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All HBS Web
(1,162)
- Faculty Publications (275)
- 2020
- Working Paper
Determinants of Early-Stage Startup Performance: Survey Results
To explore determinants of new venture performance, the CEOs of 470 early-stage startups were surveyed regarding a broad range of factors related to their venture’s customer value proposition, product management, marketing, technology and operations, financial...
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Keywords:
Startups;
Survey Research;
Performance Analysis;
Entrepreneurship;
Performance;
Analysis;
Business Startups;
Failure;
Surveys
Eisenmann, Thomas R. "Determinants of Early-Stage Startup Performance: Survey Results." Harvard Business School Working Paper, No. 21-057, October 2020.
- November 2020
- Case
Wilderness Safaris: Responses to the COVID-19 Crisis
By: James E. Austin, Megan Epler Wood and Herman B. "Dutch" Leonard
This case is an epilogue to “Wilderness Safaris: Impact Investing and Ecotourism Conservation in Africa” (2-321-020), which ends with the emergence of the pandemic in March 2020. The final discussion area for that case can be “What should Wilderness Safari CEO Keith...
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Keywords:
Communities;
COVID-19;
Ecotourism;
Travel;
Travel Industry;
Conservation Planning;
Reopening;
Investor Relations;
Project Strategy;
Governance;
Decision Making;
Cash;
Health Pandemics;
Business and Shareholder Relations;
Tourism Industry;
Africa
Austin, James E., Megan Epler Wood, and Herman B. "Dutch" Leonard. "Wilderness Safaris: Responses to the COVID-19 Crisis." Harvard Business School Case 321-077, November 2020.
- September 2020 (Revised July 2022)
- Technical Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
This note focuses on algorithmic bias in marketing. First, it presents a variety of marketing examples in which algorithmic bias may occur. The examples are organized around the 4 P’s of marketing – promotion, price, place and product—characterizing the marketing...
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Keywords:
Algorithmic Data;
Race And Ethnicity;
Promotion;
"Marketing Analytics";
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analysis;
Data Analytics;
E-Commerce Strategy;
Discrimination;
Targeting;
Targeted Advertising;
Pricing Algorithms;
Ethical Decision Making;
Customer Heterogeneity;
Marketing;
Race;
Ethnicity;
Gender;
Diversity;
Prejudice and Bias;
Marketing Communications;
Analytics and Data Science;
Analysis;
Decision Making;
Ethics;
Customer Relationship Management;
E-commerce;
Retail Industry;
Apparel and Accessories Industry;
United States
Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Technical Note 521-020, September 2020. (Revised July 2022.)
- September 2020 (Revised February 2024)
- Teaching Note
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Teaching Note for HBS No. 521-021,521-022,521-037,521-043. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and...
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- September 2020 (Revised July 2022)
- Exercise
Artea (B): Including Customer-level Demographic Data
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...
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Keywords:
Targeting;
Algorithmic Bias;
Race;
Gender;
Marketing;
Diversity;
Customer Relationship Management;
Demographics;
Prejudice and Bias;
Retail Industry;
Apparel and Accessories Industry;
Technology Industry;
United States
Ascarza, Eva, and Ayelet Israeli. "Artea (B): Including Customer-level Demographic Data." Harvard Business School Exercise 521-022, September 2020. (Revised July 2022.)
- September 2020 (Revised July 2022)
- Exercise
Artea (C): Potential Discrimination through Algorithmic Targeting
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...
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Keywords:
Targeting;
Algorithmic Bias;
Race;
Gender;
Marketing;
Diversity;
Customer Relationship Management;
Prejudice and Bias;
Retail Industry;
Apparel and Accessories Industry;
Technology Industry;
United States
Ascarza, Eva, and Ayelet Israeli. "Artea (C): Potential Discrimination through Algorithmic Targeting." Harvard Business School Exercise 521-037, September 2020. (Revised July 2022.)
- September 2020 (Revised July 2022)
- Exercise
Artea (D): Discrimination through Algorithmic Bias in Targeting
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...
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Keywords:
Targeted Advertising;
Discrimination;
Algorithmic Data;
Bias;
Advertising;
Race;
Gender;
Marketing;
Diversity;
Customer Relationship Management;
Prejudice and Bias;
Analytics and Data Science;
Retail Industry;
Apparel and Accessories Industry;
Technology Industry;
United States
Ascarza, Eva, and Ayelet Israeli. "Artea (D): Discrimination through Algorithmic Bias in Targeting." Harvard Business School Exercise 521-043, September 2020. (Revised July 2022.)
- September 2020 (Revised June 2023)
- Exercise
Artea: Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and targeting. Parts (B),(C),(D) Introduce algorithmic bias. The...
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Keywords:
Algorithmic Data;
Race And Ethnicity;
Experimentation;
Promotion;
"Marketing Analytics";
Marketing And Society;
Big Data;
Privacy;
Data-driven Management;
Data Analytics;
Data Analysis;
E-Commerce Strategy;
Discrimination;
Targeted Advertising;
Targeted Policies;
Targeting;
Pricing Algorithms;
A/B Testing;
Ethical Decision Making;
Customer Base Analysis;
Customer Heterogeneity;
Coupons;
Algorithmic Bias;
Marketing;
Race;
Gender;
Diversity;
Customer Relationship Management;
Marketing Communications;
Advertising;
Decision Making;
Ethics;
E-commerce;
Analytics and Data Science;
Retail Industry;
Apparel and Accessories Industry;
United States
Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised June 2023.)
- September 2020 (Revised June 2023)
- Supplement
Spreadsheet Supplement to Artea Teaching Note
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to Artea Teaching Note 521-041. This collection of exercises aims to teach students about 1)Targeting Policies; and 2)Algorithmic bias in marketing—implications, causes, and possible solutions. Part (A) focuses on A/B testing analysis and...
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- 2022
- Working Paper
Where the Cloud Rests: The Location Strategies of Data Centers
By: Shane Greenstein and Tommy Pan Fang
This study provides an analysis of the entry strategies of third-party data centers in the United States. We examine the market before the pandemic in 2018 and 2019, when supply and demand for data services were geographically stable. We compare with the entry...
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Greenstein, Shane, and Tommy Pan Fang. "Where the Cloud Rests: The Location Strategies of Data Centers." Harvard Business School Working Paper, No. 21-042, September 2020. (Revised June 2022.)
- 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...
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Hsieh, Nien-hê. "A Note on Ethical Analysis." Harvard Business School Background Note 321-038, August 2020. (Revised December 2020.)
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools...
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Keywords:
Machine Learning;
Statistics;
Econometric Analyses;
Experimental Methods;
Data Analysis;
Data Analytics;
Forecasting and Prediction;
Analytics and Data Science;
Analysis;
Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- June 2020
- Article
How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections
By: Maria Ibanez and Michael W. Toffel
Accuracy and consistency are critical for inspections to be an effective, fair, and useful tool for assessing risks, quality, and suppliers—and for making decisions based on those assessments. We examine how inspector schedules could introduce bias that erodes...
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Keywords:
Assessment;
Bias;
Inspection;
Scheduling;
Econometric Analysis;
Empirical Research;
Regulation;
Health;
Food;
Safety;
Quality;
Performance Consistency;
Governing Rules, Regulations, and Reforms
Ibanez, Maria, and Michael W. Toffel. "How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections." Management Science 66, no. 6 (June 2020): 2396–2416. (Revised February 2019. Featured in Harvard Business Review, Forbes, Food Safety Magazine, Food Safety News, and KelloggInsight. (2020 MSOM Responsible Research Finalist.))
- June 2020
- Article
The Isolated Choice Effect and Its Implications for Gender Diversity in Organizations
By: Edward H. Chang, Erika L. Kirgios, Aneesh Rai and Katherine L. Milkman
We highlight a feature of personnel selection decisions that can influence the gender diversity of groups and teams. Specifically, we show that people are less likely to choose candidates whose gender would increase group diversity when making personnel selections in...
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Keywords:
Behavior And Behavioral Decision Making;
Organizational Studies;
Decision Analysis;
Economics;
Decision Making;
Behavior;
Analysis;
Organizations;
Diversity;
Gender
Chang, Edward H., Erika L. Kirgios, Aneesh Rai, and Katherine L. Milkman. "The Isolated Choice Effect and Its Implications for Gender Diversity in Organizations." Management Science 66, no. 6 (June 2020): 2752–2761.
- May 2020
- Teaching Note
Big Boom Beverages: Fight or Flight? (Brief Case)
By: Stephen A. Greyser and William Ellet
Teaching Note for HBS Brief Case No. 920-557. The case addresses analysis and decisions related to the entrepreneurial life of a distinctive energy beverage, including its niche market launch, early problems, reformulation, social media impact, market success, and...
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- Article
The Changing Landscape of Auditors' Liability
By: Colleen Honigsberg, Shivaram Rajgopal and Suraj Srinivasan
We provide a comprehensive overview of shareholder litigation against auditors since the passage of the Private Securities Litigation Reform Act (PSLRA). The number of lawsuits per year has declined, dismissals have increased, and settlements in recent years have...
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Keywords:
Auditor Litigation;
Tellabs;
Section 10(b);
Section 11;
Audit Quality;
Janus;
PSLRA;
Class-action Litigation;
Accounting Audits;
Lawsuits and Litigation;
Legal Liability
Honigsberg, Colleen, Shivaram Rajgopal, and Suraj Srinivasan. "The Changing Landscape of Auditors' Liability." Journal of Law & Economics 63, no. 2 (May 2020): 367–410.
- April 2020 (Revised June 2022)
- Technical Note
Quantitative Analysis in Marketing
By: Sunil Gupta
Marketing is a combination of art and science that requires both qualitative and quantitative analysis to arrive at effective decisions. This note highlights how quantitative analysis can help in the following marketing decisions: estimating market size, determining...
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Gupta, Sunil. "Quantitative Analysis in Marketing." Harvard Business School Technical Note 520-091, April 2020. (Revised June 2022.)
- January 2020
- Case
Hurtigruten: Sailing into Warm Water?
By: Jan W. Rivkin and Kerry Herman
As this case opens in 2019, CEO Daniel Skjeldam and his team have successfully reinvigorated Hurtigruten, a storied but struggling Norwegian ferry and cruise operator, and have established it as the leading provider of polar expedition cruises. They now face a critical...
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Keywords:
Relative Cost Analysis;
Market Attractiveness;
Diversification;
Decision Making;
Expansion;
Tourism Industry;
Norway
Rivkin, Jan W., and Kerry Herman. "Hurtigruten: Sailing into Warm Water?" Harvard Business School Case 720-410, January 2020.
- 2020
- Working Paper
Consumer Protection in an Online World: An Analysis of Occupational Licensing
By: Chiara Farronato, Andrey Fradkin, Bradley Larsen and Erik Brynjolfsson
We study the effects of occupational licensing on consumer choices and market outcomes in a large online platform for residential home services. We exploit exogenous variation in the time at which licenses are displayed on the platform to identify the causal effects of...
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Keywords:
Occupational Licensing;
Consumer Protection;
Governing Rules, Regulations, and Reforms;
Consumer Behavior;
Decision Making;
Customer Satisfaction
Farronato, Chiara, Andrey Fradkin, Bradley Larsen, and Erik Brynjolfsson. "Consumer Protection in an Online World: An Analysis of Occupational Licensing." NBER Working Paper Series, No. 26601, January 2020.
- November 2019
- Article
When and Why Defaults Influence Decisions: A Meta-analysis of Default Effects
By: Jon M. Jachimowicz, Shannon Duncan, Elke U. Weber and Eric J. Johnson
When people make decisions with a pre-selected choice option—a “default”—they are more likely to select that option. Because defaults are easy to implement, they constitute one of the most widely employed tools in the choice architecture toolbox. However, to decide...
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Jachimowicz, Jon M., Shannon Duncan, Elke U. Weber, and Eric J. Johnson. "When and Why Defaults Influence Decisions: A Meta-analysis of Default Effects." Behavioural Public Policy 3, no. 2 (November 2019): 159–186.