We quantify the impact of social interactions and peer effects in the context of prescription choices by physicians. Using detailed individual-level prescription data, along with self-reported social network information, we document that physician prescription behavior is significantly...

This chapter presents a selective review of a literature in marketing that analyzes diffusion and pricing over the product life-cycle. I primarily focus on empirical work, and on papers that deal with the dynamics of pricing over time. I discuss how recent empirical work has linked outcomes to...

How consumers use review content in their decision making has remained a black box due to the labor-intensive nature of extracting content from review text and the lack of data on review-reading behavior. In this study, we overcome this challenge by applying deep-learning- based natural language...

We examine whether and how ride-sharing services influence the demand for home-sharing services. Our identification strategy hinges on a natural experiment where Uber/Lyft exited Austin in May 2016 in response to new regulations. On a 12-month longitudinal data spanning 11,423 Airbnb properties,...

We investigate the economic impact of images and lower-level image factors that influence property demand in Airbnb. Using Difference-in-Difference analyses on a 16-month Airbnb panel dataset spanning 7,711 properties, we find that units with verified photos (taken by Airbnb's photographers)...

We theorize peer awards’ effects on the volume and novelty of creative user-generated content (UGC) produced at online platform communities. We then test our hypotheses via a randomized field experiment on Reddit, wherein we randomly and anonymously assigned Reddit’s Gold Award to 905...

A lack of interpretability limits the use of common unsupervised learning techniques (e.g., PCA, t-SNE) in contexts where they are meant to augment managerial decision-making. We develop a generative deep learning model based on a Variational AutoEncoder (“InnoVAE”) that converts...

This paper uses deep learning and natural language processing (NLP) methods on the US patent corpus to evaluate their predictive power in estimating two measures of patent value: (i) investor reaction to patent announcements as measured in Kogan et al., 2017 and (ii) forward citations. While...