Breaking the Cycle: How the News and Markets Created a Negative Feedback Loop in COVID-19
New research from CBS Professor Harry Mamaysky reveals how negativity in the news and markets can escalate a financial crisis.
New research from CBS Professor Harry Mamaysky reveals how negativity in the news and markets can escalate a financial crisis.
Adapted from “Global Value Chains in Developing Countries: A Relational Perspective from Coffee and Garments,” by Laura Boudreau of Columbia Business School, Julia Cajal Grossi of the Geneva Graduate Institute, and Rocco Macchiavello of the London School of Economics.
Adapted from “Online Advertising as Passive Search,” by Raluca M. Ursu of New York University Stern School of Business, Andrey Simonov of Columbia Business School, and Eunkyung An of New York University Stern School of Business.
This paper from Columbia Business School, “Meaning of Manual Labor Impedes Consumer Adoption of Autonomous Products,” explores marketing solutions to some consumers’ resistance towards autonomous products. The study was co-authored by Emanuel de Bellis of the University of St. Gallen, Gita Johar of Columbia Business School, and Nicola Poletti of Cada.
Co-authored by John B. Donaldson of Columbia Business School, “The Macroeconomics of Stakeholder Equilibria,” proposes a model for a purely private, mutually beneficial financial agreement between worker and firm that keeps decision-making in the hands of stockholders while improving the employment contract for employees.
At Columbia Business School, our faculty members are at the forefront of research in their respective fields, offering innovative ideas that directly impact the practice of business today. A quick glance at our publication on faculty research, CBS Insights, will give you a sense of the breadth and immediacy of the insight our professors provide.
As a student at the School, this will greatly enrich your education. In Columbia classrooms, you are at the cutting-edge of industry, studying the practices that others will later adopt and teach. As any business leader will tell you, in a competitive environment, being first puts you at a distinct advantage over your peers. Learn economic development from Ray Fisman, the Lambert Family Professor of Social Enterprise and a rising star in the field, or real estate from Chris Mayer, the Paul Milstein Professor of Real Estate, a renowned expert and frequent commentator on complex housing issues. This way, when you complete your degree, you'll be set up to succeed.
Columbia Business School in conjunction with the Office of the Dean provides its faculty, PhD students, and other research staff with resources and cutting edge tools and technology to help push the boundaries of business research.
Specifically, our goal is to seamlessly help faculty set up and execute their research programs. This includes, but is not limited to:
All these activities help to facilitate and streamline faculty research, and that of the doctoral students working with them.
Using publicly available data from 299 pre-registered replications from the social sciences, we find that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the paper characteristics, study design and results, author information, and replication effort. To understand why, we analyze the textual differences between replicable and nonreplicable studies.
Using publicly available data from 299 pre-registered replications from the social sciences, we find that the language used to describe a study can predict its replicability above and beyond a large set of controls related to the paper characteristics, study design and results, author information, and replication effort. To understand why, we analyze the textual differences between replicable and nonreplicable studies.
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines — which we define as repeated behaviors with recurring, temporal structures — for customer management. One reason for this dearth is the difficulty of measuring routines from transaction data, particularly when routines vary substantially across customers. We propose a new approach for doing so, which we apply in the context of ridesharing.
Routines shape many aspects of day-to-day consumption. While prior work has established the importance of habits in consumer behavior, little work has been done to understand the implications of routines — which we define as repeated behaviors with recurring, temporal structures — for customer management. One reason for this dearth is the difficulty of measuring routines from transaction data, particularly when routines vary substantially across customers. We propose a new approach for doing so, which we apply in the context of ridesharing.
In light of the widely discussed political divide and increasing societal polarization, we investigate in this paper whether the polarization of political ideology extends to consumers’ preferences, intentions, and purchases. Using three different data sets—the publicly available social media data of over three million brand followerships of Twitter users, a YouGov brand-preference survey data set, and Nielsen scanner panel data—we assess the evolution of brand-preference polarization.
This paper studies product ranking mechanisms of a monopolistic online platform in the presence of social learning. The products’ quality is initially unknown, but consumers can sequentially learn it as online reviews accumulate. A salient aspect of our problem is that consumers, who want to purchase a product from a list of items displayed by the platform, incur a search cost while scrolling down the list. In this setting, the social learning dynamics, and hence the demand, is affected by the interplay of two unique features: substitution and ranking effects.
As financial technology improves and data becomes more abundant, do market prices reflect this growing information and allocate capital more efficiently? While a number of recent studies have documented rises in aggregate price efficiency, we show that there are large cross-sectional differences. The previously-documented increases are driven by a rise in the informativeness of large, growth stocks. The informational efficiency of smaller assets' prices or prices of assets with less growth potential are either flat or declining.
An important challenge for many firms is to identify the life transitions of its customers, such as job searching, expecting a child, or purchasing a home. Inferring such transitions, which are generally unobserved to the firm, can offer the firms opportunities to be more relevant to their customers. In this paper, we demonstrate how a social network platform can leverage its longitudinal user data to identify which of its users are likely to be job seekers. Identifying job seekers is at the heart of the business model of professional social network platforms.
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 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.
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.
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.
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).
Bitcoin provides its users with transaction-processing services which are similar to those of traditional payment systems. This article models the novel economic structure implied by Bitcoin’s innovative decentralized design, which allows the payment system to be reliably operated by unrelated parties called miners. We find that this decentralized design protects users from monopoly pricing. Competition among service providers within the platform and free entry imply no entity can profitably affect the level of fees paid by users.
Bitcoin provides its users with transaction-processing services which are similar to those of traditional payment systems. This article models the novel economic structure implied by Bitcoin’s innovative decentralized design, which allows the payment system to be reliably operated by unrelated parties called miners. We find that this decentralized design protects users from monopoly pricing. Competition among service providers within the platform and free entry imply no entity can profitably affect the level of fees paid by users.
In modern equity markets, participants have a choice of many exchanges at which to trade. Exchanges typically operate as electronic limit order books under a "price-time" priority rule and, in turn, can be modeled as multi-class FIFO queueing systems. A market with multiple exchanges can be thought of as a decentralized, parallel queueing system. Heterogeneous traders that submit limit orders select the exchange, i.e., the queue, in which to place their orders by trading off financial considerations against anticipated delays until their orders may fill.
In modern equity markets, participants have a choice of many exchanges at which to trade. Exchanges typically operate as electronic limit order books under a "price-time" priority rule and, in turn, can be modeled as multi-class FIFO queueing systems. A market with multiple exchanges can be thought of as a decentralized, parallel queueing system. Heterogeneous traders that submit limit orders select the exchange, i.e., the queue, in which to place their orders by trading off financial considerations against anticipated delays until their orders may fill.
Marketing is the functional area primarily responsible for driving the organic growth of a firm. In the age of digital marketing and big data, marketers are inundated with increasingly rich data from an ever-expanding array of sources. Such data may help marketers generate insights about customers and competitors. One fundamental question remains: How can marketers wrestle massive flows of existing and nascent data resources into coherent, effective growth strategies?
We evaluate the impacts of tax policy on asset returns using the U.S. municipal bond market. In theory, tax-induced ownership segmentation limits risk-sharing, creating downward-sloping regions of the aggregate demand curve for the asset. In the data, cross-state variation in tax privilege policies predicts differences in in-state ownership of local municipal bonds; the policies create incentives for concentrated local ownership. High tax privilege states have muni-bond yields that are more sensitive to variations in supply and local idiosyncratic risk.
Current accounting practice expenses many investments in intangible assets to the income statement, confusing earnings from current revenues with investments to gain future revenues. This has led to increasing calls to book those investments to the balance sheet. Drawing on the relevant research, this paper proposes solutions for the accounting for intangible assets that contrast with balance sheet recognition, and compares them to current practice and the IFRS standards that dictate practice.
Using US Census employer-employee matched data, I show that employer financial distress accelerates the exit of employees to found start-ups. This effect is particularly evident when distressed firms are less able to enforce contracts restricting employee mobility into competing firms. Entrepreneurs exiting financially distressed employers earn higher wages prior to the exit and after founding start-ups, compared to entrepreneurs exiting non-distressed firms. Consistent with distressed firms losing higher-quality workers, their start-ups have higher average employment and payroll growth.
How do ethical arguments affect AI adoption in business? We randomly expose business decision-makers to arguments used in AI fairness activism. Arguments emphasizing the inescapability of algorithmic bias lead managers to abandon AI for manual review by humans and report greater expectations about lawsuits and negative PR. These effects persist even when AI lowers gender and racial disparities, and when engineering investments to address AI fairness are feasible. Emphasis on status quo comparisons yields opposite effects.
How do ethical arguments affect AI adoption in business? We randomly expose business decision-makers to arguments used in AI fairness activism. Arguments emphasizing the inescapability of algorithmic bias lead managers to abandon AI for manual review by humans and report greater expectations about lawsuits and negative PR. These effects persist even when AI lowers gender and racial disparities, and when engineering investments to address AI fairness are feasible. Emphasis on status quo comparisons yields opposite effects.
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.
In this research, we investigate the prevalence, robustness and possible reasons underlying the polarity of online review distributions with the majority of the reviews at the positive end of the rating scale, a few reviews in the mid-range and some reviews at the negative end of the scale.
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.
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.
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.
This paper modifies the standard returns-earnings regression in accounting research to show that financial reports convey both cash-flow news and discount-rate (expected-return) news. The paper points to the realization principle, associated as it is with the resolution of risk, as the accounting feature that conveys expected-return news. The modified returns-earnings regressions indicate that the information so conveyed pertains to priced risk.
We use five years of bidding data to examine the reaction of advertisers to widely disseminated press on the lack of effectiveness of brand search advertising (queries that contain the firm's name) found in a large experiment run by eBay (Blake, Nosko and Tadelis, 2015). We estimate that 11% of firms that did not face competing ads on their brand keywords, matching the case of eBay, discontinued the practice of brand search advertising.
As a result of the digital revolution, new topics and themes have entered consumer research, and, as the digital revolution enters a new phase, additional new concepts and research questions will emerge. To illustrate the variety of themes on digital technology that consumer researchers have studied, I am presenting a collection of five articles that represent this active new research area. Moreover, I will look into the future and propose a research agenda to address key consumer behavior issues occurring during the next phase of the digital transformation.
Mass customization interfaces typically guide consumers through the configuration process in a sequential manner, focusing on one product attribute after the other. What if this standardized customization experience were personalized for consumers on the basis of how they process information? A series of large-scale field and experimental studies, conducted with Western and Eastern consumers, shows that matching the interface to consumers’ culture-specific processing style enhances the effectiveness of mass customization.
Two modern economic trends are the increase in firm size and advances in information technology. We explore the hypothesis that big data disproportionately benefits big firms. Because they have more economic activity and a longer firm history, large firms have produced more data. As processor speed rises, abundant data attracts more financial analysis. Data analysis improves investors’ forecasts and reduces equity uncertainty, reducing the firm’s cost of capital. When investors can process more data, large firm investment costs fall by more, enabling large firms to grow larger.
On search keywords with trademarked terms, the brand owner ("focal brand") and other relevant firms compete for consumers. For the focal brand, paid clicks have a direct substitute in the organic links below the paid ad(s). The proximity of this substitute depends on whether competing firms are bidding aggressively to siphon off traffic. We study the returns to focal brands and competitors using large-scale experiments on Bing with data from thousands of brands.
The rise of "Big Data" had a big impact on marketing research and practice. In this article, we first highlight sources of useful consumer information that are now available at large scale and very little or no cost. We subsequently discuss how this information — with the help of new analytical techniques — can be translated into valuable insights on consumers' psychological states and traits that can, in turn, be used to inform marketing strategy.
The rise of "Big Data" had a big impact on marketing research and practice. In this article, we first highlight sources of useful consumer information that are now available at large scale and very little or no cost. We subsequently discuss how this information — with the help of new analytical techniques — can be translated into valuable insights on consumers' psychological states and traits that can, in turn, be used to inform marketing strategy.
Customer Relationship Management (CRM) campaigns have traditionally focused on maximizing the profitability of the targeted customers. In this paper we investigate the social effects of CRM campaigns. We demonstrate that, in business settings that are characterized by network externalities, a CRM campaign that is aimed at changing the behavior of specific customers propagates through the social network, thereby also affecting the behavior of non-targeted customers.
The goal of this research was to test whether including an inexpensive nonfood item (toy) with a smaller-sized meal bundle (420 calories), but not with the regular-sized meal bundle version (580 calories), would incentivize children to choose the smaller-sized meal bundle, even among children with overweight and obesity. Logistic regression was used to evaluate the effect in a between-subjects field experiment of a toy on smaller-sized meal choice (here, a binary choice between a smaller-sized or regular-sized meal bundles).
Despite the popularity of prediction, markets among economists, businesses, and policymakers have been slow to adopt them in decision-making. Most studies of prediction markets outside the lab are from public markets with large trading populations. Corporate prediction markets face additional issues, such as thinness, weak incentives, limited entry, and the potential for traders with biases or ulterior motives — raising questions about how well these markets will perform.
We present an end-to-end text mining methodology for relation extraction of adverse drug reactions (ADRs) from medical forums on the Web. Our methodology is novel in that it combines three major characteristics: (i) an underlying concept of using a head-driven phrase structure grammar (HPSG) based parser; (ii) domain-specific relation patterns, the acquisition of which is done primarily using unsupervised methods applied to a large, unlabeled text corpus; and (iii) automated post-processing algorithms for enhancing the set of extracted relations.
I view the research articles presented here as prototypical examples of what may be called “the new wave” in studying Asian markets and consumers. This emerging “new wave” has a different focus than research done over the last few decades. Research is shifting from an emphasis on traditional Asian culture toward a focus on consumer culture and how this consumer culture manifests itself in various Asian markets. The “new wave” research also focuses less on general concepts and more on uniquely Asian phenomena. Finally, methodologically research is shifting from “East” vs.
We report the results of a survey conducted in November 2014 in which 29 quantitative marketing scholars from around the world reflected on the present and future of their field. The survey focused on substantive areas, methods and tools, practical and managerial relevance, doctoral training, and promotion and tenure. The results of the survey revealed several general insights on the challenges and opportunities faced by the field of quantitative marketing research.
We report the results of a survey conducted in November 2014 in which 29 quantitative marketing scholars from around the world reflected on the present and future of their field. The survey focused on substantive areas, methods and tools, practical and managerial relevance, doctoral training, and promotion and tenure. The results of the survey revealed several general insights on the challenges and opportunities faced by the field of quantitative marketing research.
We report the results of a survey conducted in November 2014 in which 29 quantitative marketing scholars from around the world reflected on the present and future of their field. The survey focused on substantive areas, methods and tools, practical and managerial relevance, doctoral training, and promotion and tenure. The results of the survey revealed several general insights on the challenges and opportunities faced by the field of quantitative marketing research.
The authors discuss the current state and future scenarios of brand experience — a new concept that they contributed to the brand management literature. Specifically, they present three research and practical trends, and marketing challenges: (i) the proliferation of settings and media that evoke brand experiences; (ii) the role of brands in consumption experiences; and (iii) the need of brand experiences to reach positive psychological outcomes.
The value of a business depends on its future not its past. Nonetheless, some managers base key decisions on backwards-looking metrics or models that have been shown to be inappropriate in many situations.
What metrics and models can provide managers with steering control for maximizing the value of their business? This editorial provides some ideas.
We present a method that dynamically designs elicitation questions for estimating preferences, focusing on the parameters of cumulative prospect theory and time discounting models. Typically these parameters are elicited by presenting decision makers with a series of choices between alternatives, gambles or delayed payments. The method dynamically (i.e., adaptively) designs such choices to optimize the information provided by each choice, while leveraging the distribution of the parameters across decision makers (heterogeneity) and capturing response error.
We present a method that dynamically designs elicitation questions for estimating preferences, focusing on the parameters of cumulative prospect theory and time discounting models. Typically these parameters are elicited by presenting decision makers with a series of choices between alternatives, gambles or delayed payments. The method dynamically (i.e., adaptively) designs such choices to optimize the information provided by each choice, while leveraging the distribution of the parameters across decision makers (heterogeneity) and capturing response error.
Web 2.0 provides gathering places for internet users in blogs, forums, and chat rooms. These gathering places leave footprints in the form of colossal amounts of data regarding consumers' thoughts, beliefs, experiences, and even interactions. In this paper, we propose an approach for firms to explore online user-generated content and "listen" to what customers write about their and the competitors' products. Our objective is to convert the user-generated content to market structures and competitive landscape insights.