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The algorithm-based management exercised by digital gig platforms has created information and power asymmetries, which may undermine the stability of gig work. Although the design of these platforms may foster unbalanced relationships, in this paper, we outline how freelancers and clients on the gig platform Upwork can leverage a network of alliances with external digital platforms to repossess their displaced agency within the gig economy. Building on 39 interviews with Upwork freelancers and clients, we found a dynamic ecosystem of digital platforms that facilitate gig work through and around the Upwork platform. We use actor-network theory to: 1) delineate Upwork's strategy to establish a comprehensive and isolated platform within the gig economy, 2) track human and nonhuman alliances that run counter to Upwork's system design and control mechanisms, and 3) capture the existence of a larger ecosystem of external digital platforms that undergird online freelancing. This work explicates the tensions that Upwork users face, and also illustrates the multiplicity of actors that create alliances to work with, through, around, and against the platform's algorithmic management.
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PACM on Human-Computer Interaction, Vol. 3, No. CSCW, Article 212, Publication date: November 2019.
Gig Platforms, Tensions, Alliances and Ecosystems:
An Actor-Network Perspective
ELISCIA KINDER, University of North Carolina at Chapel Hill, USA
MOHAMMAD HOSSEIN JARRAHI, University of North Carolina at Chapel Hill, USA
WILL SUTHERLAND, University of Washington, USA
The algorithm-based management exercised by digital gig platforms contributes to information and power
asymmetries that are pervasive in the gig economy. Although the design of these platforms may foster
unbalanced relationships, in this paper, we outline how freelancers and clients on the gig platform Upwork
can leverage a network of alliances with external digital platforms to repossess their displaced agency
within the gig economy. Building on 39 interviews with Upwork freelancers and clients, we found a
dynamic ecosystem of digital platforms that facilitate gig work through and around the Upwork platform.
We use actor-network theory to: 1) delineate Upwork’s strategy to establish a comprehensive and isolated
platform within the gig economy, 2) track human and nonhuman alliances that run counter to Upwork’s
system design and control mechanisms, and 3) capture the existence of a larger ecosystem of external
digital platforms that undergird online freelancing. This work explicates the tensions that Upwork users
face, and also illustrates the multiplicity of actors that create alliances to work with, through, around, and
against the platform’s algorithmic management.
CCS Concepts:
• Social and professional topics → Computer supported cooperative work
Gig economy, gig platforms, information asymmetry, Upwork, actor-network theory, ecosystems
ACM Reference format:
Tensions, Alliances and Ecosystems: An Actor-Network Perspective. In Proceedings of the ACM on Human-
Computer Interaction, Vol. 3, No. CSCW, Article 212, November 2019. ACM, New York, NY, USA. 26 pages.
Gig work has been on the rise in recent years due to changing labor norms and the proliferation
of gig platforms [58]. Digital labor platforms such as Uber, TaskRabbit and Upwork play a
central role in organizing and managing gig work by providing affordances like creating access
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to gigs and connecting providers and recipients of various services [17,55]. The gig economy
has been lauded as the vanguard of a new era in work and employment that brings about new
business models and mega platform-organizations such as Uber and Airbnb [10]. Businesses are
reaping the benefits of the gig economy by saving labor costs and accessing human resources
on demand [1], while new labor platforms are flourishing, delivering business values and profits
to shareholders [53]. Specifically, these platforms are seen as engines of the new (platform)
economy characterized by flexible work environments and opportunities to work independently
However, recent research sheds light on unintended (and intended) consequences of the
digital platform’s method of managing workers and clients, which has been termed ‘algorithmic
management.’ [64] Indeed, most management functions are completed automatically by a suite
of algorithms, web forms, and other automated processes [36,46]. These decision-making
processes may create and maintain information asymmetries between workers and the
platform; for example, workers remain unaware of how the platform makes key choices
regarding ranking and evaluation [32,45]. These studies further discuss how gig workers that
directly contribute to the gig economy may not reap the same level of benefits it in comparison
to the organizer of the platform (like most traditional forms of for-profit organizations in a neo-
liberal, growth based economic system) [13,29,64]. For example, Ma et al. [38] recently
integrated a stakeholder perspective to highlight the ways algorithmic decision-making
marginalizes Uber drivers who consequently struggle against power asymmetries maintained by
the platform. This line of research makes it clear that platforms may be skewed towards the
interests of only one party (the platform organizers and shareholders) at the expense of others
(often the gig worker) [49,64].
The emerging literature on the impact of digital labor platforms to date focuses on
identifying roots of power and information asymmetries between the centralized platform and
gig workers [13,46] or the worker and the customer [24,39]. However, less attention has been
paid to how gig workers exert and manifest their agency in the above noted power imbalance
by working around the digital labor platform and by forging alliances and enlisting external
tools or platforms. In doing so, research on gig work needs to go beyond dyadic (asymmetric)
relationships between the platform and gig workers or the customer and gig workers, and
establish heterogeneities of players and digital platforms that explicate their roles within a
broader ecosystem. This ecosystem emerges at the confluence of a diversity of technological
options beyond the centralized platform. It thus reflects the divisions of work and power
between various parties who carry divergent interests but may form alliances to advance their
agenda [48,55]. Multiple parties, including transacting parties and external digital platforms,
come together and actively shape and organize the gig economy.
To address these issues, we focus on the platform Upwork, which is considered the world’s
largest online freelancer platform. Upwork facilitates knowledge work carried out by skilled
workers, ranging from web design or digital marketing to strategic business consulting and
intellectual property law. To broaden the focus of current research on the asymmetric
relationship between platform and gig workers, we draw on interviews with both freelancers
and clients of Upwork. A majority of extant studies of gig work and digital labor focus only on
the perspective of the worker (with a few exceptions such as Kumar, Jafarinaimi, and Morshed
[30]). In conceptualizing relationships among different parties, we build on actor-network
theory (ANT), which incorporates various human and nonhuman actants. ANT specifically
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helps explain how networks of alliances between multiple actants may develop through the
concept of translation.
The research questions pursued in the article are:
What are the sources of power and information asymmetries in gig work conducted on
How do various stakeholders work to strengthen their positions within the gig
ecosystem formed around Upwork?
2.1 Information and Power Asymmetries in the Gig Economy
The autonomy paradox is cited as a commonplace characteristic of gig work within CSCW and
other intellectual communities [4,15,17]. While gig workers are provided with certain flexibility
and autonomy (e.g. to work versatilely across time and space), the way gig platforms manage
their work may displace aspects of workers’ autonomy [40,49]. For example, workers may have
to constantly overcome the opacity of algorithms and other systems that manage their work
[2,27,45]. As such, gig labor platforms may be sources of tensions when users face information
and power asymmetries [2,23]. These frictions are often the result of algorithms, which
intentionally or unintentionally favor the interest of the platform over users [9,40,46]. Platforms
frequently relay little information to users regarding algorithmic decision-making, despite these
systems functioning as virtual replacements for middle-management roles in traditional
working environments [13,40]. By strategically obfuscating algorithmic information, platforms’
lack of transparency contributes to low rates of algorithmic literacy on the part of platform
users [2,61]. Ma et al. [38] critique the opaque nature of gig economy algorithms, arguing that
“while platforms and the algorithms that enable them are clearly necessary to enable these
types of exchanges, it is not clearly necessary for the mediation to be opaque, where
stakeholders have limited knowledge, agency, and autonomy over how it mediates their
interactions” (p. 10). Due to the limiting factors of platform algorithms, users may attempt to
recover some semblance of lost agency by subverting algorithmic control [27,36].
Research repeatedly demonstrates that power asymmetries typically favor users positioned
as clients over those employed as workers [3,41,46]. This imbalance manifests in various ways:
in the case of Uber, Lee et al. [36] found that not only do drivers pay more attention to ratings
than riders, but ratings negatively impact drivers psychologically and monetarily. Alkhatib et al.
[3] identify the inability for communication between platform clients and workers as an
exercise in oppressing the ability to foster community relations, which could shift the balance of
asymmetries. Importantly, platform organizers may strategically design and embed power
asymmetries within the platform to retain the overall control and governance of the platform
[30,59]. Indeed, there may be little incentive for platform owners to implement systemic
changes that bring about more equitable relationships between various parties to accommodate
the precarious reality of gig work [38]. Therefore in many cases, despite marketing themselves
as sites for flexible gig work, the algorithmic control of these technologies functions as an
automated manifestation of managerial power [30,46,61]. Importantly, the alleged “flexibility” of
gig working platforms—regardless of how platforms actually implement “flexible” work
structuresis not always synonymous with agency or versatility [37,41].
Despite directing attention to various forms of information and power asymmetries, the
current literature provides little conceptualization of multiple stakeholders and their role within
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the gig economy. Most of the relevant work tends to construe platform relationships as dyadic,
emphasizing relationships as primarily between a singular platform and its workers or workers
and clients. As an exception, Kumar et al. [30] provide a more balanced perspective on the
engagement of multiple stakeholders by including both drivers and riders of a gig platform in
their analysis. Studies of the gig economy are also commonly constrained to the boundaries of a
single platform (e.g. Uber drivers or Mechanical Turkers) and offer limited understanding of
other platforms, which are maligned to the peripheries of gig labor [55]. Our approach in this
study is inspired by the tenets of an ecological perspective espoused by HCI/CSCW research
[e.g., 6,18,42,60], which examines the interaction among multiple technologies within an artifact
or communicative ecology. In the context of the gig economy, Sutherland and Jarrahi [54] have
recently highlighted the presence of an ecosystem of platforms and applications in the way
digital nomads carry out their work.
Furthermore, the CSCW/HCI literature has been more attentive to the roles and agency of
platforms themselves in shaping opportunities or constraints for gig workers [e.g.,
2,11,22,32,57]. Following Lee et. al. [21], a number of scholars have examined how algorithmic
management impacts or is applied to workers. For instance, Rosenblat and Stark [46] point to
information asymmetries as a specific mechanism for platforms to exert power over workers.
Comparatively little attention has been paid to precisely how workers and other stakeholders
might redress tensions rooted in the platform work and recover agency that platform
asymmetries displace. This noted, we follow a line of inquiry that aspires to understand the
heterogeneity of roles and agencies of various stakeholders implicated in platform-mediated
labor. For instance, research on Mechanical Turk makes it clear that crowdworkers actively
collaborate and share administrative overhead by helping each other find work and sharing
alternate tasks in order to use platforms more effectively and fulfill technical and social needs
[19]. In doing so, Turkers embark on ‘articulation work’ to organize and collectively make sense
of platform work at a global scale [21,39]. This engagement with the platform can take an active
form such as providing mutual aid in evaluating and publicizing employers, as observed by Irani
and Silberman [24] in a study of Turkopticon (an activist system that helps Turkers rate
Because algorithmic control typically leaves workers with little recourse within the platform
[45,64], framing workers as isolated or autonomous may present them as passive or helpless
participants within the gig economy [19]. As a result, we build upon the current HCI/CSCW
research that treats gig workers and other participants as ‘social actors’ with agency to act and
change certain conditions [e.g., 12,28,39,44]. Moreover, we delineate the creative strategies these
participants deploy to address tensions and recover agency that platform asymmetries displace
while also incorporating various digital tools and platforms as key players in the gig economy.
2.2 Actor-Network Theory (ANT)
ANT originated within the sociology of science during the 1980s, most notably by Latour, Law,
and Callon [8,34,35]. ANT illustrates relationshipshow they are created, sustained, and
brokenbetween actants (a term used to capture the role of both human and nonhuman actors)
within networks [26]. ANT highlights the processes that gather previously separate entities
together and how they become mobilized through the work of spokespeople. Negotiations,
relationship tensions, and compromises are not extinguished once a network is formed; actant
motives, goals, and alliances frequently shift, reflecting a state of network instability and
fragility [33]. Although these actants can be human or nonhuman, their relative agency is
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equally treated, establishing a “generalized symmetry” during the construction of and
negotiations that take place within a given network. Specifically, ANT encourages “analytical
impartiality” when dealing with human and nonhuman actants, allowing for an equal analysis
of human and nonhuman roles within the creation or disruption of networks [56]. There are
many affordances that ANT provides, but significant to our application here is the conceptual
framework of actant agency and network creation [47]. Specifically, this paper is concerned
with tensions and alliances between humans and nonhumans directly engaged with or
peripheral to Upwork’s gig working platform that frequently result in the forging of new
heterogeneous networks.
Significant to the process of network creation is ANT’s concept of translation, which
specifically delineates how an entity (here the Upwork platform) positions itself to represent an
entire network of other actants. Importantly, as Callon notes, “to translate is to displace,”
suggesting that the establishment of new networks inherently supplants other relationships or
degrees of agency [8]. This notion of displacement repeatedly occurs throughout our findings
below and we frame these tensions (and the subsequent shifts in alliances) against translation’s
individual phases. Through the steps of translationproblematization, interessement, enrolment,
and mobilizationcompromises are made in the creation of this new representative entity.
Translation’s displacements highlight which goals, motivations, and actants are prioritized
over others. First, problematization involves finding a possible solution to a problem and
making it appear essential to other actants. Through essentializing, problematization fosters the
idea that certain actions are required from actants. In other words, the process of translation
generates obligatory passage points that, as the name implies, actants must pass through to
engage with the mobility of this new network. Partnerships are forged during interessement,
which involves defining identities, motivations, and goals while simultaneously blocking off
other parties that could break the creation of the new network. As this article is concerned with
the reality of an ecosystem of networks surrounding Upwork, the platform’s approaches to
interessement, including its methods to attempt to hinder such an ecosystem, are noteworthy.
When these phases of interessement are successful, however, enrolment occurs. Enrolment is
primarily concerned with explicating the roles of the various actants and entities involved
within the network. Finally, mobilization refers to the deployment of a spokesperson for the
allied groups enrolled in the network. This also denotes the more direct mobility of actants and
entities that were previously immobile before translation occurred. While translation can
displace particular concepts of agency, it ultimately describes the assemblage of previously
immobile actants in relation to a set of defined objectives. Moreover, although translation is
presented as a series of steps, it is never fixed; relationships, motivations, identities, and goals
are constantly being negotiated and are therefore capable of disrupting networks [33].
A translation-oriented approach to ANT encourages an analysis of agency distributed across
human and nonhuman actants [50]. This further allows us to investigate the proliferation of
related networks (what we refer to as an ecosystem) despite the platform’s systemic tactics to
quell such an environment. Our deployment of ANT therefore affords an ecosystemic
perspective of Upwork while emphasizing the agency of actants within this particular
platformic setting. Furthermore, ANT helps us understand the heterogeneity of actors and
explains the generalized symmetry that networked relationships afford to humans and
nonhumans [63]. As we demonstrate below, the agency of nonhuman actants (i.e. external
digital platforms) plays a prominent role in the disruption of Upwork’s ideal operations.
Conceptually, ANT provides a useful language to delineate between Upwork as a platform and
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the emergence and multiplicity of a larger ecosystem of external digital platforms. In utilizing
this theory, we are less concerned with furthering ANT as a theory of social science. Rather, we
primarily aim to describe the scope of the gig economy environment and the functions of
actants within it.
The data used in this study centers around the freelancing labor platform Upwork, and includes
interviews with people using the platform, discussions collected from forums where people talk
about working on the Upwork platform, and policy documents collected from the platform's
website itself. This data reflects three major positions in the platform space: the worker who
finds work on the platform in order to earn money; the client, who hires workers on the
platform in order to accomplish tasks and projects; and the platform itself, which is an array of
material constraints, as well as dynamic, human and algorithmic reconfigurations.
We interviewed 20 workers and 19 clients. However, 13 of the clients related that they had
also used the platform as workers, and were able to provide perspective on performing both
roles. The participants are indicated in this paper as Freelancer 1-20, and Client 1-19. The
authors contacted people on Upwork and on other social media sites, including,,, and through personal professional websites. Individuals were selected
for a variety of professions, which include animation, photojournalism, voice acting, industrial
design, blogging, copywriting, marketing, and user experience design, among others. The kinds
of jobs clients brought to the platform were similarly varied. They included web development,
software programming, writing, marketing, data entry, book layout design, and virtual
assistance. The ages of all participants ranged from 20 to 59. Interviews were semi-structured,
following a protocol that focused on the participants’ encounters and breakdowns with the
platform, and with other workers and clients. This included how they fit the platform into their
work processes, and their use or avoidance of specific platform features. Interviews lasted about
an hour, and all were conducted via video conferencing software.
Forum discussions were pulled from two sources. The first was Upwork's "Community
Discussions" forum, which includes separate sections for both workers and clients. Upwork
representatives are present on this forum, and they participate in discussions and answer
questions. The second source was /r/Upwork, a forum on the website This forum
was selected because it was highly active, and because Upwork representatives were not overtly
present on this forum, in contrast to the Community Discussions forum. 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 users 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 third author scanned the forums
and collected posts that had at least one response, and which had some relevance to the Upwork
platform itself, rather than focusing solely on the worker’s trade. Posts were collected and
analyzed iteratively, until new posts were no longer introducing new concepts. The data
collected from the two forums and from Upwork totaled 125 documents, ranging from 2015,
after Upwork’s rebranding from oDesk, to 2018. The people exchanging information on these
forums were not made aware of this data collection effort, as the information on these pages
was publicly available (without a need for creating an Upwork or Reddit accounts).
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The documents collected directly from Upwork numbered 26 and included both policy
statements putting forward expectations about how workers and clients could interact with the
platform, as well as technical help pages, which explain to a certain level of detail how the
platform's various mechanisms work. This included Upwork’s official terms of service
document, as well as help pages about how to become top rated, how to end a contract, how to
dispute feedback from a client, and the rules and policies of Upwork’s escrow system, among
other topics. The goal of this part of the data collection effort was to collect perspectives from
different discursive contexts, and to triangulate findings from the interviews (e.g., discerning
how Upwork offers an explanation on the way badges such as Job Success Score is calculated
versus how the interviewees interpret it). In addition, the official documents were used to
represent Upwork’s official stances and policies.
All of the documents, including both interviews and documents collected from the forums
and Upwork website, were used in data analysis. The analysis was inductive and thematic [7],
but was sensitized by the focal points of ANT: instances of tensions and alliances, the leveraging
of various representations, and points of translation. Coding was used in the qualitative coding
software, Dedoose, which subsequently allowed the three researchers to produce themes for the
data collected. The first two authors were actively engaged in the analysis of interviews while
the third author contributed more to data collection and the analysis of other sources of data.
These larger themes were facilitated by the goals for the research and were revisited as the
researchers collectively made sense of the data. Comparison of the sources was an integral part
of the iterative and collective coding process, and facilitated data triangulation.
Upwork’s platform provides a wide variety of features that facilitate transactions between
freelancers and clients. Matchmaking algorithms (which include ratings, evaluations, and
feedback) connect freelancers and clients, and profiles allow workers to promote themselves
through skill tests, certifications, and proficiencies. Upwork collects service fees through
secured monetary transactions and an escrow service. If necessary, it can handle on-platform
mediation and dispute resolution between users. In short, in lieu of traditional middle
management, Upwork utilizes various automatic features and algorithms that manage and
communicate with gig workers.
Although Upwork intends these features to be beneficial to freelancers and clients, users
interacting with the platform may develop strategies to work around it when it does not align
with their interests. We identified three core spaces within the platform wherein tensions may
impact other stakeholders, namely freelancers and clients: 1) informational spaces, 2)
transactional spaces, and 3) evaluative spaces. These spaces are akin to three functions
identified by Lee et al. [36] (informational, decisional, and evaluational), which are performed
by algorithms managing work on gig platforms. To overcome these tensions, freelancers and
clients may integrate other digital platforms and external relationships into their Upwork
workflows. In what follows, we provide an overview of these three spaces and illustrate how
users address tensions and strengthen their position on the platform.
4.1 Informational Spaces
Initial information dissemination between clients and freelancers occurs through self-
presentation on platform profiles. Although freelancers can control their profiles’ visibility
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settings, they may not always be aware that their profiles can display differently for clients.
Freelancer 3 only realized this by creating a separate client profile on Upwork. He subsequently
added client-view aggregates (e.g., “only top rated freelancer in Upwork who’s based in the US
and has a top 20% score on the UX design test”) to his job proposals as a self-marketing tactic.
Freelancer profiles are constructed from numerous fields, which allow various levels of
control. We have categorized freelancer profile components based on control limitations as
follows: customizable information, Upwork generated information, and client-supplied
information. Where customizable information spaces afford freelancers particular agency to
present themselves to potential clients, client and Upwork generated information is more
constrained through negotiations with clients as well as the platform’s algorithms.
Customizable Information. On Upwork profiles, freelancers can customize their profile
photo, title, overview, introductory video, portfolio, location, education, and other experiences
to a limited extent. We observed freelancers using this section of their profile to sell themselves
as trustworthy, capable workers while simultaneously demonstrating their range of skills. We
also observed instances of interviewees linking potential clients to external portfolios or to their
professional websites where they design and reproduce work according to their own aesthetics
and structural designs. Users repeatedly highlighted the necessity of maintaining multiple
online profiles, or “high visualization” and self-marketing, when freelancing.
This kind of social marketing encourages freelancer visibility across platforms, but also
deviates from Upwork’s more isolated platform design, which largely ignores social aspects of
self-presentation. Other reported self-promotional activities included facilitating email
marketing campaigns, utilizing other job-related websites (e.g., LinkedIn, Fiverr, AngelList), and
hiring agents to promote job skills. Even though Upwork does not encourage external
networking, we observed users utilizing social media profiles (such as Facebook, Twitter,
Instagram, and Tumblr) to increase their self-image. Freelancer 8, for example, occasionally
advertised a client’s company on Twitter to demonstrate her willingness to promote the client
outside of their relationship on Upwork. Freelancers who use Instagram noted that they can
showcase their work and reach larger audiences than they could through Upwork alone. We
observed freelancers attempting to harness larger audiences on social platforms and redirect
them to Upwork, including Freelancer 18 who advertises his Upwork profile on his LinkedIn
profile. Interviewees also noted that social media facilitates relationships better than Upwork,
which has fewer influential community-building opportunities for platform users.
Information Generated By Upwork. Freelancers have less power over Upwork-specific
information, which is generated by the platform’s algorithms. Many of these sets of information
correspond with a visual demarcation, allowing clients to quickly sort through a freelancer pool
without reviewing each freelancer’s profile. These include skill test results, certifications, Top
Rated status, and Job Success Score (JSS). JSS, reflected as a percentile out of 100, indicates
freelancers’ contract completion rate and client feedback, and requires workers to have been on
the platform for a certain period of time. Consequently, newer users on the platform will not
have a high JSS until they complete a set number of contracts that result in good reviews
(platform newcomers can instead earn Rising Talent status). That said, Upwork does not
advertise the exact calculations it uses to generate JSS. Those within the 90th JSS percentile can
attempt to achieve Top Rated status, which also requires further obligations from freelancers.
These include prolonged and sustained activity on the platform, minimum earnings, profile
completion, etc. Top Rated status is also visually reflected within the platform as a prominent
badge on the freelancer’s profile.
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These forms of information stand out on freelancers’ profiles and strongly affect their ability
to secure work. We observed clients highly valuing JSS as a limiting facet when searching
through freelancer poolsbecause a JSS icon appears on brief profiles within search results,
clients can quickly glean the quality of potential freelancers without clicking into each profile.
Client 3 described searching through freelancers as follows: “JSS is so important; you don’t even
need to click on the profile of the freelancer. The score is on the snippet of their profile, so I just
scroll through the applicants. If it’s above 80% it will just show it, so I tend to just look at those
people” (Client 3).
From a freelancer perspective, we observed two sentiments in both interviews and forum
posts: 1) frustration with the unfair or arbitrary nature of JSS and 2) pragmatic acceptance at JSS
value on Upwork, which then leads to attempts at improving or even gaming their rating.
Correspondingly, we also noticed freelancer anxiety over achieving and sustaining a high JSS.
This behavior was exhibited in forums off the Upwork platform wherein freelancers discuss
how the algorithm is potentially calculated, how to recover from a low JSS, and approaches to
offset a less than desirable JSS. On Reddit, one freelancer noted that his “score is currently
sitting at about 85%”, but speculated that his JSS is “scaring away some clients after seeing
others with a 95+%.” Another freelancer reported visiting Upwork’s community forum to gain
insight into finding and securing jobs without a specific JSS requirement (many postings specify
a preferred JSS to, again, limit the pool of perceived qualified applicants). This form of
communication indicates a mode of collective sensemaking that otherwise does not exist on the
platform itself. Confusion over how Upwork calculates JSS is central in this communal
sensemaking, with numerous online threads dedicated to the score and with one poster noting
that “[JSS] is like alchemy, nobody really has a clear formula on how it works except Upwork.”
Due to its high value on freelancer profiles, JSS also encourages transactional and evaluative
behaviors, which we discuss in more detail in the sections below.
Client-Supplied Information. Similar to the fields Upwork automatically generates, client
fields are largely outside of the freelancer’s control. These client-supplied fields appear after job
completion on a freelancer’s profile as a 5-star rating and a written description of their job
performance. The freelancer can post a response to any feedback, but once submitted, only the
client can change the rating unless the freelancer proves the client violated Upwork’s Terms of
Service or if the freelancer is Top Rated. Although feedback can be removed from a freelancer’s
profile during the latter circumstance, the review still remains visible on the client’s profile.
From an informational perspective, these fields are the most restrictive, leaving freelancers with
few options.
Because Upwork does not fully disclose its algorithms, we observed freelancers connecting
with other workers through online communities unassociated with Upwork’s forums to share
methods of recovering from negative client feedback. Examples of recommended solutions
included joining other digital labor platforms (such as Fiverr, Freelancer, Toptal), improving
their communication and job vetting skills, creating another Upwork profile from scratch, and
taking multiple lower paying jobs that will likely end with favorable reviews to both raise their
JSS and push the bad review to a less noticeable location on the worker’s profile. We also
observed that some workers recommended completing contracts, but not officially closing them
on the platform to build a backlog. They could then strategically close these contracts, thereby
prompting a series of feedback requests to potentially offset a negative review. Not only do
these behaviors indicate a platform learning curve for freelancers, but it also highlights
Upwork’s own non-disclosure policy regarding this algorithm (particularly, how it impacts JSS)
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and the limited options freelancers have to adapt and respond to a primary indicator of “quality”
Social Aspects of Information Sharing. Upwork provides a public communication channel, the
Community Forums, for users to gain platform literacies and connect with their global
colleagues. As an extension of the platform’s job searching/creating function, these forums are
monitored by the platform and forum moderators frequently respond to posts from users. Since
interactions are not anonymous (Community Forum activity requires an Upwork account),
certain topics, particularly any that may imply violating Terms of Service, are not discussed.
Community Forums are the only way for clients and freelancers to interact freely, as a
messaging feature between users is absent from the platform. As a result, user-to-user
communication is limited to clients connecting with freelancers through the specific jobs they
have posted.
Despite providing a Community Forum, dynamic freelancer and client communities are
missing from the platform. Such collective gatherings are significant aspects of information
seeking as they facilitate the social function of information sharing. Social gatherings also
influence how users inform other users (especially newer ones) about Upwork functionality,
particularly JSS and other algorithms that are more obscure. We observed that external forums
had consistent exchanges regarding unclear JSS algorithms and topics that would violate
Upwork’s Community Forum Guidelines, such as discussing alternative payment methods. The
Upwork subreddit has over 5,000 members and the comment threads on Upwork-focused
YouTube videos demonstrate an active Upwork community that exists external from the
platform itself.
4.2 Transactional Spaces
Finding Work. To apply for job postings, freelancers on Upwork use the “connects” currency to
submit proposals for gigs. Those signed up with “Freelancer Basic” (the free account) are
allowed 60 connects per month with no rollover and those with “Freelancer Plus” (the paid
account) with 70 and the option to pay for more connects, which can roll over up to a maximum
limit. Most proposals require two connects, so Basic workers can apply for up to 30 gigs per
month. However, some freelancers get frustrated with clients wasting their connects by
supposedly not hiring anyone for posted jobs. In the Community Forum one worker
complained: “Clients who don't hire are destroying freelancers. [They] need to step in and do
something about clients posting jobs and not hiring.” Freelancer 3 admitted to creating a
separate client account to post jobs and get estimates on price points for different tasks. Since
the quoted prices were outside of their price range, they ended up not hiring any of the
freelancers who applied, but instead retained some of their contact information for possible
future collaborations.
Freelancers may also use other gig platforms and social media sites to supplement their
Upwork jobs. We noticed workers’ tendency to use these alternative sources because they could
access larger and likely more diverse audiences, even at the risk of less commitment from job
posters and losing the opportunity to strengthen their Upwork portfolio and ratings. For
example, Freelancer 9 notes: “Most of my work I get through my social media accounts. My
Tumblr account is the place where I get most of my work because I have been there the longest
with a larger audience. I’ve been building up a Twitter following too.” Freelancer 11, who also
uses Twitter to locate prospective jobs, found the social media site a “great place” to holistically
review job postings, field trends, and to network with other workers in their field.
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Others indicate that specialized fields have robust resources, either on forums, Facebook
groups, or specific websites, that Upwork’s more generalized platform does not. Freelancer 12, a
photographer, mentioned using the global production platform Blink to quickly find location-
based jobs that align with her self-described “digital nomad” lifestyle. Freelancer 8, a writer,
indicated the value not only in joining Facebook groups and forums, but also in building
communities to foster relationships that can lead to job opportunities. She also expressed
concern at the prospect of solely using Upwork to locate gigs, both because it does not allow for
networking and also because it is too difficult to establish high ratings on the platform,
particularly as a new user.
Clients also make use of external platforms while using Upwork’s features selectively. Some
participants only access Upwork to transfer payments, and others only use it to find freelancers
before moving off-platform to conduct transactions. They also integrate Upwork into larger
workflows, which involve finding work by word of mouth, through social media, and on other
platforms. In particular, a number of clients used the freelance marketplace Fiverr for smaller or
simpler tasks such as one-time video creation or web research. For example, Client 17 notes: “I
prefer Upwork for certain tasks. If it’s somebody that’s going to be writing stuff for me, I
wouldn’t go to Fiverr, I would go to Upwork. But if it’s for jobs where I know exactly one
specific task, then I go to Fiverr."
Payment. Upwork provides resources, which legitimize clients and freelancers while also
protecting both groups against abuse of their time, money, and labor. We found that freelancers
frequently use the platform’s “verified payment method” feature as an indicator of a client’s
trustworthiness. Transactions that take place on Upwork qualify for the platform’s escrow
service and mediation assistance in case disputes arise from either transacting party. However,
we observed tensions around these resources because of the way they were leveraged by
different groups. For instance, the design of the bidding system can encourage new freelancers
to work for little pay. Client 3 noted that talented freelancers who are new to the platform can
appear competitive and get hired by bidding lower than more established workers. While this
process allows newer freelancers to build their Upwork profile, it highly favors clients, who pay
less for work without anxiety over developing and sustaining platform metrics.
Although freelancers can see when clients have verified payment methods, there are
incidents of freelancers being “scammed” or performing unpaid labor in situations when the
client did not have verified payments. Some of these circumstances occur early in a freelancer’s
time on the platform before they developed a higher level of platform literacy. Other workers
admit to being scammed by clients they suspected were untrustworthy, but they felt they had
few options because they could not secure positions with verified clients. Freelancer 1 sought
help from Upwork after her client’s credit card was declined. However, the platform said
nothing could be done since she did most of her work on a tablet and the Work Log application
only functioned on a desktop or a laptop. The freelancer ultimately received her payment from
the client after contacting a freelancer union, who sent the client a strongly-worded letter.
Upwork jobs can be billed as hourly work or at a flat price per project, and the platform
provides invoicing to streamline payments processes. Hourly contracts have the option of
monitoring freelancers’ work through a Work Log and Time Tracker. When active, the Work
Log application randomly takes screenshots of a freelancer’s work throughout the day. This can
benefit both the client and the freelancerclients can remotely confirm work that is logged and
freelancers can document the work they completed though Upwork’s system. However,
workers often find the tracker clunky and incompatible with their personal devices, or simply
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too intrusive. Clients similarly substitute external time tracking and task management tools
which are more developed, such as ticketing systems and team management software like
Asana. Client 14 described a programming job through Upwork during which he never
communicated directly with his team members or with the client, but rather completed all of his
work through an external issue-tracking system.
Freelancers and clients also circumvent Upwork’s secured payment system by using
platforms like Stripe, Payoneer, Venmo, or CashApp in order to avoid the platform’s transaction
fees. Upwork charges freelancers a service fee on a sliding scale, which decreases as the
freelancer earns more money with a single client (20% for the first $500, 10% between $500-
$10,000, and 5% beyond that) . After establishing trust, Freelancer 11 and her client were able to
successfully move their transactions off the platform, affording the client to pay less and the
freelancer to earn more:
“I had a client who had a pretty big job (it was like $300). But Upwork usually take 20%
as profit. So, for me to have gotten that $300, the client would have had to pay $350 or
$360, which was a bit out of her budget. She we completed everything on Upwork, she
had a $5 placeholder on Upwork, but she paid me through PayPal. Most people are fine
using Upwork, but every so often if it’s a long term relationship where you’re going to
be doing a lot of work or a lot of business, sometimes people will ask, once they’ve
built that trust, if it’s okay if they pay on PayPal.” (Freelancer 11)
Unless they have paid Upwork an “opt-out” fee, clients and freelancers risk account
suspension and possible monetary loss for moving transactions off-platform. Furthermore, these
external financial transactions increase the risk of getting scammed (out of either labor or
finances), as the money is not in Upwork’s escrow system and is consequently uninsured by the
platform. Upwork attempts to curb this behavior because it cannot reap transaction fees from
off-platform exchanges. In fact, users may have their accounts deactivated or suspended after
mentioning “PayPal” in Upwork’s chat or messaging system, which could result in a loss of
earnings that the platform was processing or holding in escrow.
Communication & File Sharing. Upwork offers important communication functions such as
internal emails, chats, video conferencing, and file sharing. Since the platform uses these
communications as possible evidence during client/freelancer disputes, users are encouraged to
utilize these for most, if not all, communications. However, we observed users incorporating
external communication and cloud services into their workflows. Client 15 indicated that he will
“try to get off the system as much as possible” because Upwork’s video and chatting options
don’t “fit into” his work practices. Both clients and freelancers frequently mention Skype as a
preferred communication application. Freelancer 4 uses it because it is “almost universal” to
online freelancers and easily functions across device types and operating systems. Client 11 uses
Skype to interview freelancers for projects. Several Upwork users described Skype or Google
Hangouts as more well-established communication tools as opposed to Upwork’s embedded
communication features.
File sharing also takes place on external cloud-based platforms, despite Upwork’s file sharing
capabilities. Freelancer 14, a photographer, noticed that Upwork would reduce the size of his
higher resolution images when transferred through the site, impacting the quality of his work
product. Although a client hired him through Upwork, this photographer sought external file
sharing methods (in this case, linking to a Dropbox folder) to adequately display and deliver his
final deliverables. Similarly, Freelancer 12, also a photographer, uses Chrome Photos due to
Upwork’s size restrictions on her files. Freelancer 20 was hired through Upwork to create a
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lengthy video, which the platform ultimately could not support: “I did a job that required two
hours’ worth of video. I can’t deliver that through Upwork; I’ve got to deliver that on Dropbox.
...that’s the only way we can do it” (Freelancer 20). Freelancer 15 uses a combination of
Dropbox, for easy access and file size capacity, and Dropbox Paper for collaborative document
editing, commenting, and downloading between clients.
4.3 Evaluative Spaces
Platform Evaluations. Through Upwork’s evaluative mechanisms, competition and power
asymmetries between workers and clients emerge on the platform. Both workers and clients
rate each other once a job is completed, and the client’s feedback is then processed through
Upwork’s algorithm to generate a JSS for freelancers. A worker’s success in competing against
many other freelancers is contingent upon maintaining a higher JSS, a system that Freelancer 3
described as “five stars or fail.” Additionally, because evaluations are constructed through
singular individuals (the contracted client) and then fed through an algorithm (the structure of
which Upwork does not fully disclose), there are fewer options for freelancers to work around
negative feedback.
Although clients are also rated, their ratings are not as critical to their success on the
platform as workers’ are. When questioning clients about their ratings from previous
freelancers, we observed a range of nonchalance. Several clients vaguely knew about the client
rating system on the platform but noted that they either did not care about it or they rarely
tracked its progress. These comments were framed as comparisons to freelancers’ ratings with
Client 1 noting that workers are “very active and aggressive about guarding their reputation
and that’s not true for me.” Others were sympathetic towards freelancers due to this power
imbalance and took their evaluations of freelancers as a moral obligation.
Ultimately, the pool of freelancers is larger than that of clients, so the latter have a greater
advantage in finding workers regardless of their reputation than vice versa. Client 8 commented
on the “crazy” environment of freelancers panicking over a 4 out of 5 score, but was
unconcerned when asked about their own ratings “because people will bid regardless, even if
half of the people said oh, ‘really difficult client to work with’ I would still get 100 applications.
I’m not that concerned there.” If client ratings drop too low, one client noted that anyone can
just create another Upwork profile without inquiry from the platform, thereby erasing any
history of negative feedback.
Freelancers attempting to change their JSS either go directly through the client who provided
the feedback or through the Upwork platform. The former approach involves contacting the
client and asking them to change the score. Because feedback is optional when closing a
contract, freelancers will frequently remind clients to leave comments/ratings if they anticipate
a good review. We found others, however, were willing to exchange money earned through the
contractin one case, the freelancer offered a full refund for their workto avoid potentially
negative feedback. Client 9 described giving a freelancer a lower score before the worker offered
to complete additional jobs for free if the client would be willing to change their review. Some
barter with the client for mutual positive reviews or no reviews entirely from both parties.
Workers also noted that some clients simply refuse to give five-star reviews as a matter of
principle, and no amount of conversation or high-quality work can change this. Other clients
are happy to help workers artificially inflate their ratings by closing and opening jobs or
logging their payment in shorter, higher-pay hours. Still, the asymmetry regarding the
importance of client and worker ratings puts freelancers at a disadvantage in these negotiations.
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Freelancers approaching JSS from a systemic perspective are more experimental, primarily
due to Upwork’s refusal to disclose its algorithm for calculating JSS. Freelancer 3 described
attempting to “reverse engineer” the algorithm by roughly calculating the value of time spent
on the platform, testimonials versus rated feedback, and reviews on worker profiles as opposed
to reviews on the client’s profile, amongst other possible criteria. External forums, such as
Reddit, contain numerous threads on maintaining a good JSS and managing poor reviews.
Freelancer 4 described a common tactic for new users on the platform, but also applied it to
workers trying to boost their JSS: “Go in cheap, start off cheap, start getting small projects.”
Speaking from experience as a former freelancer, Client 18 disapproved of this practice because
it capitalizes on low-paying work and manipulates workers by holding their reputation on the
platform hostage. While we encountered both freelancers and clients who do not trust online
rating systems entirely, many clients noted the value of JSS and correspondingly, many
freelancers felt pressure to maintain this assessment tool.
Extending Platform Evaluations. Both clients and workers found the platform’s evaluations
unsatisfactory in some ways and extended their evaluative measures to other resources and
processes, often in conflict with Upwork’s Terms of Service. Some clients, for instance, pointed
out that freelancers’ ratings were inflated, and subsequently, they implemented their own
evaluations to ensure that they were hiring good workers. These tests ranged from minor tasks
designed to ensure the freelancer actually read the job description to performing smaller pilot
jobs before taking on more involved assignments.
Freelancers also sought to extend the platform’s evaluative mechanisms by merging them
with their larger online reputation. Online reputation for a freelancer is holistic and materializes
across multiple platforms. Admittedly, some freelancers feel frustrated trying to integrate the
work history and evaluations they built on Upwork with those they developed elsewhere: “I
think that a lot of these freelance websites are kind of gardens where they kind of treat it as
we’re the only place you’re going to get any can’t import reviews from some other
site, it has to be all reviews from within Upwork, or you can’t export Upwork test results to
another site” (Freelancer 3). Unlike the customizable information fields above which are user-
populated, online reputation incorporates evaluative aspects generated by multiple parties.
Despite Upwork’s closed platform design, one freelancer advertised a 5-star Upwork review that
he received on his Twitter profile. This both reached a large audience (some 10,000 of his
Twitter followers) while also signaling his work on Upwork’s specific platform through a
screenshot of the review and the #upwork hashtag. In this way, he influenced his reputation by
utilizing a metric (the 5-star rating) that was designed to be limited to and internalized within
Upwork’s platform. Other freelancers share and link to their Upwork profile across platforms.
One worker not only linked to their Upwork profile, but also included Upwork-specific metrics,
such as jobs completed and JSS (see Fig. 1).
This Twitter thread also inadvertently allowed for community feedback on the freelancer’s
reputation, including advice on setting a higher hourly rate. Instagram also factors into online
reputation, with users posting photos of their Upwork achievements (e.g. earning Top Rated
status), examples of their work, or advertisements of their services.
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Fig. 1. Example of promoting Upwork profile on Twitter (from
It is important to note that workers in niche markets may have a different experience and
subsequently different behaviors than reflected in the above discussion. For example, they may
pay less attention to the platform’s algorithms and experience fewer tensions since the platform
works relatively well for them; some of our interviews indicated many clients actually seek
them (rather than them bidding for projects). As such, workers in niche markets do not have to
constantly learn about, navigate and work around the platform; for this reason, some of the
activities outlined above may not completely apply to these users.
Within the context of Upwork’s platform, our findings reflect two perspective models of online
freelancing: 1) Upwork as its own self-contained, idealized platform that provides a
comprehensive gig economy to users, and 2) a larger ecosystem of stakeholders and digital
platforms beyond the Upwork platform. The latter reflects the reality of Upwork-in-practice not
as an isolated platform, but as part of a complex ecosystem, or as ANT suggests a heterogeneous
assemblage. In the following section, through ANT’s concept of translation, we examine the
former perspective model reinforced by Upwork’s platformic control before describing the
larger ecosystem that has arisen from coopetition (simultaneous competition and cooperation
[62]) between humans and nonhumans alike.
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5.1 The Idealized Notion of a Self-Contained Platform
Fig. 2 presents Upwork as perceived and organized by the owner of the platform (Upwork
Global Inc.). This may be akin to the picture depicted by much of the literature on the gig
economy, which focuses on the relationship between the labor platform and workers within the
bounds of a single platform, and implicitly treats the platform as operating independently from
broader relationships and alliances. However, an application of ANT helps illuminate how
Upwork attempts to lock in users, and importantly, how tensions arising from the platform spur
alliances between other stakeholders (human and nonhuman actants), thus resulting in a larger
ecosystem (see Fig. 3-4).
Fig. 2. The gig platform as a self-contained system
When what we have labeled as informational, transactional, and evaluative spaces operate
seamlessly, Upwork’s sociotechnical relationships, like other information infrastructures, are
rendered invisible [52]. Moreover, Upwork’s design of these spaces is intended to manifest as
obligatory passage points, creating a cyclical pattern of mandatory indispensability of the
platform for users, particularly for freelancers. High valued profile badges depend upon
sustained freelancer engagement with specific aspects of the platform (ability tests, connects,
proposals, etc.), which then earn them access to quality, legitimate clients. By enticing people to
utilize the platform’s convenient resources, Upwork makes itself an obligatory passage point
within its network through a strategically designed closed system that prevents
disintermediation of the platform. For example, JSS is a material transformation, which acts as a
lever to let Upwork control the means of reputation building. Unless freelancers are looking for
a one-off job, this pattern continues indefinitely and ensures worker interaction within the
platform while Upwork reshapes how the larger freelancing economy conceives of work and
Upwork’s promotional marketing clearly demonstrates how it intends to become an
indispensable one-stop shop for freelancers and their clients (in other words, problematization,
during which a problem is identified and a solution is positioned as essential to this particular
issue): “Upwork makes it fast, simple, and cost-effective to find, hire, work with, and pay the
best professionals anywhere, any time.In this narrative, it is assumed that clients have a hard
time finding quality, creative gig work and it is difficult for freelancers to find reliable and
trustworthy jobs. Upwork then positions itself as an independent and self-sufficient platform
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that can handle every step of the gig economy for clients and freelancers alike: “Contract talent,
track work, and pay freelancers from a single platform.” Its marketing highlights multiple
membership levels ranging from a basic account—“Top-skilled freelancers and the essentials to
find and work with them”—to an enterprise account—“End-to-end technology and service
solution customized to fit your company.” Upwork also includes a spectrum of hiring options,
including short-term projects and contracted positions, and platform features that include job
posting, recruiting, mobile and desktop collaborative tools, and secured transactions.
Once Upwork provides its solution to the freelancing economy, it then classifies the different
identities and objectives of actants, while also attempting to obstruct any competitors who
could possibly detract from its own isolated network (or, interessement). This process of
interessement is both fluid, as identities and motivations shift, and also dualistic through its
method of blocking off nonhuman actants that could potentially detract from its
problematization. First, Upwork defines the roles of freelancers and clients. Freelancers are
financially motivated to earn income for their labor, which incorporates both their identity as
gig workers as well as their financial goals. Clients, according to Upwork’s above marketing
tagline, are motivated by labor costs (“cost-effective”), project timelines (“fast,” “any time”), and
quality deliverables (“the best professionals”). Once Upwork defines freelancers and clients (and
reinforces these roles through control mechanisms, such as its Terms of Service), the platform
can then block other parties from creating new alliances. In this way, Upwork maintains the
structural stability of its network through a power asymmetry that largely favors the platform.
For example, it surveils chats and intervenes when transacting parties mention potential
alliances with external platforms such as Skype or PayPal. Upwork also sustains platform-bound
relationships between clients and freelancers through its Terms of Service, which aim to restrict
the assemblage of a larger ecosystem through the threat of, for example, expulsion from the
Once interessement is successful, enrolment, or the coordination of specific roles, is then
relatively straightforward from Upwork’s perspective. Freelancers produce work, clients select,
hire, and pay workers, and the platform and its stockholders reap financial benefits from
transaction fees. Upwork maintains this equilibrium of relationships through, as mentioned
above, various levels of surveillance and threats of monetary loss. Payment is contingent upon
screenshots observing hourly work and conversations that occur on the platform’s messaging
channels are monitored to ensure that external transacting actants remain inaccessible.
Moreover, Upwork can suspend accounts and render freelancers invisible in search results if
they do not behave in certain ways and maintain specific levels of engagement on the platform.
After Upwork entices clients and freelancers to its platform, defines their motivations, and
coordinates their roles, the platform is then an acting spokesperson (or, translation’s final step,
mobilization). Herein, Upwork seeks to be the spokesperson for its own platformic interest and
the users operating within it. As spokesperson, Upwork advocates for its users through
mediation services, protected monetary transactions via escrow and payment verification, and
collating and disseminating labor. Additionally, it is also the spokesperson for the quality of
workers, as its algorithms for ratings and badges become the accepted measure of freelancer
caliber. This, in turn, fosters dynamic exchanges of creative outputs that continue to generate
and evolve on the platform over time. In this way, the platform grants mobility to previously
immobile actants.
However, our findings make it clear that Upwork’s problematization is considered
insufficient as the interests of other stakeholders are not satisfactorily fulfilled due to various
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tensions depicted in Fig. 3. The subsequent ecosystemic responses of these actants are broached
further in the remainder of this paper.
5.2 A Robust Ecosystem of Platforms
The Upwork platform aims to be perceived in simplistic terms as a resource (or in ANT’s
terminology as an obligatory passage point) for quick, easy, and cheap labor. This asymmetric
design can be understood as a “heteromated system,” through which value is unproportionally
accrued to an enterprise [14]. However, the existence of a larger ecosystem reveals numerous
tensions scattered throughout this idealized structure of the platform. Indeed, the platform's
structure facilitates information and power asymmetries that favor Upwork over clients and
freelancers (although, significantly more so for workers than clients) (see Fig. 3). Importantly,
Upwork’s systemic displacementsdisplaced autonomy and displaced power symmetryare the
very tensions that foster the creation of new alliances and a larger ecosystem of actants beyond
its idealized notion of the gig platform (depicted in Fig. 2).
Fig. 3. Tensions surrounding the gig platform
Maintaining an active presence on the platform is a significant tension for some freelancers.
As success on Upwork is driven by sustained activity, the platform reinforces itself as an
obligatory passage point while simultaneously discouraging the establishment of relationships
with other platforms. Not only must freelancers log onto the platform, but in order to avoid
Upwork automatically rendering their profile invisible to clients, workers are required to earn
money every 30 days. Rewards and badges, such as Top Rated and Rising Talent, also stipulate
various on-platform activity levels (numbers of proposals submitted, work delivered, ratings
averages) within specific time frames. While profile features such as JSS and Top Rated status
highly impact freelancer success on Upwork and are marketed as beneficial profile components,
they also operate as control mechanisms designed to contain both freelancers and clients to
Upwork’s platform. Users engaging with a larger ecosystem must therefore consider the costs
and benefits of any freelancing activities that take place off the platform.
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Despite this imperative to maintain levels of platform activity, the relationship between
clients, freelancers, and Upwork is neither binding nor exclusively contained within the
boundaries of the platform. This undercuts Upwork’s position as an obligatory passage point.
As much as Upwork’s Terms of Service and activity mechanisms attempt to systemically lock
users into place, our findings illustrate a drastic difference in the platform’s interessement
methods and the reality that Upwork is only one player within a broader network of alliances
and relationships. For many freelancers, Upwork is one channel amongst a number of others for
finding work and conducting transactions. The same applies to clients as they would typically
find and recruit online freelancers through different digital avenues. As such, those
knowledgeable about different platforms’ networks, for instance, could benefit by moving
between them or signing up for multiple services.
Since freelancers are not strongly bound to the gig platform or its technical systems, they
therefore have a greater degree of autonomy in conducting work than perceived in current
literature on the gig economy [13,45]. We observed many online freelancers may also start
building their reputation on Upwork, but once they establish the quality of their work, they
may not need resources provided by the platform over time and can use their client base
independently of the platform. Similarly, once clients have vetted the legitimacy of a freelancer,
they may not depend upon Upwork to contact new workers and can then begin negotiating
their own projects.
Whether condoned by Upwork or not, other nonhuman actants in the form of external
platforms can enable clients and freelancers to creatively accomplish transactions by the most
effective means available. In fact, once clients and freelancers establish a trustworthy
relationship, there are fewer reasons to remain committed to the platform when compared to
the benefits of conducting some or all transactions off the platform where there is no
surveillance and fewer fees imposed on financial transactions. The ability for clients and
workers to use Upwork not as a sole source of locating freelancers or freelance work, but
instead as only one part of a patchwork approach to online gig work, dismantles Upwork’s
perceived ideal isolation, thereby questioning the sustainability of the platform’s translation
mechanisms. Our findings highlight tensions that markedly run against Upwork’s projection of
itself as sole spokesperson and the reality of a robust network surrounding the platform.
The intrinsic asymmetries (between clients and workers and between workers and the
platform) built into the platform further propel workers to seek other resources not provided by
Upwork. Power asymmetries include the impact of freelancer ratings vs. client ratings, the ratio
of freelancers to clients on the platform, and the platform’s surveillance and fee-based payment
mechanisms. Our findings demonstrate that information asymmetries entailed unclear
algorithm function and impact, a lack of community building, and a sharp information literacy
learning curve on navigating the platform. These information and power asymmetries are the
impetus for Upwork users assembling their own ecosystem through human and nonhuman
negotiations. Such a gathering of actants demonstrates the agency of gig economy users, which
is often glossed over in the current literature [27]. Indeed, when users subvert the platform or
seek solutions outside of it, they reclaim some of their agency lost to Upwork’s algorithmic
management and control. Notably, this impacts freelancers more than clients, as clients have
more autonomy within the platform and, as we observed, are less pressured to maintain a well-
developed reputation. While this does not necessitate that all clients have a laissez-faire attitude
towards the platform’s asymmetriesindeed, we observed notable exceptions to this
generalization in our Findings aboveand conversely, that clients will not take advantage of
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this imbalance, but systemically, they are far less pressured to adhere to the performance
quality and quantity standards Upwork enforces upon freelancers.
Despite Upwork’s power imbalance, both freelancers and clients repeatedly subvert
Upwork’s imposed communication channels, transaction fees, and file sharing services,
prompting an array of alliances between clients, freelancers, and nonhumans (external digital
platforms). Upwork’s fees on client payments repeatedly drove both clients and freelancers to
use PayPal, CashApp, or Venmo transactions, despite the risk of getting “scammed” or Upwork
suspending their account for violating its Terms of Service. In this case, Upwork’s designed
obligatory passage point for financial transactions is in direct competition with other monetary
services and applications. The viability of nonhuman actants such as PayPal or Venmo’s digital
platforms, particularly their perceived trustworthiness and familiarity, factored into client and
freelancer decisions to circumvent Upwork’s payment method. Instances of clients transferring
nominal payments to freelancers on the platform while paying larger amounts through these
external payment applications suggests that clients understand the site’s inherent power
imbalance when it comes to modes of financial transactions. Furthermore, the indispensability
of these digital actants positions them as more successful spokespeople within the arena of
financial transactions. Such instances of nonhuman actant agency subsequently bolster the
alliances between clients and workers that constitute a larger ecosystem beyond the Upwork
Similarly, external communication and file sharing resources such as personal email
accounts, Skype, DropBox, and Google Drive, maintained a competitive advantage over
Upwork’s features. Despite Upwork’s instantaneous communication tools and the ability to link
communications with specific projects, numerous clients preferred external digital platforms
and technological resources with which they had already stabilized a relationship. In these
instances, the ubiquity of applications like Skype and Google Drive held competitive advantages
over Upwork, even with the latter’s added benefits of using communication and collaboration
records for synchronous project design and as evidence for possible disputes. Moreover, a
significant tension between visual freelancers and Upwork’s platform resulted in workers using
external digital platforms by necessity. For example, the inability to transfer large files was cited
as a considerable flaw in Upwork’s platform. Despite marketing itself as a synchronous gig
working environment, Upwork’s inability to transfer particular files made it susceptible to other
established, reliable, and similarly collaborative platforms to forge alliances with Upwork users,
which, as our Findings illustrate, readily aligned themselves with the needs of Upwork users.
Within such ecosystemic alliances, these external technological actants afford freelancers and
clients the ability to both defy and protect themselves against the control and surveillance
exercised by Upwork’s intended design, functionality, and often Terms of Service.
Where relationshipsand subsequent alliancesbetween clients and freelancers are
expected, Upwork provides only one option for freelancers to connect with other freelancers
(and clients with other clients) through the platform’s Community Forum. Dynamic freelancer-
freelancer and client-client relationship building therefore conflicts with Upwork’s platform
design, which only allows communication between clients and freelancers once the latter has
submitted a proposal to a posted job. Freelancers in particular, already under time constraints to
sustain their activity levels on Upwork, gain no benefit from helping other freelancers,
especially on external digital platforms that, again, divert attention away from their activity
Upwork. And yet, prevalent freelancer communities on sites like Reddit emphasize the
importance of information seeking and sharing. This coopetition between freelancers illustrates
Gig Platforms, Tensions, Alliances and Ecosystems: An Actor-Network Perspective 212:21
PACM on Human-Computer Interaction, Vol. 3, No. CSCW, Article 212, Publication date: November 2019.
the value of community building for gig workers. Furthermore, the popularity of external
forums, especially those that afford a level of anonymity, forge networks of freelancer-forum-
freelancer alliances and attempt to redress Upwork’s information asymmetry. Social media
platforms’ recurring presence in informational, evaluative, and transactional spaces is indicative
of their wide influence for gig workers wishing to compensate for Upwork’s intentional
asymmetries. As nonhuman actants, social media afford freelancers the ability to connect with
other freelancers and clients, supplement Upwork job searches with niche postings, foster
relationships with specific communities within the gig economy, and share deliverables and
ideas with a wider audience than Upwork can provide.
Despite Upwork’s largely successful presence within the field of online freelancing [20,43],
this accomplishment does not result in Upwork’s goal of platformic isolation or serving as
spokesperson for all involved stakeholders. In this way, Upwork has attempted to construct a
sustainable system that facilitates online freelancing within the bounds of its own platform.
However, our analysis informed by tenets of ANT demonstrates that tensions rooted in
information and power asymmetries as well as other constraints of the platform propel workers
and clients to seek alliances with other digital platforms outside Upwork. This conceptualization
of stakeholders stands in contrast to the common understanding of gig workers as helpless
‘users’ that forgo their agency. Gig workers (and clients) reclaim some of their agency as social
actors [5,31] by enlisting external digital actants, giving rise to a larger ecosystem.
The presence of an ecosystem of external digital platforms (see Fig. 4) first and foremost
demonstrates the comparative advantage of external actants, while also highlighting the
instability of Upwork’s intended isolation.
Fig. 4. The larger ecosystem of digital platforms facilitating online freelancing
The ecosystem that we have illustrated is less a result of the direct implementation of Upwork’s
algorithmic control, and more of a product of shifting alliances and negotiations between clients
212:22 Eliscia Kinder, Mohammad Hossein Jarrahi, & Will Sutherland
PACM on Human-Computer Interaction, Vol. 3, No. CSCW, Article 212, Publication date: November 2019.
and freelancers. When stakeholders, particularly freelancers, are faced with tensions power
asymmetry, information asymmetry, and Upwork’s technical limitations—on Upwork’s
platform, they seek out and negotiate alliances as ecosystemic support. While the notion of the
platform represents the will and interest of the organizer/owner of the platform (an idealized
narrative from its perspective), the larger ecosystem epitomizes the agency of the workers and
clients in negotiating alliances with other external digital platforms that subsequently
perforates Upwork’s attempt at platformic isolation. While others have distinguished between
the two concepts of platforms and ecosystems in other research contexts [e.g., 16,48,51], the
distinction needs to be integrated in the research on the gig economy to reveal important
dynamic of competition and cooperation between various involved stakeholders.
Freelancers and clients on Upwork circumvent the platform’s various control mechanisms. ANT
allowed us to map the existence of two divergent perspectives of Upwork’s platform: Upwork’s
own idealistic, isolated, one-stop-shop platform and the reality of a complex and robust
ecosystem of digital platforms. ANT also afforded us the language to delineate the shifting
alliances, negotiations, and coopetition between an array of human and nonhuman actors that
surround Upwork’s workflows. These two pictures, and the actants within them, illustrate the
multiplicity of relationships present within a platform intended to operate in strict isolation.
This ANT-based analysis adds to the current ecological conceptualizations of gig work [e.g., 54]
by formulating how the design of platforms may give rise to tensions (by reinforcing a single
system of organizing work) and how other participants may break out of these restrictions and
enlist the help of other platforms.
Upwork is one of many gig working platforms that prioritize its own position as stakeholder
while obscuring its algorithmic controls from users. Considering the data collected for this
paper, larger implications for further studies include examining digital platform ecosystems
throughout the gig economy. Relationships between gig platforms, users, and external digital
platforms, while constantly in flux, reflect dynamic negotiations and coopetition at work.
Moreover, our study found that users actively aim to protect agency and subvert algorithmic
control, complicating the generalization that platform users are passive recipients of algorithmic
Our analysis provides implications for the design and management of labor platforms. First, a
perspective only concerned with the interests of shareholders of the platform may leave other
stakeholders disenfranchised. The interests of the platform are subsequently embedded within
the algorithmic management of transactions and relationships. Even though algorithms
automatically manage millions of transactions, their opaque nature may also displace the
autonomy of gig workers. Clearer guidelines on how certain badges are computed could assist
workers aiming to raise their understanding of algorithmic evaluation.
Second, algorithmic control may also lower the flexibility of gig work as Upwork strives to
bring many workflows under the same umbrella. This is at odds with the digitally-mediated
work practices of knowledge workers, which are materially supported by a multiplicity of tools
and platforms, as outlined by the artifact ecology perspective [25]. Promoting the labor platform
as a one-stop shop has clear benefits, but an ecosystemic perspective, outlined in this paper,
calls for giving the user the option to draw on other technological platforms. External platforms
such as Dropbox or Skype enjoy economies of scale and consequently a higher quality and
critical mass in offering of their specialized services. Recognizing the unique affordances of
Gig Platforms, Tensions, Alliances and Ecosystems: An Actor-Network Perspective 212:23
PACM on Human-Computer Interaction, Vol. 3, No. CSCW, Article 212, Publication date: November 2019.
other platforms helps transacting parties more cohesively and effectively conduct transactions.
In this way, the platform gives users the liberty to construct their own artifact ecologies based
on varying social and technological needs. After all, both workers and clients perceive online
freelancing as an ecosystem beyond the Upwork platform and may treat concepts like online
reputation as broader than one’s activities within the bounds of a single platform.
Third, the design of platforms may require a pragmatic approach, ceding some of the control
and retaining it wherever absolutely necessary. Espousing a policy that is too restrictive and
control-centric could propel workers to either abandon the platform altogether (particularly
those with more experience who enjoy an already established network), or more actively
circumvent the platforms and its control mechanisms. For example, Upwork can continue
reinforcing the use of its own payment system (as it directly impacts the business bottom-line),
but allow workers and clients to choose their own preferred ways of communication and file
We appreciate all the interview participants who took the time to share with us their experience
with online freelancing and Upwork. We also acknowledge the contribution of Sarah Beth
Nelson and Steve Sawyer to this research study. Finally, we thank the four anonymous
reviewers whose comments have greatly improved this manuscript.
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... We began by referring to the six papers cited in these four design directions [11,36,39,47,57,72]. Then, we looked at this prior research references and additional papers from their authors, aiming to expand our understanding of the challenges and opportunities that freelancing platforms create for workers. ...
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... Finally, in the Technological dimension, aspects of IT platforms [17,20,23] built to provide human resources to the labor market [16,22], means of payment [23], communication [3], and artifact sharing through cloud computing [20] are treated. This dimension also comprises IT resources that are specific to travelers but also generally used by DNs, including "assembling actants" [4], coworking spaces [4,25 ] and other IT infrastructure resources [4,13]. ...
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... The lack of transparency and control resulted in a feeling of dehumanization for workers [57]. These tensions have led to worker movements in gig work ecosystems [44]. ...
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The growing inequality in gig work between workers and platforms has become a critical social issue as gig work plays an increasingly prominent role in the future of work. The AI inequality is caused by (1) the technology divide in who has access to AI technologies in gig work; and (2) the data divide in who owns the data in gig work leads to unfair working conditions, growing pay gap, neglect of workers' diverse preferences, and workers' lack of trust in the platforms. In this position paper, we argue that a bottom-up approach that empowers individual workers to access AI-enabled work planning support and share data among a group of workers through a network of end-user-programmable intelligent assistants is a practical way to bridge AI inequality in gig work under the current paradigm of privately owned platforms. This position paper articulates a set of research challenges, potential approaches, and community engagement opportunities, seeking to start a dialogue on this important research topic in the interdisciplinary CHIWORK community.
Given the widespread contribution of independent contractors to organizational innovation and competitive advantage, it is timely to reassess assumptions about the HRM practices appropriate to their management and the rationale for organizations to work with them. In the original and highly influential HR architecture model of Lepak and Snell (1999), contractor status is viewed as an outcome of the low value and/or low uniqueness of human capital resulting in the proposition to externalize and manage them using either none or minimal compliance‐based HRM practices. Developments in digital technologies and algorithmic management epitomized by online labor platforms prompt us to reconsider these assumptions and to challenge the proposed links between value/uniqueness of human capital, employment mode and HRM practices that are assumed by the HR architecture model. Using insights from online labor platforms, we argue that the significant benefits to firms of working with contractors, coupled with the possibilities offered by algorithmic management to efficiently monitor and regulate their behavior, provide a compelling reason for organizations to choose external employment modes even when workers are key to value creation. We challenge the alignment and stability of the relationships proposed by the HR architecture model, and offer propositions to extend the model by reconsidering the rationale for, and nature of, HRM practices associated with contractors. This reassessment is both timely and relevant given the growing prominence of business models where externalizing workers is central alongside the development of new forms of algorithmic human resource management to control them.
Twitter is increasingly important for political outreach and networking around the world. While electoral politics and social relations in India are heavily organized by caste, a broader rhetoric of castelessness among upper-caste politicians has led to the eschewing of caste publicly to appear strategically secular. This has rendered caste dynamics more implicit than explicit. Social media, often cited as a tool for inclusion, offers a unique look into the networks of covert exclusion. Our study analyzes three structural properties of the Twitter network of Members of Parliament in India - influence, bridging capital, and mutual connectivity, to understand how caste manifests as social capital in the information economy. Our results show that those higher in the caste hierarchy are structurally poised for higher social capital through higher influence, incoming bridging capital, and higher propensity for mutual connections with other MPs in the network. Our study offers a methodological window into these invisible relations to show how structural advantages of Brahmanical supremacy are being co-produced and stabilized on social media at the highest level of politics.
Online freelance platforms can transform knowledge work. However, 'gigification' also presents challenges, including how freelance workers can access and work with knowledge, which prior research has not examined. Through a qualitative interview study, we identify disparities in how freelancers who work for enterprise companies are able to utilize knowledge as part of their work, when compared with traditional employees of similar organizations. We examine how 38 knowledge workers (21 freelancers, 17 employees) deploy knowledge, work skillfully and mobilize resources to meet knowledge needs. We find that both employees and freelancers understand their own ability to act knowledgeably as a dynamic, collaborative, negotiated and emergent accomplishment. However, for freelancers, the dynamic dimensions of knowledge work - such as helping others see the meaning and value of their work, and creating ties between their work and the enterprise - are only minimally-legitimized and minimally-supported by organizing structures and tools. We present our results as 'knowledge gaps', and propose design recommendations to reduce these gaps and consequently make on-demand knowledge work more effective and sustainable.
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This paper takes as its starting place the rich context of many knowledge workers today—highly distributed, increasing project focused, typically atypical days, infrastructural—and attempts to push past extant descriptions of their practices as ‘flexible’. Using empirical data informed by a practice theory lens, we expand the understanding of flexibility with regard to work by augmenting how worker disposition, as well as the ability to engage with agility in dynamic circumstances, should be considered as a factor when examining and designing for this population. We make several contributions of interest to the wider CSCW community. First, we distinguish between those who showcase flexible practices and those who proactively orient around flexibility. We call this second group ‘elastic workers’. Second, we raise new questions for us as scholars and designers keen to exploit the conceptual and pragmatic intersection of technology and work. These questions create opportunities to explore different methods for understanding complex phenomena such as flexibility, as well as understanding how we might design for this phenomenon with more foresight in the future.
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Financial inclusion has been defined and understood primarily in terms of access, thereby constituting ‘inclusion’/‘exclusion’ as a binary. This paper argues such a view to be myopic that risks treating financial inclusion as an end in itself, and not as means to a larger end. ‘Access’ oriented perspectives also fail to take into account considerations of structural factors like power asymmetries and pay inadequate attention to user practices. Through the case of auto-rickshaw drivers in Bangalore, India, and their use of Ola, a peer-to-peer taxi hailing service similar to Uber, we show that access is a necessary, but not sufficient condition to achieve financial inclusion in a substantive sense. By examining in detail, the financial needs and practices of rickshaw drivers, we identify the opportunities and constraints for digital technology to better support their financial practices and enhance their wellbeing. The paper proposes adding ‘autonomy’ and ‘affordances’ as two crucial factors to be included in the discourse on financial inclusion. Finally, we outline design implications for P2P technologies to contribute towards the financial inclusion of drivers.
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We theorize on the heterogonous network of people, visions, concepts, technological artifacts, and organizations that come together to enable product innovation. Drawing on the conceptual framing and mechanisms of actor-network theory (ANT), we focus on the relationships among human and non-human actors and their roles to enact new products. We do this to contribute both evidence and theory regarding the concept of a sociotechnical assemblage that serves as the innovation network. Advancing a sociotechnical conceptualization of innovation focuses attention on the contributions of, and linkages among, different types of actors; individuals and organizations, visions and concepts, and technological artifacts and prototypes together create a means for innovation to occur. The empirical basis for this theorizing comes from a detailed study of the community of research scientists, faculty, and graduate students; institutions such as research labs, funding sources, and product companies who were (and mostly still are) involved in tabletop computing. Analysis highlights the centrality of visions, concepts and technological artifacts in the innovation network. We also find that formal organizations play important, but often unrealized, roles in supporting innovation.
Conference Paper
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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.
Conference Paper
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This workshop addresses the changing nature of work and the important role of exchange platforms as both intermediaries and managers. It aims to bring together interdisciplinary and critical scholars working on the power dynamics of digitally mediated labor. By doing so, the workshop provides a forum for discussing current and future research opportunities on the digital economy, including the sharing economy, the platform economy, the gig economy, and other adjacent framings. Of particular interest to this workshop is the intersection between worker and provider subjectivities and the roles platforms take in managing work through algorithms and software. Our one-day workshop accommodates up to 20 participants.
Conference Paper
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Ridesharing services have been viewed as heralding the next generation of mobility and recognized for their potential to provide an alternate and more flexible model of work. These services have also been critiqued for their treatment of employees, low wages, and other concerns. In this paper, we present a qualitative investigation of the introduction of Uber in Dhaka, Bangladesh. Using interview data from drivers and riders, and content analysis of riders’ Facebook posts, we highlight how Uber’s introduction into Dhaka’s existing transportation infrastructure influenced experiences and practices of mobility in the city. Drawing on Iris Marion Young’s theory of justice, we demonstrate how the introduction of Uber in Dhaka reinforces existing modes of oppression and introduces new ones, even as it generates room for creative modes of resistance. Finally, we underline algorithms’ opacity and veneer of objectivity as a potential source of oppression, call for deepening the postcolonial computing perspective, and make a case for stronger connections between technology interventions and policy.
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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.
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Over the last few years the sharing economy has been changing the way that people share and conduct transactions in digital spaces. This research phenomenon has drawn scholars from a large number of disparate fields and disciplines into an emerging research area. Given the variety of perspectives represented, there is a great need to collect and connect what has been done, and to identify some common themes, which will serve as a basis for future discussions on the crucial roles played by digital platforms in the sharing economy. Drawing on a collection of 435 publications on the sharing economy and related terms, we identify some trends in the literature and underlying research interests. Specifically, we organize the literature around the concept of platform mediation, and draw a set of essential affordances of sharing economy technologies from the reviewed literature. We present the notion of platform centralization/decentralization as an effective organizing principle for the variety of perspectives on the sharing economy, and also evaluate scholars' treatment of technology itself. Finally, we identify important gaps in the existing literature on the relationship between digital platforms and sharing economy, and provide directions for future investigations.
Algorithmic management is rapidly emerging as a strategic management tool in the digital economy; however, little is known of the outcomes of algorithmic management for users of the sharing economy platforms. With a focus on one of the most rapidly growing peer-to-peer platforms, this research investigates how Airbnb hosts have responded to and adapted to the algorithmic management strategies employed by Airbnb. Findings suggest that asymmetry of algorithmic information can increase Airbnb’s power to influence and control Airbnb hosts’ practices. Further, such information asymmetry can significantly hinder Airbnb hosts’ sense of control. This study contributes to the emerging academic dialogue on the algorithmic management in tourism and hospitality and advances the academic research on the human resources aspect of the sharing economy.
Silicon Valley technology is transforming the way we work, and Uber is leading the charge. An American startup that promised to deliver entrepreneurship for the masses through its technology, Uber instead built a new template for employment using algorithms and Internet platforms. Upending our understanding of work in the digital age, Uberland paints a future where any of us might be managed by a faceless boss. The neutral language of technology masks the powerful influence algorithms have across the New Economy. Uberland chronicles the stories of drivers in more than twenty-five cities in the United States and Canada over four years, shedding light on their working conditions and providing a window into how they feel behind the wheel. The book also explores Uber’s outsized influence around the world: the billion-dollar company is now influencing everything from debates about sexual harassment and transportation regulations to racial equality campaigns and labor rights initiatives. Based on award-winning technology ethnographer Alex Rosenblat’s firsthand experience of riding over 5,000 miles with Uber drivers, daily visits to online forums, and face-to-face discussions with senior Uber employees, Uberland goes beyond the headlines to reveal the complicated politics of popular technologies that are manipulating both workers and consumers.