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Show Results For
- All HBS Web
(1,791)
- People (9)
- News (316)
- Research (1,044)
- Events (15)
- Multimedia (10)
- Faculty Publications (862)
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- 2022
- Working Paper
Small Campaign Donors
By: Laurent Bouton, Julia Cagé, Edgard Dewitte and Vincent Pons
In this paper, we study the characteristics and behavior of small donors, and compare them to those of large donors. We first build a novel dataset including all the 340 million individual contributions reported to the U.S. Federal Election Commission between 2005 and... View Details
Keywords: Campaign Finance; Campaign Contributions; Small Donations; ActBlue; WinRed; TV Advertising; Political Elections; Finance; Demographics; Advertising; Analysis; Analytics and Data Science
Bouton, Laurent, Julia Cagé, Edgard Dewitte, and Vincent Pons. "Small Campaign Donors." NBER Working Paper Series, No. 30050, May 2022.
- Article
Uninformed Consent
By: Leslie K. John
Companies want access to more and more of your personal data—from where you are to what’s in your DNA. Can they unlock its value while respecting consumers’ privacy? View Details
Keywords: Personal Data; Privacy; Customers; Analytics and Data Science; Ethics; Governing Rules, Regulations, and Reforms
John, Leslie K. "Uninformed Consent." Special Issue on The Big Idea: Tracked. Harvard Business Review (website) (September–October 2018).
- winter 2003
- Article
Massively Categorical Variables: Revealing the Information in Zip Codes
We introduce the idea of a massively categorical variable, a variable such as zip code that takes on too many values to be treated in the standard manner, and show how to use it directly as explanatory variables in an econometric model. In an application of this... View Details
Steenburgh, Thomas J., Andrew Ainslie, and Peder Hans Engebretson. "Massively Categorical Variables: Revealing the Information in Zip Codes." Marketing Science 22, no. 1 (winter 2003): 40–57.
- January 2019
- Supplement
Understanding the Brand Equity of Nestlé Crunch Bar (B): Data Analysis
By: Jill Avery and Gerald Zaltman
In early 2018, Nestlé announced the sale of its U.S. candy-making division and a select collection of 20 of its confectionery brands, including the Nestlé Crunch Bar, to Ferrero SpA for $2.8 billion. Luckily, an old consumer research study on the Nestlé Crunch Bar... View Details
Keywords: Brand Management; Market Research; Brand Positioning; Value Proposition; Consumer Products; Fast Moving Consumer Goods; Qualitative Methods; Zaltman Metaphor Elicitation Technique; ZMET; Data Analysis; Marketing; Marketing Strategy; Brands and Branding; Consumer Behavior; Marketing Communications; Analytics and Data Science; Analysis; Consumer Products Industry; Food and Beverage Industry; Advertising Industry; United States; North America; Italy
Avery, Jill, and Gerald Zaltman. "Understanding the Brand Equity of Nestlé Crunch Bar (B): Data Analysis." Harvard Business School Supplement 519-062, January 2019.
- 2013
- Working Paper
Span of Control and Span of Attention
By: Oriana Bandiera, Andrea Prat, Raffaella Sadun and Julie Wulf
Using novel data on CEO time use, we document the relationship between the size and composition of the executive team and the attention of the CEO. We combine information about CEO span of control for a sample of 65 companies with detailed data on how CEOs allocate... View Details
Keywords: Conferences; Analytics and Data Science; Leadership Style; Management Style; Managerial Roles; Time Management; Planning
Bandiera, Oriana, Andrea Prat, Raffaella Sadun, and Julie Wulf. "Span of Control and Span of Attention." Harvard Business School Working Paper, No. 12-053, December 2011. (Revised April 2014.)
- 16 Oct 2012
- First Look
First Look: October 16
in managing business analytics and big data at the enterprise level. It includes key applications of analytics, human and organizational issues in building analytical capabilities, and case studies of the... View Details
Keywords: Sean Silverthorne
- 05 Aug 2002
- Research & Ideas
Understanding the Process of Innovation
data-driven analytical approach to understanding new market opportunities. "That's a great process for finding gaps in well-established markets," says Christensen, "but it's a bad process for making intuitive bets."... View Details
Keywords: by Loren Gray
- January–February 2023
- Article
Forecasting COVID-19 and Analyzing the Effect of Government Interventions
By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more... View Details
Keywords: COVID-19 Pandemic; Epidemics; Analytics and Data Science; Health Pandemics; AI and Machine Learning; Forecasting and Prediction
Li, Michael Lingzhi, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis, and Dimitris Bertsimas. "Forecasting COVID-19 and Analyzing the Effect of Government Interventions." Operations Research 71, no. 1 (January–February 2023): 184–201.
- 2024
- Working Paper
Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference
By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov
Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to... View Details
Lindon, Michael, Dae Woong Ham, Martin Tingley, and Iavor I. Bojinov. "Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference." Harvard Business School Working Paper, No. 24-060, March 2024.
- May 2020
- Article
Scalable Holistic Linear Regression
By: Dimitris Bertsimas and Michael Lingzhi Li
We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting... View Details
Bertsimas, Dimitris, and Michael Lingzhi Li. "Scalable Holistic Linear Regression." Operations Research Letters 48, no. 3 (May 2020): 203–208.
- August 2018 (Revised April 2019)
- Supplement
Chateau Winery (B): Supervised Learning
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on “Chateau Winery (A).” In this case, Bill Booth, marketing manager of a regional wine distributor, shifts to supervised learning techniques to try to predict which deals he should offer to customers based on the purchasing behavior of those... View Details
Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (B): Supervised Learning." Harvard Business School Supplement 119-024, August 2018. (Revised April 2019.)
- August 2018 (Revised April 2019)
- Case
Chateau Winery (A): Unsupervised Learning
By: Srikant M. Datar and Caitlin N. Bowler
This case follows Bill Booth, marketing manager of a regional wine distributor, as he applies unsupervised learning on data about his customers’ purchases to better understand their preferences. Specifically, he uses the K-means clustering technique to identify groups... View Details
Datar, Srikant M., and Caitlin N. Bowler. "Chateau Winery (A): Unsupervised Learning." Harvard Business School Case 119-023, August 2018. (Revised April 2019.)
- January–February 2025
- Article
Want Your Company to Get Better at Experimentation?: Learn Fast by Democratizing Testing
By: Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit and Martin Tingley
For years, online experimentation has fueled the innovations of leading tech companies, enabling them to rapidly test and refine new ideas, optimize product features, personalize user experiences, and maintain a competitive edge. The widespread availability and lower... View Details
Keywords: Technological Innovation; AI and Machine Learning; Analytics and Data Science; Product Development; Competitive Advantage
Bojinov, Iavor, David Holtz, Ramesh Johari, Sven Schmit, and Martin Tingley. "Want Your Company to Get Better at Experimentation? Learn Fast by Democratizing Testing." Harvard Business Review 103, no. 1 (January–February 2025): 96–103.
- September 2025
- Article
Using Satellites and Phones to Evaluate and Promote Agricultural Technology Adoption: Evidence from Smallholder Farms in India
By: Shawn Cole, Tomoko Harigaya, Grady Killeen and Aparna Krishna
This paper evaluates a low-cost, customized soil nutrient management advisory service in India. As a methodological contribution, we examine whether and in which settings satellite measurements may be effective at estimating both agricultural yields and treatment... View Details
Keywords: Measurement and Metrics; Mathematical Methods; Analytics and Data Science; Agriculture and Agribusiness Industry; India
Cole, Shawn, Tomoko Harigaya, Grady Killeen, and Aparna Krishna. "Using Satellites and Phones to Evaluate and Promote Agricultural Technology Adoption: Evidence from Smallholder Farms in India." Journal of Development Economics 176 (September 2025).
- August 2001
- Article
Technology as a Complex Adaptive System: Evidence from Patent Data
Fleming, L., and O. Sorenson. "Technology as a Complex Adaptive System: Evidence from Patent Data." Research Policy 30, no. 7 (August 2001).
- 2016
- Working Paper
Venture Capital Data: Opportunities and Challenges
By: Steven N. Kaplan and Josh Lerner
This paper describes the available data and research on venture capital investments and performance. We comment on the challenges inherent in those data and research as well as possible opportunities to do better. View Details
Kaplan, Steven N., and Josh Lerner. "Venture Capital Data: Opportunities and Challenges." Harvard Business School Working Paper, No. 17-012, August 2016. (Forthcoming in Measuring Entrepreneurial Businesses: Current Knowledge and Challenges.)
- July 2002 (Revised June 2003)
- Case
Microsoft Financial History
Contains background financial data on Microsoft as of mid-2001. View Details
Sahlman, William A. "Microsoft Financial History." Harvard Business School Case 803-018, July 2002. (Revised June 2003.)
- October 1993 (Revised September 1994)
- Background Note
Accounting for Productivity Growth
Introduces students to the arithmetic of the accounting for national productivity growth. It defines labor productivity, capital productivity, and total factor productivity, describes the relationships among them, and discusses the phenomena that cause them to change... View Details
Keywords: Performance Productivity; Macroeconomics; Analytics and Data Science; Government and Politics; Mathematical Methods; United States; Singapore
Reinhardt, Forest L. "Accounting for Productivity Growth." Harvard Business School Background Note 794-051, October 1993. (Revised September 1994.)
- November 2023
- Article
Federated Electronic Health Records for the European Health Data Space
By: René Raab, Arne Küderle, Anastasiya Zakreuskaya, Ariel Dora Stern, Jochen Klucken, Georgios Kaissis, Daniel Rueckert, Susanne Boll, Roland Eils, Harald Wagener and Bjoern Eskofier
The European Commission's draft for the European Health Data Space (EHDS) aims to empower citizens to access their personal health data and share it with physicians and other health-care providers. It further defines procedures for the secondary use of electronic... View Details
Keywords: Analytics and Data Science; Cybersecurity; Information Management; Knowledge Sharing; Knowledge Use and Leverage; Health Industry
Raab, René, Arne Küderle, Anastasiya Zakreuskaya, Ariel Dora Stern, Jochen Klucken, Georgios Kaissis, Daniel Rueckert, Susanne Boll, Roland Eils, Harald Wagener, and Bjoern Eskofier. "Federated Electronic Health Records for the European Health Data Space." Lancet Digital Health 5, no. 11 (November 2023): e840–e847.
- October 2018
- Case
BreezoMeter: Making Air Pollution Data Actionable
By: Frank V. Cespedes, Allison M. Ciechanover and Margot Eiran
The case focuses on an Israeli startup that provides actionable air pollution data and forecasts. The company has over 50 enterprise customers and its tool reached a million people daily in 67 countries. The co-founders wrestle with which markets and customers to focus... View Details
Keywords: Startups; Entrepreneurship; Business Startups; Pollutants; Analytics and Data Science; Sales; Marketing; Decision Choices and Conditions; Technology Industry; Israel; United States
Cespedes, Frank V., Allison M. Ciechanover, and Margot Eiran. "BreezoMeter: Making Air Pollution Data Actionable." Harvard Business School Case 819-058, October 2018.