knowledge-based gig work such as web design, digital
marketing, strategic business consulting and intellectual property law, to be seen by potential
workers who can bid for these jobs. Both job seekers and job posters create accounts on Upwork, as
doing so gives them access to the see or post the work, and to take advantage of the resources and
functions that Upwork provides. The jobs or “gigs” posted to Upwork typically involve engagements
with clients that range from days to months and require more complex interactions than what is
posted to microtasking sites like Amazon Mechanical Turk (AMT) or TaskRabbit (De Stefano 2015;
Green et al. 2018; Kalleberg and Dunn 2016).
Jobs posted to Upwork typically require a relatively higher level of tacit expertise and therefore a
different approach, beyond mere algorithmic management, to managing and coordinating
transactions between service providers and receivers (Claussen et al. 2018). Kalleberg and Dunn
have argued that gig workers on Upwork may enjoy more control and flexibility, compared to workers
on ride-sharing and microtask platforms, because Upworkers are provided with mechanisms to
create a portfolio, decline projects, negotiate wages, dispute pay and work, and rate clients
(“employers”) (Jarrahi and Sutherland 2019).
As detailed below, we focus specifically on exploring Upworkers’ understanding of and experiences
with the management functionality provided, critically examining how the management functions
reflect and enforce managerial structures and principles, more broadly. Finally, by using interview
data and, particularly, the functional walkthrough method, we seek to open the black box of Upwork's
technological arrangements and managerial functions.
Related Literature
What we know about gig-work platforms and worker (or work) autonomy can be situated at the
nexus of two streams of literature: (1) the nascent literature defining and explaining some common
dynamics of the gig economy and digital platforms, and (2) the recent theorizations of platforms or
algorithms as managers. This literature provides some description of some of the mechanisms of
digital control exercised by platform spaces as well as worker responses to these mechanisms. Both
streams provide insights into the kinds of management structures, rules, and algorithms that might
be embedded in a gig-working platform.
The Sociotechnical Basis of Gig Work and Digital Platforms
Gig workers are typically characterized by the transactional nature of their relationship with
employers. Gig workers may not be “professionals” or work full time on gigs, in the sense of gig work
being their primary source of income. Rather, gig workers often work “on the side” (alongside
another job) or as a hobby (Brinkley 2016). Gig work centers around specific, finite projects (gigs),
rather than full-time employment (Wood et al. 2018).
For the gig worker, this at-will relationship implies flexibility in selecting and scheduling work, and
also flexibility in choosing both where and how they accomplish work (Friedman 2014; Spreitzer et
al. 2017). Gig work therefore lends itself to flexible, autonomous work, in which the gig worker has
more say in setting their own hours, choosing which projects to pursue, taking on a variety of
projects and roles, and in some cases, guiding their own business as an entrepreneur (Abubakar
and Shneikat 2017; Donovan et al. 2016; Torpey and Hogan 2016). In some cases, however, the gig
or digital workers may run into precarious work situations by engaging in menial work. For example,
Irani (2015) examines the divisions of labor and the cultural norms of microworkers, and concludes
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
that the notion of humans as computational services, embodied in the design of AMT, may alienate
crowdworkers and raise the question of fairness.
The gig economy is bound up with the rise of digital platforms as mediators and facilitators of
temporary, often impersonal work arrangements (Dunn 2017; Lehdonvirta 2018). Digital platforms
play an important role in connecting people, and providing rating systems and other forms of
evaluation to build trust between workers and employers (Acquier et al. 2017; Yoganarasimhan
2013). In the absence of a traditional work organization, the digital platforms’ features and functions
help to provide structure for working arrangements and the articulation and evaluation of tasks (Irani
and Silberman 2013; Lehdonvirta 2018). The centrality of these digital platforms means gig workers’
professional situations, and the amount of control they enjoy in conducting work and negotiating pay,
are bound up in both the functions provided by and the policies of the platform (Kalleberg and Dunn
2016; Kuhn and Maleki 2017). Like other structural metaphors such as ‘network’ or ‘infrastructure,’
the concept of platform means different things to different audiences. Gillespie (2010) brings to the
fore the political dynamics and rhetorical utility of the term ‘platform’ for various stakeholders (e.g.
technology vendors, advertisers and policy makers), and argues it has been used to spark
discussion of new business models, technical architecture, and information policies.
The mutual dependence of digital platforms in supporting gig work and gig work’s reliance on digital
platforms invites a more theoretically grounded understanding of digital platforms and how they
operate as distinct sociotechnical structures in mediating work (Howcroft and Bergvall-Kåreborn
2018; Kuhn and Maleki 2017; Sutherland and Jarrahi 2017).
Digital Platform as Manager
We focus on two aspects of Upwork’s managerial functions. First, we summarize the burgeoning
literature looking at the roles of algorithms and other platform features relative to their managerial
roles. Second, we focus on the concept of “programmability,” or the ability for the platform and
related functions, guidance and rules of use to adapt.
Algorithms and Other Platform Features
Central to the functioning of many digital platforms, algorithms now make autonomous decisions,
taking over practices previously handled by managers (Brynjolfsson and McAfee 2014; Lee et al.
2015). The prevalence of algorithmic management signals an important shift in how work is
conducted and managed and how gig workers make sense of their work and autonomy: what
Möhlmann and Zalmanson call “the autonomy paradox.” The autonomy paradox encapsulates the
situation where even as workers can enjoy autonomy over how they choose which work to pursue,
and when (and where) they do work, they are subject to new forms of control and surveillance. And,
this control and surveillance serves to limit aspects of their autonomy (Mazmanian et al. 2013).
Therefore, the seeming independence from direct, human-centered, managerial control in gig work
may or may not result in more autonomy (Gershon and Cefkin 2017; Lehdonvirta 2018). Recent
research points to the control exercised by gig platforms that limits workers’ autonomy (Prassl 2018;
Rosenblat and Stark 2016; Shapiro 2018; Wood et al. 2018). These scholars highlight a range of
control functions, collectively known as “algorithmic management” or the “oversight, governance and
control practices conducted by software algorithms” (Möhlmann and Zalmanson 2017, p. 4).
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Seen this way, the gig economy is an experiment in worker autonomy using under-regulated market-
making mechanisms (a well theorized issue of online markets, per Bar 2001). Workers seek out
flexible arrangements via online platforms. Nevertheless, to take a job, these workers are
increasingly being required to adhere to the time and project structures put in place by the platform.
More broadly, and as contemporary empirical work is helping make clear, worker autonomy is a
complicated concept, becoming that much more complex as overlapping digital platforms are helping
(re)define work practices (Bucher and Fieseler 2017; Lehdonvirta 2018; Shapiro 2018).
Underscoring the autonomy paradox, Gershon and Cefkin (2017) argue, “Just because a person is
continuously consenting to do work for others does not necessarily mean that the person has more
autonomy or has more equitable work relationships than a person occupying a more traditional job.”
As early success stories in the development of the gig economy, Uber and AirBnB have taken on a
larger (and perhaps too-visible) role as templates for understanding other gig economy business
models (Mikhalkina and Cabantous 2015). The term “Uberization,” for instance, has come to
encapsulate the shift toward short-term or project-based work and increased risk for workers (Aloni
2016; Davis 2015; Fleming 2017; Kalleberg and Vallas 2018). Corporaal (2018) highlights the
problem presented by this preoccupation, writing, We know surprisingly little about the diversity of
platforms that are out there and what types of work can be outsourced through them.” Specifically,
little is known about the organizing principles of the platforms supporting online freelancing, a
complex form of knowledge-intensive gig work involving skilled work, such as editorial work, public
relations, and others (Premilla D’Cruz and Ernesto Noronha 2016).
The limited understanding of the organizing and managerial principles that structure online
freelancing platforms is a significant gap. The prospect of managing knowledge-intensive projects,
often with unspecified processes and subjective deliverables, is typically more daunting than
managing tasks that can be effectively broken down into piecework such as those typically handled
by AMT (Alkhatib et al. 2017).
Moreover, the effects of algorithmic management on worker autonomy have become entangled with
the automation of decision making and the diminishing control of workers over their work (Howcroft
and Bergvall-Kåreborn 2018; Newlands et al. 2018; Rosenblat 2016; Wood et al. 2018). For
example, research on Uber helps make visible how functions on Uber’s app monitor and control
drivers’ activity through an assortment of algorithms and incentivization schemes (Banning 2016;
Rosenblat and Stark 2016; Simonite 2015). Whereas Uber proclaims that you can “be your own
boss,” the app and algorithms become a subtle and perhaps downbeat counter-rhythm. That is, you
can “be your own boss - subject to the rules and controls we put in place as we gather your data to
assess compliance.”
Simplistic discussions focus attention to algorithms performing these controls, with little or no direct
human intervention (Agrawal et al. 2017; Miller 2018). More thoughtful analyses make clear that
Uber’s algorithmic controls are part of a larger suite of material features and specific rules that bind
this complex system together (van Doorn 2017). Beyond algorithmic control, gig platforms may
leverage various technological and social mechanisms in order to manage how gig work is
conducted (Ticona et al. 2018). For example, gig work platforms may require the use of controlled
measurements, such as time trackers for hourly projects (with intrusive features like periodic
screenshots) (Kuhn and Maleki 2017), or they have policies requiring gig workers to commit to
windows of availabilitythat constrain flexibility and autonomy (Lehdonvirta 2018).
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
What these examples make clear is that platforms embody technological resources and rules that
both enable and manage work. Our premise in this paper is that some of these dynamics cannot be
reduced to algorithmic management, particularly in the context of knowledge workers conducting
work through online freelancing platforms (e.g., Upwork,, Toptal, and Fiverr); we
denote these dynamics as platformic management to make clear this is more than “just” algorithmic
functionality in play. Despite the recent scholarly attention paid to the management and control
aspects of digital platforms for gig work, both public discourse and contemporary academic research
have been largely focused on more conspicuous forms of gig work as icons of the gig economy
(Heeks 2017; Howcroft and Bergvall-Kåreborn 2018; Sutherland and Jarrahi 2018).
A recent report by Ticona et al. (2018) provides a typology of job-seeking platforms, and argues that
the management model used by market-making platforms (e.g., Upwork,, or Fiverr)
is more complicated than those of on-demand platforms such as Uber. This is because market-
making platforms must provide more than “automated matching between clients and workers”; they
need to mediate a complex hiring process “through sorting, ranking, and rendering visible large
pools of workers” with varying levels and forms of skill (Ticona et al. 2018, p. 3).
The limited understanding of the organizing and managerial principles that structure online
freelancing platforms is a significant gap. The prospect of managing knowledge-intensive projects,
often with unspecified processes and subjective deliverables, is typically more daunting than
managing tasks that can be effectively broken down into piecework such as those typically handled
by AMT (Alkhatib et al. 2017).
Programmability and Autonomy
One of the core features of a digital platform is “programmability,” which describes how platforms
actively invite user innovation by offering opportunities for “programmability”, allowing a bottom-up
extension of the base beyond the designer’s intentions (Plantin et al. 2016). In their role as
mediating gig-workers and gig work, these “programmable” platforms often take on managerial tasks
and roles relative to structuring work (Howcroft and Bergvall-Kåreborn 2018; Rosenblat 2016). Lee
et al. (2015) identify three specific managerial tasks that have been taken over by algorithms within
the Uber and Lyft platforms: assigning work, providing information to workers, and evaluating their
performance. Algorithms draw on large data sets to provide guidance on how to assign tasks to
workers through filtering, ranking, and coordinating activities (Lustig et al. 2016; Raval and Dourish
2016). The traditional (middle) management structure of human supervisors is replaced with the
automated enforcement of decisions based on large amounts of data (Aneesh 2009; Möhlmann
2015; Schildt 2017).
Most conceptualizations of algorithmic and data-centric decision making take a human-centered
approach. That is, understanding the algorithms demands seeing these as merely analytical and
technological systems of formal mathematical techniques (e.g., Knuth 1997). Rather than fixed
systems of procedural formulas, these algorithms are heterogenous, dynamic sociotechnical
systems, enacted through the practices of those who utilize them (Dourish 2016; Seaver 2017),
which undergo evolution in deployment. For platforms, this is not just a fact, but rather a core
criterion. The platform should be programmable, intentionally allowing and benefitting from specific
kinds of appropriation by a crowd of users. This “programmability,” and the ostensibly participatory
relations it suggests, is in fact the basis of the platform’s apparent neutrality: the notion of the
platform as an intermediary that simply connects the activities of otherwise disconnected actors
(Gillespie 2014).
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Much of the literature on digital platforms has been aimed at dispelling this rhetoric of neutrality, and
outlining the specific ways in which platforms restructure relations. Platforms’ reliance on data
requires a level of surveillance that is not present in most workplaces, and which may create
significant information and power asymmetries between the worker and the platform core
(Möhlmann and Zalmanson 2017; Wagenknecht et al. 2016). For example, Kingsley et al. (2015)
discusses how AMT’s platform design generates information asymmetry and consequently power
asymmetry between AMT workers and those who post tasks. Furthermore, workers report that the
management processes of on-demand platforms are opaque: hard to see or understand (Chan
2019; Rosenblat and Stark 2016). Given the lack of transparency around how the algorithms work,
gig workers face significant sensemaking efforts in order to become familiar with the management
and control processes under which their work is structured (Möhlmann 2015; Raval and Dourish
2016; Wagenknecht et al. 2016). These information asymmetries, combined with the impersonality
of platform spaces, contribute to a rearrangement of client and worker interactions such that human
labor is accessible programmatically (Raval and Dourish 2016), or, as Irani and Silberman (2013)
describe it, they provide ‘humans-as-a-service’. The workers then find themselves working in an
impersonal and inscrutable system (Möhlmann and Zalmanson 2017).
The working environment presented by the platform therefore both supports and obstructs the gig
worker’s desire for more professional flexibility and autonomy. Building off of Mazmanian et al.
(2013), Möhlmann and Zalmanson (2017) discuss an autonomy paradox, in which platform workers
gain work flexibility and autonomy, while simultaneously subjecting themselves to controlling
information asymmetries and surveillance policies. In this way platform-based work invites
meticulous surveillance and a host of algorithmically enforced control mechanisms which tie the
worker to the platform and curtail their agency, while ostensibly enhancing their autonomy (Alkhatib
et al. 2015; Howcroft and Bergvall-Kåreborn 2018; Rosenblat and Stark 2016). Balancing these
tendencies towards programmability and algorithmic control, it is possible to see a relation between
worker and algorithm that is mutual and emergent (Jarrahi and Sutherland 2019).
This dual role of the platform, as both market-making and managing, is central to our thesis. As
noted in the introduction, the dynamics of control and autonomy in the platform space have largely
been explored in either microtasking sites (Alkhatib et al. 2017; e.g., Irani and Silberman 2013;
Lehdonvirta 2018) or in the specific context of ridesharing (e.g., Lee et al. 2015; Ma et al. 2018;
Rosenblat and Stark 2016). The notion of algorithmic management has largely developed to
describe on-demand platforms such as Uber, in which many of the decision-making responsibilities
have been assumed by algorithms (Ticona et al. 2018). The lack of research on platforms mediating
knowledge-intensive work is a significant absence, as the gig worker’s experience with a platform,
and particularly their autonomy, is highly dependent on the kind of work they are doing and the way
the platform structures that work (Kalleberg and Dunn 2016; Lehdonvirta 2018). We address this gap
by focusing on a platform space, which is less rigidly orchestrated by algorithms, and by focusing on
a broad array of platform features as managerial mechanisms.
Digital Platforms and Boundary Resources
To help pursue this deeper understanding, we draw on the concept of boundary resources (Eaton et
al. 2015; Ghazawneh and Henfridsson 2010; Karhu et al. 2018). The concept of boundary resources
has emerged of late from the information systems research community as a useful conceptual
mechanism for describing the paradoxical affordances of these digital features, which may enable
both worker autonomy and platformic control (Barrett et al. 2015; Eaton et al. 2015; Ghazawneh and
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Henfridsson 2013, 2015; Schreieck et al. 2016). The boundary resource draws on the concept of the
boundary object as a way of discussing the cooperation of heterogeneous groups (Farshchian and
Thomassen 2019). However, boundary resource is a concept used in platform studies and
information infrastructure research to describe the relationship between the organizer/owner of a
platform and users as ‘complementors’ of the platform. In this context, boundary resources are those
specific resources and facilities provided to a group of platform users with the intent of facilitating
innovation through controlled coordination (Ghazawneh and Henfridsson 2010).
A boundary resource is provided by the platform or service and delivers both specific functions and
“the interface for the arm’s-length relationship” between the platform and participants or contributors
(Ghazawneh and Henfridsson 2013, p. 23). In the case of gig platforms, boundary resources might
include search and matching algorithms, badges that signal skills and abilities, templated profiles to
communicate expertise, financial services to help manage project payment, messaging systems to
support discussions between those offering and those seeking work, and arbitration policies and
support. In each of these examples, it is both the functionality and the intermediating relationship of
the platform that make them boundary resources.
Boundary resources are provided to a large group of complementors (users of the platform) by a
digital platform (core or owner) with the intention of enabling and facilitating their participation and
contribution to the platform’s network of value, while maintaining control of the complementors’
activities and assuring the quality of contributions (Ghazawneh and Henfridsson 2013). Analysis of
boundary resources, therefore, focuses attention to the struggle between the users’ appropriation of
a boundary resource and the platform’s design of the resource in order to promote and reinforce
particular usages (Eaton et al. 2015).
Boundary resources are, therefore, valuable tools in understanding the managerial relations
unfolding on digital labor platforms. We use this theoretical grounding to analyze a collection of data
about the Upwork platform’s functions, features, use policies, and guidance on use (e.g., the
frequently asked questions or FAQs). Furthermore, we seek to uncover the variety of relationships,
which might exist between the platform and workers of different professions, levels of experience,
ages, and genders. In this analysis, we understand Upwork and its platformic management from the
perspective of the gig worker, while inferring from the design of the platform the perspective of the
Upwork designers and Upwork leadership.
Research Approach
We pursued an exploratory case study of Upwork, seeking multiple sources and forms of data in
order to minimize the limitations of any one source or form. We chose Upwork as the digital platform
supporting gig work we would study for three reasons. Firstly, it has a large population, and this
community is accessible to the researchers through a number of social media sites (World Economic
Forum 2016). Secondly, and as noted, much of the work, which occurs through Upwork, is highly
skilled or creative work (Green et al. 2018). Thirdly, Upwork provides a large and evolving suite of
tools and resources to workers, which will help us better understand and theorize on the
sociotechnical arrangements of platformic management.
Data Collection
Three types of data were collected: (1) Upwork forum discussions and other documents found
online, (2) interview data with Upworkers, and (3) data created from a walkthrough analysis. The
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
interview data were used as the primary source of analysis in this research, and other methods
helped coborrate findings from the interviews.
Forum discussions were retrieved from the Upwork forums and from a reddit forum2 dedicated to
Upwork. Additionally, the researchers collected data from official Upwork web pages, help pages,
and excerpts from the Upwork Terms of Service. The goal of this part of the data collection effort
was to collect perspectives from different discursive contexts. The official documents were used to
represent Upwork’s official stances and policies, whereas the Upwork forum is a context in which
workers are able to communicate with each other, but are under the supervision of Upwork
moderators, and often interact with these moderators directly. The reddit forum is a space in which
workers and clients are not supervised by Upwork moderators and cannot be connected to their
Upwork accounts, meaning that they are able to speak more frankly about activities, which
contravene Upwork’s Terms of Service. The researchers read through each forum, beginning with
the most recent posts. They collected posts that 1) had more than one response, and 2) in some
way related to the workers’ interactions with the platform, rather than concerning the state of their
respective professions more broadly or freelancing in general. Researchers stopped collecting posts
when new posts were no longer causing the researchers to reevaluate themes established in the
coding process. The data collected from the two forums and from Upwork totaled 118 documents,
ranging from 2015, after Upwork’s rebranding from oDesk, to early 2018.
Interview participants were recruited by identifying freelancers through their professional websites,
and social media sites where users are not anonymized (e.g. Twitter and the question-and-answer
site Quora). Because these sources are not anonymous, the researchers could evaluate whether the
individuals were, in fact, gig workers. The sites also had contact information. Potential participants
were also chosen so as to provide a variety of professions, genders, and levels of experience with
the platform.
The resulting pool of 20 participants comprised Upwork community members, successful workers,
and those who were new to the platform. All workers performed digital work that required
specialized, skilled labor. Some worked through Upwork as a primary source of income, whereas
others used it as an ancillary form of employment, fitting gigs into their free time around other, more
stable jobs or responsibilities. For some it was a stopgap form of employment that they were
pursuing temporarily, with the intention of moving into a more stable position or a dream job. The
interview protocol covered a few major themes:
1. The general experience of the workers with the Upwork platform, and how they use it to
connect with clients.
2. Constraints or obstacles that they have encountered in working with the platform.
3. How they make sense of different platform functions and how they work around its
Interviews were semi-structured and lasted approximately an hour. Interviewers followed up on
certain parts of interviewees’ answers as themes developed throughout the course of the study. For
instance, as many of the participants mentioned going off-platform in order to conduct transactions,
interviewers increasingly inquired about how and why this was done when participants mentioned it.
After the interviews were conducted, they were transcribed and included, along with the forum data,
in the process of coding. Participants were interviewed by phone or via web conference, as they
lived in locations across the world. This limited our ability to observe body language and facial
2 /r/upwork
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expressions during our conversations. However, doing remote interviews allowed us to include a
greater diversity of participants than would have been possible otherwise. More importantly, we had
to fit into the schedules of individuals who work from their mobile devices throughout the day. To
many of our participants, scheduling the interviews this way felt like less of a demand on their time.
As summarized in Table 1, these data are gathered from nine females and 10 males. The average
age of our participants is about 37: the youngest is 25, the oldest is 58 (and one person declined to
provide their age). Fourteen respondents have extensive Upwork experience (they are classified as
established) and six are new to the platform. We also report the Job Success Score that Upwork
posts for each person. This score is partially based on completed jobs, so newer workers do not
have this distinction yet. There are a range of professions represented, and their hourly work rates
range from $11 to $150 per hour.
Table 1: Participant information
Knowledge Domain
Hourly Rate
(in USD)
Job Success
Score (%)
Industrial Design
Lifestyle Writing
UX Design
Systems Administration
Blog / Article Writing
Research Blog Writing
Blog Writing
Voice Acting
Content production
Survey Analytics
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Creative Storytelling
Legal Writing
Content Writing
Our participants worked on jobs through Upwork that could take anywhere from a few hours to
several months to complete. Participant 17, for instance, who worked as a copywriter and legal
writer, typically took gigs lasting a couple months, but also had a few ongoing gigs, which involved
doing occasional tasks for the same client over a number of years. Participant 14, a photographer,
typically worked on one-time projects, requiring a couple hours each, and only occasionally took
longer gigs lasting around a month. Some participants formed good working relationships with
clients such that they chose to work on multiple projects together.
In addition to these worker-centered methods, we also interacted directly with the platform. In
studies of algorithmic or platformic management, in particular, application features and their
constraints were points of control and breakdown. For this reason, we used the platform walkthrough
method, per Light et al. (2016). The walkthrough method recommends evaluating a platform based
on its governance of users, the expected uses, assumptions embedded in the design, and its
emphasis on, or obfuscation of, particular pieces of information (Light et al. 2016). In learning the
platform, we had to spend time working through various help pages and the Terms of Service. Doing
so furthered our understanding of the platform’s official stance on issues regarding employment
contracts, the role of Upwork, and mediation processes.
Conducting the walkthrough provided us with direct empirical observations of the Upwork platform
and the structure of its design and functionality. For the walkthrough, one author engaged another
author in work through the platform, requesting a job and going through the process of contacting,
interviewing, and executing a transaction through the workspace. To avoid wasting other Upworkers’
efforts, the job remained private (unobservable to most workers), and no other workers were
contacted or interviewed for this job.
The job was basic proofreading and editing of a piece of academic writing. The author taking the role
of a “gig worker” used Upwork’s time tracker while doing this editing. The “worker” and “client” used
Upwork’s chat and video conferencing system to arrange the job and interview for it. They then
conducted the transaction through the platform, paying through Upwork’s escrow system. The
platform walkthrough method allowed us to mimic, but not exactly replicate, the process of hiring on
the platform. We identified specific platform features and related them with the accounts provided by
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workers. Furthermore, this approach allowed us to experience parts of the platform that are only
observable to transacting parties.
Data Analysis
As is common for exploratory work that relies on multiple forms and sources of data, analysis was
done through a process of inductive coding, guided by the research questions (Corbin and Strauss
2008). After the initial data were collected, the research team began the first round of independent
analysis, which led to memo writing and extended conversation. Doing so allowed the team to
become familiar with the collected data and to collaboratively engage in sense-making. Rather than
calculating percentages of agreement/disagreement among researchers, we followed the norm of
qualitative research, which encourages conversation and “the negotiated agreement method” among
researchers (Campbell et al. 2013, p. 306). This required the research team to meet regularly to
work through differences and understand the data. The goal was to achieve consensus in relation to
the final coding scheme, categories, and relevant sets of evidence. This pursuit of consensus meant
that for each question, data were explored to identify specific control or management activities.
These included instances in which the platform exhibited its platformic management by coordinating
things and providing boundary resources, and workers interacted with the platform as an organizing
At first, these codes represented specific actions taken by both the platform and workers or
expressions of their opinions and strategies. In the second round of coding, the original codes were
combined and abstracted to represent broader managerial affordances of the platform. In this round,
the different data sources informed each other. For example, workers’ understandings of policies
and processes on Upwork could be compared to the published policies themselves, and features of
the platform described in interviews and forums were experienced directly through the walkthrough
method. Final codes are a combination of direct observation of the platform’s resources, the
perspectives and experiences of breakdowns associated with those resources, and examples of
circumventions or strategic usages.
Participants’ accounts converged on a set of common affordances and constraints of the Upwork
platform, such as the benefits of trust and greater exposure versus the drawbacks of surveillance
and technical breakdowns. Accounts similarly agree about the core platformic management
functions performed by the platform, referencing its quality requirements, its provision of resources,
and its match-making affordances. This noted, participants had distinct interactions with the
affordances, constraints, and functions of the platform. For those who had extensive experience with
Upwork, the platform’s functions/algorithms and their role in organizing gig work was considerably
less visible.These gig workers in our dataset were not as cognizant of Upwork’s infrastructure, since
the platform’s arrangements tended to reinforce their already established positions (e.g., by listing
them higher in searches). However, those who had recently begun to use Upwork tended to pay
more attention to the underlying technological mechanisms of the platform, and how they could
harness these to create a competitive advantage.
In what follows, we will include examples from all the participants, but place Participant 3 in the
spotlight to present a more vivid illustration of an experience with online freelancing. As a 32-year-
old user experience (UX) professional, he approached freelancing out of necessity: “I started out in
freelancing because I got fired from a corporate job and at the time I was just a burned out software
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developer who did not want to write another code in his life and so in 2012 I was having health
problems for the last several months and I was in corporate because the stress was just getting to
me and so when I went into freelancing it was a matter of necessity because I couldn’t go back into
the corporate world.” In this transition, Upwork (and its predecessors, O-Desk and eLance) served
as a critical tool; we will illuminate its role further below.
Common Affordances of the Platform
Almost all the participants appreciated the unique affordances of the Upwork platform, which
undergird online freelancing. Several gig workers in our sample had worked as freelancers before;
however, online freelancing increasingly differs from traditional freelancing, in the presence of digital
platforms, which are now entangled in the development of digital gig work. Effective implementations
of these affordances enable the platform to distribute work, facilitate transactions, provide means to
resolve conflicts, and help establish some level of trust among transacting parties. The platform is,
furthermore, dynamic in supporting these needs. Workers populate their profile with customized
descriptions and portfolios, and negotiate specific milestones and hourly contracts with clients.
These technical resources are open enough to facilitate the workflows of a wide array of professions.
In this way, they are not so much programmable, in the sense of an API, but rather they are open to
appropriation by workers and clients for the specific needs of a given project.
Participants viewed the mediating role of the digital platform as consequential in achieving scale and
extending their reach by providing them with access to a global network of clients. Participant 20
appreciates how using Upwork helped expand his reach beyond his local area, giving him access to
projects in other geographic locations, as well as projects with more variety, thereby “...expanding
the playing field geometrically so far beyond what I could do as a local freelancer...” The scale of the
platform’s population therefore lends the worker more options and more flexibility in landing gig
Upwork and its affordances helped Participant 3 establish himself as a top-rated freelancer and turn
freelancing into a viable career option with enough flexibility to work remotely while traveling back
and forth between the US and Australia: “I have more of a need for more stable income ... at the
same time I also have the need to stay remote because I’m engaged to a woman who lives in
Australia.” He sees freelancing as a “feast or famine” career, as he had worked for one of the world’s
largest consumer goods companies for a year, typically billing 64 hours in a week, but he also went
through periods of “famine” with very little work. Through its vast network of clients, Upwork provides
a steady revenue stream, helping him deal with the precarity of online freelancing. In particular, he
was able to build on the reputation system provided by Upwork (positive reviews and UX tests): “I’m
the only top rated freelancer in Upwork who’s based in the US and has a top 20% score [in UX]. So
I’m using that in my marketing now.” The positive reviews from several small contracts he has
completed over months have also enabled him to reach $70K lifetime earnings on Upwork in 2016
and attract more clients with a higher hourly rate ($79). In situations like this, the platform lends
stability to typically sporadic gig work.
The benefits of network externalities and scale go hand in hand with the platform as a digital
infrastructure, building on the processing power of computational and network-based systems, and
conducting automatic and semi-automatic decision-making. For example, Upwork facilitates
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matchmaking between thousands of clients and gig workers through two mechanisms: (1) Upwork
draws on algorithmic assignment, which automatically connects gig workers with clients based on a
set of attributes, and features that enable the two parties to actively search and sort projects or gig
workers. (2) The platform also enables users to reach scale and lower transaction costs by providing
communication and reputation systems, transaction services, and contractual agreements, which
facilitate professional interactions. Concretely, the Upwork platform supports security and efficiency
in transactions, such that the overhead of logistical problem-solving which is required of the gig
worker is lessened, as is the uncertainty of conducting transactions with strangers.
Common Constraints of the Platform
Despite important affordances provided to gig workers, the Upwork platform creates information
asymmetries between the users and the platform, in which users may lack important information on
how the platform works and how various automatic decisions are made (we will provide examples of
information asymmetries in the rest of the findings section). These information asymmetries may turn
into power asymmetries that favor Upwork or clients over gig workers. For example, when
Participant 3 and his client (a startup company) agreed to amicably end a contract because of the
ambitious timeline: “They told me they were not going to leave me a review as long as I didn’t leave
them one, so I held up my end of the bargain and then a few days after the contract ended I noticed
my job success score went down from 99 to 93% and I stopped getting any new leads at that point.
It was because there was actually hidden feedback, so they had left me with a bad rating but they
left it in such a way that only Upwork could see it, but Upwork uses those ratings when they’re
computing the job success score...and this is a platform where it’s 5 stars or fail.
Information and power asymmetries coupled with the technological limitations of the platform (e.g.,
inefficient communication channel or file sharing features between workers and clients), constrain
the work practices of gig workers and may impinge upon their sense of autonomy. Such constraints
can serve as the impetus for workers to work around the platform and its managerial mechanisms to
retrieve some of their professional autonomy (we detail some of these strategies that help workers
circumvent the platform) or extend the platform with their own configurations or practices. In
situations where the resources and functions provided by the platform are inefficient, workers (and
clients) may seek resources outside the Upwork platform. The use of external communication
applications and external websites is a good example of this, as workers can bring these external
resources together with the matchmaking affordances of Upwork in order to accomplish more
efficient transactions. These practices may contravene Upwork’s terms of service, but as several
participants note they improve the experience for both workers and clients.
In the remainder of the findings section, we return to our research questions and describe platformic
management functions the Upwork platform encompasses and the ways gig workers may
understand and interact with them.
Core Functions of Platformic Management
Data show that Upwork manages through a combination of algorithmic decision-making,
technological features, and business rules. We identify six management functions performed by the
Upwork platform: (1) managing transactions, (2) channeling communication, (3) resolving conflicts,
(4) providing information, (5) evaluating performance, and (6) gatekeeping. As we note in the
Discussion section, these parallel some of the basic roles of managers as articulated by Henry
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Mintzberg (1989). In what follows, we describe these functions and the ways in which gig workers
make sense of and appropriate them in relation to their work autonomy.
Managing Transactions
Upwork provides functionality that helps coordinate the tasks and people by structuring and
automating transactions. This is a feature of the platform that is not so much algorithmic as
automatic. Although algorithms may underlie even the simplest tasks the platform undertakes,
releasing funds to a freelancer upon completion of a project is very different from ranking freelancers
in search results. This functionality benefits the worker primarily by automating and securing
transactions, addressing some of the central difficulties of conducting work independently. However,
automation and security come integrated with norms of surveillance and tie the worker to platform-
provided tools and procedures.
One of Upwork’s primary coordinating resources is a developed contracting system workflow that
automates much of the administrative and clerical work of invoicing and time tracking. This workflow
provides automatic invoicing, automatic currency conversion, and tax withholding information in the
form of a spreadsheet. These resources are useful for gig workers, who typically must coordinate
their own transactions and projects. Participant 15 finds that, as a freelancer, just “chasing down late
paychecks” from clients can take up a lot of his time, and that Upwork’s automatic escrow system
helps with this. Participant 12 notes: “I have a time tracker which I turn on and then I do my work and
I turn it off when I’m done and it automatically goes to Upwork and it’s an automatic pay system, like
you don’t have to invoice anyone.” Although freelancers could potentially get paid more quickly if
they worked off-platform (no waiting period), many found the invoicing and conversion services to be
a benefit. Participant 15 adds that he may ask clients approaching him off-platform to hire him
through Upwork, simply to “keep everything in one place.
In addition to automating transactions, Upwork provides transaction security through an escrow
service. Workers in the forums and in interviews indicate that this escrow service, along with
Upwork’s ‘payment protection’ policy, is an important resource because scams and unreliable clients
are a persistent threat when working independently. Receiving the security benefit of these
resources requires the freelancer to follow Upwork’s policies and protocols. According to an Upwork
moderator responding to a question on the forum: “In order to be eligible for our Upwork Payment
Protection, you will need to be hired on an official contract and track your time with our desktop app
if its hourly or have the full agreed amount funded in Escrow if it is fixed price.” Upwork’s time tracker
runs on the gig worker’s device, recording hours worked and taking screenshots six times per hour.
These are then provided to the client. In another forum post, a worker explains that Upwork needs
this documentation to enforce payment. Without it, “all Upwork can do is suspend the client if there is
proof of them paying outside the platform.”
Some workers circumvent Upwork’s contract system in order to avoid Upwork’s transaction fees,
maintain privacy, or to preserve their autonomy. Participant 6 initially uses the platform to coordinate
with new clients, but then moves off-platform when she has established that they are trustworthy: “I
tell them I can charge you less because I’m not paying a fee now, so they pay less, I make more and
everybody is happy except for Upwork but who cares what they think.” Similarly, because the escrow
system is tied to a system of surveillance, many workers avoid Upwork’s hourly contracts, and use
their fixed-price system instead, which provides escrow based on milestones rather than time
tracking. Participants 13 and 15 and forum contributors, report that certain clients, especially larger
organizations, wished to conduct transactions off of Upwork because they had preferred payment
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systems, such as Paypal or Venmo, or established hiring and billing systems through their own
human resources departments. Transacting off platform in this way contravenes Upwork’s terms of
service, and the worker risks their account being flagged or suspended. In these situations, being
tied to Upwork’s provided resources constrains the worker to certain technologies and norms of
surveillance, and the workers circumvent these control mechanisms in order to maintain autonomy
and privacy in coordinating transactions.
Channeling Communication
Upwork’s platform provides functionality to facilitate communication between transacting parties.
Algorithmic management is not part of these communications. Upwork simply provides
communication tools through the platform. The mobile app and desktop extension allow workers to
receive messages instantaneously and communicate on the go. “If a client that I’m working with
needs to discuss something or if they send a message throughout the day, even though I’m not at
my computer, it will ping on my phone and when I have a minute I can respond” (Participant 14). It
also allows more involved activities like bidding and sending files, making it easy to secure contracts
and conduct work remotely and quickly. A gig worker on the forum shared: “Upwork's ‘interviews,’ for
me, consist of a brief text conversation with a prospective client about defining their needs, and
determining whether I can meet them, and at what cost.” The nature of communications between
clients and gig workers, with the exception of long-term engagements, is in line with the notion of
impersonal interactions noted by the current studies of gig work (Alkhatib et al. 2017). Most
communication is handled remotely with little face-to-face communication. Some workers, however,
push back on this norm. Participant 15 suggests “I do everything I can to try to find out who they are
and what their deal is.” Participant 1 likes to talk to the client on the phone before accepting a job
because “you can usually tell if you talk to somebody for a half hour if they’re a crackpot or not, and
that helps.”
Although Upwork’s suite of communication channels connects workers with their contracted
employers, it is also designed to tie workers to the platform. Workers therefore substitute their own
applications to retain flexibility. To help enforce communication through the platform, Upwork
automatically generates pop-up warnings when certain words such as “skype, phone, tel, email
(from the forum) are typed into the chat. As Participant 6 puts it, the messages remind workers
make sure that you don’t work outside of Upwork because blah, blah, bad people out there.”
Upwork also sends similar messages when workers and clients share email addresses or phone
numbers, or talk about using other cloud sharing platforms like Google Drive or Dropbox. Upwork
sometimes punishes workers for what it considers more serious infractions. For example, Participant
5 had his account frozen for 48 hours after he sent a client to his website to view his writing samples.
Even though several participants highlight the centrality of personal websites, as these provide a
more extensive and flexible presentation of their past projects and their portfolio, the Upwork profile
does not provide a space for workers to link to their websites. This may reflect the broader attitude
on the part of Upwork regarding information that facilitates connecting off platform. In summary,
most participants feel that communications through Upwork are monitored, and Upwork actively
encourages communication within the platform, particularly when it comes to payment.
Upwork’s communication channels have technical constraints and some workers must work around
them because they are not designed for their kind of work. Although it is possible to deliver files
through Upwork or attach them to Upwork’s messages, the application tends to compress images in
damaging ways, and some files are too large to send through Upwork (e.g. “two hours worth of
video,” Participant 20). Cloud services like Dropbox are popular alternatives. Upwork also provides a
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
mobile app, but Participants 16 and 19 state that this had limited functionality, such as not allowing
the worker to look at their money. Participants and forum contributors also reported technical
problems with Upwork’s other communication channels, and found them to be less reliable than
Skype or Slack.
Evaluating Performance
Upwork’s platform provides several mechanisms designed to perform the evaluation role of
managers. These include processing and presenting ratings and reviews left by other clients,
providing an aggregate rating called the “job success score,” posting badges earned for
accomplishments, monitoring of worker behavior, and directly evaluating the worker’s skills via tests.
The goal of these evaluations is to build trust and confidence in a worker’s capability by presenting
many types of information about them, including their past success, their responsiveness, and their
technical skills. Many of these ratings and evaluations are likely factored into a freelancers ranking in
client search results by Upwork’s algorithm.
As resources, calculated badges and ratings, such as the job success score, help workers promote
themselves to clients but also constrain them to the sometimes fickle client review process. All
participants noted their rating strongly influenced their experience with the platform. Highly rated
workers have to put less effort into bidding on projects, as they often have clients approaching them
with work. In this sense, the rating is an important resource for gaining attention in the network and
winning jobs. Conversely, workers are constrained to maintaining a good rating, and must go out of
their way to protect their rating. For instance, Participant 6 has given some clients a full refund to
avoid any negative feedback. Gig workers are also smart, in that theygame the system” by nudging
happy clients to give them a review but saying nothing to unhappy clients. Participant 2 makes sure
to remind clients to write a review before closing a contract. Similarly, workers must follow specific
rules for maintaining various badges. New workers can move toward Rising Talent status by making
sure their profile is completely filled out, including a headshot and portfolio. Upwork also monitors
how quickly workers respond to job invitations, and gives a “Response in 24 Hours” badge to those
who consistently respond in under 24 hours. In this way, the platform can encourage certain
behaviors and norms by making them implicit in the platform’s calculations of value.
Upwork also helps workers present and promote their skills by providing a number of proficiency
tests (e.g., knowledge of English grammar, Javascript, or payroll management). The opinions about
the real affordances of these competency tests vary, but they seem to be more critical in the case of
highly competitive jobs (e.g., copyediting) and for newcomers to the platform, who need to use as
many means as possible to showcase and promote their competencies. Participant 14 states that
the tests do not accurately reflect his capability. Participants 14 and 15 report that taking tests does
not make much difference in securing work, and other participants (such as Participants 7 and 20)
do not think that clients look at tests when hiring. However, Participant 15 reported that taking a test
will help rank him higher on searches for that skill.
Gig workers extend the platform’s evaluation and reputation systems in ways not necessarily
intended by the designer. Workers, for instance, might maintain professional websites and social
media accounts, which supplement and are supplemented by their Upwork profiles. Upwork
conceals workers’ last names and forbids them from taking clients “off-platform.” This said, workers
do mesh on-platform and off-platform reputational resources, maintaining some autonomy in how
they market themselves and providing proof of their quality. Participant 15 describes how he pasted
client testimonials from off-platform onto his Upwork profile in order to gain some credibility when he
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
first joined Upwork. Participant 5 advertises his Upwork rating off-platform by taking screenshots of
five-star reviews and sharing them on Twitter. Non-Upwork clients find him through Twitter, helping
him establish his reputation outside Upwork. Participant 1 described how her Upwork profile led
clients to her professional website:they’ll say I found you on Upwork and I Googled you and I found
your website so here I am.” These strategies allow the worker to retain some autonomy in their own
branding and professional development.
Upwork has in place certain vetting policies, which are meant to ensure the safety and integrity of
the work which occurs through the platform while also retaining control over how the platform’s
resources are used. Many of these policies are designed to encourage, or punish, certain behaviors
and professional tactics; some enforce conformity with the platform’s other policies. It seems that
gatekeeping is done automatically by the platform, but may also involve human actors.
Upwork requires identity verification of workers in order to prevent duplicate accounts, or
misrepresentation of a worker’s location (Upwork Help Center 2018). Workers on the forum note that
they cannot submit new proposals until they get their profile verified, and those who fail the
verification process cannot complete their profile or accept new jobs. Workers may be asked to
provide a digital copy of a valid government-issued photo ID and/or a recent billing statement. In
some cases, Upwork also requires the gig worker to participate in a brief video chat with an Upwork
representative. This is an example of human involvement in an otherwise, largely automated
Upwork also hides or blocks users in order to manage supply and demand in the network and to
ensure that workers presented in the network are active and present themselves well. During our
walkthrough, one of the authors was rejected from joining as a freelancer because there were
already too many freelancers with a similar skillset.” According to the message received by the
author, this rejection was based in part on the prevalence of their skills on Upwork, but also on the
incompleteness of his profile. We were unable to ascertain if this had been reviewed manually or
algorithmically. The message could have been automatically generated and sent out to any new
freelancers of that skill set with an incomplete profile. It is also possible a human looked at the profile
and made the decision to send the message.
Upwork is similarly concerned with making sure that workers are active. Participant 6 notes: “If you
don’t work for 30 days, like you don’t earn any income on that site they change your profile status to
private so nobody can find you, so I’m forced to go and look for jobs and I don’t like that at all.” Not
working enough is not officially against the rules, but it makes the worker a less desirable part of the
platform. In this way, the ability of workers to use Upwork’s resources to publish themselves to the
network is contingent on their offering some valuable good, and on their professional conduct.
Upwork also limits and monitors workers’ interactions with clients through a currency-like resource
called “connects.” Workers are allocated a certain number of connects per month and they are
expended when a worker submits a proposal on a project. Gig workers’ accounts can get suspended
due to overuse of connects. A poster on the forum speculates that proposals are limited because in
the past, workers got “trigger happy.” They were sending proposals to a large number of clients
hoping something would stick. The poster suggests that limiting proposals is a way to force workers
to focus on jobs that fit their skills. A recent announcement from Upwork explains: “We see
freelancers who aren’t successful in their attempts to find clients through Upwork. They’re regularly
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
submitting many proposals but aren’t winning projects nor earning money. This isn’t good for any of
us. With this in mind, in the next couple of days we’re going to start closing the freelance accounts of
some users with a history of not delivering winning proposals to clients” (Upwork Community 2016).
Controlling workers’ accounts in this fashion is also a way of keeping people on the platform. The
announcement goes on to say that workers who submit many proposals but do not enter into
contracts on the platform “might be violating our Terms of Service3, specifically our policies on
circumvention4(Upwork Community 2016).
Providing Information
The informational affordances of Upwork’s management functionality include: (1) algorithms to
provide best matches (between clients and gig workers) and (2) general guidance about how to
function as a successful gig worker on the platform.
Finding new projects is both a critical and constant challenge for most gig workers. Upwork
alleviates this by making a large network of clients visible and searchable. It does this by providing
specific resources, which enable the worker to search and evaluate clients. Workers can search by
keywords or category and can filter results based on the qualifications required (e.g. experience
level) or by country. Participant 7 limits his searches to US companies, because they pay closer to
market rates.”
Experienced Upworkers can describe how they make judgements about clients based on information
gleaned from job descriptions and hiring history. For instance, Participant 6 describes evaluating a
client’s hiring patterns and the clarity of their job description: “I can see if they’re primarily hiring in
the country or out of the country, and if their directions are pretty clear in their job description …
because then I know it’s going to be like pulling teeth to try and figure out exactly what they want. So
I like more information.” In this regard, Upwork’s matching process is not one of algorithmic
assignments as used in some other gig platforms (Lee et al. 2015), but rather the provision of
information and searching algorithms which help clients and workers match themselves.
Upwork’s algorithmic matching systems, which send job recommendations to workers through the
site or through email, are much less used in comparison with the searching features. Some
participants found Upwork’s job recommendations useful, but many reported that they were largely
inaccurate and shared jobs that did not match their skillset. According to Participant 10, “it’s sort of a
running joke among the Upwork community just that you know you can pretty much count on the
recommendations to be worthless.
Through these functions, Upwork can shape how the network of work opportunities is visible and
searchable for workers and clients, even as it controls certain information asymmetries, which
influence interactions between workers and clients. Upwork’s functionality maintains semi-anonymity
by displaying only the first names and last initials of workers. Conversely, clients’ identities are not
required and a number of participants have worked for clients whose names were not known to
them. Participant 2 disliked that on Upwork “you don’t know who they [clients] are.” She wished they
would “at least tell me your first name.” Clients can also leave private reviews, which are not seen by
the worker but which affect their job success score. The lack of transparency on the platform can be
somewhat isolating, and in general, more information is provided to the client than to the worker.
Participant 14 laments that on eLance (Upwork’s predecessor) it was possible to see who else had
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
bid on a project and who ended up getting the project. He argues that even if workers did not
communicate directly, this created a competitive camaraderie between them, which is missing from
Upwork. Because of these information-sharing asymmetries, workers seek to supplement
information provided by Upwork with advice from other successful Upworkers and may experiment
with the platform themselves. For example, Participant 15 relates how he received a lot of concrete
advice about how to use the platform from an online class he took, taught by an experienced Upwork
freelancer. Other participants (e.g. Participant 3) created and used a client account in order to learn
how things (particularly their own profile) appear to clients, and how to improve them.
Upwork provides some transparency concerning the operations of the platform itself. Upwork‘s
official information resources are aimed at educating workers on how the platform works, and
encouraging certain professional behaviors and conformity with platform policies. A recent post on
Upwork’s official blog gives the basic formula for the Job Success Score: “(successful contract
outcomes [minus] negative contract outcomes) / total outcomes.” The blog post also enumerates
the various pieces of information that are considered in calculating the score: “Job Success contains
more than just public feedback. It also includes private feedback, long-term contracts, and repeat
contracts.” Despite the information provided about the platform’s algorithms, workers continue to
have uncertainties about what behaviors will get their accounts flagged or banned, and how
calculations about the Job Success Score or Top Rated badge are made. Gig workers are not
completely clear, for instance, on what will be considered a positive or negative contract outcome in
calculating their Job Success Score.
Resolving Conflict
Another core managerial function performed by Upwork is arbitrating conflicts between gig workers
and clients and resolving them by dispensing payment. As with Upwork’s other managerial functions,
the structuring of conflicts and the ability of the platform to help resolve conflicts is embedded in
rules, guidelines, workflow, and algorithms. Human Upwork employees can become part of this
management task, reviewing available information to make a determination.
Disputes arise when there are misunderstandings about how the platform structures transactions. A
client notes on the forum: “The very first freelancer I hired got confused as to the contract we agreed
upon. It was a ‘fixed’ contract (for said amount); however, the freelancer thought said amount was an
‘hourly rate.’ I realized the mistake 2 days into a 2 month deadline.Conflicts may also occur in
relation to what work needs to be done for the contract to be fulfilled. Participant 7 provides an
example of a client asking for “tons of extra revisions” when Participant 7 felt that he had completed
the project as described. When the automated process of payment breaks down in this manner, a
dispute” is initiated in which an Upwork representative reviews the disagreement and arbitrates
about who should receive the money in escrow. As part of this, Upwork can examine all the
communication between the gig worker and client. In the example involving Participant 7, Upwork
could use all of their recorded communications, the contract, and work completed to determine
whether or not the gig worker has completed the job as described. Then Upwork could release funds
from escrow to pay the worker.
Upwork’s understanding about a dispute is largely dependent on its surveillance of the work being
done and on the worker’s and client’s using of its contracting resources. Upwork’s management of
disputes draws on provided functions of escrow, time tracking, and the contract systems. This also
includes the worker- or client-provided documentation of the work/project. A forum contributor
described how a client disputed his work, and “he ended up getting the full refund he was asking for
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
because I had not inputted any memos while working, which means my hours weren't protected
under the Upwork protection guarantee for freelancers.” Failure to use these boundary resources as
instructed by Upwork means losing the security benefits advertised and provided by it.
Furthermore, Upwork only has control over money, which is sitting in escrow, and does not
otherwise attempt to retrieve money from workers or clients, leading both clients and workers to
pursue external methods for resolving disputes. A client on the forum described how a former worker
tried to retrieve money through a debt collector: “I hired a person to do some API integration. He was
over budget and when I checked his work he was integrating with the wrong service. I caught it but
he said he would only fix his mistake if I paid more. I declined and paid someone else to do it right.
Now I'm getting emails from a debt collector.” Similarly, Participant 1 approached the Freelancer’s
Union of New York City to get pro bono legal advice on how to push her client to pay.
Discussion and Implications
What Upwork provides via its platform policies, workflow, data collection, algorithmic management,
and human interventions resembles many of the basic roles of managers (per Henry Mintzberg,
(1989)). The Upwork platform acts as a resource allocator by structuring financial transactions. It
acts as a liaison by facilitating communication between different parties and a disturbance handler
by dealing with conflicts and disputes. Upwork can be understood as a disseminator of information
and finally, acts as a monitor by conducting automatic evaluation and gatekeeping. In this way,
Upwork provides more than algorithmic management, it provides platformic management.
Platformic management as described in this paper can also be understood as the “organizing
affordances” of the platform (Zammuto et al. 2007). In what follows, we further discuss platformic
management relative to the flexibility and autonomy it affords, and control it exerts through provision
of boundary resources. We finally return to what platformic management entails beyond algorithmic
management in the context of knowledge-intensive gig work, focusing on the ways in which this
mutes some of the roles of managing, while also altering what is expected of the worker.
Platformic Management as Boundary Resources
Findings make clear that the concept of boundary resources provides insight into the ways in which
platform-provided resources mediate the relationship between the platform owner and users (Eaton
et al. 2015). Workers act as “complementors” and through their uses extend the scope and diversity
of a platform (Farshchian and Thomassen 2019). At the same time, the platform owner leverages
the same resources and uses them to exert control over the platform and complementors, making
the platform a boundary resource (Ghazawneh and Henfridsson 2013). This makes boundary
resources a useful conceptual frame for describing how Upwork carries out platformic management
and how gig workers negotiate their autonomy and reliance on the platform.
Conceptualizing platforms as a boundary resource foregrounds the materiality of platform features,
and positions agency as balancing platform organizers, the employers and the workers. Seeing
platformic management as a boundary resource allows us to move from a concept of automated
rules, or algorithmic management, towards a concept of dynamic negotiations between people and
the constraints and affordances of plastic, digital material artifacts. This allows us to highlight the
ways in which platform-based management has departed from the traditional modes of
organizational management.
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Two dimensions of the platform form the core of the boundary resource: (1) the provision of material
features and policies that enable and enhance the autonomy of gig workers (as a primary draw of
the gig economy (De Stefano 2015), and (2) the use of the services provided by the platform to
guide workers to conform to particular norms and structures of work (Wood,et al. 2018).
Boundary Resources Enabling Autonomy and Flexibility
As mechanisms of managerial control, the resources provided by Upwork are designed to
coordinate, facilitate, and inform gig-working freelancers. Gig workers use these to support their
temporal and spatial flexibility; giving them freedom in selecting projects, while providing for a higher
level of autonomy (in that they need not negotiate with peer workers or a manager in doing so)
(Spreitzer et al. 2017).
As discussed in the findings section, workers are able to use the platform to reach a larger
clientbase and systematize their transactions. This improves workers’ ability to operate flexibly and
sustainably by allowing them to find work reliably and spend less time on the coordination and
administrative aspects of multiple, ongoing gigs. In this way, the platform’s boundary resources
address some of the central difficulties of gig work.
The precarity of gig work stems in large part from the unpredictability of finding work, and the
increased responsibility of the worker for performing all of the ancillary marketing and negotiating
tasks associated with finding and carrying out work. Upwork provides a framework, which automates
many of these tasks while remaining nonspecific, meaning that it can facilitate workers in
undertaking work of different types and timeframes at will. The worker is therefore able to work
flexibly, without shouldering the full managerial overhead.
The platform provides communication channels and evaluation metrics, which allow for remote hiring
and job seeking. These boundary resources enhance worker flexibility and are most valuable when
there is a large enough population of clients to allow workers to find consistent work. Through these
mechanisms, the platform aids the worker and the client in making matches that could not have
been accurately predicted by algorithmic assignment.
Some of the most useful boundary resources are those that provide the gig worker some
customizability or interpretability, while remaining durable enough to support complex work and
interactions. Given the complexity of many of the gigs posted to Upwork, the platform is unable to
make firm determinations about the appropriate transaction procedures because the nature of the
work (tools used, extent of communication, length of project, etc.) is variable. And, the quality of the
work is also difficult to measure or quantify.
The platform’s digital features also help enforce standards. For instance, contracts provide a
structure for clients and freelancers that clarify agreed-upon milestones and enforce (through the
execution of code) a particular structure to a working relationship. Ideally, these resources have
enough plasticity to allow clients and freelancers to negotiate their own milestones and hours but
also enough structure to enforce those agreements. In line with what has been described in previous
work on platform labor, both customizable and constraining aspects of the platform can serve to
increase the flexibility and stability of work (Lehdonvirta 2018). This emphasizes the dual nature of
boundary resources; they are “plastic” enough to be appropriated for worker use, but also rigid
enough to provide structure across several heterogeneous parties (Ghazawneh and Henfridsson
2010, p. 4).
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Boundary Resources as a Means of Platformic Control
Even as they enable some level of worker autonomy, the boundary resources provided by Upwork
also impose constraints. These constraints are built into the interface, enforced algorithmically, and
enabled with deliberate information asymmetries. By participating on the platform, workers take on
these constraints. Findings reveal two different objectives of these constraints: (1) structuring
working relationships, and (2) protecting the platform from disintermediation.
To provide structure, the platform focuses on specific information asymmetries, and these serve,
intentionally or unintentionally, to curb workers’ autonomy (Deleuze 1995). Upwork’s services reflect
some information asymmetries similar to those reported in prior work on algorithmic management
(Möhlmann and Zalmanson 2017; Rosenblat and Stark 2016; Wood et al. 2018). That is, these
services limit the worker’s ability to fully understand how the platform works (e.g., how they are
evaluated) and controls their work. The boundary resources supporting Upwork’s reputation system
do not share workers’ last names, prevent them from posting links to their personal websites, and
warn them to use platform-provided communication channels or face sanctions.
Boundary resources are also leveraged by the platform to mitigate concerns with disintermediation.
Upwork’s concern about being disintermediated in favor of other payment or communication
channels is reflected in the design of platform resources such as automated monitoring of the chat
application, flagging of accounts, and the “non-circumvention agreement.” The platform’s interfaces
and messages leverage the threat of scams as an incentive to keep all communications and
transactions on the platform. These measures describe an attempt on Upwork’s part to maintain
what Ghazawneh and Henfridsson (2013) call platform “sovereignty,” or the platform’s control over
its own system and resources. However, in a number of cases, these attempts to prevent
disintermediation become obstacles themselves to the smooth coordination of clients and
freelancers on the platform and to gig workers’ flexibility in leveraging various technologies in aid of
their work. By joining the platform, workers are taking up a set of effective boundary resources in the
form of tools and a network, but they are also adopting a semi-closed system, which may not
interoperate with some of their own preferred work processes and those of their clients.
More broadly, the tensions between workers’ autonomy and control imposed by information systems
is known and has been captured in the concept of the autonomy paradox (Mazmanian et al. 2013).
Gig work-enabling platforms are just another medium to help surface these tensions. This noted, the
concept of boundary resources helps foreground two important dynamics of digital platforms: 1)
platform stability and central control, and 2) platform generativity (opening up the platform to multiple
flexible uses). Our application of the concept of boundary resources extends the current
conceptualization of the concepts by presenting gig workers as ‘complementors’ of the platform, who
integrate boundary resources in their practices to extend the core of the platform (e.g., Eaton et al.
2015; Ghazawneh and Henfridsson 2013). They complement the platform by creating new norms
and work practices around platform-centered work, and even workarounds that facilitate their
interactions with platformic management. The existing applications of the concept of boundary
resources only embrace application developers and technical workers who extend the technological
core of the platform by adding explicit technical resources/features (e.g., creating new Firefox add-
ons). Finally, it is noteworthy that the term ‘resource’ in the concept of boundary resources can have
a misleading connotation. Reflecting critically on the limits of this connotation, we recognize that the
term ‘resource’ does not effectively convey the constraining roles that platform features and
management system may play. In addition to providing opportunities for workers to facilitate
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
platform-mediated gig work, they may act as control or lock-in mechanisms, restraining workers’
sense of autonomy.
Platformic Management beyond Algorithmic Management
We use the study of Upwork to advance our conceptualization of platformic management as broader
than that of algorithmic management (Veen et al. 2019). Both are sociotechnical, involving
technologies and practices (e.g., how users make sense of the system and engage the platform).
However, platformic management conceptualizes a wider array of technological features than
algorithms. Clearly, Upwork draws upon algorithmic decision-making and evaluation. This noted,
Upwork also relies on other design features and policies to control the way workers interact in the
platform space. These include templated profiles, specially designed communication systems,
textual suggestions or descriptions, user options, and non-circumvention policies.
It may be that differences in the management of labor on Upwork and the management of workers
on microtasking and ridesharing platforms demands a broader suite of rules and functionality. For
example, in contrast to AMT where a “thousand to one worker-to-requester ratio” makes
communication between them almost impossible (Irani and Silberman 2013, p. 4), projects on
Upwork often require frequent communication between parties. Furthermore, measuring the work
quality of the more open-ended and complex projects found on Upwork requires multiple evaluation
and monitoring methods. Upwork allows workers to control tasks, choosing not to create means to
decompose or deskill themunlike what is found in some studies of algorithmic management
(Alkhatib et al. 2017; Irani and Silberman 2013; Lehdonvirta 2018). Platformic control mechanisms
such as the work diary and time tracker present a more complex data space for tracking than, say,
GPS positioning relative to time on ride-sharing apps.
Additionally, matching clients and freelancers for knowledge work is complex enough that many
workers reported that the platform’s algorithmically generated suggestions were not useful.
Compared to ridesharing platforms, Upwork has to rely, to a greater extent, on worker and client
participation in filling out descriptions, scrolling through possibilities, contacting each other, and
negotiating a job. The process of matching relies on a combination of search algorithms, the
platform’s structuring of search and matching activities, and also the contributive matching and
negotiating activities themselves, carried out by a crowd of gig workers and clients. Although the
process of matching is chaperoned by the platform’s algorithms and interfaces, it is more reliant on
the platform dynamics than the kind of algorithmic assignment, which infringes so significantly on the
autonomy of Uber and Lyft drivers (Lee et al. 2015; Rosenblat and Stark 2016).
These points noted, the agency of the gig workers and their practices helps to shape management
relations on the platform. That is, gig workers are not passive recipients of platformic management
and use creative strategies to appropriate or work around issues. For example, we observed that gig
workers negotiate or avoid non-circumvention policies in various ways in order to fit the platform into
their work processes.
It is useful to broaden the discussion from gig workers’ relationships with algorithms and other
automated aspects of platformic management to also consider their role as embedded in a larger
ecosystem of workers and clients and as workers negotiating with centralized platforms. In other
words, platforms do not organize people just through the “doing” of digital procedures, but rather, gig
workers and platforms enact managerial relations through negotiations over information,
algorithmically enforced rules, and the use of digital boundary resources.
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Upwork seeks to be a market-making platform: a place that brings together workers and clients by
maintaining its control of the whole online freelancing marketplace through an information and power
asymmetry that largely strengthens the position of the platform owner/organizer (Upwork Global Inc.)
(Bar 2001). From this perspective, it makes sense for Upwork to choose to not fully disclose how
certain badges are acquired, and to carefully surveil workersinteractions with the platform, progress
reports, and communications with clients. The power asymmetry comes from Upwork’s ability to
gather this data from many sessions, clients and workers and use this to guide policies and
algorithms. Doing so contributes to work precarity by diminishing workers’ control over their work
Freelancers on Upwork may work around this asymmetric system envisioned and organized by the
platform by negotiating with clients, circumventing Upwork’s Terms of Service (and its position as the
sole mediator), enlisting the help of external digital platforms in their work practices (e.g., Paypal and
Dropbox), and assembling their own ecosystem. Within this broader ecosystem Upwork plays a
central role, but the ecosystem is more of a product of shifting alliances and negotiations between
clients and freelancers. When workers subvert the platform or seek solutions outside of it to alleviate
uncertainty and precarity of work, they reclaim some of their agency lost to Upwork’s platformic
management and control.
Our research question and findings focus attention on the interactions with the platform from the
perspective of gig workers. We know clients are also key stakeholders of the platform. Future
research is needed to accommodate the dynamics of interactions and alliances among these
stakeholders. Such studies can provide a more comprehensive understanding of the concept of
autonomy, which is negotiated with different parties and through disembedded labor relations.
There are at least two other implications of this work we have not pursued here, leaving these for
future work and perhaps for others. First, the move to transaction-based work relations and
arrangements such as what Upwork supports leads to the worker doing more and more unpaid labor
to present, explain, negotiate and support their work. In traditional labor relations, these tasks were
apportioned between worker and manager, negotiated and often discursive. We don’t have a
language or set of concepts to easily express this labor shift in which all of this is laid on the worker.
Two sources of guidance for how to conceptualize this shift of added work to the worker seem
promising. First, there is the literature on the move to having consumers do work that was once done
by vendors (e.g., completing sales forms, providing information) (e.g., Boltanski and Chiapello 2005;
Rieder and Günter 2010). Second, the literature on labor markets provides some guidance on how
to better conceptualize the shift of risk from shared to primarily on the worker (Cuñat and Melitz
The empirical context of our study, Upwork, also serves to illustrate how the automated
management of knowledge work presents a more complex set of work-matching needs than that of
the more commonly reported forms of gig-working and algorithmic management. Compared to
ridesharing and microtasking, workers on Upwork may enjoy higher levels of task autonomy and
control, (since they can negotiate things like the order of tasks, methods of work, and speed/rate of
work (Maestas et al. 2017, p. 54). Such knowledge workers are harder to arrange programmatically,
and the workers themselves are more active (as complementors) in contributing to and extending
platforms. This broader and more complex remit requires a coherent platformic management: one
that embeds but includes more than algorithms to effectively serve as a market-maker.
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
In this paper we have advanced the concept of platformic management, focusing specifically on
ways gig-work-enabling platforms like Upwork serve to structure these workers’ autonomy and work
flexibility. We conceptualize platformic management and its digital features and protocols as critical
points of control in the emerging labor relations of the gig economy. We also use the concepts of
boundary resources to help characterize worker flexibility and autonomy in relation to the structuring
role of platforms in freelance work. Findings from this work have ramifications relative to the design
and control of digital features and protocols, which support work (among other things) and are
increasingly coming under the purview of private, centralized platforms, as articulated by Plantin et
al. (2016).
Abubakar, Abubakar Mohammed; and Belal Hamed Taher Shneikat. (2017). eLancing motivations.
Online Information Review, vol. 41, no. 1, pp. 5369.
Acquier, Aurélien; Thibault Daudigeos; and Jonatan Pinkse. (2017). Promises and paradoxes of the
sharing economy: An organizing framework. Technological Forecasting and Social Change, vol. 125,
pp. 110.
Agrawal, Ajay; Joshua Gans; and Avi Goldfarb. (2017). How AI will change the way we make decisions.
Harvard Business Review.
Accessed 15 June 2019.
Alkhatib, Ali; Michael S. Bernstein; and Margaret Levi. (2017). Examining Crowd Work and Gig Work
Through the Historical Lens of Piecework. In CHI ’17. Proceedings of the 2017 CHI Conference on
Human Factors in Computing Systems, Denver, Colorado, May 06 11, New York, NY, USA: ACM,
pp. 45994616.
Alkhatib, Ali; Justin Cranshaw; and Andrés Monroy-Hernandez. (2015). Laying the Groundwork for a
Worker-Centric Peer Economy (No. MSR-TR-2016-50). Microsoft Research.
economy. Accessed 15 June 2019.
Aloni, Erez. (2016). Pluralizing the Sharing Economy. Washington Law Review, vol. 91, 2016, pp. 1397.
AMT. (2019). Amazon Mechanical Turk. Accessed 20 June 2019.
Aneesh, A. (2009). Global Labor: Algocratic Modes of Organization. Sociological Theory, vol. 27, no. 4,
pp. 347370.
Banning, Marlia E. (2016). Shared entanglements Web 2.0, info-liberalism & digital sharing. Information,
Communication and Society, vol. 19, no. 4, pp. 489503.
Bar, François. (2001). The construction of marketplace architecture. In The BRIE-IGCC Economy Project
Task Force on the Internet (Ed.), Tracking a Transformation: E-commerce and the Terms of
Competition in Industries. Brookings Inst Press, Washington, DC.
Barrett, Michael; Elizabeth Davidson; Jaideep Prabhu; and Stephen L. Vargo. (2015). Service innovation
in the digital age: key contributions and future directions. MIS Quarterly, vol. 39, no. 1, pp. 135154.
Boltanski, Luc; and Eve Chiapello. (2005). The New Spirit of Capitalism. International Journal of Politics,
Culture, and Society, vol. 18, no. 3, pp. 161188.
Brinkley, Ian. (2016). In search of the Gig Economy. Lancaster: The Work Foundation, 2016.
economy_June2016.pdf. Accessed 20 June 2019.
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Brynjolfsson, Erik; and Andrew McAfee. (2014). The Second Machine Age: Work, Progress, and
Prosperity in a Time of Brilliant Technologies. New York, NY: W. W. Norton & Company.
Bucher, Eliane; and Christian Fieseler. (2017). The flow of digital labor. New Media & Society, vol. 19, no.
11, pp. 18681886.
Campbell, John L.; Charles Quincy; Jordan Osserman; and Ove K. Pedersen. (2013). Coding In-depth
Semistructured Interviews: Problems of Unitization and Intercoder Reliability and Agreement.
Sociological Methods & Research, vol. 42, no. 3, pp. 294320.
Chan, Ngai Keung. (2019). “Becoming an expert in driving for Uber”: Uber driver/bloggers’ performance of
expertise and self-presentation on YouTube. New Media & Society, First published online: April 1,
Chapman, Cameron. (2018, May 9). 25 Top Sites for Finding the Freelance Jobs You Want. SkillCrush. Accessed 5 July 2019.
Claussen, Jörg; Pooyan Khashabi; Tobias Kretschmer; and Mareike Seifried. (2018). Knowledge Work in
the Sharing Economy: What Drives Project Success in Online Labor Markets? Accessed 15 June 2019.
Corbin, Juliet; and Anselm Strauss. (2008). Basics of qualitative research: Techniques and procedures for
developing grounded theory. Thousand Oaks, CA: Sage.
Corporaal, Gretta. (2018). Organizing with on-demand freelancers in the platform economy. Oxford
Internet Institute.
economy-part-one. Accessed 15 June 2019.
Cuñat, Alejandro; and Marc J. Melitz. (2012). Volatility, Labor Market Flexibility, and the Pattern of
Comparative Advantage. Journal of the European Economic Association, vol. 10, no. 2, pp. 225254.
Davis, Gerald F. (2015). What might replace the modern corporation: Uberization and the web page
enterprise. Seattle University Law Review, vol. 39, pp. 501.
Deleuze, Gilles. (1995). Postscript on control societies. Negotiations: 1972--1990, 1995, pp. 177182.
De Stefano, Valerio. (2015). The Rise of the Just-in-Time Workforce: On-Demand Work, Crowdwork, and
Labor Protection in the Gig-Economy. Comp. Lab. L. & Pol’y J., vol. 37, pp. 471.
Donovan, Sarah A.; David H. Bradley; and Jon O. Shimabukuru. (2016). What Does the Gig Economy
Mean for Workers?, 2016. Accessed 18
April 2018.
Dourish, Paul. (2016). Algorithms and their others: Algorithmic culture in context. Big Data & Society, vol.
3, no. 2, pp. 111.
Dunn, Michael. (2017). Digital Work: New Opportunities or Lost Wages. American Journal of
Management, vol. 17, no. 4, pp. 1027.
Eaton, Ben; Silvia Elaluf-Calderwood; Carsten Sorensen; and Youngjin Yoo. (2015). Distributed tuning of
boundary resources: the case of Apple’s iOS service system. MIS Quarterly, vol. 39, no. 1, pp. 217
Farshchian, Babak A.; and Hanne Ekran Thomassen. (2019). Co-Creating Platform Governance Models
Using Boundary Resources: a Case Study from Dementia Care Services. Computer Supported
Cooperative Work, vol. 28, no. 3-4, pp. 549–589.
Fleming, Peter. (2017). The Human Capital Hoax: Work, Debt and Insecurity in the Era of Uberization.
Organization Studies, vol. 38, no. 5, pp. 691709.
Friedman, Gerald. (2014). Workers without employers: shadow corporations and the rise of the gig
economy. Review of Keynesian Economics, vol. 2, no. 2, pp. 171188.
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Gershon, Ilana; and Melissa Cefkin. (2017). The Problem with Jobs (De-Provincializing Development).
melissa-cefkin-de-provincializing-development-series. Accessed 10 July 2019.
Ghazawneh, Ahmad; and Ola Henfridsson. (2010). Governing third-party development through platform
boundary resources. ICIS 10. Proceedings of the International Conference on Information Systems.
St. Louis, MO, Dec 15-18, Atlanta, GA: AIS, pp. 118.
Ghazawneh, Ahmad; and Ola Henfridsson. (2013). Balancing platform control and external contribution in
third-party development: the boundary resources model. Information Systems Journal, vol. 23, no. 2,
pp. 173192.
Ghazawneh, Ahmad; and Ola Henfridsson. (2015). A paradigmatic analysis of digital application
marketplaces. Journal of Information Technology, vol. 30, no. 3, pp. 198208.
Gillespie, Tarleton. (2010). The politics of “platforms.” New Media & Society, vol. 12, no. 3, pp. 347364.
Gillespie, Tarleton. (2014). The relevance of algorithms. In T. Gillespie, P. J. Boczkowski, and K. A. Foot
(Eds.), Media Technologies: Essays on Communication, Materiality, and Society. Cambridge, MA:
MIT Press.
Green, Daryl D.; Craig Walker; Abdulrahman Alabulththim; Daniel Smith; and Michele Phillips. (2018).
Fueling the Gig Economy: A Case Study Evaluation of Management and Economics
Research Journal, vol. 4, pp. 104112.
Hannák, Anikó; Claudia Wagner; David Garcia; Alan Mislove; Markus Strohmaier; and Christo Wilson.
(2017). Bias in Online Freelance Marketplaces: Evidence from TaskRabbit and Fiverr. In CSCW ’17.
Proceedings of the ACM Conference on Computer-Supported Cooperative Work & Social
Computing, Portland, OR, February 25 March 1, New York, NY: ACM, pp. 19141933.
Heeks, Richard. (2017). Digital Economy and Digital Labour Terminology: Making Sense of the “Gig
Economy”, “Online Labour”, “Crowd Work”, “Microwork”, “Platform Labour”, Etc. GDI Development
Informatics Working Papers, no. 70. The University of Manchester, Manchester.
Howcroft, Debra; and Birgitta Bergvall-Kåreborn. (2018). A Typology of Crowdwork Platforms. Work
Employment and Society, vol. 33, no. 1, pp. 2138.
Irani, Lilly. (2015). The cultural work of microwork. New Media & Society, vol. 17, no. 5, pp. 720739.
Irani, Lilly; and M. Six Silberman. (2013). Turkopticon: Interrupting Worker Invisibility in Amazon
Mechanical Turk. In CHI ’13. Proceedings of the SIGCHI Conference on Human Factors in
Computing Systems, Paris, 27 April 2 May, New York, NY, USA: ACM, pp. 611620.
Jarrahi, Mohammad Hossein; and Will Sutherland. (2019). Algorithmic Management and Algorithmic
Competencies: Understanding and Appropriating Algorithms in Gig work. In Proceedings of
iConference 2019, Washington D.C., March 31- April 3. Cham, Switzerland: Springer.
Kalleberg, Arne L.; and Michael Dunn. (2016). Good Jobs, Bad Jobs in the Gig Economy. LERA for
Libraries, vol. 20, no. 1-2, pp. 1013.
Kalleberg, Arne L.; and Steven P. Vallas. (2018). Precarious Work: Causes, Characteristics, and
Consequences. Bingley, UK: Emerald.
Karhu, Kimmo; Robin Gustafsson; and Kalle Lyytinen. (2018). Exploiting and Defending Open Digital
Platforms with Boundary Resources: Android’s Five Platform Forks. Information Systems Research,
vol. 29, no. 2, pp. 479497.
Kingsley, Sara Constance; Mary L. Gray; and Siddharth Suri. (2015). Accounting for market frictions and
power asymmetries in online labor markets. Policy & Internet, vol. 7, no. 4, pp. 383400.
Knuth, Donald Ervin. (1997). The Art of Computer Programming. London, UK: Pearson Education.
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Kuhn, Kristine M.; and Amir Maleki. (2017). Micro-entrepreneurs, Dependent Contractors, and Instaserfs:
Understanding Online Labor Platform Workforces. The Academy of Management Perspectives, vol.
31, no. 3, pp. 183200.
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 CHI ’15. Proceedings of
the SIGCHI Conference on Human Factors in Computing Systems, Seoul, South Korea, April 18
23, New York, NY: ACM, pp. 16031612.
Lehdonvirta, Vili. (2018). Flexibility in the gig economy: managing time on three online piecework
platforms. New Technology, Work and Employment, vol. 33, no. 1, pp. 1329.
Light, Ben; Jean Burgess; and Stefanie Duguay. (2016). The walkthrough method: An approach to the
study of apps. New Media & Society, vol. 20, no. 3, pp. 881–900.
Lustig, Caitlin; Katie Pine; Bonnie Nardi; Lilly Irani; Min Kyung Lee; Dawn Nafus; and Christian Sandvig.
(2016). Algorithmic Authority: The Ethics, Politics, and Economics of Algorithms That Interpret,
Decide, and Manage. In CHI EA ’16. Proceedings of the CHI Conference on Human Factors in
Computing Systems (Extended Abstracts), San Jose, CA, May 7 12, New York, NY: ACM, pp.
Maestas, Nicole; Kathleen Mullen; David Powell; Till von Wachter; and Jeffrey Wenger. (2017). The
American Working Conditions Survey Finds That More Than Half of Retirees Would Return to Work.
RAND Corporation. Accessed 10 July
Malone, Tom; Robert Laubscher; and Tammy Johns. (2011). The age of hyper specialization. Harvard
Business Review, vol. 89, no. 7-8, pp. 56.
Ma, Ning F.; Chien Wen Yuan; Moojan Ghafurian; and Benjamin V. Hanrahan. (2018). Using Stakeholder
Theory to Examine Drivers’ Stake in Uber. In CHI ’18. Proceedings of the CHI Conference on Human
Factors in Computing Systems, April 21-26, Montréal, Canada, New York, NY: ACM, pp. 83:183:12.
Mazmanian, Melissa; Wanda J. Orlikowski; and Joanne Yates. (2013). The autonomy paradox: The
implications of mobile email devices for knowledge professionals. Organization Science, vol. 24, pp.
Mikhalkina, Tatiana; and Laure Cabantous. (2015). Business Model Innovation: How Iconic Business
Models Emerge. In C. Baden-Fuller and V. Mangematin (Eds.), Advances in Strategic Management.
Bingley, UK: Emerald.
Miller, Alex P. (2018). Want less-biased decisions? Use algorithms. Harvard Business Review. Accessed 10 June 2019.
Mintzberg, Henry. (1989). Mintzberg on Management: Inside Our Strange World of Organizations. New
York, NY: Simon and Schuster.
Möhlmann, Mareike. (2015). Building Trust in Collaborative Consumption Services Facilitated through an
Online Platform. Academy of Management Proceedings, no. 1, January 2015.
Möhlmann, Mareike; and Lior Zalmanson. (2017). Hands on the wheel: Navigating algorithmic
management and Uber drivers’ autonomy. In ICIS ’17. Proceedings of the International Conference
on Information Systems, Seoul, South Korea, December 10-13. Atlanta, GA: AIS.
Molla, Rani. (2017). The gig economy workforce will double in four years. Recode.
Accessed 15 April 2018.
Newlands, Gemma; Christoph Lutz; and Christian Fieseler. (2018). Collective action and provider
classification in the sharing economy. New Technology, Work and Employment, vol. 33, no. 3, pp.
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Plantin, Jean-Christophe; Carl Lagoze; Paul N. Edwards; and Christian Sandvig. (2016). Infrastructure
studies meet platform studies in the age of Google and Facebook. New Media & Society, vol. 20, no.
1, pp. 293310.
Prassl, Jeremias. (2018). Humans as a Service: The Promise and Perils of Work in the Gig Economy.
Oxford: Oxford University Press.
Premilla D’Cruz; and Ernesto Noronha. (2016). Positives outweighing negatives: the experiences of
Indian crowdsourced workers. Work Organisation, Labour & Globalisation, vol. 10, no. 1, pp. 4463.
Raval, Noopur; and Paul Dourish. (2016). Standing out from the crowd: Emotional labor, body labor, and
temporal labor in ridesharing. In CSCW 16. Proceedings of the ACM Conference on Computer-
Supported Cooperative Work & Social Computing, San Francisco, CA, February 27March 2. New
York, NY: ACM, pp. 97107.
Rieder, Kerstin; and G. Günter. (2010). The working customer--an emerging new type of consumer.
Psychology of Everyday Activity, vol. 3, no. 2, pp. 210.
Rosenblat, Alex. (2016). The Truth About How Uber’s App Manages Drivers. Harvard Business Review. Accessed 20 June 2018.
Rosenblat, Alex; and Luke Stark. (2016). Algorithmic labor and information asymmetries: A case study of
Uber’s drivers. International Journal of Communication, vol. 10, pp. 37583784.
Schildt, Henri. (2017). Big data and organizational design--the brave new world of algorithmic
management and computer augmented transparency. Innovations, vol. 19, no. 1, pp. 2330.
Schreieck, Maximilian; Manuel Wiesche; and Helmut Krcmar. (2016). Design and Governance of Platform
Ecosystems-Key Concepts and Issues for Future Research. In ECIS ’16. Proceedings of the
European Conference on Information Systems,İstanbul, Turkey, June 12-15. Atlanta, GA: AIS,
Research Paper 76.
Seaver, Nick. (2017). Algorithms as culture: Some tactics for the ethnography of algorithmic systems. Big
Data & Society, vol. 4, no. 2, pp. 112.
Shapiro, Aaron. (2018). Between Autonomy and Control: Strategies of Arbitrage in the “On-demand”
Economy. New Media & Society, vol. 20, no. 8, pp. 29542971.
Simonite, Tom. (2015). When Your Boss Is an Uber Algorithm. MIT Technology Review December, Accessed 1 July
Spreitzer, Gretchen M.; Lindsey Cameron; and Lyndon Garrett. (2017). Alternative Work Arrangements:
Two Images of the New World of Work. Annual Review of Organizational Psychology and
Organizational Behavior, vol. 4, no. 1, pp. 473499.
Sutherland, Will; and Mohammad Hossein Jarrahi. (2017). The Gig Economy and Information
Infrastructure: The Case of the Digital Nomad Community. Proceedings of the ACM on Human-
Computer Interaction, vol. 1, no. 1, October 2017, Article No. 97.
Sutherland, Will; and Mohammad Hossein Jarrahi. (2018). The Sharing Economy and Digital Platforms: A
Review and Research Agenda. International Journal of Information Management, vol. 43, pp. 328
Ticona, Julia; Alexandra Mateescu; and Alex Rosenblat. (2018). Beyond Disruption How Tech Shapes
Labor Across Domestic Work & Ridehailing. Data & Society.
disruption/. Accessed 1 July 2019.
Torpey, Elka; and Andrew Hogan. (2016). Working in a gig economy. U.S. Bureau of Labor Statistics. Accessed 15 July 2019.
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Upwork Community. (2016). Addressing accounts that don’t show work activity.
activity/td-p/274042. Accessed 10 April 2018.
Upwork Help Center. (2018). Identity Verification.
Verification?flash_digest=85c87cfdd992431da0b1d8f378a10da4a1669b74. Accessed 10 March
van Doorn, Niels. (2017). Platform labor: on the gendered and racialized exploitation of low-income
service work in the “on-demand” economy. Information, Communication and Society, vol. 20, no. 6,
pp. 898–914.
Veen, Alex; Tom Barratt; and Caleb Goods. (2019). Platform-Capital’s “App-etite” for Control: A Labour
Process Analysis of Food-Delivery Work in Australia. Work, Employment and Society, First Published
Online 25 March 2019.
Wagenknecht, Susann; Min Lee; Caitlin Lustig; Jacki O’Neill; and Himanshu Zade. (2016). Algorithms at
Work: Empirical Diversity, Analytic Vocabularies, Design Implications. In CSCW ’16 Companion.
Proceedings of the 19th ACM Conference on Computer Supported Cooperative Work and Social
Computing Companion, San Francisco, CA, February 27March 2. New York, NY: ACM, pp. 536
Wood, Alex J.; Mark Graham; Vili Lehdonvirta; and Isis Hjorth. (2018). Good gig, bad gig: autonomy and
algorithmic control in the global gig economy. Work, Employment and Society, vol. 33, no. 1, pp. 56
Wood, Alex J.; Vili Lehdonvirta; and Mark Graham. (2018). Workers of the Internet unite? Online
freelancer organisation among remote gig economy workers in six Asian and African countries. New
Technology, Work and Employment, vol. 33, no. 2, pp. 95–112.
World Economic Forum. (2016). Hosting the world’s largest on demand freelance talent marketplace for
companies to source talent. Accessed 12
April 2018.
Yoganarasimhan, Hema. (2013). The Value of Reputation in an Online Freelance Marketplace. Marketing
Science, vol. 32, no. 6, pp. 860891.
Zammuto, Raymond F.; Terri L. Griffith; Ann Majchrzak; Deborah J. Dougherty; and Samer Faraj.
(2007). Information Technology and the Changing Fabric of Organization. Organization Science, vol.
18, no. 5, pp. 749762.
Forthcoming; Journal of Computer Supported Cooperative Work (CSCW) by Springer. DOI: 10.1007/s10606-019-09368-7
Appendix: Upwork Job Example
Here we present an example of an Upwork job, contrasting this with the more commonly used
examples of Amazon Mechanical Turk (AMT) and Uber. For example, AMT tasks include “the
moderation of web and social media content, categorization of products or images, and the
collection of data from websites or other resources” (“AMT” 2019). These tasks include specific
instructions on how they ought to be completed and are often tightly time-bound. For example, in
AMT, a worker may be asked to categorize one image per task and only be able to categorize that
image in a handful of ways. In contrast, most jobs advertised on Upwork involve larger projects with
fewer instructions from clients on how exactly the jobs should be completed. These can be open-
ended projects that require high skills or specialization in certain areas (reflecting what Malone et. al.
(2011) calls hyperspecialization of work). Figure 1 provides an example of such a project posted on
Figure 1. Example of a knowledge-intensive job on Upwork
Projects on Upwork, such as what is presented in Figure 1, tend to have a longer scope and less
specification than task-based, ride-sharing or delivery gigs. This means the freelancer must make a
plan of action and update this in the face of changes to the scope, needs and deliverables that arise.
This also requires ongoing communication between the worker and the client, and perhaps others,
as part of the work.
... Particularly, people with caring responsibilities, chronic health conditions, disability, and even socially marginalised groups have found in online freelancing a viable career alternative, enabling work under their own terms [17,38,39]. Jarrahi et al. [45] characterise freelancing platforms as allowing for flexibility, for example freelancers can set their own rates, choose their working hours, and choose the types of work they wish to pursue. Online freelancers have also capitalised on freelancing platforms to leverage career and entrepreneurial development. ...
... A controversial feature of online platforms is how they enforce managerial control and oversight through algorithms, creating challenges for freelancers. Examples of this 'platformic management' [45] include evaluating freelancers' performance through ranking systems (e.g., reflecting aggregated client reviews) [61], constraining client-freelancing relationships to the platform environment [47], and even monitoring work processes (e.g., quantifying keystrokes and active time on the platform) [69]. Researchers have examined the challenges resulting from platformic management, for instance, working long, odd hours to earn decent wages [84,85], racial and gender disparities in price setting and algorithmic evaluations [35,41,61], and asymmetric power relationships with clients [4]. ...
... However, other participants remained sceptical about the plausibility of reputation portability as this speculative feature goes against current platforms' core business goals. While it may be very appealing for freelancers to get clients from multiple sources, platforms' profit depends on the mediation of such transactions [16,45,47,49], making such a feature unrealistic: "I would really like [to transfer my portfolio] but I think the platform wouldn't like it ((chuckles)) because maybe not for a corporate job, but for finding other clients that are not on those platforms" (P11.3). As platforms currently compete to attract the most transactions, there was no perceived incentive for them to facilitate transferability between competitors: "most of these platforms are in competition with each other [...] So, if you are kind of looking for a way to like transfer your profile from one platform to another I think most of them [platforms] would want to like uphold your reputation of your existing customers. ...
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Freelancing platforms, such as Upwork and Fiverr, have become a viable source of work for millions of freelancers worldwide. However, these gig economy systems are not typically designed in ways that centre workers' preferences and wellbeing. In this paper, we describe the development and evaluation of 'Freelance Grow, ' a design fiction portraying a freelancing platform that prioritises freelancers' professional development and peer support. The design fiction was informed by a systematic literature assessment, using recommendations from twenty-six sources for improving online freelancers' experiences. We then used the design fiction in focus groups with 23 online freelancers to investigate their views on the ideas suggested in our design fiction. Based upon a thematic analysis of the focus group transcripts, we present three opportunities and considerations for designing systems that further enable freelancers' work autonomy, entrepreneurial development, and peer support. Ultimately, we contribute an expanded understanding of design approaches to support online freelancers in the gig economy. CCS CONCEPTS • Human-centered computing → Empirical studies in HCI; Empirical studies in collaborative and social computing.
... No obstante, aunque el algoritmo ocupe un papel central en la gestión de los trabajadores en las plataformas cualificadas, que requieren interacciones más complejas, los algoritmos se utilizan, más bien, como un elemento más en un conjunto más amplio de prácticas (algunas más tecnológicas y otras menos). Prácticas que un estudio reciente basado en la plataforma cualificada Upwork (la plataforma de trabajo cualificado de referencia en crear mercado) han denominado platformic management [gestión de plataformas] (Jarrahi, Sutherland et al., 2020) y que amplía el conjunto de características, políticas y normas que estructuran el trabajo. Así, de acuerdo con este estudio, la gestión del trabajo de plataformas no puede reducirse al algorithmic management y requiere gestionar y coordinar transacciones complejas que necesitan de mecanismos más generales en la gestión de proyectos (e.g., crear portfolios [cartera de clientes], declinar proyectos, negociar tarifas, resolver conflictos entre clientes y trabajadores...). ...
... El profesor de VIPKid, emprendedor de sí mismo (Rose, 1992(Rose, , 1998, necesita buscar oportunidades, atraer clientes y venderse con videos, en las redes sociales, a través de su página en VIPKid, con recomendaciones, con el cuidado de su reputación, en la misma línea que señalan investigaciones recientes en plataformas cualificadas (Wood, Graham et al., 2019;Jarrahi, Sutherland et al., 2020). La venta de uno mismo, este marketing del yo no es algo directo, además necesita de entrenamiento y formación, no garantiza resultados, pero también intensifica. ...
Full-text available
Hacia la plataformación: el caso de una plataforma digital cualificada. Autores: Gloria Álvarez Hernández (UC3M) y Óscar Pérez Zapata (UPComillas) Fronteras del trabajo asalariado / Alberto Riesco Sanz (dir.), 2020 Utilizamos el caso de estudio de una plataforma china de trabajo cualificado VIPKid, que pone en comunicación a profesores norteamericanos o nativos de habla inglesa, distribuidos en distintos lugares del mundo, con niños chinos para ofrecer clases de inglés de una duración de veinticinco minutos. Opera en un sector específico (educación), lo que permite investigar el caso de un trabajador más especializado y cualificado (profesores), y su éxito comercial sugiere a priori un potencial caso de best in class [mejor en su categoría] en términos de condiciones de trabajo. En particular, nos interesará estudiar en qué medida se encuentran dinámicas similares o diferentes respecto a otros estudios de plataformas cualificadas, con la mirada puesta en anticipar potenciales tendencias más generales para el conjunto del mercado de trabajo. Por ello, en los apartados se comienza por situar teóricamente las plataformas cualificadas en el contexto más amplio de plataformización y por introducir los antecedentes destacados de la revisión de la literatura. A continuación, se presenta la metodología del caso de estudio, VIPKid, los principales resultados del análisis de las condiciones de trabajo a partir de testimonios de los propios profesores/trabajadores, y terminamos con una discusión de nuestra contribución en el contexto de la investigación precedente.
... As for crowd work, guided self-organization is the golden mean between safeguarding worker autonomy FIGURE 1 | System architecture displaying the steps taken by the system in accordance with the hackathon design starting from the initialization of the agents and proceeding to the formation of teams assessed across ten rounds. and protecting digital work platforms from disintermediation (Jarrahi et al., 2020). In the past, the principles of guided selforganization (albeit under a different name) have touched upon collaborative knowledge production (Lykourentzou et al., 2010) and crowdsourcing teams . ...
... Under the light of these inherent limitations of fully bottom-up solutions to crowdsourcing TFPs, we also model a blended approach inspired by Prokopenko (2009) who point that self-organization can (and should) be guided by algorithmic top-down mediation. Similar works (Lykourentzou et al., 2010Martius and Herrmann, 2012;Nurzaman et al., 2014;Jarrahi et al., 2020)-either through conceptualization or reallife implementations-have proposed guided self-organization as the ideal strategy linking worker agency with algorithmic optimization. Our implementation of guided self-organization differs in the way it is applied to a simulated collaborative crowdsourcing scenario where workers are recommended by the algorithm whether to change teams or not. ...
Full-text available
Modern crowdsourcing offers the potential to produce solutions for increasingly complex tasks requiring teamwork and collective labor. However, the vast scale of the crowd makes forming project teams an intractable problem to coordinate manually. To date, most crowdsourcing collaborative platforms rely on algorithms to automate team formation based on worker profiling data and task objectives. As a top-down strategy, algorithmic crowd team formation tends to alienate workers causing poor collaboration, interpersonal clashes, and dissatisfaction. In this paper, we investigate different ways that crowd teams can be formed through three team formation models namely bottom-up, top-down, and hybrid. By simulating an open collaboration scenario such as a hackathon, we observe that the bottom-up model forms the most competitive teams with the highest teamwork quality. Furthermore, we note that bottom-up approaches are particularly suitable for populations with high-risk appetites (most workers being lenient toward exploring new team configurations) and high degrees of homophily (most workers preferring to work with similar teammates). Our study highlights the importance of integrating worker agency in algorithm-mediated team formation systems, especially in collaborative/competitive settings, and bears practical implications for large-scale crowdsourcing platforms.
... The literature shows that the work of "giggers" as new type of knowledge workers is compared to that of mobile workers, who can be the main source of new ideas and new knowledge for the organization (Hasija et al., 2020;Vallas et al., 2020). In particular, the so-called "new blood", the diversity of new employees is also important to access the diversity of knowledge that leads to new combinations, sharing and stimulation of organizational learning (Jarrahi et al., 2020;Lenart-Gansiniec, Sułkowski, 2020;Lenart-Gansiniec, 2021). The result of knowledge acquisition and sharing could be new organizational routines/behaviors or, in other words, new organizational learning (Storey and Davis, 2018). ...
Research Proposal
Full-text available
The Learning Organization Journal Scopus CiteScore 2020: 5.1 ISSN: 0969-6474 Call for papers for a Special Issue on Gig Workers and Learning Organizations Opening date for manuscripts submissions: 01/08/2022 Closing date for manuscripts submission: 01/11/2022
... For example, they enable a greater automation of physical and/or cognitive tasks, formerly executed by humans (Wang & Siau, 2019), and can also help directors, managers, and employees in overall strategic and daily decision-making (Brynjolfsson & McAfee, 2014;Duan et al., 2019;Hughes et al., 2019;Jarrahi, 2018;Leicht-Deobald et al., 2019;Lindebaum et al., 2020). Moreover, algorithmic systems are integrated into organizational operations to automate tasks typically carried out by managers (Duggan et al., 2020;Griesbach et al., 2019;Hughes et al., 2019;Jarrahi et al., 2020;Kellogg et al., 2020;Lee, 2018;Lee et al., 2015;Möhlmann & Zalmanson, 2017;Schildt, 2017). Lee et al. (2015) coined the term "algorithmic management" (AM) to describe this phenomenon. ...
Full-text available
Algorithmic management (AM) is rapidly spreading across industries and significantly changing the nature of work, thanks to advances in artificial intelligence. Since 2015, the advent of the first publications on this topic, AM has captured and sustained the focus of researchers in the social sciences. This enthusiasm can be explained not only by the rapid expansion of the phenomenon but also by the important issues it raises regarding the influence of management on worker motivation, performance, and well-being. We review the existing literature to identify the known effects of the use of AM on worker motivation, using the lens of self-determination theory (SDT). We uncovered mostly negative effects of AM on worker need satisfaction and motivation; however, features of algorithmic management systems and management utilization practices have moderating effects on the impact of AM on work motivation. Future research should leverage motivational knowledge derived from self-determination theory to inform the design of algorithmic management and how organizations use it.
... Algorithmic management outside of the realm of gig work has been met with a mixed reception but has generally been accepted to produce previously untapped synergy and streamline work processes [64]. Most gig workers express some level of displeasure with algorithmic management but feel powerless to stand up to the technology giants that are their employers [50,65]. Several issues-algorithmic and managerial opacity, constant behavior and performance tracking, and isolation from support-result in frustration and burnout, and could possibly explain the high rate of turnover among gig workers [65]. ...
Full-text available
Zusammenfassung Der Beitrag widmet sich im Rahmen eines Systematic Literature Reviews sowie einer qualitativen Inhaltsanalyse der Untersuchung von Online-Arbeitsmärkten und der dort vermittelten Arbeit. Vor dem Hintergrund einer unübersichtlichen Literaturlage besteht das Ziel in der Analyse und Systematisierung der Besonderheiten von Plattformarbeit. Dafür werden 235 zwischen 2010 und 2020 erschienene thematisch relevante Publikationen daraufhin untersucht, (1) welche Disziplinen mit welchen Methoden in welchen Kontexten plattformbasierte Arbeit erforschen; (2) welche Dimensionen von Arbeit sie thematisieren; (3) welche Akteurinnen und Akteure und Institutionen Arbeit prägen; und (4) auf welche Art und Weise sie dies tun. Der Beitrag analysiert den Stand der Forschung zu den Einflussfaktoren plattformbasierter Arbeit und identifiziert Forschungsdesiderata. Zudem bietet er eine Heuristik an, die die oftmals kleinteiligen Forschungsergebnisse systematisiert und aufeinander bezieht. Die Ergebnisse zeigen, dass vor allem Plattformen (als technische und organisatorische Systeme), Kundinnen und Kunden sowie die Community der Tätigen als neuartige Prägekräfte verschiedene Aspekte von Arbeit maßgeblich beeinflussen.
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Digital labor platforms are gaining in popularity in our societies. Information systems and software engineering disciplines have focused on organizational and technological aspects of these platforms, favoring the views of platform owners. At the same time, extensive knowledge of how workers use these platforms, and how they are affected by them, is emerging within computer-supported collaborative work and human-computer interaction disciplines. These two strands of research, one favoring the views of the platform owners and the other advocating the views of the platform users, are mainly developed in parallel and without influencing each other much. In this paper, we describe a case study of designing a digital labor platform for person-centered dementia care in a small company. Dementia care illustrates an extreme case of a complex type of work. This complexity helps us debate some of the benefits and shortcoming of current platforms and platform governance models. We analyze our case using an adaptation of the platform boundary resources model. This model helps us illustrate the tensions between platform owners and workers. A focus on platform governance models and how we co-create such models can hopefully lead to better designs for both views.
Conference Paper
Full-text available
Data-driven algorithms now enable digital labor platforms to automatically manage transactions between thousands of gig workers and service recipients. Recent research on algorithmic management outlines information asymmetries, which make it difficult for gig workers to gain control over their work due a lack of understanding how algorithms on digital labor plat-forms make important decisions such as assigning work and evaluating workers. By building on an empirical study of Upwork users, we make it clear that users are not passive recipients of algorithmic management. We explain how workers make sense of different automated features of the Up-work platform, developing a literacy for understanding and working with algorithms. We also highlight the ways through which workers may use this knowledge of algorithms to work around or manipulate them to retain some professional autonomy while working through the platform.
Full-text available
Conditions in the sharing economy are often favourably designed for consumers and platforms but entail new challenges for the labour side, such as substandard social-security and rigid forms of algorithmic management. Since comparatively little is known about how providers in the sharing economy make their voices heard collectively, we investigate their opinions and behaviours regarding collective action and perceived solidarities. Using cluster analysis on representative data from across twelve European countries, we determine five distinct types of labour-activists, ranging from those opposed to any forms of collective action to those enthusiastic to organise and correct perceived wrongs. We conclude by conjecturing that the still-ongoing influx of new providers, the difficulty of organising in purely virtual settings, combined with the narrative of voluntariness of participation and hedonic gratifications might be responsible for the inaction of large parts of the provider base in collectivist activities.
Full-text available
This article evaluates the job quality of work in the remote gig economy. Such work consists of the remote provision of a wide variety of digital services mediated by online labour platforms. Focusing on workers in Southeast Asia and Sub-Saharan Africa, the article draws on semi-structured interviews in six countries (N = 107) and a cross-regional survey (N = 679) to detail the manner in which remote gig work is shaped by platform-based algorithmic control. Despite varying country contexts and types of work, we show that algorithmic control is central to the operation of online labour platforms. Algorithmic management techniques tend to offer workers high levels of flexibility, autonomy, task variety and complexity. However, these mechanisms of control can also result in low pay, social isolation, working unsocial and irregular hours, overwork, sleep deprivation and exhaustion.
There is a new cast of self-proclaimed experts offering “how-to-succeed” resources aimed at coaching and inspiring gig workers. The emergence of such resources raises questions about the performance of expertise regarding the workings of algorithmic labor platforms. This article examines how Uber driver/bloggers—workers who are driving for Uber, while also creating Uber-related video content—perform expertise in driving for Uber on YouTube. I conducted in-depth interviews with 11 driver/bloggers and a qualitative analysis of the textual and video content published by driver/bloggers. Through the data, I show how driver/bloggers’ empowerment narratives became intertwined with their individualistic aspirations to develop dual careers as Uber drivers and YouTubers. Driver/bloggers employed three self-presentation strategies to perform expertise, including the construction of uniqueness and “know-how,” realness, and relatability with audiences. The study concludes with implications for our collective understandings of gig workers, expertise, and online curation across a wider platform ecology.
Data-driven algorithms now enable digital labor platforms to automatically manage transactions between thousands of gig workers and service recipients. Recent research on algorithmic management outlines information asymmetries, which make it difficult for gig workers to gain control over their work due a lack of understanding how algorithms on digital labor platforms make important decisions such as assigning work and evaluating workers. By building on an empirical study of Upwork users, we make it clear that users are not passive recipients of algorithmic management. We explain how workers make sense of different automated features of the Upwork platform, developing a literacy for understanding and working with algorithms. We also highlight the ways through which workers may use this knowledge of algorithms to work around or manipulate them to retain some professional autonomy while working through the platform.
This qualitative case study adopts a labour process analysis to unpack the distinctive features of capital’s control regimes in the food-delivery segment of the Australian platform-economy and assess labour agency in response to these. Drawing upon worker experiences with the Deliveroo and UberEATS platforms, it is shown how the labour process controls are multi-facetted and more than algorithmic management, with three distinct features standing out: the panoptic disposition of the technological infrastructure, the use of information asymmetries to constrain worker choice and the obfuscated nature of their performance management systems. Combined with the workers’ precarious labour market positions and the Australian political-economic context, only limited, mainly individual, expressions of agency were found.
The rise of the gig economy is disrupting business models across the globe. Platforms' digital work intermediation has had a profound impact on traditional conceptions of the employment relationship. The completion of 'tasks', 'gigs', or 'rides' in the (digital) crowd fundamentally challenges our understanding of work in modern labour markets: gone are the stable employment relationships between firms and workers, replaced by a world in which everybody can be 'their own boss' and enjoy the rewards-and face the risks-of independent businesses. Is this the future of work? What are the benefits and challenges of crowdsourced work? How can we protect consumers and workers without stifling innovation? Humans as a Service provides a detailed account of the growth and operation of gig-economy platforms, and develops a blueprint for solutions to the problems facing on-demand workers, platforms, and their customers. Following a brief introduction to the growth and operation of on-demand platforms across the world, the book scrutinizes competing narratives about 'gig' work. Drawing on a wide range of case studies, it explores how claims of 'disruptive innovation' and 'micro-entrepreneurship' often obscure the realities of precarious work under strict algorithmic surveillance, and the return to a business model that has existed for centuries. Humans as a Service shows how employment law can address many of these problems: gigs, tasks, and rides are work-and should be regulated as such. A concluding chapter demonstrates the broader benefits of a level playing field for consumers, taxpayers, and innovative entrepreneurs.