Platform playbook: a typology of consumer strategies against algorithmic control in digital platforms

Abstract

Digital Platforms consist of algorithms and rules that shape consumer behaviour. When faced with these embodiments of the platform's interests, how do consumers protect their own interests? Through multi-method, qualitative fieldwork focused on commuters using ridehailing platforms in Metro Manila, this paper shows that consumers develop strategies to achieve better terms for themselves. This paper contributes to the literature on algorithmic control and user agency in two ways. First, it proposes a fine-grained typology of consumer strategies in algorithmic digital platforms, consisting of 5 major types and 18 sub-types. Second, the typology sheds light on the distinct characteristics of consumer strategies and their implications. Future studies into user strategies and algorithmic systems, and digital platforms will benefit from the typology and implications laid out here.

Coping with Algorithmic Risks

“…Finally, the use of self-help strategies illustrates that while algorithmicselection applications exert power over their users, the users also have agency to use those platforms to their ends by acting strategically (Bakardjieva, 2005;Ramizo, 2021;Selwyn & Pangrazio, 2018;van Dijck, 2009). The interaction with algorithmic-selection applications can in turn influence the algorithms as in an online environment, humans and algorithms form a recursive loop (Bucher, 2017;Gillespie, 2014).…”

“…Self-help strategies-such as adjusting one's privacy settings-provide a complementary governance choice for internet users to cope with algorithmic risks (Boerman et al, 2018;Latzer & Just, 2020). Therefore, how users engage with algorithmic-selection applications and cope with their risks warrants attention (Kitchin, 2017;Ramizo, 2021).…”

The emergence of algorithmic solidarity: unveiling mutual aid practices and resistance among Chinese delivery workers

“…The tactics we have explored in this article are aligned with two categories discussed by Ramizo (2021), that is Optimization and Boundary-hunting . The former means riders use different tactics “to maximize the benefits of using the algorithmic platforms, including the leveraging of loopholes without directly violating platform rules” (Ramizo, 2021: 12). The tactics we shed light on such as working for multiple platforms, not following the routes arranged by the platforms, and refusing to accept orders from a certain region proactively fall into this category.…”

“…The tactics we have explored in this article are aligned with two categories discussed by Ramizo (2021), that is Optimization and Boundary-hunting . The former means riders use different tactics “to maximize the benefits of using the algorithmic platforms, including the leveraging of loopholes without directly violating platform rules” (Ramizo, 2021: 12).…”

“…The tactics we shed light on such as working for multiple platforms, not following the routes arranged by the platforms, and refusing to accept orders from a certain region proactively fall into this category. The latter means riders identify and exploit the “limits of enforceable platform rules” (Ramizo, 2021: 12), that is, using tactics that obviously violate the rules of the platform and risk being punished. Using cheating bots and “shuadan” fall into this second category.…”