Recent years have seen the rapid global emergence of the "gig economy". Today’s technology has made it easier than ever to take on platform-enabled jobs on the side or even full-time, whether that’s renting out your home, delivering food, or giving others a lift. But that, researchers from Ireland say, could be problematic.
In a new article published in the Human Resource Management Journal, they discuss “gig work” as another chapter in an all-too-familiar story: how technology has outpaced our capacity to fully consider its effects on humans. As the gig economy continues to grow, gig workers themselves could be growing more distant from the platforms that provide at least some portion of their livelihood. While the work may offer the flexibility many seek, there’s clear cause for concern over the well-being of workers, signaling an urgent need for dedicated research.
Part of the problem lies in how gig work is discussed. Understanding the issues the gig workforce faces means being able to differentiate one type of work from another. To address that, the authors classify gig work into three variants: capital platform work, where individuals use a digital platform to sell goods or lease assets, such as Etsy or Airbnb; crowdwork, which refers to platforms through which workers complete tasks remotely, such as Amazon Mechanical Turk or Fiverr; and finally, there’s app-work, which relies on applications such as Uber, UberEats, or Deliveroo to connect consumers to workers who provide local services.
Focusing on app-work, the authors discuss the implications of many platforms’ preferred style of management: management by algorithm. Opting for artificial intelligence over human knowhow to decide when to recruit, fire or incentivize workers is a clear disruption of the traditional employment relationship and human resource management. And early evidence bears that out. Digital platforms don’t appear to invest heavily in ensuring workers are fit for their role or for the organization. Penalties calculated algorithmically could strip app-workers of the autonomy and flexible work schedule advertised by platforms. And work performance often hinges on the subjective experiences of customers.
How sustainable is algorithmic management as presently conceived? How do individuals perceive their work? How ethical and appropriate is gig work overall? Might algorithmic management seep into more traditional forms of work?
The authors pose these and other questions as legitimate lines of research into the nature of gig work. Addressing these issues will be important as the gig workforce continues to grow. For academics, digital platforms, and policymakers alike, it could provide critical insight into how to make data-driven gig work more human and how best to support and regulate it.