Abstract
The rapid expansion of platform-based gig work has fundamentally transformed contemporary labour markets, presenting workers with distinctive challenges relating to pay transparency, algorithmic scheduling, and income volatility. This dissertation synthesises existing scholarly literature to examine how gig workers navigate these interconnected challenges through adaptive strategies and collective action. The literature synthesis method draws upon peer-reviewed research spanning human resource management, information science, and labour economics. Key findings reveal that workers develop sophisticated “algorithmic literacy” to decode opaque pay structures, engage in crowd-sourced data sharing through third-party tools and online communities, employ multi-platform working arrangements to mitigate scheduling constraints, and adopt stringent financial management practices to buffer income instability. The analysis demonstrates that whilst these grassroots strategies provide meaningful coping mechanisms, they ultimately fail to eliminate the structural precarity inherent to gig work. The dissertation concludes that sustainable improvements require a combination of worker-led initiatives, platform accountability measures, and regulatory intervention. These findings contribute to ongoing debates concerning decent work standards in digitally mediated employment and highlight priorities for future research examining the long-term welfare implications of algorithmic management.
Introduction
The emergence of platform-mediated gig work represents one of the most significant transformations in contemporary labour markets. Characterised by short-term, task-based engagements facilitated through digital applications, the gig economy encompasses diverse occupational categories including ride-hailing, food delivery, freelance knowledge work, and domestic services. Whilst platforms such as Uber, Deliveroo, and Upwork position themselves as neutral technological intermediaries connecting workers with consumers, scholarly research increasingly reveals the profound implications of algorithmic management systems for worker autonomy, earnings stability, and overall wellbeing.
The academic significance of this topic lies at the intersection of multiple disciplinary concerns. From a human resource management perspective, algorithmic systems represent a fundamental departure from traditional employment relationships, raising questions about control, consent, and worker voice (Duggan et al., 2019). Information science scholars examine how workers interact with and attempt to understand automated decision-making systems that govern their working lives (Jarrahi and Sutherland, 2019). Labour economists investigate the implications of platform work for income security and long-term career trajectories (Farrell and Greig, 2016). Meanwhile, sociologists explore how platform-mediated work reshapes class relations and collective organisation possibilities.
The social and practical relevance of understanding gig worker navigation strategies cannot be overstated. The International Labour Organization estimates that digital labour platforms have grown five-fold over the past decade, with millions of workers globally now dependent upon platform-mediated income (International Labour Organization, 2021). In the United Kingdom, research commissioned by the Department for Business, Energy and Industrial Strategy indicates that approximately 4.4 million people engage in some form of gig work, representing a substantial and growing segment of the workforce (Department for Business, Energy and Industrial Strategy, 2018). These workers frequently operate outside traditional employment protections, facing distinctive vulnerabilities relating to unpredictable earnings, lack of benefits, and limited recourse against platform decisions.
Three interconnected challenges define the gig work experience. First, pay transparency remains profoundly problematic as platforms frequently employ opaque or frequently modified compensation algorithms that workers struggle to understand or predict (Calacci and Pentland, 2022). Second, algorithmic scheduling systems exert considerable control over work allocation, timing, and intensity, constraining the flexibility that platforms ostensibly offer (Griesbach et al., 2019). Third, income volatility creates significant financial stress, with earnings fluctuating dramatically based upon platform decisions, consumer demand, and competitive pressures (Kumar, 2024).
This dissertation addresses a critical gap in understanding: whilst substantial research documents the challenges facing gig workers, less systematic attention has been paid to how workers actively navigate, resist, and adapt to these conditions. Understanding worker agency within algorithmically mediated work arrangements provides essential insights for policymakers, platform designers, and labour advocates seeking to improve working conditions.
Aim and objectives
The overarching aim of this dissertation is to examine critically how gig workers navigate the interconnected challenges of pay transparency deficits, algorithmic scheduling constraints, and income volatility through individual and collective strategies.
To achieve this aim, the following specific objectives guide the analysis:
1. To synthesise existing scholarly literature examining the nature and extent of pay opacity, algorithmic control, and earnings instability within platform-mediated gig work.
2. To identify and categorise the strategies that gig workers employ to navigate pay transparency challenges, including information-sharing practices, third-party tools, and algorithmic literacy development.
3. To analyse how workers respond to algorithmic scheduling and control mechanisms through multi-platform working, strategic engagement, and collective knowledge sharing.
4. To evaluate the financial coping strategies workers adopt to manage income volatility and assess their effectiveness in providing economic security.
5. To critically assess the extent to which worker-developed navigation strategies mitigate or fail to address the structural precarity inherent to gig work arrangements.
6. To identify implications for platform governance, regulatory intervention, and future research directions.
Methodology
This dissertation adopts a literature synthesis methodology, systematically reviewing and integrating findings from existing peer-reviewed research to address the stated aim and objectives. Literature synthesis represents an established approach within social sciences for consolidating knowledge across multiple studies, identifying patterns and contradictions, and generating integrative insights that transcend individual research contributions (Snyder, 2019).
The literature search strategy employed multiple academic databases including Web of Science, Scopus, and Google Scholar. Search terms combined concepts relating to gig work, platform labour, algorithmic management, pay transparency, income volatility, and worker strategies. Boolean operators facilitated comprehensive coverage whilst maintaining relevance. Initial searches were supplemented through backward citation tracking from key articles and forward citation searches to identify recent contributions.
Inclusion criteria prioritised peer-reviewed journal articles, with particular emphasis on empirical studies employing qualitative, quantitative, or mixed methods approaches. Conference proceedings from established venues within information science and human-computer interaction were included where peer review processes were documented. Grey literature from recognised international organisations provided contextual data regarding the scale and scope of gig work globally.
The analytical approach followed thematic synthesis principles. Following initial reading of identified sources, findings were coded according to their relevance to the three primary challenge domains (pay transparency, algorithmic scheduling, income volatility) and associated worker navigation strategies. Iterative comparison across sources identified convergent findings, methodological strengths and limitations, and areas requiring further investigation.
Quality assessment considered factors including methodological rigour, sample characteristics, theoretical grounding, and relevance to the research questions. Studies employing systematic data collection procedures, transparent analytical methods, and appropriate attention to ethical considerations received greater weight in the synthesis.
Limitations of the literature synthesis approach warrant acknowledgement. The analysis necessarily depends upon the scope and focus of existing research, which may reflect particular geographic contexts (predominantly North American and European) and platform types (disproportionate attention to ride-hailing and delivery). Additionally, the synthesis cannot generate novel empirical data, instead consolidating and interpreting existing findings.
Literature review
The architecture of pay opacity in platform work
Platform compensation systems exhibit distinctive characteristics that distinguish them from traditional wage arrangements. Whilst conventional employment typically involves explicit hourly rates or salaries established through employment contracts and potentially collective bargaining, platform pay frequently operates through complex algorithms that workers cannot fully observe or comprehend. Calacci and Pentland (2022) describe these as “black-box” systems that calculate compensation based upon undisclosed combinations of factors potentially including distance, time, demand conditions, worker ratings, and platform-determined base rates.
The opacity of these systems serves multiple platform interests. Maintaining informational asymmetry enables platforms to adjust compensation without explicit worker consent, implement location-based or time-based pricing differentials, and resist regulatory characterisation as employers setting wages. Griesbach et al. (2019) document how food delivery platforms progressively modified pay calculations, often reducing effective per-delivery compensation whilst introducing new bonus structures that obscured these changes. Workers reported discovering pay cuts only through careful tracking of their own earnings data over time.
Research consistently demonstrates that pay opacity undermines worker trust and perceived fairness. Semujanga and Parent-Rocheleau (2024) examined the relationship between algorithmic compensation transparency and worker stress, finding that clearer compensation rules increased perceived procedural justice—the sense that pay determination processes were fair—even when substantive outcomes remained unchanged. Notably, however, transparency alone did not reduce worker stress, suggesting that knowledge of unfavourable terms may create its own psychological burdens.
The dynamics of pay opacity extend beyond calculation formulas to encompass rating systems that indirectly affect earnings. Platforms typically restrict access to high-value tasks or impose penalties upon workers with ratings below certain thresholds. Kadolkar, Kepes and Subramony (2024) synthesise research demonstrating how rating-based access creates additional uncertainty, as workers cannot always determine which customer interactions, task rejections, or platform rule violations might affect their standing.
Algorithmic control and the limits of flexibility
Platform marketing consistently emphasises flexibility as a primary benefit of gig work—the ability to work when, where, and as much as one desires. Academic research, however, reveals significant constraints upon this ostensible autonomy. Duggan et al. (2019) introduced the concept of “algorithmic HRM control” to describe how automated systems perform functions traditionally associated with human managers, including task allocation, performance monitoring, and discipline.
Algorithmic scheduling systems shape worker behaviour through multiple mechanisms. Push notifications encourage workers to log on during high-demand periods, whilst acceptance rate requirements effectively penalise selectivity. Griesbach et al. (2019) found that delivery platforms pressured workers to remain available during specified hours by restricting future scheduling access for those who failed to maintain minimum activity levels. Workers who declined tasks or logged off during shifts faced reduced access to subsequent scheduling opportunities.
The consequences of algorithmic control extend to the granular level of individual tasks. Platforms determine routing, timing expectations, and acceptable completion parameters. Duggan et al. (2023) examined worker perspectives on algorithmic HRM control, documenting frustration with systems that failed to account for real-world conditions such as traffic, weather, or customer availability. Workers described feeling caught between platform expectations and practical constraints, with limited ability to explain or appeal automated assessments.
Kadolkar, Kepes and Subramony (2024) provide systematic review evidence that algorithmic management in gig contexts exhibits distinctive characteristics compared to automated systems in conventional employment. The absence of human supervisory relationships, combined with platform-controlled information environments, creates conditions where workers have minimal understanding of how decisions affecting their work are made and limited recourse when they disagree.
Income volatility and financial precarity
Earnings instability represents perhaps the most consistently documented challenge in gig work research. Farrell and Greig (2016) analysed transaction data from millions of platform workers, finding that month-to-month income swings of 30-50% were commonplace. This volatility derived from multiple sources including fluctuating consumer demand, platform-initiated changes to compensation structures, seasonal variations, and competition from other workers.
The financial implications of income volatility extend beyond immediate cash flow challenges. Jackson (2022) examined long-run labour supply effects, finding that heavy reliance on gig income depressed long-term earnings trajectories. Workers who used platform work as a secondary buffer during temporary unemployment fared better than those who remained primarily dependent upon gig income. This pattern suggests that gig work may function more effectively as a supplementary income source than as a primary livelihood strategy.
Singh and Awasthi (2025) situate gig work income volatility within broader concerns about unorganised workers in digital platforms, highlighting how instability intersects with lack of benefits, inadequate social protections, and limited access to skill development opportunities. Kumar (2024) similarly characterises the gig economy as generating “precarious prosperity”—surface-level employment growth that masks underlying wage inequality and security deficits.
The psychological consequences of income volatility warrant attention alongside material impacts. Griesbach et al. (2019) document worker reports of stress, anxiety, and difficulty planning for the future. The unpredictability of earnings complicates budgeting, saving, and major financial decisions such as housing or family planning. Workers described constant vigilance regarding platform changes, consumer ratings, and competitive dynamics that might affect their income.
Worker navigation strategies: pay transparency
Despite operating within constrained and opaque systems, gig workers demonstrate considerable agency in developing navigation strategies. Research documents diverse approaches to addressing pay transparency deficits, ranging from individual data tracking to collective information-sharing infrastructure.
Individual-level strategies centre upon systematic earnings monitoring. Workers track their own compensation across tasks, time periods, and conditions to identify patterns that platform-provided information obscures. This practice enables detection of pay rate changes that platforms may not explicitly announce. Calacci and Pentland (2022) describe how workers compile personal datasets that reveal the true compensation implications of different task types, locations, and timing choices.
Collective information-sharing extends individual tracking into shared knowledge resources. Online forums, social media groups, and dedicated communities enable workers to compare experiences and aggregate information that no individual could compile alone. Griesbach et al. (2019) document how food delivery workers used Facebook groups and Reddit communities to share observations about pay rate changes, bonus structures, and platform policy modifications.
Third-party tools represent a more formalised approach to pay transparency navigation. Calacci and Pentland (2022) analyse the “Shipt Calculator,” a worker-developed tool that reverse-engineers platform pay formulas by collecting and analysing crowd-sourced delivery data. Such tools enable workers to audit platform claims, identify hidden pay cuts, and make more informed task acceptance decisions. The development of these tools demonstrates sophisticated technical and analytical capabilities within worker communities.
Dedema and Rosenbaum (2024) situate these pay transparency strategies within broader patterns of worker response to socio-technical issues in platform-mediated work. Their systematic review identifies consistent themes of workers developing “algorithmic competencies” through experimentation, observation, and knowledge sharing. Workers learn to interpret platform signals, game algorithmic systems where possible, and adapt their behaviour based upon accumulated understanding.
Worker navigation strategies: algorithmic scheduling
Workers navigate algorithmic scheduling constraints through strategies that exploit platform design features, leverage multiple platform relationships, and share collective knowledge about optimal practices.
Multi-homing—simultaneously working across multiple platforms—represents a primary scheduling navigation strategy. Duggan et al. (2019) identify multi-platform working as a rational response to algorithmic control, enabling workers to shift activity toward whichever platform offers superior conditions at any given moment. Kadolkar, Kepes and Subramony (2024) confirm that multi-homing provides workers with reduced dependence upon any single platform’s scheduling decisions and compensation changes.
Strategic logging on and off enables workers to target high-demand periods and locations. Workers learn through experience and community knowledge which times and areas generate surge pricing or increased task availability. Jarrahi and Sutherland (2019) describe how workers develop sophisticated mental models of demand patterns, adjusting their availability accordingly. This strategy requires substantial unpaid time monitoring platform conditions and positioning for optimal opportunities.
Knowledge sharing about algorithmic behaviour helps workers anticipate and respond to scheduling system logic. Dedema and Rosenbaum (2024) document how workers share “tips and tricks” regarding factors that appear to influence task allocation, such as maintaining certain acceptance rates, positioning in particular locations, or responding quickly to initial task offers. Whilst workers cannot verify the accuracy of these theories given platform opacity, collective experimentation generates working hypotheses that inform individual decisions.
Rating management represents a defensive scheduling strategy, as maintaining high ratings ensures continued access to work. Kadolkar, Kepes and Subramony (2024) and Duggan et al. (2023) describe how workers engage in “over-servicing” customers—providing service levels beyond platform requirements—to secure positive ratings. This strategy imposes additional costs upon workers but represents a rational response to rating-based access systems.
Worker navigation strategies: income volatility
Financial coping strategies address income volatility through budgeting practices, savings accumulation, and digital financial tools. These individual-level strategies aim to smooth consumption despite earnings fluctuations.
Strict budgeting represents the most commonly documented financial coping mechanism. Casalhay, Guevarra and Bragas (2025) examine financial challenges faced by freelancers in the gig economy, finding widespread adoption of conservative spending practices, expense tracking, and variable budgeting approaches that adjust consumption based upon recent earnings. Workers described treating good earning periods as exceptional rather than baseline, maintaining frugal habits even during temporary abundance.
Emergency savings accumulation provides a buffer against earnings shortfalls. Griesbach et al. (2019) document worker efforts to build savings despite volatile income, though many reported that accumulation proved difficult when earnings barely covered essential expenses. The aspiration toward financial cushions frequently exceeded actual savings capacity.
Digital financial tools offer technological assistance with budgeting, expense tracking, and income smoothing. Casalhay, Guevarra and Bragas (2025) note that whilst freelancers adopt various financial applications, these tools cannot compensate for fundamental income inadequacy. Technology may facilitate better management of existing resources but cannot generate additional income when platform conditions deteriorate.
Income diversification extends beyond platform multi-homing to encompass non-platform income sources. Farrell and Greig (2016) find that gig work often supplements rather than replaces other income, with workers using platform earnings to buffer fluctuations in primary employment or other self-employment activities. This pattern suggests that gig work may function most effectively as part of diversified income portfolios rather than as exclusive livelihood strategies.
Collective action and structural interventions
Beyond individual coping strategies, research documents collective efforts to address gig work challenges through campaigns for minimum rates, platform accountability, and regulatory intervention.
Worker campaigns for minimum compensation standards represent attempts to establish floors beneath market-driven rates. Heeks et al. (2021) developed the Fairwork framework for evaluating gig work against decent work standards, with fair pay constituting a primary criterion. Platform evaluation against such standards provides leverage for worker advocacy and consumer pressure.
Dedema and Rosenbaum (2024) note that whilst collective campaigning occurs, downward pressure on rates simultaneously drives some workers toward underbidding rivals, creating competitive dynamics that undermine collective interests. The tension between individual survival strategies and collective advancement reflects broader challenges in organising workers within platform-mediated arrangements.
Regulatory interventions in various jurisdictions have attempted to address gig work challenges through employment classification determinations, minimum wage requirements, and transparency mandates. Whilst detailed regulatory analysis exceeds this dissertation’s scope, the existence of policy interventions reflects recognition that individual worker navigation strategies cannot fully address structural issues.
Discussion
The literature synthesis reveals consistent patterns in how gig workers navigate pay transparency deficits, algorithmic scheduling constraints, and income volatility. These findings warrant critical analysis regarding their implications for the stated objectives and broader debates concerning platform labour governance.
Algorithmic literacy as a form of worker agency
Workers demonstrate remarkable capacity for developing what Jarrahi and Sutherland (2019) term “algorithmic competencies”—practical understandings of how platform systems operate that inform strategic behaviour. This finding addresses the second and third objectives concerning navigation strategies for pay transparency and algorithmic scheduling. Workers are not passive recipients of algorithmic control but active interpreters who experiment, observe, and share knowledge to decode platform logic.
However, this agency operates within significant constraints. Algorithmic literacy requires substantial investment of unpaid time and cognitive effort. Workers must continuously update their understanding as platforms modify systems, often without notification. The benefits of algorithmic literacy accrue individually, potentially disadvantaging workers with less time, technical capacity, or community access for learning. Furthermore, even sophisticated understanding cannot overcome fundamental power asymmetries when platforms retain authority to change rules unilaterally.
Collective information infrastructure as platform counter-power
The development of crowd-sourced pay calculators, online communities, and shared knowledge repositories represents a significant form of collective action adapted to platform contexts. Tools such as the Shipt Calculator documented by Calacci and Pentland (2022) demonstrate technical sophistication and organisational capacity within worker communities.
These collective information infrastructures serve multiple functions. They enable pay auditing that platforms might prefer to avoid, creating accountability pressures. They provide mutual support and validation for workers experiencing platform-related difficulties. They generate collective knowledge that exceeds what any individual could accumulate.
Yet collective information strategies face limitations. Platforms may respond to transparency initiatives by further obscuring systems or targeting tool developers. Not all workers participate in or benefit from collective knowledge resources. The information shared may be incomplete, outdated, or inaccurate. Most significantly, collective information does not translate automatically into collective bargaining power when workers remain legally classified as independent contractors with limited labour rights.
The paradox of coping strategy effectiveness
The fourth objective sought to evaluate the effectiveness of financial coping strategies in addressing income volatility. The evidence presents a paradox: workers adopt sensible, reasonable strategies that provide meaningful but ultimately insufficient protection.
Strict budgeting, emergency savings, and digital financial tools represent rational responses to unpredictable income. These practices help workers manage cash flow fluctuations, avoid acute financial crises, and maintain some stability despite volatile earnings. In this sense, coping strategies demonstrably “work.”
However, as Casalhay, Guevarra and Bragas (2025) and Griesbach et al. (2019) observe, these strategies cannot compensate for fundamental income inadequacy. Workers cannot budget their way to security when earnings consistently fall below needs. They cannot save adequately when surpluses rarely occur. Individual financial management strategies address symptoms rather than causes of income volatility.
This paradox extends to navigation strategies more broadly. Multi-platform working helps individual workers but may intensify competition that depresses rates for all. Algorithmic literacy benefits those who acquire it whilst potentially disadvantaging those who cannot. Over-servicing to maintain ratings imposes costs upon workers that benefit platforms and consumers. Individual coping strategies, whilst rational and sometimes effective for particular workers, may collectively perpetuate or even worsen structural conditions.
Transparency, fairness, and worker wellbeing
The relationship between pay transparency and worker outcomes proves more complex than simple assumptions might suggest. Semujanga and Parent-Rocheleau (2024) demonstrate that clearer compensation rules increase perceived procedural justice but do not reduce stress. This finding has important implications.
On one hand, transparency has inherent value. Workers benefit from understanding how their pay is calculated, even when they cannot change outcomes. Procedural justice perceptions matter for worker dignity and trust, independent of substantive outcomes.
On the other hand, transparency alone cannot address problematic compensation levels or structures. Knowing precisely how low pay is calculated may clarify rather than resolve unfairness. Full transparency about algorithmic control mechanisms might increase stress by revealing the extent of surveillance and constraint. These nuances suggest that transparency advocacy should complement rather than substitute for substantive improvements in pay and conditions.
Structural precarity and the limits of individual navigation
The fifth objective required critical assessment of whether worker navigation strategies address structural precarity. The evidence strongly suggests they do not. Workers actively and often effectively navigate immediate challenges, but the underlying conditions generating those challenges remain.
Platforms retain fundamental control over compensation structures, task allocation algorithms, and access conditions. Workers cannot collectively bargain from positions of legal and economic weakness. Regulatory frameworks in many jurisdictions inadequately protect platform workers. Market competition among platforms may improve conditions marginally, but competitive dynamics also drive cost-cutting that harms workers.
This assessment does not diminish the value or significance of worker navigation strategies. Individual and collective coping mechanisms demonstrably help workers survive and sometimes thrive within difficult conditions. Understanding these strategies provides insights for worker education, support services, and platform design improvements. However, sustainable improvements likely require structural interventions beyond worker-led adaptation.
Implications for platform governance and regulation
The sixth objective addresses implications for platform governance, regulatory intervention, and future research. Several implications emerge from the literature synthesis.
Platform design choices significantly influence worker experiences. Platforms could provide greater transparency without fundamental changes to business models. They could reduce algorithmic control intensity, provide more predictable scheduling, or stabilise compensation structures. That many platforms do not make such choices reflects business priorities rather than technical constraints.
Regulatory intervention may be necessary where market mechanisms and platform self-governance prove inadequate. Potential regulatory approaches include minimum compensation standards, transparency requirements, algorithmic accountability mandates, and reconsideration of employment classification. Different jurisdictions are experimenting with various approaches, generating natural experiments for evaluating policy effectiveness.
Worker organisation, whilst challenging in platform contexts, offers possibilities for collective power that individual navigation strategies cannot achieve. Support for worker association rights, sectoral bargaining mechanisms, or platform cooperative alternatives might enable structural improvements beyond what individual coping achieves.
Conclusions
This dissertation has examined how gig workers navigate pay transparency deficits, algorithmic scheduling constraints, and income volatility through a systematic synthesis of existing scholarly literature. The analysis addressed six objectives concerning the nature of platform work challenges, worker navigation strategies, their effectiveness, and implications for governance and future research.
Regarding the first objective, the literature consistently documents that platforms employ opaque compensation algorithms, exert substantial scheduling control through automated systems, and generate income volatility that creates significant financial stress for workers. These challenges are interconnected, with pay opacity enabling unannounced rate changes, algorithmic control limiting worker ability to respond, and resulting earnings instability compounding financial difficulties.
The second and third objectives were addressed through identification of diverse navigation strategies. For pay transparency, workers engage in individual earnings tracking, collective information sharing through online communities, and development of third-party audit tools. For algorithmic scheduling, workers employ multi-platform working, strategic timing and location decisions, and collective knowledge sharing about platform system behaviour. These strategies demonstrate worker agency and capacity for sophisticated adaptation.
The fourth objective concerning financial coping strategy effectiveness reveals a nuanced picture. Workers adopt sensible budgeting, savings, and financial management practices that provide meaningful but ultimately insufficient protection against income volatility. These strategies help manage symptoms without addressing structural causes.
The fifth objective required critical assessment of whether navigation strategies address structural precarity. The evidence indicates they do not. Whilst worker strategies enable individual coping and sometimes thriving, they cannot overcome fundamental power imbalances, inadequate regulatory protections, or market dynamics that perpetuate difficult conditions. This conclusion does not diminish the importance of understanding and supporting worker agency, but it does indicate the limits of individual adaptation as a solution to collective problems.
The sixth objective concerning governance and research implications suggests several directions. Platform design improvements, regulatory interventions, and support for worker organisation each offer possibilities for addressing challenges that individual navigation cannot resolve. These structural approaches warrant continued attention alongside research examining worker experiences and strategies.
The significance of these findings extends beyond academic interest. Millions of workers globally depend upon platform-mediated income, and their wellbeing depends substantially upon how platform work challenges are understood and addressed. This dissertation contributes to that understanding by documenting worker resilience and agency whilst honestly acknowledging the limits of individual coping strategies.
Future research priorities include longitudinal studies examining how navigation strategies and their effectiveness evolve over time as platforms and workers adapt to each other. Comparative research across jurisdictions with different regulatory approaches would illuminate policy effectiveness. Studies examining mental health and wellbeing consequences of various navigation strategies could inform support services. Research on platform cooperative alternatives might identify structural innovations beyond conventional platform governance.
In conclusion, gig workers demonstrate remarkable agency in navigating difficult conditions through algorithmic literacy development, collective information sharing, multi-platform strategies, and stringent financial management. These strategies provide meaningful coping capacity but cannot eliminate the structural precarity inherent to contemporary platform work arrangements. Sustainable improvements require combining support for worker-led initiatives with platform accountability measures and appropriate regulatory intervention.
References
Calacci, D. and Pentland, A., 2022. Bargaining with the black-box: Algorithmic pricing and worker resistance. *Proceedings of the ACM on Human-Computer Interaction*, 6, pp. 1-24. https://doi.org/10.1145/3570601
Casalhay, S., Guevarra, C. and Bragas, C., 2025. The gig economy: Financial challenges and opportunities faced by freelancers. *International Journal of Research Publication and Reviews*. https://doi.org/10.55248/gengpi.6.0525.1716
Dedema, M. and Rosenbaum, H., 2024. Socio-technical issues in the platform-mediated gig economy: A systematic literature review: An Annual Review of Information Science and Technology (ARIST) paper. *Journal of the Association for Information Science and Technology*, 75, pp. 344-374. https://doi.org/10.1002/asi.24868
Department for Business, Energy and Industrial Strategy, 2018. *The characteristics of those in the gig economy*. London: BEIS.
Duggan, J., Carbery, R., McDonnell, A. and Sherman, U., 2023. Algorithmic HRM control in the gig economy: The app-worker perspective. *Human Resource Management*. https://doi.org/10.1002/hrm.22168
Duggan, J., Sherman, U., Carbery, R. and McDonnell, A., 2019. Algorithmic management and app-work in the gig economy: A research agenda for employment relations and HRM. *Human Resource Management Journal*. https://doi.org/10.1111/1748-8583.12258
Farrell, D. and Greig, F., 2016. Paychecks, paydays, and the online platform economy: Big data on income volatility. *JPMorgan Chase Institute*.
Griesbach, K., Reich, A., Elliott-Negri, L. and Milkman, R., 2019. Algorithmic control in platform food delivery work. *Socius*, 5. https://doi.org/10.1177/2378023119870041
Heeks, R., Graham, M., Mungai, P., Belle, J. and Woodcock, J., 2021. Systematic evaluation of gig work against decent work standards: The development and application of the Fairwork framework. *The Information Society*, 37, pp. 267-286. https://doi.org/10.1080/01972243.2021.1942356
International Labour Organization, 2021. *World employment and social outlook 2021: The role of digital labour platforms in transforming the world of work*. Geneva: ILO.
Jackson, E., 2022. Availability of the gig economy and long run labor supply effects for the unemployed. *Working paper*.
Jarrahi, M. and Sutherland, W., 2019. Algorithmic management and algorithmic competencies: Understanding and appropriating algorithms in gig work. *Information in Contemporary Society: iConference 2019 Proceedings*, pp. 578-589. https://doi.org/10.1007/978-3-030-15742-5_55
Kadolkar, I., Kepes, S. and Subramony, M., 2024. Algorithmic management in the gig economy: A systematic review and research integration. *Journal of Organizational Behavior*. https://doi.org/10.1002/job.2831
Kumar, R., 2024. Precarious prosperity: The gig economy’s role in employment growth and wage inequality. *International Journal of Advanced Research in Commerce, Management and Social Science*. https://doi.org/10.62823/ijarcmss/7.4(i).7184
Semujanga, B. and Parent-Rocheleau, X., 2024. Time-based stress and procedural justice: Can transparency mitigate the effects of algorithmic compensation in gig work? *International Journal of Environmental Research and Public Health*, 21. https://doi.org/10.3390/ijerph21010086
Singh, R. and Awasthi, S., 2025. The status of unorganized workers in digital platforms (gig economy): Opportunities and challenges. *Journal of Informatics Education and Research*. https://doi.org/10.52783/jier.v5i2.2625
Snyder, H., 2019. Literature review as a research methodology: An overview and guidelines. *Journal of Business Research*, 104, pp. 333-339.
