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- March 2021
- Supplement
Artea (A), (B), (C), and (D): Designing Targeting Strategies
By: Eva Ascarza and Ayelet Israeli
Power Point Supplement to 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...
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- February 2021
- Article
Health Care Measurements That Improve Patient Outcomes
By: Robert S. Kaplan, Lara Jehi, Clifford Y. Ko, Andrea Pusic and Mary Witkowski
This article describes the challenges and solutions in determining whether a patient’s treatment has been successful. Such an assessment depends on multiple factors, including the patient’s pretreatment status; the qualifications of personnel performing the treatment;...
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- Winter 2021
- Article
Dealmaking Disrupted: The Unexplored Power of Social Media in Negotiation
By: James K. Sebenius, Ben Cook, David A. Lax, Isaac Silberberg and Paul Levy
While social media has had profound effects in many realms, the theory and practice of negotiation have remained relatively untouched by this potent phenomenon. In this article, we survey existing research in this area and develop a broader framework for understanding...
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- January 2021
- Article
Machine Learning for Pattern Discovery in Management Research
By: Prithwiraj Choudhury, Ryan Allen and Michael G. Endres
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used for exploratory inductive or abductive research, or for post-hoc analysis of regression results to detect...
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- November 2020
- Teaching Note
Valuing Celgene's CVR
By: Benjamin C. Esty and Daniel Fisher
When Bristol-Myers Squibb (BMS) acquired Celgene Corporation in November 2019, Celgene shareholders received cash, BMS stock, and a contingent value right (CVRs) that would pay $9 if the U.S. Food and Drug Administration (FDA) approved three of Celgene’s late stage...
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- November 2020
- Case
Valuing Celgene's CVR
By: Benjamin C. Esty and Daniel Fisher
When Bristol-Myers Squibb (BMS) acquired Celgene Corporation in November 2019, Celgene shareholders received cash, BMS stock, and a contingent value right (CVRs) that would pay $9 if the U.S. Food and Drug Administration (FDA) approved three of Celgene’s late stage...
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- November 2020
- Supplement
Valuing Celgene's CVR
By: Benjamin C. Esty and Daniel Fisher
When Bristol-Myers Squibb (BMS) acquired Celgene Corporation in November 2019, Celgene shareholders received cash, BMS stock, and a contingent value right (CVRs) that would pay $9 if the U.S. Food and Drug Administration (FDA) approved three of Celgene’s late stage...
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- 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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- 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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- September 2020
- 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;
E-commerce;
Discrimination;
Targeting;
Targeted Advertising;
Pricing Algorithms;
Ethical Decision Making;
Customer Heterogeneity;
Marketing;
Race;
Ethnicity;
Gender;
Diversity;
Prejudice and Bias;
Marketing Communications;
Data and Data Sets;
Analysis;
Decision Making;
Ethics;
Customer Relationship Management;
Retail Industry;
Apparel and Accessories Industry;
United States
- September 2020 (Revised April 2021)
- 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;
E-commerce Strategy;
Discrimination;
Targeted Advertising;
Targeted Policies;
Targeting;
Pricing Algorithms;
A/b Testing;
Ethical Decision Making;
Customer Base Analysis;
Customer Heterogeneity;
Coupons;
Marketing;
Race;
Gender;
Diversity;
Customer Relationship Management;
Marketing Communications;
Advertising;
Decision Making;
Ethics;
Retail Industry;
Apparel and Accessories Industry;
United States
- September 2020 (Revised April 2021)
- 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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- September 2020 (Revised April 2021)
- 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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- September 2020 (Revised March 2021)
- 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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- September 2020 (Revised December 2020)
- 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 March 2021)
- 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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- 2020
- Working Paper
Where the Cloud Rests: The Economic Geography 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, an industry with assets worth hundreds of billions of dollars. We examine the market in 2018 and 2019, and compare with the physical entry of private data centers...
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- 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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- 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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- 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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