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.
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Anchoring refers to a biased judgment on a stimulus based on the initial assessment of another stimulus and the insufficient adjustment away from that initial assessment. Previous research indicates that anchoring seems to be a general phenomenon, underlying a wide variety of processing strategies (Epley and Gilovich 2001; Johnson and Puto 1987; Tversky and Kahnemann 1974). Every time when individuals form an impression or an image about a stimulus while another stimulus is present, these impressions may be subject to anchoring effects.
This research models the dynamics of customer relationships using typical transaction data. Our proposed model permits not only capturing the dynamics of customer relationships but also incorporating the effect of the sequence of customer-firm encounters on the dynamics of customer relationships and the subsequent buying behavior. Our approach to modeling relationship dynamics is structurally different from existing approaches.
We propose a framework for designing adaptive choice-based conjoint questionnaires that are robust to response error. It is developed based on a combination of experimental design and statistical learning theory principles. We implement and test a specific case of this framework using Regularization Networks. We also formalize within this framework the polyhedral methods recently proposed in marketing.
We identify gaps and propose several directions for future research in preference measurement. We structure our argument around a framework that views preference measurement as comprising three interrelated components: (1) the problem that the study is ultimately intended to address; (2) the design of the preference measurement task and the data collection approach; (3) the specification and estimation of a preference model, and the conversion into action. Conjoint analysis is only one special case within this framework.
We identify gaps and propose several directions for future research in preference measurement. We structure our argument around a framework that views preference measurement as comprising three interrelated components: (1) the problem that the study is ultimately intended to address; (2) the design of the preference measurement task and the data collection approach; (3) the specification and estimation of a preference model, and the conversion into action. Conjoint analysis is only one special case within this framework.
Following a successful idea generation exercise, a company might easily be left with hundreds of ideas, generated by experts, employees, or consumers. The next step is to screen these ideas, and identify those with the highest potential. In this paper we propose a practical approach to involving consumers in idea screening. Although the number of ideas may potentially be very large, it would be unreasonable to ask each consumer to evaluate more than a few ideas. This raises the challenge of efficiently selecting the ideas to be evaluated by each consumer.
We propose and test a new approach for modeling consumer heterogeneity in conjoint estimation based on convex optimization and statistical machine learning. We develop methods both for metric and choice data. Like hierarchical Bayes (HB), our methods shrink individual-level partworth estimates towards a population mean. However, while HB samples from a posterior distribution that is influenced by exogenous parameters (the parameters of the second-stage priors), we minimize a convex loss function that depends only on endogenous parameters.
Polyhedral methods for choice-based conjoint analysis provide a means to adapt choice-based questions at the individual-respondent level and provide an alternative means to estimate partworths when there are relatively few questions per respondent as in a web-based questionnaire. However, these methods are deterministic and are susceptible to the propagation of response errors. They also assume, implicitly, a uniform prior on the partworths.
Idea generation (ideation) is critical to the design and marketing of new products, to marketing strategy, and to the creation of effective advertising copy. However, there has been relatively little formal research on the underlying incentives with which to encourage participants to focus their energies on relevant and novel ideas. Several problems have been identified with traditional ideation methods. For example, participants often free ride on other participants' efforts because rewards are typically based on the group-level output of ideation sessions.
Building on the work of Dhar, Menon, and Maach (2004), this commentary describes how the compromise effect models developed in the work of Kivetz, Netzer, and Srinivasan (2004) can be extended to predict complex (business-to-business) purchase decisions and additional behavioral context effects. The authors clarify their general modeling approach and outline how it applies to choices among solutions (augmented products) and group decision making.
Building on the work of Dhar, Menon, and Maach (2004), this commentary describes how the compromise effect models developed in the work of Kivetz, Netzer, and Srinivasan (2004) can be extended to predict complex (business-to-business) purchase decisions and additional behavioral context effects. The authors clarify their general modeling approach and outline how it applies to choices among solutions (augmented products) and group decision making.
The authors propose and test a new "polyhedral" choice-based conjoint analysis question-design method that adapts each respondent's choice sets on the basis of previous answers by that respondent. Polyhedral "interior-point" algorithms design questions that quickly reduce the sets of partworths that are consistent with the respondent’s choices. To identify domains in which individual adaptation is promising (and domains in which it is not), the authors evaluate the performance of polyhedral choice-based conjoint analysis methods with Monte Carlo experiments.
Despite the obvious importance of understanding how business cycle fluctuations affect both individual companies and whole industries, not much marketing research focuses on the subject. Often, one only has aggregate information on the state of the national economy, even though cyclical contractions and expansions generally do not have an equal impact on every industry, nor on all firms in any given industry.
The purpose of this paper is to understand buyer/seller adoption dynamics in independent, buyer-side B2B exchanges. In a stylized model, we assume that the main role of the exchange is to reduce search costs for buyers. Buyers and sellers enter or exit the exchange based on the relative economic surplus (loss) they receive inside vs. outside the exchange. We contrast two situations: one where participants' switching cost to join the institution is negligible and another, in which it is significant.
We propose and test new adaptive question design and estimation algorithms for partial-profile conjoint analysis. Polyhedral question design focuses questions to reduce a feasible set of parameters as rapidly as possible. Analytic center estimation uses a centrality criterion based on consistency with respondents' answers. Both algorithms run with no noticeable delay between questions. We evaluate the proposed methods relative to established benchmarks for question design (random selection, D-efficient designs, adaptive conjoint analysis) and estimation (hierarchical Bayes).
For many years, average bed occupancy level has been the primary measure that has guided hospital bed capacity decisions at both policy and managerial levels. Even now, the common wisdom that there is an excess of beds nationally has been based on a federal target of 85% occupancy that was developed about 25 years ago. This paper examines data from New York sate and uses queueing analysis to estimate bed unavailability in intensive care units (ICUs) and obstetrics units. Using various patient delay standards, units that appear to have insufficient capacity are identified.
This paper studies dynamic competition in markets characterized by the introduction of technologically advanced next-generation products. Firms invest in new product effort in an attempt to attain industry leadership, thus securing high profits and benefiting from advantages relevant for the success of future product generations. The analysis reveals that when the current leader possesses higher research and development (R&D) competence, it tends to invest more in R&D than rivals and to retain its lead position.
The author studies the pricing of information with private value (e.g., management consulting, legal advice, medical diagnosis). Anecdotal evidence shows that in some of these markets, competing information sellers split the business to sell only first or second opinions to their customers. The author explains this pricing practice by showing that second-opinion markets are a result of temporal differentiation.
The authors investigate two competing hypotheses about how chronic vividness of imagery interacts with the vividness and salience of information in decision making. Results from four studies, covering a variety of decision domains, indicate that chronic imagery vividness rarely amplifies the effects of vivid and salient information. Imagery vividness may, in fact, attenuate the effects of vivid and salient information. This is because, relative to nonvivid imagers, vivid imagers rely less on information that appears obvious and rely more on information that seems less obvious.
The authors propose a new methodology called the "coupled-hazard approach" to study the global diffusion of technological innovations. Beyond its ability to describe discontinuous diffusion patterns, the method explicitly recognizes the conceptual difference between the timing of a country's introduction of the new technology (the so-called implementation stage; Rogers 1983) and the timing of the innovation's full adoption in the country (the confirmation stage).
The authors study global adoption processes where the units of observation are countries, which sequentially adopt a particular technology. The authors’ goal is to provide a better understanding of how exogenous and endogenous country characteristics affect this diffusion process. They develop a general model of global adoption processes, which allows researchers to test extant theories of cross-country adoption, and illustrate the approach using data from the cellular telephone industry for 184 countries.
Marketers all over the world agree that the Internet will have a major impact on the way firms do business. What changes will exactly occur, however, is hard to predict as the Internet is in a phase of rapid growth and constant change. Patterns are difficult to isolate, especially since despite its explosive growth, today, the Net is still in its infancy, only being available to a small proportion of people. In spite of this general lack of reliable patterns, one consensus among managers seems to be that the Internet is likely to intensify price competition.
This article analyzes how Knowledge Management (KM) is likely to affect competition in the management consulting industry. KM represents a fundamental and qualitative change in this industry's basic production technology. Because management consultants acquire information directly from their customers, for these firms, KM technology exhibits increasing returns to scale. As such, although KM clearly represents an opportunity for some consultants to build a sustainable competitive advantage, it is likely to lead to a shake-out.
This article proposes a method that overcomes a number of problems associated with new product diffusion models noted in the marketing literature. We illustrate the methodology in the context of better understanding global variances in new product adoption. Building on existing diffusion models and sample matching principles from international consumer research, we suggest a "staged estimation procedure." The procedure provides both sensible and robust estimates and remains usable even if the diffusion process is in its earliest stage in most or all countries.
This paper presents an applied methodology to assist managers in strategically setting prices and allocating resources over the product, brand, or adoption (diffusion) life cycle. While substantial theoretical work has been achieved in this area in the management science and operations research disciplines, approaches which can be implemented as managerial tools are generally lacking.
Selling information that is later used in decision making constitutes an increasingly important business in modem economies (Jensen 1991). Information is sold under a large variety of forms: industry reports, consulting services, database access, and/or professional opinions given by medical, engineering, accounting/ financial, and legal professionals, among others.
We use discounted cash flow analysis to measure the projected fiscal capacity of the U.S. federal government. We apply our valuation method to the CBO’s projections for the U.S. federal government’s primary deficits between 2022 and 2052 and projected debt outstanding in 2052. The discount rate for projected cash flows and future debt must include a GDP or market risk premium in recognition of the risk associated with future surpluses. Despite current low interest rates, we find that U.S. fiscal capacity is more limited than commonly thought.