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- All HBS Web (314)
- Faculty Publications (144)
Show Results For
- All HBS Web (314)
- Faculty Publications (144)
- January 2025
- Case
AI Meets VC: The Data-Driven Revolution at Quantum Light Capital
By: Lauren Cohen, Grace Headinger and Sophia Pan
Ilya Kondrashov, CEO of Quantum Light Capital, was driven to harness AI for identifying high-potential scale-ups. Collaborating with Nik Storonsky, founder of Revolut, the duo observed that most venture capital (VC) decisions were heavily influenced by emotion, with... View Details
Keywords: Artificial Intelligence; Business Finance; Data Analysis; Angel Investors; Cognitive Biases; Scale; Venture Capital; Investment; Business Model; Forecasting and Prediction; Technological Innovation; Innovation Strategy; Behavior; Cognition and Thinking; Public Opinion; Private Sector; Business Strategy; Competitive Advantage; Business Earnings; Behavioral Finance; AI and Machine Learning; Analytics and Data Science; Business Startups; Financial Services Industry; London; United Kingdom
Cohen, Lauren, Grace Headinger, and Sophia Pan. "AI Meets VC: The Data-Driven Revolution at Quantum Light Capital." Harvard Business School Case 225-053, January 2025.
- July – August 2011
- Article
Foundations of Organizational Trust: What Matters to Different Stakeholders?
By: Michael Pirson and Deepak Malhotra
Prior research on organizational trust has not rigorously examined the context specificity of trust nor distinguished between the potentially varying dimensions along which different stakeholders base their trust. As a result, dominant conceptualizations of... View Details
Keywords: Trust; Competency and Skills; Forecasting and Prediction; Ethics; Framework; Analytics and Data Science; Surveys; Organizations; Business and Stakeholder Relations; Identity; Perspective
Pirson, Michael, and Deepak Malhotra. "Foundations of Organizational Trust: What Matters to Different Stakeholders?" Organization Science 22, no. 4 (July–August 2011): 1087–1104.
- Web
Accounting & Management - Faculty & Research
Mind the (Gender Pay) Gap By: June Huang and Shirley Lu We study whether voluntary gender diversity disclosure is predictive of gender diversity performance. Exploiting a mandate in the United Kingdom that requires firms to disclose 2017... View Details
- 2011
- Chapter
Regional Trade Integration and Multinational Firm Strategies
By: Pol Antras and C. Fritz Foley
This paper analyzes the effects of the formation of a regional trade agreement on the level and nature of multinational firm activity. We examine aggregate data that captures the response of U.S. multinational firms to the formation of the ASEAN free trade agreement.... View Details
Keywords: Forecasting and Prediction; Trade; Foreign Direct Investment; Multinational Firms and Management; Globalized Markets and Industries; Analytics and Data Science; Agreements and Arrangements; United States
Antras, Pol, and C. Fritz Foley. "Regional Trade Integration and Multinational Firm Strategies." In Costs and Benefits of Regional Economic Integration in Asia, edited by Robert J. Barro and Jong-Wha Lee. Oxford University Press, 2011.
- April–May 2024
- Article
Gone with the Big Data: Institutional Lender Demand for Private Information
By: Jung Koo Kang
I explore whether big-data sources can crowd out the value of private information acquired through lending relationships. Institutional lenders have been shown to exploit their access to borrowers’ private information by trading on it in financial markets. As a shock... View Details
Keywords: Analytics and Data Science; Borrowing and Debt; Financial Markets; Value; Knowledge Dissemination; Financing and Loans
Kang, Jung Koo. "Gone with the Big Data: Institutional Lender Demand for Private Information." Art. 101663. Journal of Accounting & Economics 77, nos. 2-3 (April–May 2024).
- January 2018
- Article
Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life
By: Edward L. Glaeser, Scott Duke Kominers, Michael Luca and Nikhil Naik
New, "big" data sources allow measurement of city characteristics and outcome variables at higher frequencies and finer geographic scales than ever before. However, big data will not solve large urban social science questions on its own. Big data has the most value for... View Details
Glaeser, Edward L., Scott Duke Kominers, Michael Luca, and Nikhil Naik. "Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life." Economic Inquiry 56, no. 1 (January 2018): 114–137.
- 09 Jan 2024
- In Practice
Harnessing AI: What Businesses Need to Know in ChatGPT’s Second Year
crowdsource diverse viewpoints and innovative solutions to their most complex solutions. Take the Netflix challenge. In 2006, Netflix launched an open competition for the best collaborative filtering algorithm to predict user ratings for... View Details
- 2012
- Working Paper
~Why Do We Redistribute so Much but Tag so Little? Normative Diversity, Equal Sacrifice and Optimal Taxation
Tagging is a free lunch in conventional optimal tax theory because it eases the classic tradeoff between efficiency and equality. But tagging is used in only limited ways in tax policy. I propose one explanation: conventional optimal tax theory has yet to capture the... View Details
Keywords: Forecasting and Prediction; Cost; Framework; Policy; Taxation; Analytics and Data Science; Performance Efficiency; United States
Weinzierl, Matthew. "~Why Do We Redistribute so Much but Tag so Little? Normative Diversity, Equal Sacrifice and Optimal Taxation." Harvard Business School Working Paper, No. 12-064, January 2012. (Revised August 2012. NBER Working Paper Series, No. 18045, August 2012)
- 2017
- Working Paper
Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity
By: Edward L. Glaeser, Hyunjin Kim and Michael Luca
Can new data sources from online platforms help to measure local economic activity? Government datasets from agencies such as the U.S. Census Bureau provide the standard measures of economic activity at the local level. However, these statistics typically appear only... View Details
Glaeser, Edward L., Hyunjin Kim, and Michael Luca. "Nowcasting the Local Economy: Using Yelp Data to Measure Economic Activity." Harvard Business School Working Paper, No. 18-022, September 2017. (Revised October 2017.)
- 26 May 2015
- First Look
First Look: May 26
successfully unified all analytics talent and resources into one group over a three-year period. Rapid increases in computing power and decreases in data storage costs had enabled DA2's data architects to build View Details
Keywords: Sean Silverthorne
- February 2021
- Case
Digital Manufacturing at Amgen
By: Shane Greenstein, Kyle R. 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... View Details
Keywords: Digital Technologies; Change; Change Management; Decision Making; Cost vs Benefits; Decisions; Information; Analytics and Data Science; Innovation and Invention; Innovation and Management; Innovation Leadership; Innovation Strategy; Technological Innovation; Jobs and Positions; Knowledge; Leadership; Organizational Culture; Science; Strategy; Information Technology; Technology Adoption; Biotechnology Industry; Pharmaceutical Industry; United States; California; Puerto Rico; Rhode Island
Greenstein, Shane, Kyle R. Myers, and Sarah Mehta. "Digital Manufacturing at Amgen." Harvard Business School Case 621-008, February 2021.
- July 2023
- Article
Takahashi-Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations
By: Dawson Beutler, Alex Billias, Sam Holt, Josh Lerner and TzuHwan Seet
In 2001, Dean Takahashi and Seth Alexander of the Yale University Investments Office developed a deterministic model for estimating future cash flows and valuations for the Yale endowment’s private equity portfolio. Their model, which is simple and intuitive, is still... View Details
Beutler, Dawson, Alex Billias, Sam Holt, Josh Lerner, and TzuHwan Seet. "Takahashi-Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations." Journal of Portfolio Management 49, no. 7 (July 2023): 144–158.
- June 2023
- Article
How New Ideas Diffuse in Science
By: Mengjie Cheng, Daniel Scott Smith, Xiang Ren, Hancheng Cao, Sanne Smith and Daniel A. McFarland
What conditions help new ideas spread? Can knowledge entrepreneurs’ position and develop new ideas in ways that help them take off? Most innovation research focuses on products and their reference. That focus ignores the ideas themselves and the broader ideational... View Details
Keywords: Innovation Adoption; Natural Language Processing; Knowledge; Science; Innovation and Invention; Knowledge Sharing; Analytics and Data Science
Cheng, Mengjie, Daniel Scott Smith, Xiang Ren, Hancheng Cao, Sanne Smith, and Daniel A. McFarland. "How New Ideas Diffuse in Science." American Sociological Review 88, no. 3 (June 2023): 522–561.
- 26 Oct 2010
- First Look
First Look: October 26, 2010
set of analytic and practical challenges arises, which this article explores via three cases: 1) a cross-border, large-dollar complex sales effort requiring interlocking financial, political, and organizational negotiations among dozens... View Details
Keywords: Sean Silverthorne
- Forthcoming
- Article
Slowly Varying Regression Under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details
Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression Under Sparsity." Operations Research (forthcoming). (Pre-published online March 27, 2024.)
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
Chang, Peter W., Leor Fishman, and Seth Neel. "Feature Importance Disparities for Data Bias Investigations." Working Paper, March 2023.
- 07 Feb 2022
- Research & Ideas
Digital Transformation: A New Roadmap for Success
described the proliferation of "digital positions" in their companies, from Digital Project Manager or Digital Director to Chief Transformation Officer or Chief Innovation Officer. In one roundtable discussion, participants predicted... View Details
- 23 Oct 2018
- First Look
New Research and Ideas, October 23, 2018
allow testing for and predicting firm-specific coefficients, thereby distinguishing between effects that have a significant mean versus significant variance. RCMs may also be used to explore the sources of firm heterogeneous effects. We... View Details
Keywords: Dina Gerdeman
- 2025
- Working Paper
Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning
Reinforcement learning (RL) offers potential for optimizing sequences of customer interactions by modeling the relationships
between customer states, company actions, and long-term value. However, its practical implementation often faces significant
challenges.... View Details
Keywords: Dynamic Policy; Deep Reinforcement Learning; Representation Learning; Dynamic Difficulty Adjustment; Latent Variable Models; Customer Relationship Management; Customer Value and Value Chain; Foreign Direct Investment; Analytics and Data Science
Ma, Liangzong, Ta-Wei Huang, Eva Ascarza, and Ayelet Israeli. "Dynamic Personalization with Multiple Customer Signals: Multi-Response State Representation in Reinforcement Learning." Harvard Business School Working Paper, No. 25-037, February 2025.
- 18 Apr 2017
- First Look
First Look at New Ideas, April 18
interest rates than predicted by the standard expectations hypothesis. We find that, since 2000, such high-frequency "excess sensitivity" remains evident in U.S. data and has, if anything, grown stronger. By contrast, the positive... View Details
Keywords: by Sean Silverthorne