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Show Results For
- All HBS Web
(1,917)
- People (2)
- News (215)
- Research (1,370)
- Events (15)
- Multimedia (13)
- Faculty Publications (906)
- 16 Sep 2019
- Research & Ideas
Crowdsourcing Is Helping Hollywood Reduce the Risk of Movie-Making
anything as monumental as a multimillion-dollar feature film." The answer resonates beyond Hollywood, carrying implications for any industry in which the stakes are high, uncertainty looms large, and alternative methods to de-risk ideas... View Details
- 2024
- Case
EPCorp: What Story Does the Data Tell?
By: Jacob M. Cook
In EPCorp: What Story Does the Data Tell?, the Quick Case begins with Shivani Bahl researching problems with her company's website so that she can begin to analyze which option would help EPCorp most: selling all its products on Amazon or improving its own data... View Details
Cook, Jacob M. "EPCorp: What Story Does the Data Tell?" Harvard Business Publishing Case, 2024.
- 30 Dec 2014
- Working Paper Summaries
Return on Political Investment in the American Jobs Creation Act of 2004
- 26 Apr 2016
- First Look
April 26
April 2016 Review of Economic Studies Landing the First Job: The Value of Intermediaries in Online Hiring By: Stanton, Christopher, and Catherine Thomas Abstract—Online markets for remote labor services allow workers and firms to contract with each other directly.... View Details
Keywords: Sean Silverthorne
- October–December 2022
- Article
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; AI and Machine Learning; Forecasting and Prediction
Yang, Mochen, Edward McFowland III, Gordon Burtch, and Gediminas Adomavicius. "Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem." INFORMS Journal on Data Science 1, no. 2 (October–December 2022): 138–155.
Stephen P. Bradley
Professor Bradley is the William Ziegler Professor of Business Administration Emeritus at the Harvard Business School. In addition to teaching Management and Strategy in the Owner President Management Program and leading an... View Details
- 2001
- Chapter
Publicly Funded Science and the Productivity of the Pharmaceutical Industry
By: Rebecca Henderson and Ian Cockburn
U.S. taxpayers funded $14.8 billion of health related research last year, four times the amount that was spent in 1970 in real terms. In this paper we evaluate the impact of these huge expenditures on the technological performance of the pharmaceutical industry. While... View Details
Keywords: Public Sector; Science-Based Business; Research and Development; Sovereign Finance; Pharmaceutical Industry
Henderson, Rebecca, and Ian Cockburn. "Publicly Funded Science and the Productivity of the Pharmaceutical Industry." In Innovation Policy and the Economy, Volume 1, edited by Adam B. Jaffe, Josh Lerner, and Scott Stern, 1–34. MIT Press, 2001.
- 2021
- Working Paper
Technology Differentiation and Firm Performance
By: Sam Arts, Bruno Cassiman and Jianan Hou
Prior work has extensively studied how investing in R&D and building a technology portfolio relate to superior firm performance. However, the value of a firm’s technology portfolio should also be driven by the degree to which it is more unique and technologically... View Details
Keywords: Technology Portfolio; Differentiation; Competitiveness; Organizations; Technology; Performance; United States
Arts, Sam, Bruno Cassiman, and Jianan Hou. "Technology Differentiation and Firm Performance." Harvard Business School Working Paper, No. 22-040, December 2021.
- 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
Hollenbach, F.M., I. Bojinov, S. Minhas, N.W. Metternich, M.D. Ward, and A. Volfovsky. "Multiple Imputation Using Gaussian Copulas." Special Issue on New Quantitative Approaches to Studying Social Inequality. Sociological Methods & Research 50, no. 3 (August 2021): 1259–1283. (0049124118799381.)
- Article
Four Things No One Will Tell You About ESG Data
By: Sakis Kotsantonis and George Serafeim
As the ESG finance field and the use of ESG data in investment decision-making continue to grow, the authors seek to shed light on several important aspects of ESG measurement and data. This article is intended to provide a useful guide for the rapidly rising number of... View Details
Keywords: ESG; ESG (Environmental, Social, Governance) Performance; ESG Reporting; Data Analytics; Sustainability; Sustainability Reporting; CSR; Transparency; Investment Management; Socially Responsible Investing; Sustainable Finance; Sustainable Development; Inclusion; Inclusive Growth; Corporate Social Responsibility and Impact; Corporate Accountability; Investment; Management; Climate Change; Corporate Governance; Diversity; Integrated Corporate Reporting
Kotsantonis, Sakis, and George Serafeim. "Four Things No One Will Tell You About ESG Data." Journal of Applied Corporate Finance 31, no. 2 (Spring 2019): 50–58.
- 28 Sep 2006
- Working Paper Summaries
Scale without Mass: Business Process Replication and Industry Dynamics
- 05 Jul 2006
- Working Paper Summaries
A Survey-Based Procedure for Measuring Uncertainty or Heterogeneous Preferences in Markets
- November 2004 (Revised March 2007)
- Case
10 Uncommon Values®: Optimizing the Stock-Selection Process
By: Paul M. Healy and Boris Groysberg
In 2003, Steve Hash, research director at Lehman Brothers, prepared to initiate the firm's "Ten Uncommon Values" stock-picking process for the year. An investment committee had to pick the 10 best stocks from about 100 stock ideas presented by the firm's analysts. The... View Details
Keywords: Stocks; Investment; Financial Strategy; Decision Making; Groups and Teams; Financial Services Industry; United States
Healy, Paul M., and Boris Groysberg. "10 Uncommon Values®: Optimizing the Stock-Selection Process." Harvard Business School Case 405-022, November 2004. (Revised March 2007.)
- TeachingInterests
Marketing Models Doctoral Seminar
This course is a doctoral level course on Quantitative Marketing. We will cover methodological as well as substantive topics this semester. Methodological topics include: Choice models, Entry and Exit models, Dynamic structural models, Bayesian estimation methods... View Details
Robert C. Merton
Robert C. Merton is the School of Management Distinguished Professor of Finance at the MIT Sloan School of Management.
Merton is University Professor Emeritus at Harvard University and was the George Fisher Baker Professor of... View Details
- Article
Manage the Suppliers That Could Harm Your Brand: Know When to Avoid, Engage, or Drop Them
By: Jodi L Short and Michael W. Toffel
The pandemic has placed a new spotlight on working conditions in factories that supply global companies. To avert problems, firms often impose codes of conduct on their suppliers and perform audits to assess compliance. Do these measures help identify unethical... View Details
Keywords: Auditing; Agency Cost; Quality And Safety; Quality Management System; Quality Management; Unions; Environmental Management; Globalization; Goods and Commodities; Governance; Labor; Labor Unions; Wages; Working Conditions; Operations; Supply Chain; Safety; Quality; China; Bangladesh; Asia; Pakistan
Short, Jodi L., and Michael W. Toffel. "Manage the Suppliers That Could Harm Your Brand: Know When to Avoid, Engage, or Drop Them." Harvard Business Review 99, no. 2 (March–April 2021).
- 01 Mar 2021
- News
Manage the Suppliers That Could Harm Your Brand
- 14 Jul 2020
- Research & Ideas
Restarting Under Uncertainty: Managerial Experiences from Around the World
or exploring new training methods such as role plays between employees to introduce safety protocols. Overall, a common theme emerging from our interviews is the attempt to deliver a highly individualized HR management approach to support... View Details
- 2022
- Article
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a... View Details
Keywords: Machine Learning Models; Counterfactual Explanations; Adversarial Examples; Mathematical Methods
Pawelczyk, Martin, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, and Himabindu Lakkaraju. "Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 25th (2022).
- 12 Mar 2008
- Working Paper Summaries
Allocating Marketing Resources
Keywords: by Sunil Gupta & Thomas J. Steenburgh