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The Digital Boss: Algorithms, Housekeeping Apps, and the Digitalization of Domestic Work in Colombia

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Abstract

Domestic work in Colombia has historically been undervalued and unpaid, with informality continuing to rise despite legal efforts to dignify it, such as the ratification of ILO C189. In this context, domestic work gig platforms have emerged as a source of formal employment. Unlike most gig economy models, these platforms operate as agencies that directly hire workers, ensuring key conditions like stable income and social security. However, gaps remain in understanding the algorithmic design of these platforms. Through an ethnographic study of one domestic work platform interface called Hogaru and semi-structured interviews with workers and customers, we examine algorithmic governance and its impact on domestic workers’ experiences, particularly concerning employment and social relations. Our research focuses on the platform’s matching system, ranking, and pricing mechanisms. We conclude with implications for designing more effective and equitable platforms, specifically tailored to the unique context of Latin America.
https://doi.org/10.1177/08969205241304790
Critical Sociology
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The Digital Boss: Algorithms,
Housekeeping Apps, and the
Digitalization of Domestic
Work in Colombia
Laura Clemencia Mantilla-León
Universidad del Rosario, Colombia
Isabella Jaimes Rodríguez
York University, Canada
Oscar Javier Maldonado Castañeda
Universidad del Rosario, Colombia
Abstract
Domestic work in Colombia has historically been undervalued and unpaid, with informality
continuing to rise despite legal efforts to dignify it, such as the ratification of ILO C189. In
this context, domestic work gig platforms have emerged as a source of formal employment.
Unlike most gig economy models, these platforms operate as agencies that directly hire
workers, ensuring key conditions like stable income and social security. However, gaps remain
in understanding the algorithmic design of these platforms. Through an ethnographic study
of one domestic work platform interface called Hogaru and semi-structured interviews with
workers and customers, we examine algorithmic governance and its impact on domestic
workers’ experiences, particularly concerning employment and social relations. Our research
focuses on the platform’s matching system, ranking, and pricing mechanisms. We conclude with
implications for designing more effective and equitable platforms, specifically tailored to the
unique context of Latin America.
Keywords
algorithmic governance, science and technology studies, domestic work, gig economy, matching,
rating, pricing, undervaluation
Corresponding author:
Laura Clemencia Mantilla-León, Universidad del Rosario, Calle 12C No 6A-15, Bogotá 111711, Colombia.
Email: laurac.mantilla@urosario.edu.co
1304790CRS0010.1177/08969205241304790Critical Sociology X(X)Mantilla-León et al.
research-article2024
Article
2 Critical Sociology 00(0)
Introduction
The platform economy has thrived in Colombia, significantly reshaping the landscape of tradi-
tional labor while also revealing additional challenges for worker social protection (ILO, 2021;
Woodcock and Graham, 2019). Within this context, low-skilled jobs in the service sector of the
economy have transitioned toward technological frameworks. These frameworks enable the con-
tinuous availability of workers who undertake gigs for a predetermined period in exchange for
economic compensation (Bukht and Heeks, 2017). Specifically, in the case of remunerated domes-
tic work, various platforms have emerged in Colombia since 2013 to digitally mediate an occupa-
tion marked by historical issues such as non-payment, lack of provision of work equipment, social
security, and job stability (Diaz, 2011). The promise of digital intermediaries for domestic work in
Colombia has aimed at facilitating the integration of workers into the labor market, the contribu-
tions to the social security system, and service management for clients, all through technological
devices (Mantilla-León and Maldonado Castañeda, 2024).
Remunerated domestic work employs over 600,000 women in Colombia, although this number
may be significantly higher due to the informal nature of this employment sector. According to the
National Department of Statistics (DANE in spanish, 2020), only 17% of women engaged in
domestic work have access to the social security system. This system provides crucial benefits,
including medical assistance, pensions, maternity leave, unemployment protections, and safe-
guards against work-related accidents or disabilities.
In this context, platforms that mediate domestic work present a promising opportunity for
employment. In Colombia, these platforms operate under a markedly different model than the typi-
cal gig economy (Fairwork Colombia, 2022, 2023). Notably, domestic work platforms in Colombia
are regulated by the International Labor Organization (ILO) Convention No. 189, which was for-
mally adopted by the Colombian government in 2012 through Law 1595. This Convention grants
domestic workers various rights, including the right to written contracts, statutory benefits, social
security coverage, freedom of association, and protection against all forms of violence (Mantilla-
León and Maldonado Castañeda, 2024).
Under this regulation, employers of domestic workers—whether private households or com-
panies, such as domestic work platforms—are required to uphold specific labor standards. These
standards include providing a written employment contract, enrolling workers in social security,
offering bonus pay, adhering to minimum wage requirements, ensuring that working hours do
not exceed the legally mandated 8-hour limit, and compensating for overtime hours, among
other stipulations.
In Colombia, domestic work platforms have emerged as intermediaries that connect cleaning
service providers with customers in both households and offices through a mobile application.
These platforms facilitate the hiring of cleaning staff while ensuring that the fees charged to cus-
tomers cover all legal entitlements for the workers mentioned earlier. This model not only stream-
lines the hiring process but also aims to promote fair labor practices and enhance the overall quality
of employment in the domestic work sector. By incorporating these legal protections, domestic
work platforms can help formalize the industry, providing workers with greater security and ben-
efits while delivering reliable services to clients.
While this operational model seems promising, gaps persist in understanding the algorithmic
design of these platforms. In this paper, we will explore algorithmic governance (Burrell and
Fourcade, 2021) and its impact on the specific experiences of domestic workers, with particular
attention to the operations of the national domestic work platform Hogaru. We will focus on the
matching system, rating, and pricing mechanisms to understand how workers relate with a digital
boss while navigating the city cleaning strangers’ houses. The research question we seek to explore
Mantilla-León et al. 3
is: How do algorithmic systems of pricing, rating, and matching impact the working experiences
and conditions of domestic workers on platforms?
First, we dissect various theoretical perspectives on algorithmic governance in gig work, focus-
ing particularly on the scope and limitations for platform-mediated domestic labor. Here, we exam-
ine the nature of the algorithms that underpin the digital mediation of domestic work and explore
the characteristics identified in the literature related to matching, rating, and pricing systems. This
serves as the foundation for our discussion on algorithmic governance. Second, we outline the
methodology employed to study algorithmic governance in domestic work. Employing an ethno-
graphic methodology, we explored Hogaru’s interface and conducted semi-structured interviews
with various stakeholders to understand the specific impacts of algorithmic management. Our find-
ings reveal unique aspects of these systems in operation, particularly in how they shape the labor
experiences of domestic workers in Latin America. Concluding, we argue for the design of more
equitable platform models that consider the unique socioeconomic contexts of the Global South.
We frame our discussion on the increasing influence of algorithms in dictating work parameters in
digital labor markets, ultimately affecting worker autonomy, client interactions, and income.
Hogaru, the Working of the Platform
Hogaru was founded in 2013 by two foreign men living in Colombia, who were surprised by the
prevalence of domestic workers in households compared to their experiences in Europe. In
response, they created a word-of-mouth business model similar to the Yellow Pages, gathering
resumes of household staff. These resumes were manually organized into an Excel database, and
leveraging social networks, they facilitated the provision of cleaning services and other household
tasks like carpentry and plumbing. Recognizing the customers’ preference for cleaning services,
the founders decided to focus on this sector, aligning it with the company’s mission. As the demand
for the service increased, the creators recognized the need for technological assistance to optimize
worker schedules and enhance customer-worker allocations. Consequently, the initial prototype of
the Hogaru app was developed in 2015. Today, it serves as the primary source of employment for
a network of 630 domestic workers, referred to as cleaning professionals, who provide services to
approximately 3,900 customers.1
Hogaru aims to formalize domestic work in the country, stating, ‘We want the company to reach
20,000 women who can say, “I receive social security payments and work benefits thanks to
Hogarú”’ (Hogaru manager, personal communication, 2024). Accordingly, the app formally
employs all domestic workers and covers all associated costs, including supplies such as cleaning
gloves, slip-resistant shoes, uniforms, and transportation. In addition, Hogaru provides a written
employment contract, social security, insurance, and a compensation structure aligned with mini-
mum wage regulations. This distinguishes Hogaru from traditional gig economy platforms, such as
Uber. Furthermore, the application relies on data-driven decision-making processes (Pasquale,
2015) that automate the matching of customers with workers, integrate incentive and evaluation
mechanisms for domestic workers, and digitize client payments for services. However, what are
the specific characteristics of Hogaru’s algorithmic management? How does the platform function
in practice on a daily basis?
The platform allows customers to book cleaning services for either 4 or 8 hours, depending on
their needs and the size of the space. Hogaru matches workers to customers based on various fac-
tors, including the specific cleaning requirements and standards set by customers, the experience
and expertise of the workers, and the distance between the workers’ and customers’ residences.
Once a customer books a service, they pay based on the duration of the shift, the type of shift, and
the day of the week. For instance, an 8-hour service costs approximately 140,000 COP (around 37
4 Critical Sociology 00(0)
USD) per day. Customers then specify the tasks they would like the workers to complete and, in
some cases, provide details on their preferred cleaning methods. The worker assigned to the service
can view the customer’s name, the destination address, directions for getting there, and the
requested tasks on their app. In contrast, the customer receives nearly biographical details about
the worker, including their full name, personal ID, legal background, residence address, and proof
of the social security payments that Hogaru covers.
On the day workers travel to their assigned locations, they are required to activate the GPS on
their phones so that Hogaru’s call-center personnel can assist them if they encounter difficulties.
Workers often reside quite far from their customers, sometimes on the opposite side of the city.
Upon arrival, workers must notify Hogaru via the app that they have reached their destination, and
Hogaru’s call-center staff typically follows up with the customers to confirm their arrival. Upon
completing their shifts,2 both customers and workers have the opportunity to rate each other.
Customers evaluate workers based on their attitude and the quality of their work, while workers
reflect on their experience, indicating whether it was positive, neutral, or negative. High ratings are
crucial for workers as they enable them to accumulate ‘kibos’—points awarded by the application
that can be redeemed for various benefits beyond their base salary, such as movie tickets and addi-
tional leisure time. When workers accept split shifts—comprising 4 hours at one location and
another 4 hours at a different site—they have the opportunity to earn even more points.
Workers are allowed a maximum of two shifts per day, totaling 8 hours, with the majority work-
ing 6 days a week. Hogaru aims to ensure that workers’ schedules are fully booked to facilitate
full-time employment. Workers receive the legally mandated minimum wage, along with addi-
tional compensation for mobile data and commuting expenses. Furthermore, they are eligible for a
financial bonus of approximately 80,000 COP (around 19 USD) for maintaining perfect attendance
throughout the month.
The integration of algorithms into gig work raises ongoing questions regarding their transpar-
ency for workers, the surveillance practices they enable, and their impact on workers’ labor experi-
ences (Maldonado Castañeda and Sanchez Vargas, 2020; Popan, 2021; Sanchez Vargas et al.,
Forthcoming). While there have been attempts to address algorithmic governance in care work
platforms (Anwar et al., 2021; Flanagan, 2019; Ticona and Mateescu, 2018), we argue that it is
necessary to uncover how domestic workers experience, resist, and adapt to algorithmic implemen-
tation, particularly in the case of the agency model prevalent in domestic work platforms in
Colombia. Algorithmic management is a situated experience that is shaped by regulatory and eco-
nomic contexts. Colombia exemplifies the configuration of digital platforms in the Global South,
in which automation is partially relying on different types of analogical work, personal social con-
nection, and the economy of emotions between customers and workers.
Algorithmic Governance and Gig Work
Algorithmic management represents a pivotal shift in the organization and control of work, funda-
mentally altering the dynamics between employers and employees. This change is particularly
evident in the context of digital platforms and the gig economy, where algorithms play a central
role in coordinating, monitoring, and evaluating work (Sanchez Vargas and Maldonado Castañeda,
2022). The surge of digital platforms such as Uber, Rappi, Deliveroo, TaskRabbit, among thou-
sands, has led to the widespread implementation of algorithmic management practices. These plat-
forms use algorithms to assign tasks, set prices, and evaluate performance, often in real time and
with minimal human oversight (Rosenblat and Stark, 2016). This system of management is marked
by its reliance on data-driven decision-making processes, which are believed to enhance efficiency,
scalability, and objectivity (Lee et al., 2015; Pasquale, 2015). Labor process theory, as articulated
Mantilla-León et al. 5
by Braverman (1974), has provided a useful lens through which to examine algorithmic manage-
ment. Braverman’s analysis of the degradation of work and the separation of conception from
execution can be applied to the context of digital labor, where algorithms increasingly dictate the
pace, nature, and evaluation of work. The centralization of control within the algorithmic system
often results in a loss of autonomy for workers, who are subjected to tightly controlled and moni-
tored working conditions (Moore and Robinson, 2016). Furthermore, the opacity of these algo-
rithms makes it difficult for workers to understand the basis on which decisions about their work
are made, challenging their ability to contest or negotiate working conditions (Kellogg et al., 2020).
The implementation of algorithmic management has raised questions about the nature of work
and employment in the digital age. Digital platforms blur the boundaries between formal and infor-
mal work (Sanchez Vargas et al., 2022), between being employed and self-employed (Prassl and
Risak, 2016). This has profound implications for workers’ rights, social protections, and the very
definition of what it means to be an employee. Moreover, the reliance on algorithms for the man-
agement of labor introduces new forms of inequality and discrimination, as biases embedded in
algorithms can lead to unfair treatment of certain groups of workers, for instance, migrants
(Rosenblat et al., 2017; Sánchez Vargas et al., 2024). The rise of algorithmic management has not
gone uncontested. Workers, activists, and scholars have raised concerns about the implications of
this mode of management for workers’ rights, privacy, and autonomy. Various forms of resistance
have emerged, ranging from individual acts of gaming the system to collective organizing and
legal challenges (Rosenblat and Stark, 2016; Woodcock and Graham, 2019). These efforts are
aimed at demanding transparency, accountability, and fairness in algorithmic decision-making
processes.
In addition, there is a growing call for the regulation of digital platforms and algorithmic man-
agement practices. Scholars argue for the need to develop legal and regulatory frameworks that
can protect workers in the digital economy, ensuring fair wages, working conditions, and the right
to organize (De Stefano, 2016; Fairwork Colombia, 2022, 2023). This includes addressing the
challenges posed by the classification of platform workers as independent contractors rather than
employees, which significantly impacts their access to labor rights and protections. Algorithmic
management embodies a significant evolution in the way work is organized, monitored, and con-
trolled. While it offers the potential for increased efficiency and scalability, it also poses signifi-
cant challenges to traditional notions of employment, workers’ rights, and organizational control.
Algorithmic management is rendered visible through specific realms of control such as matching
systems between supply and demand of services, rating systems to score performance and satis-
faction of customers, and finally, price and the costing of labor. Digital platforms introduce
dynamic pricing as a strategy to value workers’ work based on the always changing conditions of
the market and the workplace, matching diverse data about demand for services, supply, weather,
performance, rating, among others.
Domestic Work and Algorithmic Management
The integration of digital platforms and algorithmic management into domestic work has led to sig-
nificant transformations in its operation and the commoditization of services in the labor market.
Although domestic work has been examined in relation to care burdens, and the reproduction of
gender and racial gaps and stereotypes in platform labor (Benvegnù and Kampouri, 2021; Yin, 2024),
there is a need to emphasize how algorithmic management is shaping domestic work, particularly in
the Global South, and how current application models dictate workers’ rhythms and practices. Digital
platforms play a critical role in connecting low-income women to work opportunities (Kasparian et
al., 2023), but these platforms vary in their structure and operation, leading them to adapt to different
6 Critical Sociology 00(0)
socioeconomic contexts and navigate complex territories that cover both informal and formal eco-
nomic spheres.
Research on digital platforms and the gig economy shows that algorithmic management signifi-
cantly impacts the working conditions of gig workers, particularly women. In some cases, the
introduction of women to the gig economy reinforces stereotypes and gender divisions, contribut-
ing to lower wages and greater uncertainty regarding certain aspects of their working conditions
(Schoenbaum, 2016).
Studies on beauty-platform work, as discussed by Anwar et al. (2021), have delved into various
forms of control, surveillance, and rules within the platforms. Specifically, these studies describe
‘participatory surveillance,’ where workers, such as those in beauty services, are not only con-
trolled and subjected to discipline but also actively leverage and reappropriate monitoring tech-
nologies for agency and empowerment. These works highlight how women in the gig economy
recognize and navigate both explicit and implicit forms of control. They have been subjects of
algorithmic governance and control, where algorithms are employed to restrict, recommend, and
evaluate workers through recording and rating. These systems also discipline workers by enforcing
replacement and offering rewards (Anwar et al., 2021).
On the other hand, studies have delved into the role of platforms like HomeServers and
HouseHelp, which can enhance autonomy and flexibility for women in the care industry. These
platforms reportedly offer safer and more liberating alternatives to traditional workplaces, allow-
ing women to tailor their work schedules to personal commitments (D’Cruz and Noronha, 2016).
However, while facilitating a degree of empowerment, these platforms also introduce new chal-
lenges. Anwar et al. (2021) highlight that, alongside the benefits, there is an increase in surveil-
lance, control, and the discipline of women’s bodies, echoing findings from other studies (e.g.
Ticona and Mateescu, 2018).
Digital platforms often have rules that limit how much work a worker can do, based on task
availability and acceptance. One of the first systems integrating the current models of algorithmic
management is the matching system. Often, platforms seek to offer ‘trustworthy’ employees, in
line with what is observed in the literature on platforms in the sector (Schoenbaum, 2016; Sibiya
and du Toit, 2022; Ticona and Mateescu, 2018). As exemplified by Micha et al. (2024), Zolvers (a
domestic work platform in Argentina) promotes contact with ‘verified’ workers—those who have
positive references from previous employers and have passed psychological evaluations and crimi-
nal background checks. These digital platforms use digital tools to foster trust but also expose
domestic workers to vulnerabilities and discrimination by publishing personal data and compara-
ble information.
In addition to these verification processes, the matching mechanisms often replicate and exac-
erbate pre-existing inequalities in offline domestic work, such as racial and gender discrimination.
These systems enable clients to select workers based on characteristics like gender, race, and social
class (Rodríguez-Modroño et al., 2024), reinforcing exploitative employment practices and restrict-
ing workers’ access to labor rights (Micha, citing Hunt and Machingura, 2016; van Doorn, 2020;
Rodríguez-Modroño et al., 2024). Hunt and Machingura (2016) also highlight how these dynamics
contribute to the rise of precarious employment in the on-demand domestic work sector.
Importantly, these matching processes are often one-sided, where platforms present domestic
workers’ information based on various criteria that are not necessarily related to skills, but rather
personal preferences of clients. This process does not allow workers the opportunity to choose in
return (ILO, 2021). Another key aspect of the matching process is viewing the housecleaner as a
worker, rather than as a collaborator with the platform. Recognizing the domestic worker as an
employee could enable clients to meet the professionalization needs of these workers, such as fair
pay, benefits, and job security (Rivera, 2012; Raval and Pal, 2019). However, under the on-demand
Mantilla-León et al. 7
models frequently used by domestic work platforms, the platforms generally act solely as interme-
diaries and, as a result, do not significantly contribute to formalization. Their role is often restricted
to the matching service (Digital Future Society, 2021; Ticona and Mateescu, 2018). Even though
these platforms may increase transparency and visibility in the working relationship, they typically
classify domestic workers as independent contractors, limiting their access to professionalization
opportunities and employment benefits (Hunt and Machingura, 2016; van Doorn, 2020).
Another relevant system we want to deepen is the rating mechanisms that platforms use to con-
trol labor conditions. As Ticona and Mateescu point out, ‘by design, ranking and rating systems are
“reactive”, meaning they do not simply count and rank things, but actively intervene in shaping
people’s behaviors by creating or reinforcing the categories used to evaluate people, goods, and
services’ (Espeland and Stevens, 2008). Those consequences are embedded in the visibility of
potential workers in the platforms to compare in standardized ways. Rating systems are therefore
important to workers because ‘the processes by which reputation is calculated and displayed affect
market outcomes’ (Irani, 2015; Ticona and Mateescu, 2018).
Again, most digital platforms apply one-way rating systems where workers are evaluated, but
clients are not (Flanagan, 2019; Sedacca, 2022; van Doorn, 2017). One example is Zolvers, where
worker ratings depend exclusively on employer assessments, often resulting in temporary deacti-
vations. In addition, on various platforms, the continuity of the domestic worker’s employment is
often determined by their performance within the score and rating system. This creates an exploita-
tive situation: since a worker’s future economic opportunities rely heavily on receiving a positive
rating from clients, the worker often feels compelled to meet every demand (Ticona and Mateescu,
2018). Therefore, platforms tend to prioritize the ability of clients and platform companies to man-
age their risks and trust workers, while neglecting the workers’ ability to assess and trust their cli-
ents (Ticona and Mateescu, 2018).
Ultimately, exploring the existing scholarship on pricing algorithms reveals that digital plat-
forms promote services at hourly rates that vary according to the weekly workload. In many cases,
these prices align with or slightly exceed the legal minimum wage established for the sector.
However, most platforms face the challenge of maintaining commission rates at a level that ensures
company growth while simultaneously keeping service prices competitive for clients and wages
sufficient for workers. This balance is crucial if on-demand domestic work is to become a viable
option for decent work (Hunt and Samman, 2020).
Although some platforms initially set higher prices for customers, bookings only started to
materialize after prices were lowered. Once the demand is established, clients often gain control
over the price of services. The higher hourly cost of hiring a platform worker is offset by lower
transaction costs, such as those related to selecting, screening, and supervising a worker indepen-
dently. By delegating these processes to the platform, clients are assured of a ‘professionalized’
service in exchange for paying higher prices (Hunt and Samman, 2020).
In the integration of algorithms within gig work, particularly domestic labor platforms, we
encounter significant challenges around transparency, surveillance, and the overall impact on
workers’ experiences. Popan (2021) and Sanchez Vargas et al. (Forthcoming) highlight ongoing
concerns with the opacity of these systems and the monitoring capabilities they enable, raising
critical questions about fairness and autonomy in labor processes. Previous studies, such as those
by Ticona and Mateescu (2018), Flanagan (2019), and Anwar et al. (2021), have addressed algo-
rithmic governance in care work platforms but suggest a need for deeper exploration into how
domestic workers in regions like Colombia experience, resist, and adapt to these systems. This
paper builds on this foundation by examining the ‘agency model’ prevalent in Colombia, where
digital platform configurations are deeply intertwined with analog work, personal connections, and
an economy of emotions that influence the labor dynamics between customers and workers.
8 Critical Sociology 00(0)
Our approach in this study involves dissecting theoretical frameworks of algorithmic governance
in gig work, focusing on the nature of algorithms that mediate domestic work, and their implementa-
tion in aspects like matching, rating, and pricing systems. We conceptualize these algorithms not
just as tools but as representational objects that embody certain values and assumptions, which blur
and blend into a singular, opaque entity in the perceptions of workers and managers, exerting a form
of non-human agency that deeply affects workplace dynamics and worker autonomy.
Methods
To capture the complexities of algorithmic governance in domestic work platforms, we adopted a
methodological approach aligned with Seaver’s (2017, 2018) ethnographic perspective. According
to the author, in every algorithmic decision, we can discern human choices, meaning that ‘algo-
rithms are not autonomous technical objects, but complex sociotechnical systems’ (Seaver, 2018),
which are unstable, malleable, and utterly reliant on human sense-making (Seaver, 2018). In the
algorithmic governance of domestic work platforms, we also encounter the arbitrariness of per-
sonal preference (Seaver, 2018) and a series of human actions that fuel it. Therefore, our objective
was to shed light on the matching, rating, and pricing systems of the Hogaru app, through an eth-
nographic sensibility finding the people within these systems (Seaver, 2018).
As Seaver (2017, 2018) argues, algorithms are collectively produced, and any system takes ‘the
shape of the organizations that make them’ (p.375). Therefore, our aim was to explore the human
practices that contribute to their development within the realm of digitally mediated domestic
work. The contribution of this methodological approach lies in uncovering the human imprint
behind data-driven decision-making processes in digitally mediated domestic work. Scholars
adopting this approach seek to identify all the actors that influence a company’s systems, including
engineers, software developers, and even non-technical personnel (Devendorf and Goodman,
2014; Seaver, 2017; Seaver, 2018). The techniques employed in this endeavor may vary signifi-
cantly based on researchers’ expertise and methodological backgrounds. As Seaver (2017) points
out, when examining the human and organizational traces of algorithms, a data scientist might
utilize mathematical analysis, an anthropologist could apply ethnographic methods, and a com-
puter scientist may conduct efficiency tests. This diversity underscores that the representation of
algorithms is as multifaceted as the objectives of the researchers involved. In adapting a methodo-
logical approach to study algorithms, it is essential to acknowledge that ‘as my discipline’s meth-
ods are poorly suited to determining the efficiency of an algorithm in asymptotic time, so the
computer scientists’ are poorly suited to understanding the cultural situations in which algorithms
are built and implemented’ (Seaver, 2017: 5).
To begin, we conducted interviews with management personnel at Hogaru who possessed insight
into the design process of the three specific algorithmic systems relevant to our study. Through
semi-structured interviews, we engaged with an app programmer and a manager to: (1) trace the
evolution of the app designed for managing domestic work, (2) elucidate the features of the app
interface for both workers and clients, (3) grasp the underlying design principles (such as usability
and accessibility), (4) understand the motivations driving the creation of an algorithmic system for
domestic work management, and (5) investigate into the mechanics of the matching, rating, and
pricing systems (see Annex 1). Furthermore, we explored Hogaru management’s perceptions regard-
ing the relationship between domestic workers and these algorithmic systems.
We also conducted two focus groups with domestic workers professionally linked to Hogaru. One
group consisted of long-term workers, with over 4 years of experience using the app, while the other
included new workers who had been working for the platform for only 1 year. The interviewed
women were between 30 and 40 years old, and they have dedicated their lives to caregiving and
Mantilla-León et al. 9
cleaning jobs before joining Hogaru, significantly contributing to their families’ economic support.
Our aim in interviewing them was to comprehend their usage of the app in managing their daily work
routines, explore their handling of the application and technological devices, the challenges they have
faced while using them, and its flaws. We also explored the workers’ understanding of Hogaru’s
matching mechanism and their clarity regarding rating and earnings allocation systems.
During these interviews, our focus was on examining the learning processes and strategies uti-
lized by domestic workers to leverage the algorithmic systems for their benefit (see Annex 2). The
interviewees showed their apps to us during the interview, providing detailed explanations on how
they use its interface, what work-related issues they could manage through it, and which ones they
could not. In addition, we reconstructed the workers’ experiences relying on narratives from more
than 30 domestic workers linked to Hogaru, who were interviewed by the authors as part of the
Fairwork Colombia project.
Finally, we conducted interviews with six Hogaru customers in Bogotá, all of whom were
women. Our objectives in engaging with them were to gather insights into their experiences with
domestic workers prior to utilizing apps, explore their motivations for adopting digital intermediar-
ies, and elucidate any relationships or connections they maintain with the workers of these interme-
diaries. One of the customers consented to show the app, enabling us to gain a deeper understanding
of surveillance, monitoring, and control mechanisms over workers; customers’ access to informa-
tion concerning workers entering their homes; service rating systems; and the relationship between
service expenses and worker earnings (see Annex 3). These interviews were conducted via video
call, recorded with consent, and transcribed preserving the anonymity of the participants.
Findings
Navigating the App and the City: Asymmetric Information and Urban Matching
Challenges in Hogaru
The matching system is a crucial component of the platform’s algorithmic management. It is
designed to meet customer demands and address subjective perceptions regarding who is
allowed into specific spaces, such as offices or households. This system uses specific criteria to
create a model that effectively pairs customers with professionals. As one of the organization
leaders explains,
our main principle are marriages, a customer who is married to a professional, we don’t break that [. . .].
They will hopefully last five years working together. To achieve this, we start from the availability to
provide the service (Hogaru manager, personal communication, 2024).
The availability is the first variable considered within the matching system; customers and profes-
sionals available at specific times and days are identified through the automated system. The next
variable that influences the decision of the matching system is distance. Distance is a critical factor
that influences the decisions made by the matching system. While the system uses GPS to ensure
geographic proximity, its impact extends beyond the digital realm, significantly affecting the daily
lives of workers. Although the platform aims to reduce commute times and has reportedly suc-
ceeded in some cases, the reality is often different. Workers still endure lengthy commutes across
the city. This discrepancy highlights a significant gap between the platform’s algorithmic goals and
the actual workers’ on-ground experiences. One of the customers noted:
I had a very good one [cleaning professional], but she lived so far away that it was a struggle for her to
make it here. Consequently, she often called in and couldn’t come. I told the company, ‘Listen, she’s really
nice and willing, but it doesn’t help me if she cancels due to the distance. Please, find me someone closer
10 Critical Sociology 00(0)
because this girl lives who knows where, and there’s a bus only every two hours’ (Hogaru’s customer,
personal communication, 2023).
Similarly, one of our participants expressed her frustration, ‘Imagine, tomorrow I go to the 170s
street and I live towards Usme. The entire city from one side to the other’ (Hogaru Professional,
personal communication, 2024). Another participant’s experience reinforces this affirmation: ‘I
was in the 26th, at the Chamber of Commerce, and I had to run or I wouldn’t make it on time. Many
times, because of five minutes, the customer won’t receive [the service]’ (Hogaru Professional,
personal communication, 2024). These testimonies underscore the inefficiencies in the system, not
only resulting in practical challenges for the workers but also adversely impacting their ability to
meet customer expectations effectively. Similarly, these inaccuracies in distance calculations lead
to uncertainties about the time and money workers will spend during a day’s work. Such issues
result in no guarantees regarding additional, unpaid time and expenses and tend to create an unsta-
ble environment for the workers.
The short time between shifts is another challenge noted by Hogaru professionals. They have
the option to choose either a single 8-hour shift at one site or split shifts, comprising 4 hours at one
site and another 4 hours at a different site. Despite the tight schedule, some participants prefer the
split shift option because it allows them to accumulate ‘kibos’—points awarded by the application,
which can be redeemed for different benefits beyond the base salary, such as movie tickets and free
time. As one of the platform’s leaders mentioned: ‘the problem for workers was the distance. That
has improved a lot. Now, the issues are about split shifts, customer characteristics not informed,
and replacements’ (Hogaru manager, personal communication, 2024).
Workers are often assigned split shifts despite having preferences or restrictions against these.
Shift transitions pose significant logistical challenges for Hogaru professionals, impacting both
their personal time and client satisfaction. The tight scheduling between shifts, often with scant
regard for the workers’ personal needs such as meals, leads to stressful situations. One professional
expressed:
Sometimes they set very little time to switch shifts . . . I got out at 1 PM . . . When it was one o’clock, they
called me from the call center, ‘Where are you?’ I was three blocks away, but I hadn’t even been a minute
late when they called me and said the customer wasn’t going to receive me (Hogaru professional, personal
communication, 2024).
Another added, ‘I also have to reach because I more or less . . . It was 1:03 PM when they called
me and said: “Marisol, the client won’t let you in”. And I said, “Really? Why?” When I had already
arrived in front of the point’ (Hogaru professional, personal communication, 2024). These experi-
ences underscore the disconnect of the platform’s scheduling system with the workers’ practical
realities and the resultant impact on service quality and client relations.
The tight shift scheduling, without adequate consideration for the workers’ basic needs such as
mealtimes, creates a high-stress environment. The prompt call from the call center, even before the
employees are officially late, reflects an overly rigid system that fails to accommodate even minor
delays, which are often beyond the workers’ control. This approach not only puts undue pressure
on the professionals but also risks damaging customer relationships, as evidenced by clients refus-
ing to receive workers for minor delays. Ultimately, this scenario highlights a critical flaw in the
platform’s scheduling system, suggesting a need for more empathetic and realistic planning that
considers the human aspect of its workforce.
Replacement situations are particularly challenging for Hogaru professionals. Being assigned to
unfamiliar customers brings about elements of uncertainty and apprehension, negatively impacting
their emotional and psychological well-being. One of the platform leaders said: ‘the anxiety they
Mantilla-León et al. 11
feel when going to a new address is horrible . . . It’s like, will I be treated well? Are they going to
be rude?’ (Hogaru manager, personal communication, 2024). Despite the platform’s integration of
advanced features such as GPS navigation to assist cleaners in locating and traversing the city and
its efforts in configuring incentives and providing information about client needs, these technologi-
cal advancements do not fully extend to the more nuanced challenges faced in offline contexts. The
quote from one of the platform leaders emphasizes a significant gap in the digital interface’s ability
to mitigate uncertainties inherent in new service encounters. These include the dynamics of inter-
personal interactions behind closed doors, the unpredictability of client behavior, and the anxiety
of meeting potentially divergent expectations. This discrepancy highlights a crucial aspect related
to the gig economy: the complexity of human interactions in service delivery through digital tools
shows that technological solutions can be limited in addressing the emotional and psychological
dimensions of such labor.
On the other hand, discrimination based on race, gender, and class bias is among the challenges
faced by Hogaru professionals in terms of the matching systems. As one of the platform leaders
states,
Another complaint we get is that the client has no influence over the worker’s profile, and often the profile
ends up being based on nationality or race. Sorry, we do not discriminate here. It was Maria, and it is
Maria. It doesn’t matter to you; she will do the job (Hogaru manager, personal communication, 2024).
This is manifested in both online and offline settings. In fact, the application, through its interface,
attempts to foster inclusivity by not explicitly revealing the ethnic identities of the cleaners when
matching with a client. However, its power is limited behind closed doors, as it cannot control how
a customer treats a professional, which might involve discrimination during service delivery. The
exception occurs in the professional’s evaluation of the client, where scenarios of discrimination
can be reported. These concerns reveal the complex challenges inherent in managing a service
platform, where a variety of sometimes conflicting preferences and expectations must be navi-
gated. Moreover, this situation prompts questions about achieving a balance between operational
efficiency and worker preferences.
Human Intervention Needed? Call Center vs Application in Matching Decisions
The transition from call center–based to app-based job assignments at Hogaru has produced a
spectrum of experiences among the workers. Although the platform’s intention was to streamline
the process of job assignment, workers point out persistent core issues. Reflecting on this change,
one professional commented, ‘But honestly, it’s kind of the same because sometimes they send you
far away, and the answer is that there is nothing nearby and that there aren’t available girls’ (Hogaru
professional, personal communication, 2024). Another professional showed resigned acceptance,
stating, ‘I don’t argue with them. I’ve done it; I say, okay, if they wait for me, fine’ (Hogaru profes-
sional, personal communication, 2024). These experiences reveal that challenges with the algo-
rithm persist, irrespective of the method of assignment, indicating deeper systemic issues.
While the application does introduce efficiencies in the matching system, the April 2021 strike
(Paro nacional in Spanish) highlighted the crucial role of human intervention, particularly when
unforeseen events significantly disrupt normal operations. One of Hogaru managers described the
scenario,
Without the application, it would be up to me and the whole team to start making calls, telling Maria where
she needs to go. This happened during the April 2021 strike. The chaos was such that the entire
administrative team had to assist the professionals in finding their way home. The application was almost
12 Critical Sociology 00(0)
useless, and we were all grabbing phones, calling. ‘Okay, where is this? Tell me. Ah yes, I see it. Done. No,
if she goes to such a place, that Transmilenio route isn’t working. It’s a disaster. ’ We were here until 10 at
night, ensuring the professionals got home (Hogaru manager, personal communication, 2024).
This experience underscored a critical aspect of the app’s functionality: its adaptability is nota-
bly limited in scenarios that significantly alter city navigation. This highlights the indispensa-
ble role of human assistance in such non-operational, circumstantial matching processes.
Therein lies the importance of having a human voice on the phone, available to guide the
worker through unexpected situations. This marks a significant contrast from the operations of
traditional ride-hailing and delivery platforms in the gig economy, where communication with
the platform is often restricted to chatbots, and direct human contact is rare, thereby limiting
the agency of the workers (Brown, 2019).
A Reactive Rating System: A Good Rating Buys Free Time and Time for Self-Care
Reputational and rating systems are crucial in digital work platforms as they are the means by
which the quality and reliability of services are measured (Ticona and Mateescu, 2018). In the
case of household-based jobs, such as cleaning services, these systems have important qualities to
consider and different impacts on the workers. In the case of Hogaru, the app allows customers to
rate workers through stars and comments in two categories: attitude and quality. Five stars trans-
late to excellent service, where the worker demonstrated willingness and ‘kindness’ throughout
the 4 or 8 hours spent at the customer’s house, while also meeting cleanliness standards for each
requirement. ‘For us, it’s a performance review; you know how your performance is going . . .
when we receive a good rating, we get the notification “You’re a star”’ (Hogaru domestic worker,
personal communication, 2024).
We found a correlation between the star rating system used by customers and the personal
score assigned to each worker in their app. This score is on a numerical scale out of 10. Both
groups of workers lacked clarity on the precise conversion of stars to numerical values, but they
understood that 5 stars equate to a perfect score out of 10. In addition, they emphasized the sys-
tem’s sensitivity to any decrease in their personal rating. According to the interviewees, receiving
just one rating of less than 5 stars is sufficient to lower the 10-point rating that ‘everyone wants
to have’. One worker shared her experience, stating, ‘I previously had a perfect ten out of ten rat-
ing, but a customer gave me four stars, it automatically decreased my score. You can have many
excellents but rising a lowered score takes time . . . even months’ (Hogaru domestic worker, per-
sonal communication, 2024).
The management highlighted how ratings profoundly affect workers emotionally, as these
reflect the importance customers assign to their work. He stated, ‘It hurts them when they aren’t
evaluated’ (Hogaru Management, personal communication, 2024). Rating systems in platform-
based work are reactive (Ticona and Mateescu, 2018); these go beyond mere numbers, shaping
workers’ sense of self-worth. In the context of domestic platform-based work, these ratings can
influence client relationships and result in highly significant rewards for workers. In the first case,
Hogaru’s management drew a crucial distinction between ratings for workers in apps like Uber and
those in domestic work apps:
In Uber, the chances of encountering the same driver again are minimal. But here, the worker and the
customer will see each other again. So eventually, the workers say, ‘That was the lady who rated me
poorly’, and they start to show resentment, saying, ‘She rated me badly’, and then the relationship is
damaged (Hogaru, personal communication, 2024).
Mantilla-León et al. 13
In domestic work rating systems, metrics are highly personalized and deeply human, shaping the
experiences of both workers and customers and influencing their attitudes toward one another. As
a reflection of this, one of the professionals mentioned during a cleaning service with a client:
I tried to do everything to make her house nice. And she wanted more, but time was running out. And she
rated me badly. There, my performance went down. Right now it’s at 9 quality. And I was 10 out of 10
since I came in. And I was crying. What did I do wrong, my God (Hogaru professional, personal
communication, 2024).
From their perspective, it emphasizes the vulnerability of women workers when faced with rating
systems, in which their sources of livelihood and self-esteem are closely linked to subjective evalu-
ations that do not always take into account the complexities and limitations of their work
environment.
In focus groups with workers and interviews with clients, we identified that domestic workers
can also rate those who hire their services. The system they use consists of a three-label scale—
‘excellent’, ‘good’, and ‘bad’—accompanied by mood-reflected faces. It is noteworthy that the
platform takes into consideration workers’ impressions and evaluations of customers, for example,
avoiding assigning them to someone they have not felt comfortable with:
I had one who locked us in. We were with another worker, and the customer said, ‘You two are not leaving
until you finish this, this, and this. There’s another worker who comes and does it all in one day; you two
can do it in four hours’. And she left us locked in . . . I reported it, and they told me that the lady had been
blacklisted (Hogaru domestic worker, personal communication, 2024).
Returning to the case of rewards accessible to workers with high ratings, we discovered that Hogaru
implements a points-based—kibos—system that offers various benefits to workers such as free
time, movie tickets, and household appliances. Contributing to this system serves as a major incen-
tive for workers to deliver exceptional service. By maintaining a near-perfect rating in quality and
attitude, they can accumulate ‘loyalty points’, as described by the manager we interviewed.
Through discussions with the workers, we learned that this points system allows them to address
caregiving needs for themselves and their families. Their primary interest lies in using the points
to secure afternoons or days off, which they dedicate to spending time with their children, attend-
ing medical appointments, or simply resting. As one worker puts it, ‘For us mothers, it’s not just
about the rewards, it’s about spending time with our children’ (Hogaru domestic worker, personal
communication, 2024). According to the workers, receiving a 5-star rating for a service only earns
them 6 points, while an afternoon off is valued at 1500 points. They have strategically identified
actions that generate the most points. They explained that split shifts—working in two houses in
one day, 4 hours in each—and taking on less desirable shifts such as Friday and Saturday after-
noons, or Sundays and holidays, enable them to accumulate points more easily. However, these
tactics impact their working dynamics, as interviewees noted they often have to overwork to meet
their goals. One worker shared, ‘I once worked two Sundays in a row, straight through without a
break. It was very rewarding, but extremely challenging’ (Hogaru domestic worker, personal com-
munication, 2024).
Finally, the workers have identified which customers might lower their personal rating and
therefore their chances to accumulate more points, so they ask them not to rate the services: ‘I told
her “You lowered my average, from 10 to 9.8, and that’s bad for me”, it takes months to raise it,
even with 50 good ratings, it wouldn’t increase’ (Hogaru domestic worker, personal communica-
tion, 2024). In this regard, one worker mentioned that it had taken her a year to raise her score back
14 Critical Sociology 00(0)
to 10. Workers have developed strategies to handle customers and their ratings, ensuring their
points accumulation remains unaffected: ‘as soon as you walk in and are greeted with “good morn-
ing” or “good afternoon”, you know what to expect from the customer’ (Hogaru domestic worker,
personal communication, 2024).
Pricing System: A Steady Income Maintaining the Undervaluation of
Domestic Work?
The pricing system we found in Hogaru reflects the regularization of domestic work in Colombia.
Unlike delivery or ride-hailing apps in the gig economy, which use algorithms to calculate gig work-
ers’ earnings based on factors like distance (van Doorn, 2020), Hogaru employs a fixed earning struc-
ture aligned with the prevailing legal minimum wage in the country. It is crucial to recognize that the
establishment of a minimum wage and work-related benefits for domestic workers has been achieved
through the historical efforts of domestic workers’ unions in the country. Currently, ongoing discus-
sions and legislative initiatives aim to improve the earnings of domestic workers, as the minimum
wage is increasingly viewed as inadequate compensation given the complexities of the tasks and
responsibilities they undertake (Mantilla-León and Maldonado Castañeda, 2024). Moreover, platforms
must go beyond the minimum legal requirements and provide fair compensation for domestic work.
However, the platform offers a range of incentives aimed at motivating workers to boost their
earnings. For instance, during focus groups, workers discussed a compliance bonus, which is paid
monthly to workers who do not miss any service, regardless of the circumstances, be it due to ill-
ness or attending a medical appointment. This bonus also contributes to the points-based system
mentioned earlier. This incentive reflects a highly productive approach to domestic work that bor-
ders on mechanization, as exemplified by one of the domestic workers: ‘you can’t get sick, inca-
pacity is what kills the most’ (Hogaru domestic worker, personal communication, 2024).
We also found that workers who accept split shifts during their workdays are awarded an additional
bonus intended to cover the costs of traveling between homes. Nevertheless, domestic workers perceive
this bonus as an additional source of income comparable to an extra benefit that makes being assigned
to this shift pattern attractive despite the physical tiredness, as noted by the interviewed workers.
Hogaru’s pricing system and incentives subtly ties the seniority of workers to the potential of
making higher earnings. Those who manage to adapt to the pace of working with the app for more
than a year earn a seniority bonus paid annually. But that seniority is only achieved through ‘endur-
ance’. Both groups of interviewed workers highlighted the challenge of sustaining employment on
this platform over time: ‘During the interview and selection process, out of 80 candidates, only 15
of us were selected, and now there are only two or one left’ (Hogaru domestic worker, personal
communication, 2024). What we noted was that the app-based model for domestic work imposes
considerable demands, both physically and mentally, for workers; ‘we’ve all lost weight since we
started working here’ (Hogaru domestic worker, personal communication, 2024). Working with a
domestic work app entails frequent changes in customers, navigating the city without complete
familiarity, mastering technology usage, and adjusting to various temperaments and cleanliness
standards. One worker with over 4 years of experience with Hogaru shared,
The other day I arrived at the office and the new girls seemed demotivated . . . I listened to them and I
encouraged them, ‘Stay positive . . . Don’t think that this is going to be tough, think about making money;
you are in control of your income’ (Hogaru domestic worker, personal communication, 2024).
It is important to highlight that the pricing system of this platform does not match the charge
they make to customers for hiring cleaning services with the remuneration received by domestic
Mantilla-León et al. 15
workers. As mentioned earlier, the cost a customer pays for a service on Hogaru has a background
where ‘the customer simply opens the door, lets the cleaning professional in, the cleaning profes-
sional does the cleaning, and the customer then closes the door in the afternoon and forgets about
everything’ (Hogaru manager, personal communication, 2024). A customer requesting an 8-hour
service pays approximately 140,000 COP per day (37 USD); this price varies depending on the
duration of the shift, the type of shift, and the day of the week. Hogaru has an automated system
that takes demand characteristics into account and assigns a price based on the mentioned varia-
bles. The motivation behind paying this amount is that the customer avoids the hassle of finding
and training the worker, hiring them, affiliating them with health and pension plans, settling
accounts, among other matters. One customer said:
You determine the schedule for their arrival and departure. Communication with them doesn’t entail
exchanging phone numbers; it’s solely conducted through the platform. Neither they nor I share our phone
numbers directly, and I have no intention of doing so as it would go against protocol (Hogaru’s customer,
personal communication, 2023).
According to the manager that we interviewed, Hogaru’s cleaning services are among the most
expensive in the domestic work app market.
Hogaru currently has 3900 clients and 630 workers. In most cases, workers provide services
from Monday to Saturday, have a full schedule, and almost always have stable clients. Considering
the cost of a cleaning service shown earlier, the question arises about the actual percentage of profit
per service that the workers receive. Regarding this, the interviewed manager pointed out:
They know how much they charge the client, and at least they know how much they earn from that cost
the client pays. No . . . Well, they could find out if they wanted to, they will see the prices and can do the
calculations, but it’s like, look, you can go on vacation, look, you have health insurance, look, you have
this and that [other labor benefits] (Hogaru manager, personal communication, 2024).
The described pricing system allows us to assert how domestic work has been categorized as
‘unskilled’ labor, perpetuating the historical undervaluation of this sector and its workers (Sarti,
2014). This is evident in existing legislation, which defines the minimum wage as the ‘appropriate’
compensation, despite it not aligning proportionately with the complexity of tasks and responsibili-
ties assumed by domestic workers.
Discussion
Based on our findings, we will develop two analysis paths that encompass algorithmic governance
in domestic work:
Juggling the App and Job Dynamics
The algorithmic systems governing gig work expose information asymmetries and surveillance
practices that affect labor processes (Anwar and Graham, 2020; Athreya, 2020; Gandini, 2019).
Workers’ ability to resist ‘techno-normative control’ influenced by algorithms (Anwar and Graham,
2020) has been addressed through subtle and daily tactics of resistance, both collectively and indi-
vidually. In the realm of domestic work, the individual nature of the labor is crucial, as workers
have limited knowledge of each other, and solidarity networks via WhatsApp or Facebook groups,
common in other sectors like delivery (Sánchez Vargas et al., 2024), are scarce and less extensive.
16 Critical Sociology 00(0)
Consequently, workers have developed strategies to navigate the algorithmic systems and leverage
the availability of human personnel on the platform to their benefit. Hogaru’s cleaning profession-
als have created alternative paths to manage customers allocation, leverage the app’s visibility to
ensure trust in hiring (Ticona and Mateescu, 2018), handle inaccurate distance calculations between
services, use the point system—kibos—to earn free time, and even avoid dismissals or penalties
when clients decide to test their ‘loyalty’.
The workers have developed strategies, or ‘moves’ as they call them, to navigate the platform
and use its design to their advantage. One such strategy involves leveraging the availability of
human personnel on the platform. For example, the app suggests that workers mark when they
arrive at a service location, but if they are late, the app may automatically cancel the service. To
prevent this, they contact the call center, inform them that they are running late, and ask if they can
negotiate with the client to see if the client will still allow them to complete the assigned cleaning
service. As one cleaner explains: ‘Before leaving our homes, we always have to say we left on time
so that they get the information. For example, if you get lost or something happens, you can write,
“I’m running late”. And once you’ve already clicked that you left on time, it will show the check-
in. I mark my check-in in the app’.
Another issue they often face is handling inaccurate distance calculations between services. As
they mention, the app frequently makes errors with the GPS system, so they have other strategies
to reach client’s homes. As one cleaner explained when describing the Hogaru app:
This is the one I told you about, it’s in the morning at El Virrey.3 Right here, it shows me how many hours,
where it is, and they give you some specifications to figure out how to get there. Honestly, I sometimes
don’t even check it; I just look at Google Maps to know exactly what I need to take.
As she pointed out, using other apps like Google Maps, along with their knowledge of the city,
allows them to effectively navigate and use public transportation to complete their services.
Regarding the creation of alternative strategies for managing customer allocation, cleaners
often take advantage of the app’s ‘split shifts’ system. They typically take split shifts to earn
more kibos and gain free time. In addition, they understand that earning more kibos depends on
the client’s rating after they complete a service. Since they usually have a good sense of how
clients will rate them, they ask for evaluations from the nicer clients to gain more points and
avoid requesting evaluations from clients with whom they have had negative experiences. As
one cleaner mentioned:
And anyway, who’s giving the bad ratings if they’re reducing our kibos? That’s the most important thing.
For example, this one client who didn’t rate me, when she lowered my rating and told me she was going
to rate me, I said, ‘No, María José, don’t rate me because you lowered my average’. I showed her. ‘You
dropped me from a 10 to a 9.8, and for me, that’s bad’. She said, ‘But that’s not bad’, and I told her, ‘It is
for me, because I always aim for a 10’.
Another key strategy that cleaners employ to navigate platform constraints and gain free time is
through the point system, known as kibos. By accumulating kibos, workers can earn bonuses,
avoid penalties, and even secure time off without sacrificing their standing or loyalty within the
platform. As one worker explained, ‘If you don’t fail any shifts for a whole month, you get a bonus.
That’s why we use the kibos to get permission to take time off, without losing the bonus’. Cleaners
understand that maintaining a good record and accumulating kibos is essential to protect their job
security, allowing them to take strategic time off while still meeting the loyalty expectations
imposed by the app. This strategic use of the platform’s point system exemplifies how workers
Mantilla-León et al. 17
adapt to maximize their benefits while navigating the rigid performance metrics designed to ensure
their continuous availability.
In addition to optimizing free time, cleaners also develop strategies to resist the pervasive sur-
veillance they face in clients’ homes. Workers often encounter hidden cameras or intentional
‘traps’, like money left out to test their honesty. In response, many cleaners identify camera loca-
tions and place their belongings in front of them, using these actions to challenge the suspicion and
distrust imposed by clients. One cleaner shared how they confront such traps: ‘Some people leave
folded bills to see if you take them. They do it to test us’. This surveillance resistance highlights the
cleaners’ awareness of the constant scrutiny they are under and their deliberate actions to safeguard
their reputation and avoid any accusations that could jeopardize their job. By skillfully navigating
both the app’s expectations and the invasive surveillance in clients’ homes, cleaners manage to
maintain their professional standing while resisting exploitation.
The workers highlighted that customers sometimes set traps for them in the household, such as
leaving money to assess their trustworthiness. In addition, numerous households are equipped with
surveillance cameras to monitor domestic workers. In response, workers identify the camera’s
location and deliberately place their belongings in front of it to dispel any suspicion of theft.
The matching and rating systems described also draw from the actions taken by workers in their
workplaces to minimize changes between households and secure stable positions where they feel
respected, free from harassment, and can negotiate agreements with clients, such as leaving earlier
when they complete all tasks on time. Similarly, negotiations with the call center regarding prob-
lems with distance calculations, and the contingencies arising from the chaos of the city and public
transportation, are indicative of the ongoing construction of agreements that domestic workers
have historically undertaken to improve conditions in their workplaces (Mawii and Aneja, 2020),
even in the realm of digitalization.
The juxtaposition of automation and analog work within domestic work platforms in Colombia
has allowed domestic workers to gain experience using the app, its multiple features, benefits,
gaps, and flaws. Moreover, domestic workers have learned to utilize app systems to balance dimen-
sions such as motherhood, caregiving, and their work activities. Analysis of algorithmic govern-
ance must encompass feminized occupations and the specific needs of the workforce to think about
gender-sensitive app designs that do not leave it all to the maneuvering skills of the workers,
‘reinforcing gender inequalities of care, rather than reducing them’ (James, 2022: 12).
Surveillance, Governance and Engagement, the Affective Economies of
Domestic Work
Algorithmic management relies on a set of practices and manual labor that compensate for the
shortcoming of automation. As we have previously discussed, the app and the algorithms are never
enough for distributing tasks and the following of workers and customers. The app operates as a
digital infrastructure that is sustained by the invisible work of customers and workers that feed it
with information, the call-center staff who solve the issues that cannot be controlled by the app,
and finally by the emerging affections and emotions between customers and domestic workers.
This last relation is crucial for the operation of domestic platforms. There is an economy of
affections that provides the stability and trust necessary for making the management of domestic
workers profitable for the platform. The affection and trust that customers may feel by the domestic
workers assigned by the platforms provide a basis for the establishment of long-term business
relationships with the platforms; in addition, to stabilize the workplace, in these houses, workers
know how to do their work, so the operation goes uninterrupted. However, the platform configures
18 Critical Sociology 00(0)
trust relationships between customers and domestic workers without commitments of care between
the two parties. One of the primary selling points of domestic work platforms such as Hogaru is the
outsourced responsibility they provide; customers are relieved of concerns related to legal paper-
work, sick leave, and the potential repercussions of informal arrangements with their workers. It is
essential for platforms like Hogaru to develop strategies that address the realities faced by workers
once they enter customers’ homes. After securing a transaction, these platforms often disengage
from their responsibilities, leaving workers vulnerable.
Conclusions
In conclusion, our investigation into the dynamics of domestic work platforms in Colombia, par-
ticularly through the lens of Hogaru, offers a comprehensive view of the nuanced and multifaceted
impact of algorithmic governance in the gig economy. Despite the promises of formal employment
and better working conditions heralded by these platforms, the persistence of challenges—such as
undervaluation of work, algorithmic biases, and the precarious balancing of job dynamics—under-
scores a complex reality. Hogaru’s model, operating within a legal framework and directly employ-
ing domestic workers, emerges as a distinct approach within the platform economy in Colombia,
aiming to provide stability and social security benefits to its workforce. However, our findings
reveal that while this model mitigates some risks associated with gig work, it does not fully escape
the systemic issues that plague informal labor sectors.
Algorithmic governance, as observed in Hogaru, not only impacts the logistical aspects of
matching workers with clients but also deeply influences social relations, workers’ autonomy, and
their sense of self-worth. The platform’s attempt to minimize the informality of domestic work
through technological intermediation and formal employment contracts introduces a new layer of
complexity. Workers navigate a digital interface that dictates their daily routines, evaluates their
performance through opaque rating systems, and even influences their earnings and job security.
While Hogaru’s approach provides a semblance of formality and benefits, it simultaneously per-
petuates the undervaluation of domestic work through a pricing system that does not proportion-
ately reward the labor performed.
Furthermore, our study highlights the resilience and agency of domestic workers as they navi-
gate the constraints and opportunities presented by the platform. They engage in strategic behav-
iors to maximize their benefits, negotiate the challenges of algorithmic management, and seek to
maintain their dignity in a labor market that historically marginalizes their work. These strate-
gies, however, are not a panacea for the structural issues embedded in the gig economy model of
labor. They underscore the need for more equitable and inclusive platform designs that genu-
inely address the needs and rights of workers (Zhang et al., 2023), particularly those in histori-
cally undervalued and informal sectors.
It is imperative to rethink the design and regulation of gig economy platforms to ensure they
serve as genuine instruments of social and economic inclusion. This entails moving beyond mere
compliance with legal frameworks to actively engaging with the lived experiences of workers.
Platforms like Hogaru have the potential to redefine the landscape of domestic work in Colombia
and beyond, but this potential can only be realized through a commitment to transparency, fairness,
and worker empowerment. As digital platforms continue to reshape labor markets globally, our
study contributes to the critical discourse on algorithmic governance, worker rights, and the pursuit
of a more just and equitable platform economy.
Acknowledgements
The authors would like to express our gratitude to all the interviewees—workers, platform staff, and custom-
ers—who generously shared their experiences with us.
Mantilla-León et al. 19
Funding
The author(s) received no financial support for the research, authorship, and/or publication of this article.
ORCID iD
Laura Clemencia Mantilla-León https://orcid.org/0000-0002-3008-110X
Isabella Jaimes Rodríguez https://orcid.org/0000-0003-0895-9921
Notes
1. Information provided by managers of Hogaru.
2. There are various ‘internal’ job dynamics between customers and workers that Hogaru may not fully
recognize, which constitute an important area of analysis within the digital intermediation of domestic
work. Although this topic goes beyond the scope of this article, further insights can be found in: Revista
de Estudios Sociales (uniandes.edu.co).
3. Neighbor in the North of the city.
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Annexes
Annex 1. Interview guide to Hogaru’s management.
Section Text
Why We Want to Do
This Research
The aim of this study is to understand how the Hogaru platform manages
the work of its collaborators through the application.
Consent and
Compensation
For data-collection purposes, we will record this session. After the one we
transcribe, we’re going to delete the original recording. We will separate
your name or any other personal information with your interview.
Your participation in this interview is completely voluntary, and you
are under no obligation to participate. If you decide to participate but
change your mind later, you are free to withdraw at any time without
consequences. Your decision to retire will not influence your relationship
with the researcher in any way. If you decide to withdraw during the
session, you will still receive full compensation. You can also leave after the
interview. We will delete all your data.
Do you have any questions before you begin?
(Continued)
22 Critical Sociology 00(0)
Section Text
Hogaru Information
We have already worked with Hogaru from Fairwork for several years, but
we know that the business model of platforms evolves, and we would like
to know:
What is the company’s goal at the moment? Emphasize that they are a
technology company and why ground it to domestic work.
What are the types of services the company offers at the moment?
What is Hogaru Aporta?
How many workers do you have at the moment?
How many customers do you have at the moment?
Work Management
& Technology
What the interaction interface of the three parties looks like: worker,
customer, and company.
Since you created Hogaru, how did you come to develop the app you use
today? Inquire about how intermediation began, how it has evolved.
Why create an app for the management of domestic work?
What makes the Hogarú app different from other apps for domestic
service?
Is the app’s design designed for female workers? Why? What difficulties
have you identified in your workers when using the app?
Is the app designed according to any design principles, such as accessibility,
usability?
Is the app’s design designed for customers? Why? What difficulties have
you identified in your customers when using the app?
Is the app designed according to any design principles, such as accessibility,
usability?
Note: Inquire about the differences between the app for workers and
customers.
How often is the app updated? Is this notified to customers and workers?
Matching system If it helps, you can pull out your cell phone and guide me through the job
search process (if you use your cell phone for that)
How do you assign a client to a worker? Find out if the process is manual
or with an algorithm.
What do you take into account for this assignment? Find out if the location
of the customer and the worker, the connectivity, the feeling, the score of
the customer, and the worker influence.
How has the allocation system evolved/changed/modified since Hogaru was
created? Inquire about future plans.
What problems have workers or customers reported with the allocation
system?
How has this system made work management easier?
Rating System Why create a system of scoring and evaluating the worker and the
customer?
Describe the system they operate
Is there a classification of workers based on scoring and evaluation?
What problems have workers or customers reported with the scoring
system?
How has this system made work management easier?
Annex 1. (Continued)
(Continued)
Mantilla-León et al. 23
Annex 1. (Continued)
Section Text
Pricing System How do you calculate the cost of a service?—What they charge the
customer.
Do all services have the same cost? On what basis does the cost change?
What incentives do you have for female workers? Apart from payment
Are there income categories among female workers?
What is the percentage of profit per service for female workers?
How has this system made work management easier?
Annex 2. Interview guide to Hogaru’s cleaning professionals.
Section Text
Why We Want to Do
This Research
The aim of this study is to understand how Hogarú employees perform their
services through the application.
Consent and
Compensation
For data-collection purposes, we will record this session. After the one we
transcribe, we’re going to delete the original recording. We will separate
your name or any other personal information with your interview.
Your participation in this interview is completely voluntary, and you are
under no obligation to participate. If you decide to participate but change
your mind later, you are free to withdraw at any time without consequences.
Your decision to retire will not influence your relationship with the
researcher in any way. If you decide to withdraw during the session, you will
still receive full compensation. You can also leave after the interview. We will
delete all your data.
Do you have any questions about the consent form?
You can choose to sign this consent form or give verbal consent. Which do
you prefer?
This interview will take no more than 90 minutes.
We will ask you questions about your life, your work, and how you use
technologies. If there’s something you’re not comfortable responding to,
that’s okay.
Do you have any questions before you begin?
Personal Information
How old are you?
Where do you live?
(Continued)
24 Critical Sociology 00(0)
Annex 2. (Continued)
Section Text
Labor History and
Labor Practices
Overall & Platform
Experience
1. How she started her job cleaning
2. Why did you join Hogarú?
3. What do you like about the app?
4. What are your working hours like? Can you choose them or are they
assigned/organized/arranged for you?
5. How many customers do you have right now? Is this amount usual for
you? Is it usually greater, lesser, or equal? Does the number of customers
change seasonally?
Does the app determine any characteristics of the number of
customers or the type of customers?
What’s not to like?
Do you find any glitches within the platform? Which?
What would you like to see improved?
How do you communicate with your clients to organize their
working hours?
Does the platform have these communication channels?
What do you use to keep your work schedule organized?
Does the app have any kind of schedule display?
Thinking about the places you’ve worked in recently, how would
you describe them?
Matching System If it helps, you can pull out your cell phone and guide me through the job
search process (if you use your cell phone for that)
How do you get assigned clients within the app? How can you visualize it?
What services does the platform offer? How does assigning specific
services work with the app?
Does the app know what things it takes into account when
assigning a customer to it?
If it says location, drill down*
Should the GPS be kept on?
Have you had problems with your cell phone locations
and signal?
What would you like them to take into account when
making the match in the system?
Rating System How are the cleaning services evaluated by the client?
What evaluations have you received? How does your score work?
If it says stars, how many stars have you earned on your
services?
Do you think that form of evaluation is fair? What feedback
have your customers given you?
Can you see the comments?
Do you like that way of evaluating?
What would you like to add/remove?
Can you refuse services?
Is there a specific acceptance rate?
Are there any penalties for missing deadlines or customer
reviews?
Can you evaluate your customers? How?
(Continued)
Mantilla-León et al. 25
Annex 3. Interview guide with customers.
Topic Questions Purpose of the questions
Customers’ Personal
Information
Introduction:
To begin with, I would like you to tell me
a little more about yourself, where you are
from, how old you are, marital status.
What do you work on?
Do you have dependents?
Who do you live with?
What part of town you live in?
Reconstruct the profile of
platform customers
Time Use and
Domestic Work
You were telling me that you work as a
xxxx, how is your daily routine usually in
relation to your work? – How much of
your time your work consumes?
What role do you have in cleaning your
home in your day-to-day life?
In order for certain jobs to
occur, there are others that
support them, jobs that are
made possible by other jobs.
Experience with
digital platforms for
domestic service
How did you find out about digital
domestic service platforms?
Which ones do you use or have used, how
long have you been using them?
Analyze customer
experiences with platforms
Analyze the transformations
that these platforms
represent in the employer-
domestic worker
relationship
Analyze the tensions
between the care economy
and commodification.
What was your main motivation for hiring
home cleaning services through platforms?
What experiences led you to choose this
type of service?
What do you know about the working
conditions of these workers? (in terms of
social security, guarantees, etc.)
Do you think the profit received by the
workers is fair?
Who supplies the toiletries for the worker?
Annex 2. (Continued)
Section Text
Pricing System Thinking a little about the work/jobs you’ve done recently
How much does the app pay for service? Do rates change? How do
they change?
Do you think you’ve ever been paid unfairly?
What was that situation like?
How does the app disburse the money?
Do you know what the app’s profit per service percentage is?
Do you have a preference in the payment method? Cash, deposit,
etc.
ResearchGate has not been able to resolve any citations for this publication.
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