Human-machine networks for the sharing economy

Image based on artwork from MaxPixel (CC0 Public Domain)

The sharing economy has seen much interest as an alternative to traditional models for ownership and consumption of products and services. From the perspective of HUMANE, the sharing economy is of particular interest as a phenomenon enabled by networking of humans and intelligent digital machines.

Botsman defines sharing economy as an economic system based on sharing underused assets or services, for free or for a fee, directly from individuals. As pointed out by Belk, sharing behaviour is as old as humankind itself. However, in a networked society sharing supported by digital services becomes a whole new phenomenon with disruptive potential. While the value of the sharing economy today is relatively modest, it is expected to grow exponentially in the coming 10 years.

As part of the HUMANE work to support future thinking, we are in the process of developing a roadmap for human-machine networks supporting sharing economy services. To do this, we have applied the HUMANE typology dimensions to analyse aspects of such networks pertaining to its actors and their relations, as well as network structure and extent. The analysis is based on interviews with sharing economy service owners, policy maker representatives, and independent researchers. Here, we provide some example insights from this work.

The actor perspective: Human and machine actors in sharing economy services hold highly different roles. The human actors, typically unskilled or unprofessional, are providers and seekers in the two-sided market of the sharing economy. The machine actors are the matchmakers, predicting who is in need of the provided goods or services, and supporting the sharing process. Interestingly, as service owners want to provide efficient and easy sharing processes , the tasks and activities of the human actors are typically restricted or streamlined. In contract, the machine actors take on an increasing range of tasks requiring machine intelligence. For example, owners of online redistribution markets seek to reduce the work needed to upload ads and communicate with potential buyers by leveraging artificial intelligence for image recognition and text prediction; improving quality through reducing human agency and increasing machine agency.

The relation perspective: Whereas sharing behaviour requires trust between the actors in the sharing economy, sharing economy services do not seem to particularly encourage human actors to establish social ties. In the pre-internet era, sharing behaviour would typically require strong social ties between those that share goods or services. Not so much so in todays networked world. Botsman has discussed this phenomenon as layered trust, where the trust among individuals in the sharing economy depend on trust in the sharing platform. In our interviews, we find that the service owners have similar insights and seek to strengthen the relation between the individuals and the services, rather than to strengthen the relation between individuals. For sure, the positive experience of a sharing encounter with another person is seen as valuable; as is the users’ ratings and feedback to each other. Nevertheless, group formation is typically not strongly encouraged and the aim of the service owners is to support matching of strangers.

The future of sharing economy services will likely see the strengthening of the trends of increased machine agency and strengthening of the relationship between users and the platforms. An implication of this is the benefit of size and market share. Larger service owners will have better access to user data, enabling stronger AI-support in the sharing process. Larger service owners may also be better equipped to serve as a trusted platform for sharing. For newcomer platform providers, it will be critical to identify alternative means of supporting intelligent matching and predictions, as well as smart ways of building trust without the benefit of a household name.