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    • News  (92)
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Show Results For

  • All HBS Web  (601)
    • People  (1)
    • News  (92)
    • Research  (416)
    • Events  (6)
    • Multimedia  (1)
  • Faculty Publications  (224)
← Page 8 of 601 Results →
  • Research Summary

Consumer Response to Online Ratings and Recommendations

Jolie is currently conducting several laboratory and field experiments to assess the tendency of individuals to employ predictable heuristics in complex information aggregation tasks, thus leading to search and choice behavior that is suboptimal relative to the fully... View Details
  • September 2020
  • Teaching Note

Miami's Tech Future (B): Building the Entrepreneurial Ecosystem

By: Rosabeth Moss Kanter and Joyce J. Kim
In 2017, Miami was rated #1 among U.S. cities for startups, but about 40th for “scale-ups” – growth companies. This case shows how leaders of incubators and accelerators supported startups and a culture of entrepreneurship, but also describes some factors limiting... View Details
Keywords: Scaling; Growth; Startup; Community Impact; Community Relations; Business Startups; Entrepreneurship; Information Technology; Growth and Development Strategy; Business and Community Relations; Miami; Florida
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Kanter, Rosabeth Moss, and Joyce J. Kim. "Miami's Tech Future (B): Building the Entrepreneurial Ecosystem." Harvard Business School Teaching Note 321-045, September 2020.
  • October 2012
  • Article

The Effect of Reference Point Prices on Mergers and Acquisitions

By: Malcolm Baker, Xin Pan and Jeffrey Wurgler
Prior stock price peaks of targets affect several aspects of merger and acquisition activity. Offer prices are biased toward recent peak prices although they are economically unremarkable. An offer's probability of acceptance jumps discontinuously when it exceeds a... View Details
Keywords: Mergers and Acquisitions; Stocks; Price; Valuation; Negotiation
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Baker, Malcolm, Xin Pan, and Jeffrey Wurgler. "The Effect of Reference Point Prices on Mergers and Acquisitions." Journal of Financial Economics 106, no. 1 (October 2012): 49–71.
  • 10 Nov 2015
  • First Look

November 10, 2015

of research, the authors have concluded that four biases stand in the way: we focus too heavily on success, are too quick to act, try too hard to fit in, and rely too much on experts. Each of these biases... View Details
Keywords: Sean Silverthorne
  • September 2, 2014
  • Article

Development of In-Group Favoritism in Children's Third-Party Punishment of Selfishness

By: Jillian J. Jordan, Katherine McAuliffe and Felix Warneken
When enforcing norms for cooperative behavior, human adults sometimes exhibit in-group bias. For example, third-party observers punish selfish behaviors committed by out-group members more harshly than similar behaviors committed by in-group members. Although evidence... View Details
Keywords: Ontogeny; Cooperation; Equality and Inequality
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Jordan, Jillian J., Katherine McAuliffe, and Felix Warneken. "Development of In-Group Favoritism in Children's Third-Party Punishment of Selfishness." Proceedings of the National Academy of Sciences 111, no. 35 (September 2, 2014): 12710–12715.
  • Summer 2021
  • Article

Predictable Country-level Bias in the Reporting of COVID-19 Deaths

By: Botir Kobilov, Ethan Rouen and George Serafeim
We examine whether a country’s management of the COVID-19 pandemic relate to the downward biasing of the number of reported deaths from COVID-19. Using deviations from historical averages of the total number of monthly deaths within a country, we find that the... View Details
Keywords: COVID-19; Deaths; Reporting; Incentives; Government Policy; Health Pandemics; Health Care and Treatment; Country; Crisis Management; Outcome or Result; Reports; Policy
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Kobilov, Botir, Ethan Rouen, and George Serafeim. "Predictable Country-level Bias in the Reporting of COVID-19 Deaths." Journal of Government and Economics 2 (Summer 2021).
  • August 2020 (Revised December 2020)
  • Background Note

A Note on Ethical Analysis

By: Nien-hê Hsieh
To engage in ethical analysis is to answer such questions as “What is the right thing to do?” “What does it mean to be a good person?” “How should I live my life?” Ethical analysis, on its own, is often not adequate for doing the right thing or being a good... View Details
Keywords: Ethics; Framework; Decision Making; Prejudice and Bias
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Hsieh, Nien-hê. "A Note on Ethical Analysis." Harvard Business School Background Note 321-038, August 2020. (Revised December 2020.)
  • May 2016
  • Article

When Performance Trumps Gender Bias: Joint Versus Separate Evaluation

By: Iris Bohnet, Alexandra van Geen and Max Bazerman
We examine a new intervention to overcome gender biases in hiring, promotion, and job assignments: an "evaluation nudge," in which people are evaluated jointly rather than separately regarding their future performance. Evaluators are more likely to focus on individual... View Details
Keywords: Prejudice and Bias; Selection and Staffing; Decision Choices and Conditions; Performance; Gender
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Bohnet, Iris, Alexandra van Geen, and Max Bazerman. "When Performance Trumps Gender Bias: Joint Versus Separate Evaluation." Management Science 62, no. 5 (May 2016): 1225–1234.
  • 13 Sep 2018
  • HBS Seminar

Ashley Swanson, Wharton, University of Pennsylvania

  • Research Summary

Analyst Disagreement, Forecast Bias and Stock Returns

We present evidence of inefficient information processing in equity markets by documenting that biases in analysts' earnings forecasts are reflected in stock prices. In particular, investors fail to account for analysts' tendency to withhold negative views and to issue... View Details
  • June 2010
  • Case

Legendary Pictures & ABRY Partners

This case focuses on the review by the private equity firm ABRY Partners of an opportunity to invest in a film financing vehicle, Legendary Pictures. Before 2004, private equity firms had avoided co-investing with film studios in film productions because of their... View Details
Keywords: Private Equity; Investment; Film Entertainment; Entertainment and Recreation Industry
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El-Hage, Nabil N., and Anne Katherine Kofol. "Legendary Pictures & ABRY Partners." Harvard Business School Case 210-094, June 2010.
  • 2007
  • Chapter

Behavioral Corporate Finance: A Survey

By: Malcolm Baker, Richard Ruback and Jeffrey Wurgler
Research in behavioral corporate finance takes two distinct approaches. The first emphasizes that investors are less than fully rational. It views managerial financing and investment decisions as rational responses to securities market mispricing. The second approach... View Details
Keywords: Decisions; Prejudice and Bias; Debt Securities; Financial Management; Price; Theory; Investment; Problems and Challenges; Behavioral Finance; Corporate Finance
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Baker, Malcolm, Richard Ruback, and Jeffrey Wurgler. "Behavioral Corporate Finance: A Survey." In The Handbook of Corporate Finance, Volume 1: Empirical Corporate Finance, edited by Espen Eckbo. New York: Elsevier/North-Holland, 2007.
  • January 2012 (Revised March 2012)
  • Case

Dimensional Fund Advisors (DFA)'s Entry into the Retirement Market

By: Lauren Cohen and Christopher Malloy
This case examines Dimensional Fund Advisors (DFA)'s decision to enter the retirement market with their new "Dimensional Managed DC" product, a complete retirement solution that aimed to provide investors with what they really wanted: the same standard of living in... View Details
Keywords: Retirement; Asset Management
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Cohen, Lauren, and Christopher Malloy. "Dimensional Fund Advisors (DFA)'s Entry into the Retirement Market." Harvard Business School Case 212-068, January 2012. (Revised March 2012.)
  • Article

How Much Should We Trust Staggered Difference-In-Differences Estimates?

By: Andrew C. Baker, David F. Larcker and Charles C.Y. Wang
We explain when and how staggered difference-in-differences regression estimators, commonly applied to assess the impact of policy changes, are biased. These biases are likely to be relevant for a large portion of research settings in finance, accounting, and law that... View Details
Keywords: Difference In Differences; Staggered Difference-in-differences Designs; Generalized Difference-in-differences; Dynamic Treatment Effects; Mathematical Methods
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Baker, Andrew C., David F. Larcker, and Charles C.Y. Wang. "How Much Should We Trust Staggered Difference-In-Differences Estimates?" Journal of Financial Economics 144, no. 2 (May 2022): 370–395. (Editor's Choice, May 2022; Jensen Prize, First Place, June 2023.)
  • November 12, 2021
  • Editorial

The Psychology Behind Meeting Overload

By: A.V. Whillans, Dave Feldman and Damian Wisniewski
Bad meetings are the bane of the corporate world — and yet despite what appears to be an overwhelming consensus that they’re often unnecessary and unproductive, many workplaces continue to struggle to avoid them. In this piece, the authors discuss the psychological... View Details
Keywords: Meetings; Collaboration; Psychology; Time Management; Communication
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Whillans, A.V., Dave Feldman, and Damian Wisniewski. "The Psychology Behind Meeting Overload." Harvard Business Review (website) (November 12, 2021).
  • 17 Oct 2016
  • News

How to Hire with Algorithms

    Mandi Nerenberg

    Mandi is a doctoral student in Organizational Behavior at Harvard Business School. She is interested in the impact of how gender and racial dynamics shape workplace evaluations. Her research explores gender biases in interpersonal professional contexts,... View Details

    • 18 Apr 2016
    • News

    How to Take the Bias Out of Interviews

      The Psyche on Automatic: Amy Cuddy Probes Snap Judgments, Warm Feelings, and How to Become an “Alpha Dog”

      Social psychologist Cuddy, an assistant professor of business administration, investigates how people perceive and categorize others. Warmth and competence, she finds, are the two critical variables. They account for about 80 percent of our... View Details
      • Research Summary

      Overview

      By: Himabindu Lakkaraju
      I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:

      1. How to build... View Details
      Keywords: Artificial Intelligence; Machine Learning; Decision Analysis; Decision Support
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