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ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue VIII, August 2025
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From Taylorism to Algorithmic Management: How Technological
Evolution Reshapes Foundational Management Principles
Mr.Vaivaw Kumar Singh, Ms. Rishika Gupta
Research Scholar, Faculty of Business Management, Sarala Birla University, Ranchi, Jharkhand, India
DOI: https://doi.org/10.51583/IJLTEMAS.2025.1408000206
Abstract: This study traces management’s evolution from Frederick W. Taylor’s early 20th-century scientific management to the
contemporary phenomenon of algorithmic management, probing how technological advancements have fundamentally transformed
managerial logic and dynamics. Taylorism emphasized optimizing labor through meticulous time-and-motion studies, task
standardization, top-down control, and managerial oversight, aimed at maximizing economic efficiency and reducing reliance on
worker discretion.
In contrast, algorithmic management entrusts managerial functions such as monitoring, performance assessment, scheduling,
goal-setting, compensation, and even dismissal to algorithms and automated systems. Enabled by real-time data collection,
predictive analytics, and digital platforms, algorithmic management enables organizations to manage dispersed labor at scale with
unprecedented precision.
While both approaches share enduring principles like efficiency, standardization, and control, the shift from human supervision to
software-mediated governance introduces distinctive dynamics. Algorithmic systems create triangular visibility regimes rather than
traditional hierarchical oversight, often obscuring decision logic and engendering information asymmetries, where employers and
increasingly algorithm designers hold more knowledge than workers do.
Moreover, the impacts of algorithmic management are complex and dual in nature. Empirical and theoretical literature suggests
such systems can simultaneously restrict and enable worker autonomy and value creation shaping workstation design, job demands,
well-being, and motivation. This duality underscores that algorithmic management’s consequences are not predetermined by
technology but are contingent on sociotechnical design and implementation choices.
The paper explores how these shifts play out in contemporary labor contexts - from gig platforms to fulfillment centers where
workers are subject to algorithmic surveillance and quantified performance regimes, compelling new forms of worker resistance,
adaptation, and negotiation of agency.
Integrating Labor Process Theory, the analysis reveals how algorithmic systems reenact power asymmetries and labor control while
posing fresh challenges and opportunities for worker autonomy and organizational justice. It argues that as management becomes
digitized, practitioners, policymakers, and scholars must critically assess algorithmic design, prioritize ethical implementation, and
craft frameworks to preserve worker dignity, transparency, and fairness.
Keywords: Taylorism, Scientific Management, Algorithmic Control, Managerial Automation, Platform Work, Labor Process
Theory.
I. Introduction
The evolution of management from the early 1900s to today reflects a continuous quest for efficiency, control, and productivity but
the tools, scope, and implications have transformed dramatically over time. In the early 20th century, Taylorism also known as
scientific management was developed by Frederick W. Taylor. He promoted a rigorous, data-driven approach to work design, using
time-and-motion studies, task decomposition, and scientifically determined "one best way" processes to elevate productivity. Taylor
emphasized systematic training, precise supervision, and a clear division of labor where managers planned while workers executed
recasting the organization as a finely tuned machine.
Although initially applied in heavy industry, Taylorism profoundly influenced organizational and management practices across
sectors. Figures like Frank and Lillian Gilbreth contributed motion studies and ergonomic insights, while Ford applied these
principles in the auto industry to revolutionize mass production via assembly-line efficiencies shrinking car assembly times and
expanding access to consumer goods. Over subsequent decades, Taylorism evolved into operations research, quality management,
and lean methodologies preserving its core tenets of efficiency while gradually integrating human and systemic considerations.
Fast forward to the 21st century: algorithmic management emerges as both an inheritor and disruptor of Taylorism. It entrusts
managerial functions such as task assignment, performance evaluation, scheduling, and feedback to algorithms and software tools,
leveraging real-time data, machine learning, and digital platforms to coordinate labor flexibly and at scale. Critics and theorists dub
it “Scientific Management 2.0,” noting clear continuities such as standardized control and impersonal decision-making; yet stark
differences emerge in visibility, organizational form, and worker experience. Rather than vertical supervision, algorithmic systems
operate via triangular visibility regimes, blending platform, user, and worker interactions under opaque algorithmic logic.
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Today’s workplaces ranging from warehouse fulfillment centers to gig-work platforms and offices are increasingly shaped by
algorithmic surveillance. Employees’ movements, productivity, and breaks become quantifiable metrics; break time or idle
moments may result in disciplinary action or termination. These technologies are often portrayed as neutral efficiency boosters, yet
their impacts on worker autonomy, privacy, well-being and organizational justice are complex and contentious. Critics argue that
algorithmic control can exacerbate dehumanization, reinforce inequalities, and obscure power dynamics in the guise of neutrality.
Within this broader historical arc from Taylor’s stopwatch to AI dashboards this paper examines how technological evolution
redefines core managerial logic. It investigates how algorithmic management inherits aspects of Taylorism while also reshaping
who controls work, how decisions are made, and how organizational systems function. The introduction sets the stage for a deeper
exploration of continuities and divergences, culminating in a discussion on what these shifts mean for organizations, workers, and
policy.
II. Methodology
This study employs a conceptual review methodology, integrating insights from a wide array of interdisciplinary literature to
explore how foundational management principles have evolved from classical Taylorism to contemporary algorithmic management
systems. The research approach is theory-driven, focusing on the synthesis and interpretation of existing knowledge rather than
primary data collection.
Drawing on foundational and contemporary scholarship in management studies, critical sociology, and science and technology
studies, the review maps historical and conceptual continuities and discontinuities in managerial logics over time. Special attention
is given to how key principles such as labor control, efficiency, surveillance, and autonomy are reconfigured through technological
systems in the digital economy.
To ground the theoretical discussion in empirical reality, the study incorporates illustrative case studies of prominent platform-
based companies specifically Amazon and Uber, which are widely recognized for their use of advanced algorithmic management
techniques. These firms serve as paradigmatic examples of how algorithmic control operates in large-scale, data-intensive work
environments. Although no primary empirical data were collected, the analysis draws from existing secondary sources, including
peer-reviewed academic articles, policy reports, and ethnographic case studies that document management practices within these
firms.
The interpretation of these cases is informed by Labor Process Theory (LPT), a critical sociological framework that emphasizes the
dynamics of managerial control, worker autonomy, and the commodification of labor under capitalism (Braverman, 1974;
Thompson & van den Broek, 2010). LPT offers a valuable lens through which to examine how algorithmic systems reshape power
relations in the workplace, reproducing some aspects of scientific management while also introducing novel forms of control that
are more opaque, individualized, and technologically mediated.
The methodological approach is inherently exploratory and reflexive, aimed at developing a conceptual bridge between historical
management theory and contemporary technological transformations in the workplace. Rather than testing hypotheses, the study
seeks to generate theoretical insights, identify emerging patterns, and suggest avenues for further empirical research on the
implications of algorithmic management for workers, managers, and institutions.
III. Literature Review
Foundations: Taylorism and Scientific Management
Taylorism, or scientific management, originated with Frederick W. Taylor’s 1911 treatise The Principles of Scientific Management,
which formalized four tenets: replacing intuition with scientific analysis, matching workers to tasks scientifically, supervising
performance rigorously, and strictly dividing labor responsibilities between planning managers and executing workers. Taylor's
method emphasized standardized processes achieved through time-and-motion studies, seeking a “one best way” to increase
productivity by dissecting tasks into discrete and optimized elements.
Henry Ford famously operationalized these ideas through mass production techniques, reducing Model T assembly from 12 hours
to just 93 minutes by enforcing specialization, conveyor belts, and process optimization. Meanwhile, Frank and Lillian Gilbreth
expanded on Taylor’s work by integrating motion studies, recording and analyzing worker movements, including film usage to
identify inefficiencies and train workers effectively.
However, Taylorism sparked fervent critique. It was accused of dehumanizing workers by treating them akin to machines, stripping
autonomy, eliminating creativity, and overlooking intrinsic motivation. Such mechanistic approaches contributed to low job
satisfaction, absenteeism, and high turnover. Workers often felt alienated, despite heightened efficiency. Cultural commentaries
like Modern Times and Brave New World satirized these rigid, efficiency-driven systems, and skilled labor backlash helped fuel
early labor union movements.
Simultaneously, early adaptations incorporated behavioral considerations to soften the steepness of Taylorism. Lillian Gilbreth’s
The Psychology of Management (1914) emphasized acknowledging workers as personalities not just economic units but integrating
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psychological insight into work design. Later, scientific management paved the way for management science models that balanced
technical control with human and systemic factors.
Rise of Digital Taylorism: Algorithmic Management
In contemporary work contexts, Digital Taylorism describes how algorithm-driven systems replicate and intensify core Taylorist
logic. Digital technologies now decompose, measure, and optimize labor at unprecedented scale, particularly within gig economy
platforms.
These systems assign tasks, monitor performance, and adjust compensation automatically often via rating systems and performance
thresholds leading to heightened standardization, deskilling, and surveillance. For instance, UPS drivers now navigate work guided
by detailed instructions and embedded sensors, mirroring digital control over human movement. Mori work across platform-based
services like Uber, food delivery, or freelancing is shaped by algorithmic assignment, customer-based ratings, and feedback loops,
constraining worker autonomy while optimizing efficiency.
Although reminiscent of Taylor’s model, algorithmic management removes human intermediaries. Instead, software algorithms
enforce standards often through opaque logics and remote monitoring, creating what some describe as “software foremen” operating
without empathy or discretion.
Labor Process Theory: A Theoretical Lens
Labor Process Theory (LPT), rooted in Marxist and critical management studies, offers a robust framework to analyze such shifts.
LPT explores how capitalism restructures labor through control mechanisms, deskilling, and the separation of planning from
execution. Braverman’s 1974 critique highlights how scientific management eroded workers' skills and autonomy in favor of
managerial control.
Ilya Konovalov extends LPT to the contemporary platform economy, highlighting the centrality of information asymmetry where
platforms and algorithm designers wield knowledge and control over workers. LPT thus helps illuminate how algorithmic
management perpetuates power imbalances, using data and digital architecture to monitor behavior, condition compliance, and
delimit worker discretion.
Effects, Criticisms & Worker Responses
Real-world evidence reinforces theoretical critiques. Workers at Amazon warehouses, for example, describe feeling dehumanized
by digital tracking systems like “Time Off Task” sensors, echoing Taylorist surveillance on digital steroids. Similarly, Woolworths
employees in Australia report increased stress, injury risk, and punitive oversight under new productivity frameworks, drawing
analogies to plantation-like control regimes and calls for resistance reminiscent of Luddite movements.
On the regulatory front, the European Union is advancing the Platform Work Directive, seeking transparency, algorithmic fairness,
and worker protection through legislative means. The directive proposes control over intrusive data use, mandates system
transparency, and defends employees against retaliation
Case Studies & Comparative Analysis
Case Studies
Amazon Fulfillment Centers: Algorithmic Management in Practice
In Amazon’s massive fulfillment centers, workers are subject to continuous monitoring via labor-tracking systems. Based on two
years of ethnographic research, warehouse employees described the experience as deeply dehumanizing where productivity quotas
and surveillance metrics transform them into extensions of the machine. In response, many engage in subtle acts of defiance labeled
"work games": small tactics to maneuver within or momentarily outsmart algorithmic controls and reclaim a semblance of agency.
Uber’s Dynamic Pricing: Impact on Worker Earnings and Autonomy
A longitudinal audit of over 1.5 million trips by 258 UK-based Uber drivers reveals stark consequences following the 2023 rollout
of dynamic pricing. Average driver earnings dropped, Uber’s commission (or "take rate") rose from about 25% to 29% and in some
instances exceeded 50%. Furthermore, income became less predictable, idle waiting time increased, and disparities among drivers
grew more pronounced. This triggered widespread concern, likening algorithmic compensation shifts to a form of digital gambling
i.e promising flexibility but delivering diminished transparency and stability
Other Platform Examples: Bias and Inequality in Algorithmic Pricing
Emerging research suggests that algorithmic price-setting may reproduce or amplify social biases like charging higher fares based
on user demographics or location. In Chicago, one study found dynamic pricing algorithms correlated with neighborhood factors
like race, age, and income indicating a risk of algorithmic discrimination.
Comparative Analysis: Taylorism vs. Algorithmic Management.
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Aspect Taylorism (Early 20th Century) Algorithmic Management (21st Century)
Control
Mechanism
Human overseers, time-and-motion
measurement
Software algorithms, sensors, real-time digital tracking
Efficiency Tools Stopwatches, assembly-line
standardization
AI-powered pricing, robotic coordination, dynamic work
allocation
Worker
Experience
Alienation, rigid routines, little
autonomy
Opacity, stress, unpredictable compensation, "invisible"
digital constraints
Forms of
Resistance
Strikes, manual sabotage,
unionization
Work games, algorithmic navigation tactics, data audits,
gig platform switching
Power Structure Visible managerial control Hidden algorithmic logic, deep-seated information
asymmetries between platforms
Synthesis
Continuity in Efficiency-First Logic
Both eras prioritize boosting productivity. Taylorism via physical optimization, algorithmic management via digital optimization
but the underlying logic persists: standardization and control remain central.
Amplified Surveillance and Opacity
Unlike earlier times, today’s systems embed control invisibly through algorithmic interfaces making oversight less tangible and
erasing opportunities for straightforward contestation.
Worker Adaptation and Pushback
From early strikes to modern-day "work games" and audits, workers continue to seek ways to reclaim autonomy, even within tightly
managed ecosystems.
Regulatory and Ethical Pressure Mounting
As the social costs of algorithmic control become more visible like declining wages, biased outcomes, mental strain and there is
increasing momentum for oversight, transparency measures, and countervailing policy frameworks.
Analysis
The emergence of algorithmic management (AM) signals a critical shift in how managerial control is exercised simultaneously
building on and reshaping foundational theories such as Taylorism, Labor Process Theory (LPT), and Self-Determination Theory
(SDT). While classical scientific management focused on optimizing labor through hierarchical control and task standardization
(Taylor, 1911), algorithmic systems have transferred these principles into the digital domain, embedding control within platforms,
metrics, and real-time feedback systems. This analysis explores the psychological consequences, power relations, forms of worker
resistance, and the inherent duality of algorithmic management, all situated within established theoretical frameworks.
Mental Strain and Loss of Autonomy
Algorithmic management significantly affects the emotional and psychological experience of work. According to SDT (Deci &
Ryan, 2000), individuals thrive when they experience autonomy, competence, and social connectedness needs that are often
compromised in algorithmic work environments. Automated workflows, inflexible schedules, and constant monitoring reduce
workers' autonomy and sense of control. Simultaneously, opaque feedback mechanisms and performance-based metrics can
undermine perceived competence, while the replacement of human supervisors with software weakens workplace relationships and
support networks.
These dynamics are particularly visible in gig and platform work, where studies have linked AM to heightened levels of stress,
burnout, and emotional exhaustion (Zhang et al., 2022). Workers face unpredictable algorithms, fragmented communication, and
limited avenues for redress, all of which contribute to deteriorating psychological well-being. In this way, algorithmic systems
continue and intensify the fragmentation and deskilling associated with early industrial labor processes (Braverman, 1974), now
mediated through digital interfaces.
Asymmetrical Control and Hidden Authority
Algorithmic systems reconfigure traditional managerial power structures, concealing control within layers of code, data, and
interface logic. Drawing on LPT (Thompson & van den Broek, 2010), these systems concentrate decision-making power at the
platform level while restricting workers' access to critical information, such as how performance is assessed or tasks are allocated.
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Unlike traditional supervisors, algorithms operate without dialogue, negotiation, or transparency effectively centralizing control
while obscuring accountability.
This form of "silent management" disempowers workers by removing their ability to contest decisions or understand the rationale
behind algorithmic outcomes. While Taylorism relied on physical supervision and rule enforcement, algorithmic control automates
these functions, embedding managerial authority into the very design of work systems (Kellogg et al., 2020). The result is an
environment where intensified oversight is disguised as neutrality or efficiency.
Resistance and Digital Agency
Despite these constraints, workers are not entirely powerless. Research highlights the various ways in which platform workers
exercise tactical resistance to regain control over their labor. Strategies such as selectively accepting tasks, using multiple platforms,
or manipulating location data are examples of how workers adapt and push back against rigid systems (Rosenblat & Stark, 2016;
Veen et al., 2020). These practices reflect a modern form of the resistance that LPT has long emphasized only now, it occurs within
algorithmically governed environments.
However, access to these forms of resistance is uneven. Workers with digital skills, economic flexibility, or insider knowledge are
better positioned to manipulate algorithmic systems to their advantage. For others, particularly those in precarious or low-income
situations, such resistance may be inaccessible or carry significant risks. Thus, digital resistance reproduces existing social and
economic inequalities, even as it demonstrates worker agency.
Ambiguity and the Double-Edged Nature of AM
The consequences of algorithmic management are not universally negative. On one hand, platforms can facilitate greater efficiency,
real-time coordination, and personalized feedback, which may improve performance and, in some cases, worker satisfaction.
Gamified systems and data-driven insights can motivate certain behaviors and streamline task distribution. On the other hand, these
same systems often result in intensified pressure, constant surveillance, and diminished opportunities for human connection.
This reflects the inherent duality of AM: it has the capacity to both optimize work and exploit labor, depending on how it is
implemented. In contexts where legal protections, collective bargaining, or participatory design processes exist such as in parts of
Europe algorithmic tools have been used more collaboratively (Hoffmann et al., 2021). These examples illustrate that technology
itself is not deterministic; rather, its impacts depend on who designs it, for what purposes, and under what regulatory and cultural
conditions (Wood et al., 2019).
Concluding Synthesis: Toward a New Logic of Control
Ultimately, algorithmic management does not break with traditional management theory; instead, it digitally reinterprets and
extends it. While it retains core Taylorist values of efficiency and control, it introduces new mechanisms of influence that are more
granular, automated, and opaque. At the same time, the presence of worker resistance, participatory design models, and growing
calls for regulation suggest that the trajectory of AM remains open and contested.
The challenge ahead lies in redefining algorithmic systems to serve both organizational goals and human well-being. This requires
ethical design, institutional oversight, and active inclusion of worker voices. In rethinking management for the algorithmic age, the
goal should not be merely to optimize labor, but to reclaim human agency and dignity in technologically mediated work.
Implications
Revitalizing Organizational Strategy & Role Design
Literature Context: Algorithmic management automates core managerial tasks like monitoring, goal setting, performance
evaluation, scheduling, compensation, and even dismissal but often reduces job autonomy and raises workload and physical
demands, hampering motivation and wellbeing.
Analysis Insight: Algorithmic systems elevate stress and burnout, especially when job design isn’t aligned with workers' strengths
and capacities. Enhancing person–job fit mitigates these negative effects.
Implication: Organizations should consciously design roles that match individual skills with algorithmic requirements, embedding
transparent interfaces into AM systems. These interfaces should offer meaningful human oversight and decision-making authority
not fully replacing managerial judgment.
Preserving Worker Autonomy, Well-Being & Purpose
Literature Context: Empirical studies show algorithmic management erodes autonomy, increases surveillance, and marginalizes
interpersonal support in fostering anxiety and undermining psychological safety.
Analysis Insight: Workers feel disconnected and stressed under gig-style, app-mediated interaction that replaces managerial
guidance with digital nudges.
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Implication: To counter this, workplaces should cultivate participatory co-design of algorithmic tools, facilitate avenues for human
support, and design feedback loops that preserve worker discretion and emotional engagement.
Ensuring Fairness, Transparency & Sustained Trust
Literature Context: Platforms commonly hide algorithmic logic, creating information asymmetries. This opacity degrades trust and
erodes the psychological contract between workers and employers
Analysis Insight: Ambiguous incentive systems and hidden performance assessment methods fuel frustration and mistrust among
employees.
Implication: Organizations must disclose key elements of algorithmic evaluation such as rating criteria while safeguarding
confidential logic. Clarity around performance expectations and accountability must be standard.
Regulatory & Ethical Imperatives
Literature Context: The EU's Platform Work Directive mandates human oversight of algorithmic decisions, employees' right to
explanations, and the ability to contest algorithmic outcomes. It also addresses key concerns like transparency, fair scheduling, and
employment classification.
Analysis Insight: However, reforms risk dilution through corporate misinterpretation or loopholes. At the same time, workers face
real harms including surveillance overload and precarious scheduling without adequate safeguards.
Implication: Policymakers must enforce the Directive robustly and close potential loopholes. Employers should engage workers in
governance, ensure algorithmic decisions are contestable, and uphold protections against algorithmic overreach.
Addressing Erosion of Craft, Creativity & Work Meaningfulness
Literature Context: Algorithmic systems often diminish the significance of tasks, disallow creative problem-solving, and narrow
the focus to data points reducing intrinsic job motivation.
Analysis Insight: The "datafication" of tasks leads to detachment from the broader purpose of work, increasing employee
disengagement.
Implication: AM systems should incorporate features that retain task significance such as custom task options, narrative feedback,
or creative problem space allowing workers to shape their work meaningfully.
Fostering Future-Ready, Human-Centric Work Systems
Literature Context: According to sociotechnical systems theory, modifying work design with transparency, fairness, and meaningful
human influence can moderate AM's negative effects.
Analysis Insight: Balancing automation with ethics prevents algorithmic systems from becoming oppressive tools of control.
Implication: Organizations should adopt a hybrid model i.e algorithmic efficiency coexisting with human-centered values. This
includes regularly evaluating the psychological impact of AM, conducting algorithmic impact audits, and forming frontline worker
panels to guide system updates.
Final Thoughts
The implications of shifting from Taylorism to algorithmic management extend far beyond operational efficiency. To preserve
human meaning and dignity, systems must be built with worker well-being, fairness, and creativity at their core. Only through
holistic design and robust regulation can digital labor systems become empowered not exploitative and spaces of work.
IV. Conclusion
The journey from Taylorism to algorithmic management reveals a persistent yet evolving tension between efficiency and human
dignity. Drawing from foundational literature, algorithmic management inherits the core Taylorist logic namely the drive for
optimization through quantification. Yet, as the literature shows, this logic has been remobilized within digital architectures that
deepen surveillance, deskill work, and amplify power asymmetries.
Our analysis further unpacks how algorithmic systems reshape the workplace: eroding autonomy, increasing stress, and
undermining well-being through opaque decision-making and relentless performance pressures. Importantly, though, the effects
are not universally detrimental. Under supportive conditions with emotional stability, social resources, and organizational support
workers may reframe algorithmic oversight as a stimulating challenge, fostering engagement and resilience (the “challenge vs
threat” dynamic) rather than exhaustion.
Building on these findings, implications for organizations, policymakers, and society emerge:
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Organizational Practice: Leaders need to shift from mere automation for efficiency to balanced systems that honor human
judgment. Practices like ensuring person–job fit, enhancing transparency around algorithmic logic, and embedding human
oversight are vital to protect worker dignity and resilience.
Worker Well-Being: Maintaining meaningful autonomy and emotional connection requires participatory system design,
supportive human interaction, and feedback mechanisms that affirm rather than surveil.
Ethics and Fairness: Organizations must confront biases, clarify algorithmic evaluation criteria, and restore worker trust.
Right-to-explanation measures and fair audit mechanisms are essential.
Regulatory Evolution: New policy frameworks, such as EU directives and “algorithmic due process” models, must be
enacted robustly with features like contestability, accountability attribution, and safeguard mechanisms lest algorithms
overreach and erode worker rights.
In the broader societal context, reports signal growing unease about AI bosses, even as AI tools proliferate. While many workers
welcome AI for decision-making support, there’s a clear preference for retaining human oversight in critical domains like hiring,
finance, or termination. Incidents such as algorithm-driven firings and punitive surveillance regimes like at Woolworths underscore
the human cost when oversight and dialogue are sidelined.
Looking ahead, the challenge lies in escaping an “algorithmic Taylorism on digital steroids” to reimagine workplaces that integrate
technology without sacrificing meaning, solidarity, or equity. This requires a hybrid model: where algorithms enhance human
capacity rather than substitute it, informed by worker voice, guided by ethical regulation, and grounded in a commitment to
autonomy and justice.
In sum, algorithmic management embodies both a continuation and transformation of Taylorist principles. Its trajectory will be
shaped not by technological inevitability but by choices of organizational, theoretical, and policy and about how work should be
organized in the digital age.
References
1. Bajwa, U., Knorr, L., & Doucet, M. (2021). The algorithmic workplace: Resistance, control, and politics in the platform
economy. Canadian Journal of Communication, 46(2), 211–232. https://doi.org/10.22230/cjc.2021v46n2a3955
2. Barley, S. R. (1990). The alignment of technology and structure through roles and networks. Administrative Science
Quarterly, 35(1), 61–103. https://doi.org/10.2307/2393551
3. Benanav, A. (2020). Automation and the future of work. Verso Books.
4. Bérastégui, P. (2021). AI and platform work: How artificial intelligence is transforming platform work. European Trade
Union Institute.
5. Braverman, H. (1974). Labor and monopoly capital: The degradation of work in the twentieth century. Monthly Review
Press.
6. Burawoy, M. (1979). Manufacturing consent: Changes in the labor process under monopoly capitalism. University of
Chicago Press.
7. Cant, C. (2021). Riding for Deliveroo: Resistance in the new economy. Polity Press.
8. Cherry, M. A. (2016). Beyond misclassification: The digital transformation of work. Comparative Labor Law & Policy
Journal, 37(3), 577–602.
9. De Stefano, V. (2016). The rise of the "just-in-time workforce": On-demand work, crowdwork, and labor protection in the
"gig economy". Comparative Labor Law & Policy Journal, 37(3), 471–504.
10. Deci, E. L., & Ryan, R. M. (2000). The "what" and "why" of goal pursuits: Human needs and the self-determination of
behavior. Psychological Inquiry, 11(4), 227–268. https://doi.org/10.1207/S15327965PLI1104_01
11. Duggan, J., Sherman, U., Carbery, R., & McDonnell, A. (2020). Algorithmic management and app‐work in the gig
economy: A research agenda for employment relations and HRM. Human Resource Management Journal, 30(1), 114–
132. https://doi.org/10.1111/1748-8583.12258
12. Eubanks, V. (2018). Automating inequality: How high-tech tools profile, police, and punish the poor. St. Martin's Press.
13. Gandini, A. (2019). Labour process theory and the gig economy. Human Relations, 72(6), 1039–1056.
https://doi.org/10.1177/0018726718790002
14. Gray, M. L., & Suri, S. (2019). Ghost work: How to stop Silicon Valley from building a new global underclass. Houghton
Mifflin Harcourt.
15. Head, S. (2014). Mindless: Why smarter machines are making dumber humans. Basic Books.
16. Heeks, R., Graham, M., & Ahmed, S. (2021). Digital labour platforms and the future of work: A development perspective.
Routledge.
17. Hoffmann, J., Reuter, M., & Bormann, R. (2021). Algorithmic management and collective bargaining in Germany.
European Trade Union Institute. https://www.etui.org/publications/algorithmic-management-and-collective-bargaining-
germany
18. Howcroft, D., & Bergvall-Kåreborn, B. (2019). A typology of crowdwork platforms. Work, Employment and Society,
33(1), 21–38. https://doi.org/10.1177/0950017018760136
INTERNATIONAL JOURNAL OF LATEST TECHNOLOGY IN ENGINEERING,
MANAGEMENT & APPLIED SCIENCE (IJLTEMAS)
ISSN 2278-2540 | DOI: 10.51583/IJLTEMAS | Volume XIV, Issue VIII, August 2025
www.ijltemas.in Page 1695
19. Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at work: The new contested terrain of control.
Academy of Management Annals, 14(1), 366–410. https://doi.org/10.5465/annals.2018.0174
20. Kalleberg, A. L., & Dunn, M. (2016). Good jobs, bad jobs in the gig economy. Perspectives on Work, 20, 10–14.
21. Kashyap, R., & Belli, L. (2021). Digital discomfort: Experiences of Indian gig workers under algorithmic management.
Centre for Internet and Society. https://cis-india.org/raw/digital-discomfort
22. Moore, P. V. (2021). The quantified worker: Technology and the future of work. Bristol University Press.
23. Rosenblat, A., & Stark, L. (2016). Algorithmic labor and information asymmetries: A case study of Uber’s drivers.
International Journal of Communication, 10, 3758–3784.
24. Schor, J. B., & Attwood-Charles, W. (2017). The sharing economy: Labor, inequality, and social connection on for-profit
platforms. Sociology Compass, 11(8), e12493. https://doi.org/10.1111/soc4.12493
25. Srnicek, N. (2017). Platform capitalism. Polity Press.
26. Taylor, F. W. (1911). The principles of scientific management. Harper & Brothers.
27. Thompson, P., & van den Broek, D. (2010). Managerial control and workplace regimes: An introduction. Work,
Employment and Society, 24(3), 1–12. https://doi.org/10.1177/0950017010376243
28. Vallas, S., & Schor, J. B. (2020). What do platforms do? Understanding the gig economy. Annual Review of Sociology,
46, 273–294. https://doi.org/10.1146/annurev-soc-121919-054857
29. van Doorn, N. (2017). Platform labor: On the gendered and racialized exploitation of low-income service work in the ‘on-
demand’ economy. Information, Communication & Society, 20(6), 898–914.
https://doi.org/10.1080/1369118X.2017.1294194
30. Veen, A., Barratt, T., & Goods, C. (2020). Platform-capital’s “app-etite” for control: A labour process analysis of food-
delivery work in Australia. Work, Employment and Society, 34(3), 388–406. https://doi.org/10.1177/0950017019836911
31. Wood, A. J., Graham, M., Lehdonvirta, V., & Hjorth, I. (2019). Good gig, bad gig: Autonomy and algorithmic control in
the global gig economy. Work, Employment and Society, 33(1), 56–75. https://doi.org/10.1177/0950017018785616
32. Zhang, C., Lu, L., & Li, N. (2022). Algorithmic management, job burnout, and turnover intentions in the gig economy: A
person–job fit perspective. Journal of Applied Psychology, 107(5), 755–770. https://doi.org/10.1037/apl0000911