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- October 2021
- Article
Confidence and Central Tendency in Perceptual Judgment
By: Yang Xiang, Thomas Graeber, Benjamin Enke and Samuel Gershman
This paper theoretically and empirically investigates the role of Bayesian noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the...
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- August 2021
- Article
Multiple Imputation Using Gaussian Copulas
By: F.M. Hollenbach, I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward and A. Volfovsky
Missing observations are pervasive throughout empirical research, especially in the social sciences. Despite multiple approaches to dealing adequately with missing data, many scholars still fail to address this vital issue. In this paper, we present a simple-to-use...
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- 2020
- Working Paper
Is Accounting Useful for Forecasting GDP Growth? A Machine Learning Perspective
We provide a comprehensive examination of whether, to what extent, and which accounting variables are useful for improving the predictive accuracy of GDP growth forecasts. We leverage statistical models that accommodate a broad set of (341) variables—outnumbering the...
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- 2021
- Working Paper
Bayesian Signatures of Confidence and Central Tendency in Perceptual Judgment
By: Thomas Graeber, Yang Xiang, Benjamin Enke and Samuel Gershman
This paper theoretically and empirically investigates the role of Bayesian noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the...
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- February 2021
- Case
Digital Manufacturing at Amgen
By: Shane Greenstein, Kyle Myers and Sarah Mehta
This case discusses efforts made by biotechnology (biotech) company Amgen to introduce digital technologies into its manufacturing processes. Doing so is complicated by the fact that the process for manufacturing biologics—or therapeutics made from living cells—is...
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Keywords:
Digital Technologies;
Change;
Change Management;
Decision Making;
Cost vs Benefits;
Decisions;
Information;
Data and Data Sets;
Innovation and Invention;
Innovation and Management;
Innovation Leadership;
Innovation Strategy;
Technological Innovation;
Jobs and Positions;
Knowledge;
Leadership;
Organizational Culture;
Science;
Strategy;
Technology;
Technology Adoption;
Biotechnology Industry;
Pharmaceutical Industry;
United States;
California;
Puerto Rico;
Rhode Island
- 2021
- Working Paper
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
By: Mochen Yang, Edward McFowland III, Gordon Burtch and Gediminas Adomavicius
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data, followed...
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- 2021
- Working Paper
Inattentive Inference
By: Thomas Graeber
This paper studies how people infer a state of the world from information structures that include additional, payoff-irrelevant states. For example, learning someone’s effort from their observable performance may require accounting for the otherwise irrelevant role of...
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- January 2021
- Article
Machine Learning for Pattern Discovery in Management Research
By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect...
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- November 2020
- Article
Disrupting the Disruptors or Enhancing Them? How Blockchain Re‐Shapes Two‐Sided Platforms
By: Daniel Trabucchi, Antonella Moretto, Tommaso Buganza and Alan MacCormack
The importance of platform‐based businesses in the modern economy is growing continuously and becoming increasingly relevant. Specifically, the deployment of digital technologies has enhanced the applicability of two‐sided business models, enabling companies to act not...
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- September–October 2020
- Article
The Air War Versus the Ground Game: An Analysis of Multi-Channel Marketing in U.S. Presidential Elections
By: Lingling Zhang and Doug J. Chung
This study jointly examines the effects of television advertising and field operations in U.S. presidential elections, with the former referred to as the “air war” and the latter as the “ground game.” Specifically, the study focuses on how different campaign...
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- 2020
- Working Paper
Conflicting Interests and the Effect of Fiduciary Duty—Evidence from Variable Annuities
By: Mark Egan, Shan Ge and Johnny Tang
We examine the drivers of variable annuity sales and the impact of a proposed regulatory change. Variable annuities are popular retirement products with over $2 trillion in assets in the United States. Insurers typically pay brokers a commission for selling variable...
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- June 2020 (Revised November 2020)
- Case
Vanguard Retail Operations (A)
By: Willy C. Shih and Antonio Moreno
The first two cases in this series are set in the financial services industry, and explore whether it is better for back-office workers to be generalists who provide the flexibility of being able to handle the complete range of transactions that the company faces or...
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- June 2020 (Revised August 2020)
- Supplement
Vanguard Retail Operations (B)
By: Willy C. Shih and Antonio Moreno
The first two cases in this series are set in the financial services industry, and explore whether it is better for back-office workers to be generalists who provide the flexibility of being able to handle the complete range of transactions that the company faces or...
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- March 2020
- Article
Diagnosing Missing Always at Random in Multivariate Data
By: Iavor I. Bojinov, Natesh S. Pillai and Donald B. Rubin
Models for analyzing multivariate data sets with missing values require strong, often assessable, assumptions. The most common of these is that the mechanism that created the missing data is ignorable—a twofold assumption dependent on the mode of inference. The first...
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- 2020
- Article
Towards Modeling the Variability of Human Attention
By: Kuno Kim, Megumi Sano, Julian De Freitas, Daniel Yamins and Nick Haber
Children exhibit extraordinary exploratory behaviors hypothesized to contribute to the
building of models of their world. Harnessing this capacity in artificial systems promises
not only more flexible technology but also cognitive models of the...
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- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Nicolas Padilla and Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can...
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- 2021
- Working Paper
Estimating Models of Supply and Demand: Instruments and Covariance Restrictions
By: Alexander MacKay and Nathan H. Miller
We consider the identification of empirical models of supply and demand. As is well known, a supply-side instrument can resolve price endogeneity in demand estimation. We show that, under common assumptions, two other approaches also yield consistent estimates of the...
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- September 2018
- Article
Assembling the Sales Team
Data and analytical tasks have lengthened productivity ramp-up times in many sales contexts, making each hire a bigger sunk cost for a longer time. Most companies adopt two common practices: They hire on the basis of “experience” and/or look at their best reps and try...
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- September 2018
- Article
Discretionary Task Ordering: Queue Management in Radiological Services
By: Maria Ibanez, Jonathan R. Clark, Robert S. Huckman and Bradley R. Staats
Work-scheduling research typically prescribes task sequences implemented by managers. Yet employees often have discretion to deviate from their prescribed sequence. Using data from 2.4 million radiological diagnoses, we find that doctors prioritize similar tasks...
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Keywords:
Discretion;
Scheduling;
Queue;
Healthcare;
Learning;
Experience;
Decentralization;
Operations;
Service Operations;
Service Delivery;
Performance;
Performance Effectiveness;
Performance Efficiency;
Performance Improvement;
Performance Productivity;
Decisions;
Time Management;
Cost vs Benefits;
Health Industry
- June 2018
- Article
Personal and Social Usage: The Origins of Active Customers and Ways to Keep Them Engaged
By: Clarence Lee, Elie Ofek and Thomas Steenburgh
We study how digital service firms can develop an active customer base, focusing on two questions. First, how does the way that customers use the service postadoption to meet their own needs (personal usage) and to interact with one another (social usage) vary across...
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