Understanding the Factors Influencing Self-Managed Enterprises of

Crowdworkers: A Comprehensive Review

Alexandre Prestes Uchoa

and Daniel Schneider

1,2 b

Postgraduate Program in Informatics, PPGI/UFRJ, Rio de Janeiro, Brazil

Tércio Pacitti Institute of Computer Applications and Research, NCE/UFRJ, Rio de Janeiro, Brazil

Keywords: Self-Managed Enterprises of Crowdworkers, Online Labor Platforms, Crowdfarms, Macrotask

Crowdsourcing.

Abstract: This paper investigates the shift in crowdsourcing towards self-managed enterprises of crowdworkers

(SMECs), diverging from traditional platform-controlled models. It reviews the literature to understand the

foundational aspects of this shift, focusing on identifying key factors that may explain the rise of SMECs,

particularly concerning power dynamics and tensions between Online Labor Platforms (OLPs) and

crowdworkers. The study aims to guide future research and inform policy and platform development,

emphasizing the importance of fair labor practices in this evolving landscape.

1 INTRODUCTION

The crowdsourcing landscape is undergoing a

significant transformation, redefining how collective

intelligence is utilized in contemporary settings.

Crowdsourcing has evolved from a universally

accessible model to a sophisticated ecosystem of

platforms that selectively bridge specific segments of

the crowd with recruiters, thereby mediating these

interactions (Kittur et al., 2013; Zhao and Zhu, 2014;

Lopez, Vukovic, and Laredo, 2010).

As these platforms began addressing more

complex tasks, they encountered challenges in

assembling teams with the necessary expertise. This

process can be both resource-intensive and costly,

particularly in fluctuating market conditions (Ho and

Vaughan, 2012). The intricacy of these tasks and

constrained platform oversight necessitated enhanced

collaboration among workers and the provision of

greater autonomy and creative freedom

(Lykourentzou et al., 2019), prompting the need for

innovative management strategies.

At the same time, “coming from the other side of

the fence”, a particularly noteworthy development in

this domain is the emergence of self-organized groups

of crowdworkers who independently manage and

execute complex macrotasks. These groups represent

https://orcid.org/0000-0002-2028-0252

https://orcid.org/0000-0003-2987-4732

a paradigm shift from the traditional, platform-centric

workforce model to a self-directed, enterprise-like

collaboration (Wang et al., 2020; Huo, Zheng, and

Tu, 2017), thus challenging the direct control exerted

by platforms. SMECs represent a potential paradigm

shift, offering crowdworkers greater autonomy and

potentially improved working conditions compared

to traditional OLP work.

However, despite its promise, SMEC is still an

emerging and localized phenomenon with scant

scholarly attention thus far (Wang et al., 2019; 2020;

2021; 2023). To bridge this research gap, we have

thoroughly reviewed the extant literature,

concentrating on specific factors and challenges

associated with macrotask platform work that are

pivotal in defining and differentiating this nascent

model from traditional platform work. We aim to

shed light on this area, thus providing crucial insights

into the possible origins and developmental

trajectories of SMECs and informing subsequent

research directions.

The rest of the paper is organized as follows. In

the next section (Background), we provide some

context on the main topics of this study. Next, a

detailed description of the methodology is presented

in Section 3, followed by the results (Section 4), and

an analysis of the results with a discussion of the main

410

Uchoa, A. and Schneider, D.

Understanding the Factors Inﬂuencing Self-Managed Enterprises of Crowdworkers: A Comprehensive Review.

DOI: 10.5220/0012703600003690

Paper published under CC license (CC BY-NC-ND 4.0)

In Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024) - Volume 2, pages 410-421

ISBN: 978-989-758-692-7; ISSN: 2184-4992

issues raised (Section 5). Finally, we depict our

limitations (Section 6), future research directions

(Section 7), and conclusions (Section 8).

2 BACKGROUND

Paid crowdsourcing is a type of socio-technical work

characterized by a triangular relationship between

recruiters (companies and individuals), crowds, and

platforms (Kittur et al., 2013). Platforms are the ones

that bring recruiters and the crowd together (Zhao and

Zhu, 2014), thus intermediating the interactions and

communications between the other two (Lopez,

Vukovic and Laredo, 2010). Nowadays, there is a

panoply of different types of crowdsourcing, ranging

from corporate to social and public contexts (Vianna,

Peinado and Graeml, 2019), and a proliferation of for-

profit crowdsourcing platforms that, in effect,

determine who can actually participate. According to

Chaves et al. (2019), different forms of public

participation and engagement can be achieved in such

platforms that harness crowd workers.

This kind of paid crowdsourcing, also called

online work, digital work, or online labor, has become

widely recognized for its effectiveness in distributing

not complex tasks to a large number of individuals in

the crowd because they do not require specific skills

and can thus be performed quickly and repetitively.

Such tasks are typically outsourced by OLPs like

Amazon Mechanical Turk, which pays very little for

them (De Stefano, 2016; Deng, Joshi and Galliers,

2016).

On the other side of the spectrum of task

complexity and size are the so-called macrotasks.

These are difficult tasks that are sometimes even

impossible to break down into smaller, easier

subtasks (Robert, 2019). For this reason, unlike their

simpler cousins, macrotasks usually require specific

skills and knowledge to be accomplished (Schmitz

and Lykourentzou, 2018). There is a wide variety of

problems and types of tasks that today are being

addressed with the help of macrotask crowdsourcing

(Wang et al., 2021; Gimpel et al., 2023; Kohler and

Chesbrough, 2019; Mcgahan et al., 2021; Geiger and

Schader, 2014).

OLPs, in general, as an intermediate, act

facilitating functions such as task and contract

management and dispute resolution (King, 1983;

Shafiei Goal, Avital and Stein, 2019), but also

deciding whether and how to divide these complex

tasks into smaller subtasks, distributing them to

workers according to their assessment of their skills

and capabilities, managing them and their

interdependencies (Kittur et al., 2013). The

distinction between both types of tasks is crucial as it

affects how OLPs handle them (Cheng et al., 2015)

and how they shape their work processes accordingly

(Leimeister et al., 2016).

Macrotask crowdsourcing may be considered to

be a manifestation of post-bureaucratic work (Barley

and Kunda, 2001; Seppänen et al., 2021), in which

expertise is distributed outside bureaucracies,

organizations, and hierarchies. Kittur et al. (2013) see

such a form of work as a new form of technological

Taylorism in which the rules and guarantees of

subordinate work do not apply. Because it is not

feasible to use traditional control mechanisms, OLPs

safeguard themselves in various ways. According to

Kornberger, Pflueger and Mouritsen (2017),

macrotask OLPs operate like “evaluative

infrastructures that create competition and incentives

out of the differences among workers and establish

power through the decentralization of control”. One

way to minimize the efforts needed to control workers

in a macrotask setup is to ensure that their aspirations

align with the OLP’s goals (Schörpf et al., 2017). In

pursuing this goal, some OLPs adopt hiring processes

comparable to traditional employers by conducting

background checks, face-to-face interviews, skills

assessments, and even test drives (Kuhn and Maleki,

2017).

Another key component of macrotasking is

collaboration among workers (Kittur et al., 2013;

Gimpel et al., 2023; Lykourentzou et al., 2019). On

the other hand, such teams require increased

coordination effort (Kerr and Tindale, 2004; Kittur

and Kraut, 2008). Using the very interdependence of

the team and employing managerial practices that

promote autonomy has shown beneficial effects

(Gagné and Deci, 2005). According to Kerr and

Tindale (2004), the quality of a group can be

measured by the ability of its members to reach an

agreement about what is to be done. Yet, giving

workers more autonomy, creative freedom, and

initiative requires the OLPs to innovate in how they

manage the crowd (Lykourentzou et al., 2019).

In this scenario, an intriguing development

emerges, which is that of independent, self-

constituted, and managed teams of crowdworkers, in

distinction from the groups constituted and

coordinated by the OLPs. Although still incipient in

the West, these self-organized teams, or companies,

called by Wang et al. (2020) “crowdfarms”, is a

phenomenon mainly observed in China, a country

with a mature crowdsourcing market where, in 2017,

there were already 30 million crowdworkers serving

more than 190,000 companies and individuals from

Understanding the Factors Inﬂuencing Self-Managed Enterprises of Crowdworkers: A Comprehensive Review

411

all over the world and generating a total turnover of

approx. $700 million (Huo, Zheng and Tu, 2017).

What was once a market dominated by individual

workers seeking extra revenue in their spare time by

executing microtasks has seen their gradual

replacement by small organizations that perform

crowdsourcing tasks en masse (Wang et al., 2019),

employ full-time salaried workers, and operate in

formal (i.e., physical) workplaces such as business

offices. The ZBJ platform, for instance, one of the

most prominent crowdsourcing OLP in China with

more than 19 million active crowdworkers, acts as a

kind of “incubator” for these crowdsourcing

companies, having already supported the creation of

more than 150,000 of them since 2016 by providing

them with services, such as financial and legal, as

well as physical workspaces in 26 major cities in

China, calling them “crowdsourcing factories”

(Wang et al., 2021). The authors attribute the

emergence of these organizations in China to the

gradual transformation and increased complexity of

tasks posted on OLPs, coupled with favorable

government policies, such as the “mass

entrepreneurship and innovation program”, as well as

support from the very OLPs, including the

aforementioned ZBJ factories.

Unlike “flash organizations” (Valentine et al.,

2017), where random solo workers are automatically

organized into a hierarchy according to their abilities

in a temporary structure computationally constructed

to handle a specific complex task, in these SMECs,

the decisions are all up to the very company,

including the breakdown of complex tasks. Thanks to

the extensive experience in crowdwork and the

mastery that the managers of these companies usually

have of their work arrangement and capabilities, they

can procure, decompose, and allocate tasks internally,

designing their own workflows by adapting them to

their teams and thus being more effective.

For the workers at these SMECs, the personalized

workflows improve their understanding of their perso-

nal duties and roles and make things easier by setting

the standards for cooperation (Wang et al., 2023).

Workers are also attracted by the better payments

complex tasks offer, by the guarantees secured

through legal contracts, and by the possibility that

these companies provide for establishing

interpersonal relationships with customers, which

leads to more business opportunities (Wang et al.,

2020; 2021). The experiences and context in these

self-managed companies end up underpinning their

motivations, the ways they engage with

crowdworking, the tasks they work on, and the OLPs

they use.

However, despite the valuable empirical insights

provided by Wang et al. (2019; 2020; 2021; 2022),

much is still unknown about crowdwork enterprises

like the crowdfarms and their workers. While

SMECs, much like OLP-controlled crowdworkers,

also have to deal with aspects such as problematic

requirements and specifications, deadlines and costs,

customer acceptance criteria, prospecting and

reputation, OLP policies and algorithms, payments

and defaults, they do it in different scales and

manners.

What exact factors and work practices do these

SMECs employ, what other forms of SMECs besides

crowdfarms can be thought of, and what barriers

hinder their emergence in other markets are just some

examples of what must be further explored. SMECs

represent an expansion of the traditional

understanding of paid crowdworking and reflect the

ongoing evolution of the field. And as with any new

socio-technical advance, it reveals new challenges

and potential spaces for investigation.

This review pinpointed eight principal areas of

contention in macrotask platform work from the

workers’ perspective, utilizing them to delve into and

refine our grasp of the underlying dynamics of this

emerging model. These areas encompass payment

schemes, trust and reputation systems, control and

autonomy, exploitation and unfair treatment,

demands for improved conditions, algorithmic bias,

crowdworker unity, and empowerment. By adopting

a socio-technical lens, the review methodically

explores this broad array of topics, highlighting the

intricate interplay of power, agency, and mutual

dependence that characterizes the relationship

between workers and OLPs.

3 METHODOLOGY

Our methodology for this review centered on

examining existing research on aspects and factors

that may underpin the emergence of SMECs. We

aimed to answer the following three key review

questions by identifying studies and assessing to what

degree they address the unique aspects that

characterize this phenomenon.

RQ1) What are some commonly overlooked aspects

of crowdworking that characterize SMECs?

RQ2) What dynamics and sources of tension

between crowdworkers and OLPs have been

investigated?

RQ3) What potential areas for further research can

be identified?

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3.1 Search Strategy

Early in the first exploratory searches for literature,

some important difficulties were already noticed. The

results were remarkably insignificant or diffuse when

searching different databases using expressions such

as “groups” or “self-managed teams of

crowdworkers” as proxies for SMECs. Research

articles on this phenomenon are still rare, and the few

that have been found have their authorship

concentrated in a few researchers (Lykourentzou,

Robert JR and Barlatier, 2022; Wang et al., 2019;

2020; 2021; 2023), probably due to the youth and

locality of the phenomenon. The lack of agreement on

a common denomination and definition for SMECs

and an objective, precise, and widely accepted

definition of macrotask and complex crowdwork also

hindered the first searches.

With this in mind, the review decided to design

the search so that it could retrieve a broader spectrum

of studies that include subjects and aspects of

macrotask crowdworking that are, we believe, at the

core of what most clearly distinguish SMECs from

typical OLP-controlled forms of crowdwork. The

four aspects of crowdworking that the review chose

were:

A1. Workflow, task decomposition, coordination;

A2. Worker selection, assignment, incentives;

A3. Team composition;

A4. Power dynamics, precarity, sources of

tension.

3.2 Sources and Search Process

To systematically select relevant literature, we

employed a multi-step search strategy using the two

major indexing databases: Scopus and Web of

Science (WoS).

1. For every search instruction tested, it was ensured

that selected articles considered referential in the

field of interest of the review were among the

search results in at least one of the databases used.

2. For each search result, the keywords of the

retrieved articles were analyzed in search of

relevant new words to search for.

3. As the process progressed, new referential articles

were added, and the new improved search

sentences had to retrieve them as well.

4. Of the successful alternatives, the one capable of

retrieving the smallest set of results and still

containing all the referential articles was chosen.

Figure 1 presents the final version of the search

instruction used, depicting the combination of terms

and logical operators used in article searches in the

chosen indexing bases.

Figure 1: Combination of terms and logical operators used

in searches for articles in the Scopus and Web of Science

indexing databases.

3.3 Inclusion and Exclusion Criteria

Our inclusion criteria were specific to peer-reviewed,

not redundant journal and conference articles

published within the past decade (criterion C1),

focusing on:

I. Paid crowdworkers;

II. Online Labor Platforms (OLPs);

III. Macro, complex, creative, knowledge-based,

interdisciplinary, non-decomposable tasks;

IV. Decomposition or workflows of tasks;

V. Collaboration or co-creation.

For this reason, all other types of outputs were

excluded, together with the following cases:

● Studies addressing impacts of COVID

pandemic;

● Reviews, workshops, tutorials, thesis &

dissertations;

● Articles with no abstract available;

● Article’s full text not available in English;

● Article’s full text not available for reading at the

time of the writing.

We also deliberately excluded studies that either

use crowdsourcing as a subsidiary means to

accomplish their objectives (such as obtaining data),

propose design principles for new crowdsourcing

endeavors, or address types of crowdsourcing that are

fundamentally distinct from the paid work for OLPs

(criterion 2), among them:

● Voluntary or unpaid crowdworking (e.g., citizen

science, contests, games);

Understanding the Factors Inﬂuencing Self-Managed Enterprises of Crowdworkers: A Comprehensive Review

413

● Sharing economy, online communities, open-

source project, collaborative crowdsourcing;

● Mobile, geographic, collocated or spatial

crowdsourcing;

● Crowdsourcing to customers or consumers;

● Enterprise, marketing, industry crowdsourcing;

● Crowdsourcing by the government;

● Passive crowdsourcing (e.g., IoT, body sensors

for active or passive data collection);

● Crowdfunding.

Exceptions would be tolerated when

contributions were deemed relevant and extendable to

crowdwork on macrotasks. Finally, the selection

should also exclude propositional studies with

contributions in the form of technological solutions,

new approaches and methods, particularly those

aimed at a single specific party. This includes those

aimed at OLPs and requesters with propositions such

as boosting worker productivity and task quality or

reducing costs and risks (criterion 3).

This approach was chosen, firstly, because a

wider scope of audience and subject often

encapsulates a more diverse range of perspectives and

insights. Secondly, such papers tend to foster

interdisciplinary dialogue and broader applicability,

extending their relevance beyond a single, specialized

domain or problem, which is especially valuable

when exploring the realm of SMECs.

The implementation of a protocol for assessing

the quality of the retrieved studies was waived by the

review process. This decision was rooted in the belief

Figure 2: Two-level sunburst chart of articles before the

application of selection criterion 3. The inner ring

categorizes articles by their intended audience/aim. The

outer ring details the main papers studied.

that imposing a quality protocol at this juncture of

such a nascent field could inadvertently narrow the

scope of our review, thus omitting valuable insights

and emerging lines of inquiry.

4 RESULTS

From an initial pool of 1003 publications (313 from

WoS and 687 from Scopus), we downloaded all

metadata, reduced 62 redundancies, and retained

recent peer-reviewed journal articles. Utilizing

OpenAI's GPT-3.5 Turbo, we programmatically

analyzed the abstracts of 713 remaining articles,

generating seven syntheses to distill their key points.

Seven syntheses were generated, addressing key

questions about each study's purpose, contributions,

evidence, central argument, type of crowdsourcing,

results, and data used. These syntheses streamlined

the application of thematic inclusion and exclusion

criteria (criterion 1 & 2), aiding in identifying studies

irrelevant to our review. This process narrowed the

field to 74 articles. GPT-3.5 Turbo was again

employed, this time analyzing the full texts of these

articles, further assisting in our research synthesis. In

order to apply criterion 3, we categorized these 74

articles according to three dimensions:

Audience/End User: Identifies the primary target

group for the study's contributions, particularly

when directed towards a specific audience.

Main Party Studied: the party that primarily benefits

from the solutions and approaches proposed in

the study (OLPs/requesters, crowdworkers, no

specific).

Aspect Studied: The predominant aspect of

crowdworking that the study's contributions

address (A1 to A3).

LLM-assisted categorization is hampered by their

sensitivity to task wording. Mitigating ambiguity and

vagueness, especially in category names, is crucial for

reliable and reproducible categorization. Figures 2

and 3 illustrate the outcomes of this categorization

process. The initial set of 74 articles primarily offered

solutions and propositions for platforms and/or

requesters, focusing on worker selection, task

assignment, and incentive mechanisms (Figure 2).

The review’s selection phase concluded by

narrowing down to 25 articles that target a more

general audience, as illustrated in Figure 4. This

choice was influenced by the fact that papers with a

broader audience typically adopt an analytical,

observational, or critical approach, aligning with our

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Figure 3: Distribution of the articles aimed at

platforms/requesters that address one or more of the three

aspects of crowdworking we choose to focus on: A1.

Worker selection, task assignment, incentives; A2. Team

formation; and A3. Workflow design, task decomposition,

coordination. Overlapping regions indicate articles

addressing multiple aspects.

review’s objectives to provide comprehensive

insights into the thematic exploration of

crowdworking dynamics.

The remaining 49 articles, focused on specific

solutions for platforms and requesters, were excluded

due to their narrower scope. This strategic decision

was based on our criteria to include studies with

broader relevance, ensuring a more universally

applicable understanding of the subject.

5 ANALYSIS AND DISCUSSION

OLPs play a pivotal role in shaping task availability,

payment, and working conditions while relying on

skilled crowdworkers, whose needs are vital for both

task completion and platform success. This interplay

directly affects crowdworkers’ autonomy and

satisfaction and is crucial in the context of SMECs, as

it influences their operation and effectiveness. Given

the variability across different OLPs and worker

groups, this area is a rich vein for research. To delve

deeper into these nuances, we have broken down this

aspect (A4) into eight key factors, each shedding light

on the complexities of this ecosystem, as follows:

F1. Payment schemes: the impact of payment

schemes on the relationship between

crowdworkers and OLPs;

F2. Trust and Reputation Systems: the role of trust

and reputation systems in shaping the

relationship between crowdworkers and OLPs;

F3. Control and autonomy: the impact of autonomy,

freedom and control over tasks;

F4. Exploitation and unfair treatment: potential for

exploitation or unfair treatment of

crowdworkers by OLPs;

F5. Demand for better conditions: collective action

or organizing among crowdworkers to advocate

for better working conditions or autonomy;

F6. Algorithmic bias: potential for algorithmic bias

or discrimination in the OLP decision-making

processes;

F7. Crowdworker Unity: crowdworker solidarity or

collaboration in response to OLP practices;

F8. Crowdworker empowerment: crowdworker

empowerment or agency in shaping the OLP

policies and practices.

We focused on determining the presence or

absence in the 25 articles including discussions

related to these factors. This approach allowed us to

quantitatively assess the extent to which these

underlying factors were considered in the body of

literature at hand. To achieve this, we categorized

each paper based on whether it explicitly mentioned

or engaged with each of our underlying factors. It’s

important to note that this analysis was binary in

nature; we marked a factor as ‘addressed’ if it was

either discussed in detail or merely pointed out in the

paper as an important aspect to be considered.

Figure 4: Classification of the 25 articles according to

whether they touch (green) or not (red) the eight factors

(Fn) related to the power dynamics, precarity, and tensions

between crowdworkers and OLPs (A4).

Understanding the Factors Inﬂuencing Self-Managed Enterprises of Crowdworkers: A Comprehensive Review

415

This method provided a broad overview of the

thematic landscape of the field, indicating which

areas have been given more or less attention in

academic discourse. However, it does not assess the

depth or quality of the coverage for each topic within

the individual papers. Our goal is to map the

prevalence of certain themes and identify potential

gaps in the literature rather than to perform a

qualitative analysis of the discussions surrounding

these themes. Consequently, the results offer insights

into the frequency of topic appearances across our set

of papers, reflecting trends and potential areas for

further research in the realm of crowdwork and OLPs.

As Figures 4 and 5 reveal, overall, the research

papers strongly focus on autonomy, control, and

unfair treatment, indicating these are key concerns in

the crowdworking research field. However, it also

suggests that there might be a potential need for more

research in understanding the collective aspects of

crowdwork, such as collective actions for better

conditions and the impacts of reputation systems and

algorithmic biases on crowdworkers. The subsequent

subsections provide a detailed analysis of the key

findings and themes identified in the selected articles.

Figure 5: Percentage of the 25 selected articles that mention

each of the eight factors (F1 to F8) underlying the aspects

of power dynamics, precarity, and tensions between OLPs

and workers (A4).

5.1 Payment Schemes

The schemes and timeliness of payments to online

workers can significantly influence employee

motivation, satisfaction, and overall engagement with

an OLP (Zhang and Van der Schaar, 2012). While

increasing pay doesn’t necessarily ensure a consistent

improvement in online work quality, it attracts more

workers faster (Mason and Watts, 2010; Rogstadius

et al., 2011). Furthermore, when extrinsic (e.g.,

payments) and intrinsic motivators are synergistically

combined, a higher level of employee satisfaction and

performance can be expected (Amabile, 1993). A

payment system that is fair, transparent, and does not

delay can foster workers’ trust and long-term

commitment to OLPs and recruiters (Rogstadius et

al., 2011).

As mentioned by almost half of the articles, the

role of payment schemes and their impact on the

relationship between crowdworkers and online OLPs

seems to be a noticeable factor from the research

perspective. Possible reasons for this number not

being higher, since this is an important aspect for

workers, may include the fact that payment schemes

in crowdworking OLPs, particularly with macrotasks,

can vary widely and be complex, possibly making

them a challenging subject for a comprehensive

study. There might also be a lack of transparency

from OLPs about their payment structures, posing a

challenge for researchers. However, given the direct

impact of payment schemes and mechanisms on the

well-being of crowdworkers, and this being a factor

linked to crowdworkers’ adherence to SMECs (Wang

et al., 2019; 2023), this could be a better and more

deeply explored aspect in the future.

5.2 Trust and Reputation Systems

Trust and reputation systems serve as mechanisms to

build trust between actual strangers in digital

marketplaces where direct interaction is limited.

Potential recruiters assess crowdworkers based on

their profiles, particularly their ratings on an OLP.

Crowdwork rating systems are thus at the core of the

control over workers exerted by both recruiters and

OLPs in this triangular relationship (Barnes, Green

and Hoyos, 2015; Blair, 2001; Schörpf et al., 2017).

A third of the articles (33.3%) selected by the

review devoted attention to this important topic that

often directly influences the ability of workers to

secure work, potentially better wages, and more

flexibility of choice, impacting workers’ livelihoods,

satisfaction, and career progression.

However, how ratings are calculated, the potential

for bias and the impact of negative reviews are critical

issues that seem to be insufficiently investigated, as

well as the impacts of these systems on workers’

psychological well-being. The stress of maintaining

high ratings, the social dynamics of feedback

systems, and how these systems can be made more

equitable and less prone to abuse seem to be

opportunities to be further explored.

5.3 Control and Autonomy

OLPs exert significant control over interactions and

dependency management among platform

participants (Schmidt, 2017), enforcing performance

monitoring through various rules, policies, and

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standards (Deng, Joshi, and Galliers, 2016; Gandini,

2019). The Upwork platform, for instance, employs

electronic monitoring, offering hourly workers a

guarantee of earnings if they consent to periodic

desktop screenshots and keystroke recording. This

data and activity ratings are shared with clients (Kuhn

and Maleki, 2017). While this system can mitigate the

risk of nonpayment by enticing workers to

compromise their privacy, it profoundly impacts their

working conditions (Kaplan, 2016).

OLPs also typically centralize task decomposi-

tion, subdividing larger tasks into smaller, executable

components (Khan et al., 2019; Lykourentzou et al.,

2019) aiming at task quality and the ability to engage

and control a broader spectrum of workers (Retelny,

Bernstein, and Valentine, 2017).

Conversely, worker autonomy—particularly over

task selection and execution—is a well-established

determinant of job satisfaction and motivation,

including the context of crowdworking (Baard, Deci,

and Ryan, 2004; Ghezzi et al., 2018). The flexibility

to select tasks, schedule work, interact with

requesters, and maintain a degree of control is crucial

in distinguishing between fulfilling and disheartening

work experiences, especially since traditional

supervisory frameworks are diminished or

restructured in crowdworking scenarios.

Numerous articles explore the dimensions of

control and autonomy, acknowledging the significant

influence of OLPs in dividing and allocating tasks,

reflecting their conventional function in structuring

crowdwork. However, the effects of Small and

Medium-sized Enterprise Contractors’ enhanced

autonomy and their proficiency in decomposing and

overseeing complex tasks on worker satisfaction and

motivation remain unclear. Similarly, how OLPs will

adapt to this evolving landscape is yet to be

comprehended.

5.4 Exploitation and Unfair Treatment

This factor is the second most mentioned among the

articles selected and is a critical area of concern in the

context of crowdworking and OLPs. Crowdwork

notably lacks the traditional safeguards that protect

workers in conventional employment (Gillespie,

2010). This absence of regulation and security can

lead to situations where crowdworkers are vulnerable

to exploitation or unfair treatment. Crowdworkers

typically face irregular work hours, unpredictable

income, and a lack of benefits such as health

insurance, paid leave, or retirement plans. In the case

of crowdfarms workers, for instance, workers

experience increased communication costs, stress

levels, and work schedules that resemble the 996

working hour system (Wang et al., 2020).

The legal status of crowdworkers is frequently

ambiguous, raising questions about whether they are

employees, independent contractors, or fall into a

distinct category altogether. This lack of clarity

introduces both ethical and legal complexities,

potentially depriving workers of the protections and

rights usually granted to traditional employees.

Instead, they must depend exclusively on the policies

and rules of OLPs, which often lack transparency and

impartiality. In the context of SMECs, the insertion

of an additional intermediary layer can exacerbate

existing legal ambiguities or voids, potentially

complicating the situation further. Additionally, the

global dimension of crowdworking amplifies these

challenges, allowing OLPs to capitalize on the

increased vulnerability of workers in certain locales.

5.5 Demand for Better Conditions

Collective action and worker organizing can be

powerful tools for workers to voice their concerns,

negotiate better terms, and ensure fair practices.

Trade unions or workers’ organizations play this role

in traditional employment sectors. However, in the

decentralized world of crowdworking, organizing can

be challenging, given the distributed nature of the

workforce.

One-fourth of the papers addressing this topic

suggest that the collective actions and demands of

crowdworkers might not be as visible or well-

documented as other aspects, making it harder for

researchers to study them. The dispersed,

individualized nature of crowdworking might lead to

fewer collective, organized movements (Alacovska,

Bucher and Fieseler, 2024; Johnston, 2020; Liu and

Wang, 2022; Wood, Lehdonvirta and Graham, 2018),

which in turn may result in less academic attention.

We suspect that there might also be a bias in the

academic community towards studying phenomena

that are more easily quantifiable or align with OLP

providers’ interests rather than worker advocacy. And

SMECs, as already established, situated, and

recognized organizations, may eventually play an

important role in collective action.

5.6 Algorithmic Bias

Recruiter ratings to crowdworkers are shaped by

algorithms into reputation scores (Seppänen et al.,

2021; Wood et al., 2019). Trust and reputation

systems play a well-known and fundamental role in

online platforms, especially in the realm of

Understanding the Factors Inﬂuencing Self-Managed Enterprises of Crowdworkers: A Comprehensive Review

417

crowdworking. Algorithms are also employed to

match and form teams (Basu et al., 2014). So, it is

surprising that only three papers directly touch on this

topic, which could imply that while recognized as an

issue, it may not be as extensively explored as other

topics in the context of OLP crowdworking.

The topic of algorithmic bias in crowdworking

might be still emerging. As awareness of the

implications of AI and algorithms grows, this could

become a more prominent research area. Research in

this area also requires a deep understanding of both

machine learning algorithms and the specific ways

they interact with labor dynamics, which might be a

barrier to some researchers.

Algorithms can inadvertently and intentionally

introduce, perpetuate, or amplify biases, leading to

unfair or discriminatory outcomes for certain

crowdworker populations. However, the extent to

which the phenomenon of SMEC is a defense or

reaction against algorithmic effects is still unknown.

5.7 Crowdworker Unity

Crowdworker unity and solidarity were mentioned by

37.5% of the articles, indicating a relatively moderate

interest. Historically, gig workers have been viewed

as isolated, but there’s an increasing awareness of the

potential for solidarity and collective bargaining,

even in such dispersed work environments (Hau and

Savage, 2023; Liu and Wang, 2022; Wood,

Lehdonvirta and Graham, 2018; Woodside, Vinodrai

and Moos, 2021).

Technology, while an enabler of the gig economy,

presents both opportunities and challenges for worker

organization (De La Torre-López, Ramírez and

Romero, 2023; Lykourentzou, Robert JR and Barlatier,

2022; Zhou and Pun, 2022). The unique nature of this

technology-mediated form of work – where workers

are often isolated and compete against each other for

tasks (Soriano, 2021) – makes it a compelling area for

research. Studies might explore how solidarity can be

fostered in an environment typically characterized by

individualized, remote work.

5.8 Crowdworker Empowerment

Crowdworker empowerment and agency is about

giving them more control, voice, and influence in

OLPs’ decision-making processes (Deng, Joshi and

Galliers, 2016; Lykourentzou et al., 2019), especially

in areas that directly affect their work, like, for

instance, workflow definition and management

(Retelny, Bernstein and Valentine, 2017). This can

lead to more equitable OLP policies, improved

worker satisfaction, and improved work outcomes.

Half of the articles mentioning this factor indicate

academic interest in the agency of crowdworkers in

shaping OLP policies and practices. This may

indicate an evolving concern about exploitation and

unfair practices, turning worker empowerment into a

crucial area to be explored. How crowdworkers can

assert their rights and influence the terms of their

engagement is central to discussions about the future

of fair work in OLPs. And as Wang et al. (2023)

indicate, a search for self-empowerment, or at least

protection, is underneath the organization of workers

around SMECs like the Chinese crowdfarms.

6 LIMITATIONS OF THE STUDY

This review, while comprehensive, acknowledges

certain limitations. The scope of literature, though

broad, may not capture all emerging research within

the SMEC and OLP domains. Analytical depth was

sought, yet further exploration into the nuances of

how identified factors influence SMECs could enrich

understanding. Methodological transparency has

been a priority; however, deeper justification for

selection criteria could enhance rigor. The review

strives for a balanced perspective, yet engagement

with contradictory evidence could be strengthened.

The limitations identified in this review set the stage

for future research.

7 FUTURE RESEARCH

DIRECTIONS

The review has unveiled numerous avenues for

further research, highlighting critical questions that

future studies could address to deepen our

understanding of SMECs and their evolving role.

These questions include:

● Working Conditions: How do SMECs

influence worker income, job security, and

overall well-being compared to traditional OLP

work? What authentic pathways do SMECs

create toward improved working conditions and

more balanced power dynamics for

crowdworkers?

● Talent Acquisition: How can SMECs attract

and retain skilled workers while maintaining a

healthy internal structure, remaining

competitive, and managing potential declines in

platform payments? How the competition for

ICEIS 2024 - 26th International Conference on Enterprise Information Systems

418

talent may influence the relationship between

SMECs and OLPs?

● Legal and Regulatory Frameworks: How can

legal frameworks evolve to recognize and

effectively regulate the unique characteristics of

SMECs? Additionally, what regulatory policies

are necessary to foster fair competition among

SMECs and between these entities and individual

workers and ensure their protection?

● OLP Adaptation: In what ways might OLPs

adapt to the emergence of SMECs? Will they

tolerate, integrate, co-opt these models, or

promote more competition?

● Scalability and Technological Advancements:

What technological advancements or

organizational structures could support

scalability within SMECs? Can we anticipate the

formation of cooperative networks among

SMECs, and if so, what might these

arrangements look like?

Addressing these questions could significantly

contribute to the knowledge of OLPs, offering

valuable insights for academics, practitioners,

policymakers, and crowdworkers.

8 CONCLUSIONS

The evolution of crowdworking towards autonomous,

enterprise-like models marks a significant shift from

traditional, platform-controlled work. Our review

focuses on the platform-centric perspective in

crowdworking, addressing RQ1 by examining how

crowdwork aspects integral to SMECs, such as task

decomposition and team coordination, are discussed

in the literature. We uncover a gap in understanding

the power dynamics between crowdworkers and

OLPs (RQ2), particularly in areas like payment,

autonomy, and algorithmic bias. Identifying future

research avenues, including exploring exploitation

and stakeholder balance (RQ3), underscores the need

for a holistic approach. This review contributes to a

more comprehensive understanding of

crowdworking, advocating for theoretical and

practical advancements that prioritize the well-being

and empowerment of crowdworkers.

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