In 2016, we set out to understand the future of driverless mobility—and specifically, how a mobility company can build products and services that will optimize the relationships between people and advanced assistive systems in an increasingly automated future. This case study will shed light on how an ethnographic approach inspired by actor-network theory allowed us to look closely at human-system interactions, build a unique perspective on the forms of agency people value most, and understand how mobility companies can harness this understanding to build automated systems that strengthen their relationships with consumers.
Drawing from the core tenets of actor-network theory, our research placed an emphasis not on individuals or even broader social ecologies—but rather, shifting networks of relationships between humans, objects, ideas, and processes. We divided our resources between two research tracks: i) human mobility, studying the complex network of relationships that gives shape to it, and ii) technology, studying networks of relationships surrounding six analogous advanced assistive technologies that are likely to prove pre-cursors to the relationship between people and driverless cars, ranging from the DaVinci surgical robot to the driverless tractor. While the objective of the former track was to understand the relationship between human agency and mobility, the latter was designed to help us understand how advanced assistive technologies might aid or impede this relationship going forward.
Studying human-system interactions within broader, complex networks allowed us to uncover an insight about agency that is core to how mobility companies should approach automation. Agency doesn’t have a single, fixed value to individuals; rather, people derive greater meaning from and thus value agency over higher-order tasks and responsibilities — often revolving around role determination and fulfillment, such as “being a good father” or “being a precision farmer” — much more than they enjoy and value agency over lower-order tasks — like paying the household bills, or keeping track of contracts with farm suppliers. The people studied aspired to preserve their enjoyable agency over higher-order tasks, and thus perceived automation as most helpful when it liberated them to higher-order responsibilities by removing the burden of lower-order ones.
This understanding allowed us to see that mobility companies can reframe mobility as much more than about getting between destinations. Instead, they should see mobility as a broader and more valuable system within which automation can be used to lessen users’ burden of control over lower-order tasks, while augmenting people’s agency over the most meaningful tasks. This could mean, for example, using automation to remove the lower-order task of navigation, so drivers can focus on curating a unique set of destinations through a city for their passengers; or removing pain points around parking that might dissuade a driver from driving to see a friend, so drivers can focus on higher-order social tasks like setting the mood for a great dinner. Since this study, this focus on unlocking the higher-order value of mobility has become a part of our client company’s approach to driverless cars and advanced automated systems. This case study will invite social scientists to consider how we might refine and continue to apply this actor-network inspired approach to build an even more granular ambition for the future of automation in mobility.
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