Assignment 1:
Chapter 1: Introduction
The platform economy is one of the most significant manifestations of the changes induced by
digitalization. An important component of the platform economy is digital labour platforms,
which include location-based platforms, where services are provided by individuals in a
specific location, and online platforms, where workers provide their services remotely. The
growth of these platforms over recent decades has opened new markets for businesses and
created new employment and income opportunities, often characterized by flexibility and low
entry barriers (International Labour Organisation). This development has contributed to the
emergence of what is commonly referred to as the gig economy, in which innovative services
have been facilitated through digital platform companies such as Uber, Deliveroo, and
Amazon’s Mechanical Turk, transforming and disrupting traditional employment systems of
full-time jobs inside firms, to flexible and independent short-term contract-based jobs, paid
only for the units of labour performed (Woodcock and Graham, 2020; Kessler, 2018).
A core driver of this new work model is algorithmic management, in which computational
algorithms assume and coordinate a wide range of traditional managerial and operational
functions, including hiring, task assignment, performance monitoring, and even discipline,
thereby enabling platforms to achieve unprecedented efficiency and flexibility. However,
scholars across disciplines, such as Möhlmann M.Zalmanson (2025) and Lif.Z. and X.LiuY.
(2025), have begun to scrutinize the double-edged nature of algorithmic management in the
gig economy, outlining that it often reproduces and intensifies managerial control, raising
urgent questions about worker autonomy, fairness, and well-being in platform-mediated work
(Chigbu, 2026). Protecting the dignity and the wherewithal of workers, and those who manage
their work, remains a critical concern as algorithmic management becomes a more
commonplace phenomenon (article#2).
With the development of new-generation digital technologies, the use of algorithms for
management has become increasingly prevalent in the gig economy (Kellogg et al., 2020),
covering a wide range of industries from car-hailing services (such as Uber and Lyft), food
delivery services (such as Meituan and Amazon Flex), to logistics, retail, healthcare, and more
(Stark and Pais, 2021; Möhlmann et al., 2021; Liu et al., 2025; Parent-Rocheleau and
Parker, 2022). Among them, food delivery platforms, as typical users of algorithmic
management, take algorithmic technology as the core to assign food-delivery orders to workers,
track their locations, appraise performance, and even implement systems of rewards and
punishments (Li et al., 2024; Rosenblat and Stark, 2016). As a supplement and substitute for
traditional labor management methods (Parent-Rocheleau and Parker, 2022), algorithmic
management requires minimal human intervention while enabling online food delivery
platforms to employ and utilize a large number of delivery workers at a relatively low cost
(Schmidt, 2017). However, compared with employees in traditional companies, these delivery
workers are more likely to exhibit lower work engagement and higher turnover rates due to the
flexibility and instability of the gig economy (Lin et al., 2020; Li et al., 2024).
Following the increasing reliance on algorithmic management within food delivery
platforms, Wolt represents a particularly relevant empirical context for examining the
sociotechnical dynamics of algorithmic management within platform-based labor. Founded in
Finland in 2014, Wolt operates as a digital food delivery platform that connects customers,
restaurants, and delivery couriers through an app-based system. As a result, the relationship
between the platform and couriers is largely mediated through digital systems rather than
traditional managerial structures.
At the same time, Wolt represents an analytically significant case due to the potential
divergence between organizational narratives surrounding algorithmic management and
workers’ lived experiences of these systems in practice. Platform-based organizations
frequently frame algorithmic management in terms of flexibility, efficiency, autonomy, and
technological optimization, while existing research suggests that workers may experience and
interpret these systems differently in practice. The relevance of Wolt as an empirical case also
emerges from personal observations developed through previous work experience in the
restaurant industry, where interactions with Wolt couriers during order pick-ups frequently
revealed tensions, frustrations, and differing perceptions regarding the platform and its
management practices. These observations raised an interest in how algorithmically managed
work is experienced from the perspective of delivery workers, particularly in relation to how
platform-based labor is presented organizationally. Consequently, rather than attempting to
determine whether algorithmic management is inherently beneficial or detrimental, this project
adopts a social constructivist perspective that seeks to explore how differing organizational
actors construct and interpret the realities surrounding the same managerial system. In this
regard, Wolt provides a valuable setting for examining how meanings, perceptions, and
experiences of algorithmic management are socially produced, negotiated, and enacted within
everyday platform work. Consequently, the following research question guides the project:
How do Wolt’s couriers experience and interpret working conditions under algorithmic
management in relation to the company’s organizational narratives?
Assignment 2:
Chapter 2: Literature Review
This chapter presents existing literature on both the gig economy market and algorithmic
management. Given the fields’ broad scopes, only a selected portion of the most relevant
literature will be included. The chapter will be divided into sub-chapters covering the concepts,
including synthesising their connection and presenting the framework of the project.
Chapter 2.1
Theme 1: Gig Economy and Independent Contractors
The ‘gig economy’ has emerged as a key theme in a recent independent review of modern
employment practices, commonly referring to platform-based work in which individuals use
digital applications to provide and sell their labor services. The rapid expansion of online
outsourcing and platform-mediated work has created new economic opportunities and
increased labor market accessibility across various industries and geographical contexts.
Further, within the gig economy, platform workers are commonly classified as independent
contractors rather than traditional employees. In legal and organizational terms, an independent
contractor refers to a worker who performs services independently and is not subject to the
same degree of direct managerial control typically associated with standard employment
relationships. Instead of companies controlling how tasks are performed, independent
contractors are generally expected to retain greater autonomy over the organization of their
work activities, including decisions regarding working hours, workload, and task acceptance.
At the same time, existing literature demonstrates that these forms of flexibility are frequently
accompanied by new challenges and uncertainties associated with platform labor. The
organizational structure of the gig economy often transfers responsibilities, operational costs,
and employment-related risks from companies onto workers themselves. Consequently, the
legal classification of platform workers as either employees or independent contractors has
become a central issue within debates surrounding gig work. This distinction is particularly
significant because employment status determines workers’ access to fundamental labor
protections, including minimum wage guarantees, collective bargaining rights, social security
provisions, and protection against unfair dismissal (Mate, 2026).
A further issue with the extent of job quality outcomes, collectively discussed by the scholars,
such as Rubery and Grimshaw (2001: 165), Mullan and Wajcman (2017), and Kalleberg
(2011: 31), conceptualizes job quality as the outcome of an interconnected relationship between
technology, power structures, organisational forms, and broader socio-environmental
conditions. The following model is presented for better visual representation of the key
dimensions and relationships within the Job Quality model (Figure 1):
Figure 1: A Model of Job Quality Determinants
Existing literature suggests that information and communication technologies (ICTs) do not
independently determine working conditions; rather, their impact is shaped by the balance of
bargaining power between employers and workers (Rubery and Grimshaw, 2001). In platformmediated employment relations, where collective protections are often weakened, workers
increasingly rely on individual bargaining resources such as labour market demand, specialized
skills, and access to alternative income opportunities (Kalleberg, 2011; Silver, 2003; Wright,
2000). These power relations influence how technological systems are implemented within
organisational forms and consequently shape workers’ autonomy, control, and overall job
quality. Furthermore, the relationship between organisational forms and job quality is mediated
by broader socio-environmental conversion factors, including healthcare systems, educational
access, and gender norms, which influence workers’ ability to transform employment
opportunities into sustainable well-being and economic security (Monteith and Giesbert, 2017).
The model, therefore, presents job quality in the gig economy not as a direct consequence of
technology alone, but as a socially and structurally mediated outcome.
Moving further, the Job Quality model can be complemented by Labour Process Theory (LPT),
which provides a deeper explanation of the capitalist control mechanisms shaping workers’
conditions. LPT argues that workplace transformations should be understood as historical
processes through which capitalism continuously restructures labor while maintaining the same
underlying logic of maximizing profit and preserving managerial control. Building on the
principles of scientific management introduced by Frederick Winslow Taylor, which promoted
the standardization and fragmentation of work tasks to improve productivity, Harry Braverman
critically examined the broader consequences of this system under capitalism. Braverman
argued that capitalist production systems progressively deskill workers by separating them
from holistic knowledge of the labor process. According to him, the fragmentation of labor
enables employers to strengthen managerial control, reduce labor costs, and increase efficiency
in ways that primarily benefit capitalist interests. Rather than viewing changes in work
organization as neutral developments, from a Labour Process Theory perspective, many
contemporary forms of labour management in the gig economy can be understood as
mechanisms that serve capitalist interests.
Braverman’s critique of capitalist labor processes has been highly influential within Labour
Process Theory and continues to shape contemporary studies of work and platform labor. Many
scholars have expanded his arguments by examining how modern forms of managerial control,
including algorithmic management and digital surveillance, can contribute to the
standardization and control of labor processes in the gig economy.
Theme 2: Algorithmic Management
Across jurisdictions, traditional tests for determining employment status have generally
focused on indicators such as subordination, managerial control, and economic dependence.
These criteria were developed in employment contexts characterized by direct human
supervision and clearly identifiable employers. However, the emergence of algorithmic
management complicates the application of such criteria, as many control functions are
increasingly embedded within digital systems that operate indirectly and often invisibly.
Examining algorithmic management within organizational contexts requires moving beyond
the assumption that algorithms operate as fully autonomous technological entities (von Krogh,
2018). Instead, algorithmic management should be understood as a sociotechnical process that
emerges through the continuous interaction between organizational actors and the algorithms
that mediate their work (Jarrahi and Sutherland, 2019). From this perspective, technological
systems and social actors are viewed as mutually constitutive, with workplace relationships
being socially constructed and continuously enacted through these interactions (Sawyer and
Jarrahi, 2014).
Figure 2: Algorithmic Management as a Sociotechnical
Phenomenon representing both Social and Technological Forces.
This understanding of algorithmic management is illustrated in Figure 2, which presents
algorithmic management as a sociotechnical phenomenon shaped through the continuous
interaction between algorithms, workers, managers, and organizational structures. Rather than
treating algorithms as autonomous technological systems, scholars argue that their
implementation, operation, and organizational effects are deeply embedded within broader
social and organizational contexts. Research within this perspective emphasizes the reciprocal
relationship between technological systems and organizational actors, demonstrating how
managerial priorities, organizational competencies, and workplace structures influence the
functioning of algorithmic systems, while these systems simultaneously reshape organizational
roles, coordination processes, and workplace power relations.
Recognizing algorithmic systems as socially constructed and embedded within existing
organizational power relations also implies that such systems neither emerge nor operate
independently from the cultural and structural biases present within the organizations in which
they are implemented (Kellogg et al., 2020). Consequently, scholars argue that when
organizations display historical biases in areas such as hiring, promotion, dismissal, or
compensation, these patterns may become embedded within the data used to train algorithmic
systems, allowing discriminatory practices to be replicated and legitimized through seemingly
objective technological processes (Keding, 2021).
Building further on the principles of scientific management discussed previously, scholars
argue that algorithmic management risks reducing workers to highly measurable, standardized,
and controllable units of labor. As noted by Brett Frischmann and Evan Selinger (2018),
workers may increasingly be treated as “programmable cogs in machines,” where algorithmic
systems prioritize efficiency, optimization, and productivity over human agency and autonomy.
Within digitally mediated work environments, such as platform labor, this dynamic may
contribute to the commodification and alienation of workers by transforming labor into
quantifiable data points that can be continuously evaluated and controlled. Existing literature
further suggests that these processes can reinforce asymmetrical power relations between
platforms and workers, as algorithmic systems centralize managerial control while limiting
workers’ ability to negotiate, challenge, or meaningfully participate in organizational decisionmaking.
Chapter 2.3: Synthesis of Theme 1 and Theme 2
…
Discussion of how the literature informs and qualifies my research question:
The reviewed literature provides the theoretical foundation for answering the research question
by identifying the key dynamics of platform-based work and algorithmic management.
Research on the gig economy and independent contracting helps contextualize the conditions
under which Wolt couriers operate and explains the overall logic and structure of the industry.
Moving to the top of the pyramid and narrowing the scope, the literature on algorithmic
management helps to examine the underlying logic and mechanisms of this form of
management, which largely shapes couriers’ everyday working experiences in Wolt.
Understanding how algorithmic systems operate and the managerial intentions behind them
provides an important foundation for fairly examining how couriers perceive and interpret their
work experiences, as the literature helps explain both the origins of these systems and the
potential implications they may have for workers.
Chapter 3: Methodology
The research strategy of this study is based on a qualitative case study approach focusing on
Wolt couriers and their experiences with algorithmic management. The literature review on the
gig economy and algorithmic management provided an important theoretical foundation for
understanding the broader dynamics, opportunities, and challenges associated with platformbased work. The literature review follows a semi-systematic approach, aiming to identify,
synthesize, and connect key theoretical perspectives and discussions relevant to gig-economy
work and algorithmic management. Rather than applying the highly rigid procedures associated
with a fully systematic literature review, the review is organized thematically to progressively
narrow down the broader research field and develop a focused conceptual understanding of the
topics most relevant to the empirical investigation.
Further, this study applies an abductive approach, in which theoretical insights from the
literature review are continuously connected to empirical findings to develop a deeper
understanding of the mechanisms shaping couriers’ experiences and interpretations of
algorithmic management. Building on this theoretical understanding, the empirical part of the
study aims to explore how couriers themselves perceive and interpret these working conditions
in practice.
To examine these experiences, the study will primarily rely on semi-structured interviews with
approximately five to seven Wolt couriers. A qualitative interview approach is considered
appropriate because it allows for deeper insights into workers’ subjective experiences,
interpretations, and reflections regarding algorithmic management. In addition to interviews,
the study will use Wolt’s official public statements, website materials, and organizational
communication as a secondary data source in order to understand how the company presents
and legitimizes its management practices and working conditions. Since access to top
management representatives may be limited due to the controversial nature of platform labor
debates, these official narratives will serve as an alternative source for understanding the
company’s perspective. Furthermore, discussions from social media communities, particularly
Facebook groups used by Wolt couriers, will be included as supplementary empirical material
to capture recurring concerns, experiences, and informal discussions among workers.
To ensure validity and reliability, the study will apply data triangulation by combining multiple
sources of empirical material, including interviews, company narratives, and online worker
discussions. This approach allows for the comparison of different perspectives and helps
strengthen the credibility of the findings. In addition, semi-structured interviews ensure that
similar themes are discussed across participants, supporting consistency within the data
collection process.