Abstract

Many innovations today are data-driven, ranging from self-driving cars to advanced medical diagnostic tools. The success of data-driven products critically depends on their access to big data. To improve the algorithms of these products, firms make substantial investments in data collection. However, for an individual firm, the accumulation of useful data can be slow, limiting the benefits of the algorithms. Therefore, a key challenge facing governments and policymakers is how to promote data sharing among individual firms. In this monograph, we first discuss unique challenges of data collection and data sharing in innovations, using the autonomous vehicle industry as an example. Then we present findings based on one of our recent research studies that seeks to understand the efficacy of a recent data sharing initiative.

Thought-leadership in Supply Chain Finance and Risk Management

This monograph contains six thought-leading contributions on various topics related to supply chain finance and risk management. The issue culminated out of a recent (May 14-16, 2021) mini-conference on “Supply Chain Finance and Risk Management” organized by The Boeing Center for Supply Chain Innovation (BCSCI), Olin Business School, Washington University in St. Louis.

In “Quadratic Hedging and Optimization of Option Exercise Policies”, Nicola Secomandi explores a model for optimizing option exercise policies under any given equivalent martingale measure and anchoring quadratic hedging to the resulting value of the policy. In “Operations Revenue Insurance”, Paolo Guiotto, Andrea Roncoroni and Roméo Tédongap propose a new framework for the optimal design of a financial instrument to hedge nonclaimable risk embedded by business and operating revenues. In “Crowdfunding Adoption in the Presence of Word-of-Mouth Communication”, Fasheng Xu, Xiaomeng Guo, Guang Xiao and Fuqiang Zhang investigate a firm’s optimal funding choice when launching a product in the market with word-of-mouth communication. In “Data Sharing in Innovations”, Zhi Chen and Jussi Keppo discuss how the success of data-driven products depends on a firm’s access to big data and the challenges of data collection and sharing in innovations using the autonomous vehicle industry as an example. In “Coordination Problems in Platform Markets Under Uncertainty”, Hamed Ghoddusi presents a dynamic coordination problem under uncertainty that is common in platform markets and provides novel insights on this problem between two sides of a platform under uncertainty. In “Value Games”, Matthew J. Sobel shows that insights and algorithms based on sequential games with a profit criterion and negligible bankruptcy risk can be adapted to maximize value.