Knowledge that Transforms

Powered by digital technologies, many peer-to-peer platforms, or what is called the sharing economy, have emerged in the past decade. Although the impact of the sharing economy has received considerable attention over the past few years, extant research has not fully documented the impact of the sharing economy on consumers, workers, industry, or society as a whole. In this study, we exploit the geographical and temporal variation in Uber’s entry to examine its impact on the personal bankruptcy rate as well as on other consumer credit default rates. We empirically document the changes in personal bankruptcy filings after Uber’s entry, and show that personal bankruptcy filings under Chapter 7 experience a drop of 0.047 per 1,000 people after Uber enters a county, which translates to a 3.26% reduction in quarterly bankruptcy filings. Uber’s entry also leads to a reduction in Chapter 13 personal bankruptcy filings, but to a smaller degree (0.018 cases per 1,000 people per quarter). We check the validity of our estimates using business bankruptcy filings, which we find are uncorrelated with Uber’s entry.

We define a prescriptive analytics framework that addresses the needs of a constrained decision-maker facing, ex ante, unknown costs and benefits of multiple policy levers. The framework is general in nature and can be deployed in any utility-maximizing context, public or private. It relies on randomized field experiments for causal inference, machine learning for estimating heterogeneous treatment effects, and on the optimization of an integer linear program for converting predictions into decisions. The net result is the discovery of individual-level targeting of policy interventions to maximize overall utility under a budget constraint. The framework is set in the context of the four pillars of analytics and is especially valuable for companies that already have an existing practice of running A/B tests. The key contribution of this work is to develop and operationalize a framework to exploit both within- and between-treatment arm heterogeneity in the utility response function in order to derive benefits from future (optimized) prescriptions. We demonstrate the value of this framework as compared to benchmark practices—i.e., the use of the average treatment effect, uplift modeling, as well as an extension to contextual bandits—in two different settings. Unlike these standard approaches, our framework is able to recognize, adapt to, and exploit the (potential) presence of different subpopulations that experience varying costs and benefits within a treatment arm while also exhibiting differential costs and benefits across treatment arms. As a result, we find a targeting strategy that produces an order of magnitude improvement in expected total utility for the case where significant within- and between-treatment arm heterogeneity exists.

In social networks, social foci are physical or virtual entities around which social individuals organize joint activities, for example, places and products (physical form) or opinions and services (virtual form). Forecasting which social foci will diffuse to more social individuals is important for managerial functions such as marketing and public management operations. In terms of diffusive social adoptions, prior studies on user adoptive behavior in social networks have focused on single-item adoption in homogeneous networks. We advance this body of research by modeling scenarios with multi-item adoption and learning the relative propagation of social foci in concurrent social diffusions for online social networking platforms. In particular, we distinguish two types of social nodes in our two-mode social network model: social foci and social actors. Based on social network theories, we identify and operationalize factors that drive social adoption within the two-mode social network. We also capture the interdependencies between social actors and social foci using a bilateral recursive process—specifically, a mutual reinforcement process that converges to an analytical form. Thus, we develop a gradient learning method based on a mutual reinforcement process that targets the optimal parameter configuration for pairwise ranking of social diffusions. Further, we demonstrate analytical properties of the proposed method such as guaranteed convergence and the convergence rate. In the evaluation, we benchmark the proposed method against prevalent methods, and we demonstrate its superior performance using three real-world data sets that cover the adoption of both physical and virtual entities in online social networking platforms.