Good Gig, Bad Gig: Autonomy and Algorithmic Control in the Global Gig Economy

Area of Science:

• Digital Labour Studies
• Gig Economy Research
• Algorithmic Management

Background:

• The remote gig economy involves digital services provided via online platforms.
• Workers in Southeast Asia and Sub-Saharan Africa are increasingly engaged in this sector.
• Understanding job quality in this context is crucial due to platform-based algorithmic control.

Purpose of the Study:

• To evaluate the job quality of remote gig work.
• To analyze the impact of algorithmic control on gig workers.
• To explore variations across different countries and work types in Southeast Asia and Sub-Saharan Africa.

Main Methods:

• Semi-structured interviews with 107 remote gig workers across six countries.
• A cross-regional survey of 679 remote gig workers.
• Analysis focused on platform-based algorithmic control mechanisms.

Main Results:

• Algorithmic control is a central feature of online labor platforms.
• Management techniques offer flexibility, autonomy, and task variety.
• However, these controls correlate with low pay, social isolation, irregular hours, overwork, and exhaustion.

Conclusions:

• Remote gig work presents a duality of flexibility and precariousness.
• Algorithmic management significantly shapes the worker experience, often negatively impacting well-being.
• Policy and platform design considerations are needed to improve job quality in the digital gig economy.