AI@CBS

This paper offers the first study of how changes in corporate R&D investment affect labor mobility. We document that increases in firm R&D have no measurable effect on employee mobility to other incumbent firms or on exit from employment, but do spur employee departures to join the founding teams of startups. These startups are more likely to be outside the R&D-investing employer's industry, suggesting that the ideas moving via employees to startups would impose diversification costs on the parent.

The multi-armed bandit problem has been the subject of decades of intense study in statistics, operations research, electrical engineering, computer science, and economics. A “one-armed bandit” is a somewhat antiquated term for a slot machine, which tends to “rob” players of their money. The colorful name for our problem comes from a motivating story in which a gambler enters a casino and sits down at a slot machine with multiple levers, or arms, that can be pulled. When pulled, an arm produces a random payout drawn independently of the past.

Words are part of almost every marketplace interaction. Online reviews, customer service calls, press releases, marketing communications, and other interactions create a wealth of textual data. But how can marketers best use such data? This article provides an overview of automated textual analysis and details how it can be used to generate marketing insights. The authors discuss how text reflects qualities of the text producer (and the context in which the text was produced) and impacts the audience or text recipient.

Once artificial intelligence (AI) is indistinguishable from human intelligence, and robots are highly similar in appearance and behavior to humans, there should be no reason to treat AI and robots differently from humans. However, even perfect AI and robots may still be subject to a bias (referred to as speciesism in this article), which will disadvantage them and be a barrier to their commercial adoption as chatbots, decision and recommendation systems, and staff in retail and service settings.

Why do young firms pay less? Using confidential microdata from the US Census Bureau, we find lower earnings among workers at young firms. However, we argue that such measurement is likely subject to worker and firm selection. Exploiting the two-sided panel nature of the data to control for relevant dimensions of worker and firm heterogeneity, we uncover a positive and significant young-firm pay premium. Furthermore, we show that worker selection at firm birth is related to future firm dynamics, including survival and growth.

Human enhancement products allow consumers to radically enhance their mental abilities. Focusing on cognitive enhancements, we introduce and study a novel factor dehumanization (i.e., denying a person emotional ability and likening them to a robot) which plays a key role in consumers' reluctance to use enhancement products. In study 1, consumers who enhance their mental abilities beyond normal levels were dehumanized, whereas consumers who use the same products to restore lost abilities were not.

Hidden Markov Models (HMMs) have been widely used in marketing to study dynamics in customer behavior. HMMs have been successfully applied to model how customers transition among a set latent states such as attention levels, web search behavior, customer's relationships, and purchase intent. While most HMMs in marketing allow for heterogeneity in the model's parameters, these models assume that the number of latent states is common across customers.

Consumer robots are predicted to be employed in a variety of customer-facing situations. As these robots are designed to look and behave like humans, consumers attribute human traits to them—a phenomenon known as the “Eliza Effect.” In four experiments, we show that the anthropomorphism of a consumer robot increases psychological warmth but decreases attitudes, due to uncanniness. Competence judgments are much less affected and not subject to a decrease in attitudes.