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Show Results For
- All HBS Web
(12,554)
- People (75)
- News (2,866)
- Research (3,663)
- Events (32)
- Multimedia (334)
- Faculty Publications (2,349)
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- 2014
- Working Paper
Modeling Money Market Spreads: What Do We Learn about Refinancing Risk?
By: Vincent Brousseau, Kleopatra Nikolaou and Huw Pill
Brousseau, Vincent, Kleopatra Nikolaou, and Huw Pill. "Modeling Money Market Spreads: What Do We Learn about Refinancing Risk?" Finance and Economics Discussion Series (Federal Reserve Board), No. 2014-112, November 2014.
- 05 Mar 2001
- What Do You Think?
Fine Coupling: Can Human Resource Management Learn from Supply Chain Management?
calculations. The ultimate irony is that the "fine coupling" of our supply chains, through the removal of inventory buffers, may have made it more difficult to "fine couple" human inventory management. Are there... View Details
Keywords: by James Heskett
- February 2024
- Teaching Note
Data-Driven Denim: Financial Forecasting at Levi Strauss
By: Mark Egan
Teaching Note for HBS Case No. 224-029. Levi Strauss & Co. (“Levi Strauss”) partnered with the IT services company Wipro to incorporate more sophisticated methods, such as machine learning, into their financial forecasting process starting in 2018. The decision to... View Details
- Forthcoming
- Article
Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments
By: Kosuke Imai and Michael Lingzhi Li
Researchers are increasingly turning to machine learning (ML) algorithms to investigate causal heterogeneity in randomized experiments. Despite their promise, ML algorithms may fail to accurately ascertain heterogeneous treatment effects under practical settings with... View Details
Imai, Kosuke, and Michael Lingzhi Li. "Statistical Inference for Heterogeneous Treatment Effects Discovered by Generic Machine Learning in Randomized Experiments." Journal of Business & Economic Statistics (forthcoming). (Pre-published online July 8, 2024.)
- 2015
- Chapter
Recasting the Corporate Model: What Can Be Learned from Social Enterprises
By: Julie Battilana
Battilana, Julie. "Recasting the Corporate Model: What Can Be Learned from Social Enterprises." In Performance and Progress: Essays on Capitalism, Business, and Society, edited by Subramanian Rangan, 435–461. Oxford: Oxford University Press, 2015.
- March 1993
- Background Note
Stages Theory, The: A Framework for IT Adoption and Organizational Learning
By: Richard L. Nolan, David Croson and Katherine Seger
Describes Professor Richard Nolan's Stages Theory of Information Technology adoption by organizations. View Details
Keywords: Information; Body of Literature; Information Management; Information Publishing; Adoption; Organizational Structure; Organizational Design; Decision Making; Information Technology Industry
Nolan, Richard L., David Croson, and Katherine Seger. "Stages Theory, The: A Framework for IT Adoption and Organizational Learning." Harvard Business School Background Note 193-141, March 1993.
- 2004
- Working Paper
The Recovery Window: Organizational Learning Following Ambiguous Threats in High-Risk Organizations
By: Amy C. Edmondson, Michael A. Roberto, Richard M.J. Bohmer, Erika M. Ferlins and Laura R. Feldman
- 04 Oct 2017
- Book
Five Leaders Forged in Crisis, and What We Can Learn From Them
Keywords: by Dina Gerdeman
- February 1, 2019
- Article
What Theresa May Might Learn from Woodrow Wilson's Failed Negotiations in 1919
By: Deepak Malhotra
On December 13, 2018, UK Prime Minister Theresa May met with her European counterparts in an attempt to renegotiate the “Brexit deal” she had reached with them only weeks earlier; the deal was facing harsh criticism and almost certain rejection at home. Perhaps only... View Details
Malhotra, Deepak. "What Theresa May Might Learn from Woodrow Wilson's Failed Negotiations in 1919." Harvard Business Review (website) (February 1, 2019).
- February 2009
- Article
Learning in a New Cardiac Surgical Center: An Analysis of Precursor Events
By: Daniel R. Wong, Imtiaz S. Ali, David F. Torchiana, Arvind K. Agnihotri, Richard Bohmer and Thomas J. Vander Salm
Wong, Daniel R., Imtiaz S. Ali, David F. Torchiana, Arvind K. Agnihotri, Richard Bohmer, and Thomas J. Vander Salm. "Learning in a New Cardiac Surgical Center: An Analysis of Precursor Events." Surgery 145, no. 2 (February 2009): 131–137.
- February 2009 (Revised September 2009)
- Case
Investing in Early Learning as Economic Development at the Minneapolis Federal Reserve Bank
By: Stacey M. Childress and Geoff Eckman Marietta
In his role as Senior Vice President and Director of Research at the Federal Reserve Bank of Minneapolis (Minneapolis Fed), Art Rolnick and his colleague, Rob Grunewald, had written "Early Childhood Development: Economic Development with a High Public Return." The... View Details
Keywords: Development Economics; Early Childhood Education; Investment Return; Demand and Consumers; Supply and Industry; Performance Effectiveness; Nonprofit Organizations; Minneapolis; Saint Paul
Childress, Stacey M., and Geoff Eckman Marietta. "Investing in Early Learning as Economic Development at the Minneapolis Federal Reserve Bank." Harvard Business School Case 309-090, February 2009. (Revised September 2009.)
- September 2006
- Article
Learning in Noisy Games: Partial Reinforcement and the Sustainability of Cooperation
By: Yoella Bereby-Meyer and Alvin E. Roth
Bereby-Meyer, Yoella, and Alvin E. Roth. "Learning in Noisy Games: Partial Reinforcement and the Sustainability of Cooperation." American Economic Review 96, no. 4 (September 2006): 1029–1042.
- 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.
- November 1992
- Article
Executive Succession and Organization Outcomes in Turbulent Environments: An Organizational Learning Approach
By: Michael Tushman, B. Virany and E. Romanelli
Tushman, Michael, B. Virany, and E. Romanelli. "Executive Succession and Organization Outcomes in Turbulent Environments: An Organizational Learning Approach." Organization Science 3, no. 4 (November 1992): 72–92.
- May 2022 (Revised July 2022)
- Supplement
AWS and Amazon SageMaker (C): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (C): The Commercialization of Machine Learning Services." Harvard Business School Supplement 622-087, May 2022. (Revised July 2022.)
- May 2022
- Supplement
AWS and Amazon SageMaker (B): The Commercialization of Machine Learning Services
By: Karim R. Lakhani, Shane Greenstein and Kerry Herman
Lakhani, Karim R., Shane Greenstein, and Kerry Herman. "AWS and Amazon SageMaker (B): The Commercialization of Machine Learning Services." Harvard Business School Supplement 622-086, May 2022.
- June, 2021
- Article
Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19
By: Edward L. Glaeser, Ginger Zhe Jin, Benjamin T. Leyden and Michael Luca
During the COVID-19 pandemic, states issued and then rescinded stay-at-home orders that restricted mobility. We develop a model of learning by deregulation, which predicts that lifting stay-at-home orders can signal that going out has become safer. Using restaurant... View Details
Keywords: COVID-19; Lockdown; Reopening; Impact; Coronavirus; Public Health Measures; Mobility; Health Pandemics; Governing Rules, Regulations, and Reforms; Consumer Behavior
Glaeser, Edward L., Ginger Zhe Jin, Benjamin T. Leyden, and Michael Luca. "Learning from Deregulation: The Asymmetric Impact of Lockdown and Reopening on Risky Behavior During COVID-19." Journal of Regional Science 61, no. 4 (June, 2021): 696–709.
- April 2024
- Article
A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification
By: Hsin-Hsiao Scott Wang, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow and Caleb Nelson
Backgrounds: Urinary Tract Dilation (UTD) classification has been designed to be a more objective grading system to evaluate antenatal and post-natal UTD. Due to unclear association between UTD classifications to specific anomalies such as vesico-ureteral reflux (VUR),... View Details
Wang, Hsin-Hsiao Scott, Michael Lingzhi Li, Dylan Cahill, John Panagides, Tanya Logvinenko, Jeanne Chow, and Caleb Nelson. "A Machine Learning Algorithm Predicting Risk of Dilating VUR among Infants with Hydronephrosis Using UTD Classification." Journal of Pediatric Urology 20, no. 2 (April 2024): 271–278.
- May 1998
- Article
Learning in High Stakes Ultimatum Games: An Experiment in the Slovak Republic
By: R. Slonim and A. E. Roth
Slonim, R., and A. E. Roth. "Learning in High Stakes Ultimatum Games: An Experiment in the Slovak Republic." Econometrica 63, no. 3 (May 1998): 569–596.