Autonomy and Algorithmic Control in the Gig Economy: Balancing Flexibility and Well-Being

Pawan Kumar (), Sumesh Singh Dadwal (), Sanjay Modi (), Arsalan Mujahid Ghouri () and Hamid Jahankhani ()
Additional contact information
Pawan Kumar: Lovely Professional University
Sumesh Singh Dadwal: London Southbank University
Sanjay Modi: Lovely Professional University
Arsalan Mujahid Ghouri: London Southbank University
Hamid Jahankhani: Northumbria University

Chapter Chapter 5 in The Dark Side of Marketing, 2025, pp 119-143 from Springer

Abstract: Abstract This chapter explores the dual nature of autonomy and algorithmic control within the gig economy, highlighting both the opportunities and challenges faced by gig workers. The gig economy, characterised by flexible work arrangements and independent contracting, offers workers significant autonomy in choosing tasks, schedules, and work environments. However, this autonomy is often counterbalanced by algorithmic management, which can impose constraints and pressures on workers. The chapter examines the impact of perceived autonomy on job satisfaction, motivation, and well-being, while also addressing the negative aspects such as isolation, job insecurity, and constant availability pressures. Through case studies and real-life examples, the chapter illustrates the varying experiences of gig workers across different platforms, such as Uber, Lyft, Upwork, and Fiverr. It concludes by discussing strategies for balancing autonomy and control, emphasising the importance of transparency, worker inclusion, and support services to enhance the overall well-being of gig workers.

Keywords: Autonomy; Algorithmic Control; Gig Economy; Job satisfaction; Well-being; Platforms; Uber; Lyft; Upwork (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-94946-3_5

Ordering information: This item can be ordered from
http://www.springer.com/9783031949463

DOI: 10.1007/978-3-031-94946-3_5

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().