Decoding Algorithmic HRM in the Gig Economy: Workers’ Interpretations, Reactions, and Strategies

Introduction 

The rise of digital platforms such as Uber, Deliveroo, Upwork, and Fiverr, alongside the shift from traditional employment, has led more individuals to engage in gig work as a primary or supplementary source of income (Wu et Huang 2024). Algorithms and AI increasingly automate human resource management (HRM) functions on these platforms (Keegan et Meijerink 2025). Algorithmic management can be defined as “a system of control where self-learning algorithms are given the responsibility for making and executing decisions affecting labor, thereby limiting human involvement and oversight of the labor process. It replaces some of the tasks and processes that workers typically engage with by using algorithms that are developed by the very same individuals’ data on the platform” (Duggan et al. 2020:119). Algorithmic management underpins gig platforms, directing task allocation, evaluation, and discipline (Kellogg, Valentine, et Christin 2020).

Platforms often implement hybrid HRM systems, combining autonomy-enhancing practices with tight control (Keegan et Meijerink 2025), which can create alignment or misalignment with strategic goals, internal consistency, and worker expectations, shaping job design and producing mixed effects. Digital platforms generate precarious work environments distinct from traditional employment in autonomy, job security, social interaction, and career prospects, while also offering flexible income opportunities (Caza et al. 2022; Wu et Huang 2024). Gig workers experience marginalization, technostress, and career precarity (Cameron et Rahman 2022; Caza et al. 2022; Cropanzano et al. 2023), yet responses vary: some withdraw or develop workarounds (Cram et al. 2022) while others adopt proactive strategies such as job crafting or countersignalling.

Algorithmic HRM represents a novel domain for HRM scholarship (Snell et al. 2023), yet prior research has emphasized efficiency and productivity, largely overlooking how gig workers react and cope with algorithmic management (Kellogg et al. 2020). Empirical studies are needed to examine how, why and under which conditions workers interact with clients, respond to misaligned practices, mobilize coping strategies, build human capital, and craft careers. Most existing studies focus on single platforms or countries, limiting generalizability. Comparative research across platforms, contexts, and countries is therefore crucial (Cropanzano et al. 2023; Keegan et Meijerink 2025; Wu et Huang 2024), reflecting within- and between-platform differences and informing inclusion, work-life balance, ethical governance, poverty reduction, engagement, well-being, and sustainable careers.

This Special Issue seeks to advance understanding of how, why, and under which conditions gig workers interpret, respond to, and craft their work under algorithmic HRM. Submissions should engage with established frameworks—such as signalling theory, attribution theory, sensemaking theory, transactional model of stress and coping, theory of conservation of resources, justice theories, social exchange, job design, or psychological contract—and aim to advance or extend these theories by examining the new conditions created by algorithmic HRM. Comparative, cross-platform, and cross-country studies are particularly encouraged to test, refine, and contextualize these frameworks in diverse digital work environments. Contributions should build on or challenge existing HRM, labor economics, or organizational behavior theories to explain gig workers’ responses, strategies, and experiences in the evolving gig economy.

List of topic areas

• Platform HRM Practices and Alignment
  • Which algorithmic HRM practices are often neglected on digital platforms (e.g., training, hiring, career development), and how do they affect gig workers' well-being, engagement, and career outcomes?
  • How do gig workers perceive the alignment or misalignment of algorithmic HRM with platform strategy, internal consistency, and contextual conditions, and how does this influence their perceptions of fairness, strategic intent, and commitment?
  • How do gig workers perceive and choose platforms as preferred employers, and how do algorithmic HRM practices shape these perceptions and choices?
  • How are algorithms designed to manage the human resource functions of hiring, performance evaluation, and compensation? What are the ethical considerations and potential for bias in these designs?
• Resistance, Agency, and Proactivity
  • How do gig workers resist, adapt to, or proactively shape algorithmic management (e.g., countersignalling, algoactivism, job and portfolio crafting)?
  • How do gig workers use digital tools and social media to organize and mobilize against perceived unfair algorithmic practices?
  • What are the limits and failures of gig worker resistance, and what are the factors that enable or constrain their collective power?
• Gig Workers' Experiences and interpretations
  • How do system-related characteristics or perceptions (e.g. fairness, transparency, trustworthiness, intensity) affect workers' attributions of the system, dignity, recognition, well-being, and work-life balance?
  • How do workers experience the duality of the effects of algorithmic HRM? Is it a double-edged sword?
  • Which coping strategies enable workers to maintain engagement under stress and uncertainty?
  • How do gig workers' reactions and coping strategies evolve over time as they gain more experience with algorithmic management?
  • How do client interactions and feedback influence gig workers' experiences, coping strategies, and service performance?
  • How workers' experience differs between the different types of gig work?
• Career Development and Human Capital
  • How do gig workers build skills and human capital to sustain meaningful careers in precarious contexts?
  • How do gig workers experience their job characteristics (e.g., autonomy, diversity, job demands)?  
  • Under what conditions can platform work provide decent and fulfilling employment?
  • How do gig workers transfer skills and experiences gained on one platform to another, and how does this affect their career mobility?
  • How does the algorithmic HRM of a platform either hinder or facilitate the development of transferable skills, as opposed to platform-specific ones?
• Identity and Inclusion
  • How do gig workers interpret algorithmic signals, make different attributions, and how do this shape professional identity and career development?
  • How do social ties and collective strategies mitigate isolation and promote inclusion?
  • How do algorithmic HRM practices differentially affect gig workers based on their demographics (e.g., gender, race, age, disability), and how does this impact their sense of belonging and inclusion?
  • How do gig workers create and sustain online or offline communities to share knowledge, offer support, and collectively interpret algorithmic signals

Submissions Information

Submissions are made using ScholarOne Manuscripts. Registration and access are available here.

Author guidelines must be strictly followed. Please see here.

Authors should select (from the drop-down menu) the special issue title at the appropriate step in the submission process, i.e. in response to ““Please select the issue you are submitting to”.

Submitted articles must not have been previously published, nor should they be under consideration for publication anywhere else, while under review for this journal.

Key deadlines

This Special Issue is now open for submissions.

Closing date for manuscripts submission: 28/02/2026

Guest Editors

Sari Mansour, TÉLUQ University, Québec, Canada, Sari.mansour@teluq.ca

Xavier Parent-Rocheleau, HEC Montreal, Quebec, Canada, xavier.parent-rocheleau@hec.ca

James Duggan, University College Cork, Ireland, jamesduggan@ucc.ie 

References

Cameron, Lindsey D., et Hatim Rahman. 2022. « Expanding the Locus of Resistance: Understanding the Co-Constitution of Control and Resistance in the Gig Economy ». Organization Science 33(1):38‑58. doi:10.1287/orsc.2021.1557.

Caza, Brianna B., Erin M. Reid, Susan J. Ashford, et Steve Granger. 2022. « Working on My Own: Measuring the Challenges of Gig Work ». Human Relations 75(11):2122‑59. doi:10.1177/00187267211030098.

Cram, W. Alec, Martin Wiener, Monideepa Tarafdar, et Alexander Benlian. 2022. « Examining the Impact of Algorithmic Control on Uber Drivers’ Technostress ». Journal of Management Information Systems 39(2):426‑53. doi:10.1080/07421222.2022.2063556.

Cropanzano, Russell, Ksenia Keplinger, Brittany K. Lambert, Brianna Caza, et Susan J. Ashford. 2023. « The organizational psychology of gig work: An integrative conceptual review ». Journal of Applied Psychology 108(3):492‑519. doi:10.1037/apl0001029.

Duggan, James, Ultan Sherman, Ronan Carbery, et Anthony McDonnell. 2020. « Algorithmic Management and App-Work in the Gig Economy: A Research Agenda for Employment Relations and HRM ». Human Resource Management Journal 30(1):114‑32. doi:10.1111/1748-8583.12258.

Keegan, Anne, et Jeroen Meijerink. 2025. « Algorithmic Management in Organizations? From Edge Case to Center Stage ». Annual Review of Organizational Psychology and Organizational Behavior 12(Volume 12, 2025):395‑422. doi:10.1146/annurev-orgpsych-110622-070928.

Kellogg, Katherine C., Melissa A. Valentine, et Angéle Christin. 2020. « Algorithms at Work: The New Contested Terrain of Control ». Academy of Management Annals 14(1):366‑410. doi:10.5465/annals.2018.0174.

Snell, Scott A., Juani Swart, Shad Morris, et Corine Boon. 2023. « The HR Ecosystem: Emerging Trends and a Future Research Agenda ». Human Resource Management 62(1):5‑14. doi:10.1002/hrm.22158.

Wu, Dongyuan, et Jason L. Huang. 2024. « Gig Work and Gig Workers: An Integrative Review and Agenda for Future Research ». Journal of Organizational Behavior 45(2):183‑208. doi:10.1002/job.2775.