AI@CBS

With heightened concerns regarding user privacy, there is a recent movement for empowering consumers with the ability to control how their private data are collected, stored, used and shared. Notably, between 2018 and 2020, the General Data Protection Regulation (GDPR) has been implemented in the European Union (EU), and the California Consumer Privacy Act (CCPA) and the California Privacy Rights Act (CPRA) have been implemented/passed in the state of California in the United States. These regulations address both consumer data security and consumer privacy rights.

We consider a liquidation problem in which a risk-averse trader tries to liquidate a fixed quantity of an asset in the presence of market impact and random price fluctuations. When deciding the liquidation strategy, the trader encounters a trade-off between the transaction costs incurred due to market impact and the volatility risk of holding the position.

The composition of natural liquidity has been changing over time. An analysis of intraday volumes for the S&P500 constituent stocks illustrates that (i) volume surprises, i.e., deviations from their respective forecasts, are correlated across stocks, and (ii) this correlation increases during the last few hours of the trading session.

We explore the relationship between the volatility of a firm's local environment and its organizational structure. Using micro-level data on managers working for a large retailer, we empirically test and provide support for our theory that a more volatile local environment results in more decentralization only when the need for coordination among sub-units is low. In contrast, more local volatility is associated with more centralization when coordination needs are high.

Logos serve a fundamental role as the visual figureheads of brands. Yet, because of the difficulty of using unstructured image data, prior research on logo design has largely been limited to nonquantitative studies. In this work, we explore the interplay between logo design and brand identity creation from a data-driven perspective.

Problem definition: We study how online platforms can leverage the behavioral considerations of their users to improve their assortment decisions. Motivated by our collaboration with a dating company, we study how a platform should select the assortments to show to each user in each period to maximize the expected number of matches in a time horizon, considering that a match is formed if two users like each other, possibly on different periods.

Smartphones have made it nearly effortless to share images of branded experiences. This research classifies social media brand imagery and studies user response. Aside from packshots (standalone product images), two types of brand-related selfie images appear online: consumer selfies (featuring brands and consumers’ faces) and an emerging phenomenon the authors term “brand selfies” (invisible consumers holding a branded product).

We study the use and economic impact of artificial intelligence (AI) technologies among U.S. firms. We propose a new measure of firm-level AI investments, using a unique combination of detailed worker resume and job postings datasets. Our measure reveals a stark increase in AI investments across sectors in the last decade. AI-investing firms see higher growth in sales, employment, and market valuations.