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  • October 2021
  • Article

Board Design and Governance Failures at Peer Firms
By: Shelby Gai, J. Yo-Jud Cheng and Andy Wu
Our study introduces board committees as a crucial determinant of board actions. We examine how directors who structurally link different board committees—referred to as multi-committee directors (MCDs)—explain why some board actions are merely symbolic while others...  View Details
Keywords: Board Committees; Board Monitoring; New Director Nomination; Peer Financial Restatements; Governing and Advisory Boards; Corporate Governance; Performance Effectiveness
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  • October 2021
  • Article

Judgment Aggregation in Creative Production: Evidence from the Movie Industry
By: Hong Luo, Jeffrey T. Macher and Michael Wahlen
We study a novel, low-cost approach to aggregating judgment from a large number of industry experts on ideas that they encounter in their normal course of business. Our context is the movie industry, in which customer appeal is difficult to predict and investment costs...  View Details
Keywords: Judgment Aggregation; Quality Uncertainty; Creative Industry; Project Evaluation And Selection; Creativity; Film Entertainment; Judgments; Motion Pictures and Video Industry
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  • 2021
  • Working Paper

When to Go and How to Go? Founder and Leader Transition in Private Equity Firms
By: Josh Lerner and Diana Noble
Leadership transition in private equity firms is an understudied field, despite the important, albeit controversial, role such firms play in developed economies. We analyzed 260 firms in an empirical study, supplemented by qualitative interviews with a small sample of...  View Details
Keywords: Buyout; Succession; Venture Capital; Leadership; Private Equity; Personal Development and Career; Performance
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  • September 15, 2021
  • Article

Improving Deconvolution Methods in Biology Through Open Innovation Competitions: An Application to the Connectivity Map
By: Andrea Blasco, Ted Natoli, Michael G. Endres, Rinat A. Sergeev, Steven Randazzo, Jin Hyun Paik, N.J. Maximilian Macaluso, Rajiv Narayan, Xiaodong Lu, David Peck, Karim R. Lakhani and Aravind Subramanian
A recurring problem in biomedical research is how to isolate signals of distinct populations (cell types, tissues, and genes) from composite measures obtained by a single analyte or sensor. Existing computational deconvolution approaches work well in many specific...  View Details
Keywords: Deconvolution; Methods; Open Innovation Competition; Genomics; Research; Innovation and Invention
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  • Fall 2021
  • Article

When to Go and How to Go? Founder and Leader Transitions in Private Equity Firms
By: Josh Lerner and Diana Noble
Leadership transition in private equity firms is an understudied field, despite the important, albeit controversial, role such firms play in developed economies. We analyzed 260 firms in an empirical study, supplemented by qualitative interviews with a small sample of...  View Details
Keywords: Leadership Transition; Private Equity; Leadership; Management Succession; Analysis
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  • 2021
  • Working Paper

BEAT Unintended Bias in Personalized Policies
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those "protected" attributes....  View Details
Keywords: Algorithmic Bias; Personalization; Targeting; Fairness; Discrimination
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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...  View Details
Keywords: Missing Data; Bayesian Statistics; Imputation; Categorical Data; Estimation
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  • August 2021
  • Article

The Undervalued Power of Self-relevant Research: The Case of Researching Retirement While Retiring
By: Teresa M. Amabile and Douglas T. (Tim) Hall
For decades, training in management research has emphasized objectivity, typically viewed as an arm’s length distance between the topic of the research and the interests of the researcher. This emphasis has led most scholars to avoid research topics of deep personal...  View Details
Keywords: Qualitative Research Methods; Case Research Methods; Organizational Behavior; Careers; Career Changes And Transitions; Self-relevant Research; Research; Personal Development and Career; Transition; Identity; Retirement
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  • 2021
  • Working Paper

Population Interference in Panel Experiments
By: Iavor I Bojinov, Kevin Wu Han and Guillaume Basse
The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in...  View Details
Keywords: Finite Population; Potential Outcomes; Dynamic Causal Effects; Mathematical Methods
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  • 2021
  • Working Paper

How Much Should We Trust Staggered Difference-In-Differences Estimates?
By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
Difference-in-differences analysis with staggered treatment timing is frequently used to assess the impact of policy changes on corporate outcomes in academic research. However, recent advances in econometric theory show that such designs are likely to be biased in the...  View Details
Keywords: Difference In Differences; Staggered Difference-in-differences Designs; Generalized Difference-in-differences; Dynamic Treatment Effects; Mathematical Methods
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  • 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...  View Details
Keywords: Machine Learning; Econometric Analysis; Instrumental Variable; Random Forest; Causal Inference; Analysis; Theory; Measurement and Metrics; Performance Consistency
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  • 2021
  • Article

Fair Algorithms for Infinite and Contextual Bandits
By: Matthew Joseph, Michael J Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
We study fairness in linear bandit problems. Starting from the notion of meritocratic fairness introduced in Joseph et al. [2016], we carry out a more refined analysis of a more general problem, achieving better performance guarantees with fewer modelling assumptions...  View Details
Keywords: Algorithms; Bandit Problems; Fairness; Mathematical Methods
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  • Article

Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
By: Kaivalya Rawal and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affected individuals. While developing such tools is important, it is even more critical to...  View Details
Keywords: Predictive Models; Decision Making; Framework; Mathematical Methods
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  • November 2020
  • Teaching Note

Social Salary Setting at Spiber
By: Ashley Whillans and John Beshears
Teaching Note for HBS Case No. 920-050. The case tells the story of Spiber, a Japanese technology start-up company. To reflect the company’s values, the leadership team implemented a new and unique salary-setting process: each employee had the authority to choose their...  View Details
Keywords: Compensation; General Management; Employee Benefits; Incentives; Motivation; Compensation and Benefits; Fairness; Motivation and Incentives; Management; Happiness; Negotiation; Cross-Cultural and Cross-Border Issues; Japan
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  • November 2020
  • Article

Taxation in Matching Markets
By: Arnaud Dupuy, Alfred Galichon, Sonia Jaffe and Scott Duke Kominers
We analyze the effects of taxation in two-sided matching markets, i.e., markets in which all agents have heterogeneous preferences over potential partners. In matching markets, taxes can generate inefficiency on the allocative margin by changing who is matched to whom,...  View Details
Keywords: Matching Markets; Labor Markets; Taxation; Labor; Markets
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  • Article

Oracle Efficient Private Non-Convex Optimization
By: Seth Neel, Aaron Leon Roth, Giuseppe Vietri and Zhiwei Steven Wu
One of the most effective algorithms for differentially private learning and optimization is objective perturbation. This technique augments a given optimization problem (e.g. deriving from an ERM problem) with a random linear term, and then exactly solves it....  View Details
Keywords: Machine Learning; Algorithms; Objective Perturbation; Mathematical Methods
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  • June 2020
  • Teaching Note

Understanding the Brand Equity of Nestlé Crunch Bar
By: Jill Avery and Gerald Zaltman
Teaching Note for HBS Case Nos. 519-061 and 519-062. In early 2018, Nestlé announced the sale of its U.S. candy-making division and a select collection of twenty of its confectionery brands, including the Nestlé Crunch Bar, to Ferrero SpA for $2.8 billion. Under the...  View Details
Keywords: Brand Management; Brand Storytelling; Brand Equity; Market Research; Qualitative Methods; Marketing; Brands and Branding; Marketing Communications; Consumer Behavior; Marketing Strategy; Food and Beverage Industry
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  • 2021
  • Conference Presentation

An Algorithmic Framework for Fairness Elicitation
By: Christopher Jung, Michael J. Kearns, Seth Neel, Aaron Leon Roth, Logan Stapleton and Zhiwei Steven Wu
We consider settings in which the right notion of fairness is not captured by simple mathematical definitions (such as equality of error rates across groups), but might be more complex and nuanced and thus require elicitation from individual or collective stakeholders....  View Details
Keywords: Algorithmic Fairness; Machine Learning; Fairness; Framework; Mathematical Methods
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  • June 2020
  • Article

Parallel Play: Startups, Nascent Markets, and the Effective Design of a Business Model
By: Rory McDonald and Kathleen Eisenhardt
Prior research advances several explanations for entrepreneurial success in nascent markets but leaves a key imperative unexplored: the business model. By studying five ventures in the same nascent market, we develop a novel theoretical framework for understanding how...  View Details
Keywords: Search; Legitimacy; Organizational Innovation; Organizational Learning; Mechanisms And Processes; Institutional Entrepreneurship; Qualitative Methods; Business Model Design; Business Model; Business Startups; Entrepreneurship; Emerging Markets; Adaptation; Competition; Strategy
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  • 2020
  • Working Paper

Iterative Coordination and Innovation
By: Sourobh Ghosh and Andy Wu
Agile management practices from the software industry continue to transform the way organizations innovate across industries, yet they remain understudied in the organizations literature. We investigate the widespread Agile practice of iterative coordination: frequent...  View Details
Keywords: Innovation; Goals; Specialization; Coordination; Field Experiment; Software Development; Organizations; Collaborative Innovation and Invention; Goals and Objectives; Integration; Software
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