Collaborative consumption, often associated with the sharing economy, takes place in organized systems or networks, in which participants conduct sharing activities in the form of renting, lending, trading, bartering, and swapping of goods, services, transportation solutions, space, or money. In this paper, a framework on the determinants of choosing a sharing option is developed and tested with two quantitative studies by applying partial least squares path modeling analysis. In study 1, users of the B2C car sharing service car2go (N = 236), and in study 2, users of the C2C online community accommodation marketplace Airbnb (N = 187) are surveyed. The results reveal the satisfaction and the likelihood of choosing a sharing option again to be predominantly explained by determinants serving users' self-benefit. Utility, trust, cost savings, and familiarity were found to be essential in both studies, while service quality and community belonging were identified solely in study 1. Four proposed determinants had no influence on any of the endogenous variables. This applies to environmental impact, internet capability, smartphone capability, and trend affinity. Finally, research and managerial implications are discussed.

Online labor platforms (OLPs) can use algorithms along two dimensions: matching and control. While previous research has paid considerable attention to how OLPs optimize matching and accommodate market needs, OLPs can also employ algorithms to monitor and tightly control platform work. In this paper, we examine the nature of platform work on OLPs, and the role of algorithmic management in organizing how such work is conducted. Using a qualitative study of Uber drivers’ perceptions, supplemented by interviews with Uber executives and engineers, we present a grounded theory that captures the algorithmic management of work on OLPs. In the context of both algorithmic matching and algorithmic control, platform workers experience tensions relating to work execution, compensation, and belonging. We show that these tensions trigger market-like and organization-like response behaviors by platform workers. Our research contributes to the emerging literature on OLPs.

The version presented here is a Working Paper (or 'pre-print') that may be later published elsewhere.

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