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- Faculty Publications (7,019)
Show Results For
- All HBS Web
(18,562)
- Faculty Publications (7,019)
- Fall 2020
- Article
Christo and Jeanne‐Claude: The Negotiation of Art and Vice Versa
Over the past two decades the Program on Negotiation at Harvard Law School (PON) has named thirteen people as Great Negotiators. The project, directed by my colleague Jim Sebenius, has given us the opportunity to commend our honorees’ outstanding work and to learn from... View Details
Wheeler, Michael A. "Christo and Jeanne‐Claude: The Negotiation of Art and Vice Versa." Negotiation Journal 36, no. 4 (Fall 2020): 471–487.
- October 2020
- Article
Collusion in Markets with Syndication
By: John William Hatfield, Scott Duke Kominers, Richard Lowery and Jordan M. Barry
Markets for IPOs and debt issuances are syndicated, in the sense that a bidder who wins a contract may invite losing bidders to join a syndicate that together fulfills the contract. We show that in markets with syndication, standard intuitions from industrial... View Details
Keywords: Collusion; Antitrust; IPO Underwriting; Syndication; "Repeated Games"; Markets; Game Theory
Hatfield, John William, Scott Duke Kominers, Richard Lowery, and Jordan M. Barry. "Collusion in Markets with Syndication." Journal of Political Economy 128, no. 10 (October 2020).
- October 2020
- Article
Comparative Statics for Size-Dependent Discounts in Matching Markets
By: David Delacretaz, Scott Duke Kominers and Alexandru Nichifor
We prove a natural comparative static for many-to-many matching markets in which agents’ choice functions exhibit size-dependent discounts: reducing the extent to which some agent discounts additional partners leads to improved outcomes for the agents on the other side... View Details
Keywords: Size-dependent Discounts; Path-independence; Respect For Improvements; Market Design; Mathematical Methods
Delacretaz, David, Scott Duke Kominers, and Alexandru Nichifor. "Comparative Statics for Size-Dependent Discounts in Matching Markets." Journal of Mathematical Economics 90 (October 2020): 127–131.
- 2022
- Working Paper
Flight to Safety: How Economic Downturns Affect Talent Flows to Startups
By: Shai Bernstein, Richard Townsend and Ting Xu
Using proprietary data from AngelList Talent, we study how individuals’ job search and application behavior changed during the COVID-19 downturn. We find that job seekers shifted their searches toward more established firms and away from early-stage startups, even... View Details
Keywords: Startup Labor Market; Flight To Safety; COVID-19; Recession; Business Startups; Human Capital; Business Cycles; Health Pandemics
Bernstein, Shai, Richard Townsend, and Ting Xu. "Flight to Safety: How Economic Downturns Affect Talent Flows to Startups." Harvard Business School Working Paper, No. 21-045, September 2020. (Revised March 2022.)
- Oct 2020
- Conference Presentation
Optimal, Truthful, and Private Securities Lending
By: Emily Diana, Michael J. Kearns, Seth Neel and Aaron Leon Roth
We consider a fundamental dynamic allocation problem motivated by the problem of securities lending in financial markets, the mechanism underlying the short selling of stocks. A lender would like to distribute a finite number of identical copies of some scarce resource... View Details
Diana, Emily, Michael J. Kearns, Seth Neel, and Aaron Leon Roth. "Optimal, Truthful, and Private Securities Lending." Paper presented at the 1st Association for Computing Machinery (ACM) International Conference on AI in Finance (ICAIF), October 2020.
- October 2020
- Article
Peer Influence on Trade Credit
By: Daniel Gyimah, Michael Machokoto and Anywhere (Siko) Sikochi
We examine the influence of peer firms on trade credit policies of listed firms in the United States. We posit and find evidence that firms mimic their peers in formulating trade credit policies. The findings are more pronounced for firms in highly competitive product... View Details
Keywords: Trade Credit; Peer Effects; Product Market Competition; Trade; Credit; Policy; Competition
Gyimah, Daniel, Michael Machokoto, and Anywhere (Siko) Sikochi. "Peer Influence on Trade Credit." Journal of Corporate Finance 64 (October 2020).
- 2020
- Working Paper
Targeting for Long-Term Outcomes
By: Jeremy Yang, Dean Eckles, Paramveer Dhillon and Sinan Aral
Decision makers often want to target interventions so as to maximize an outcome that is observed only in the long term. This typically requires delaying decisions until the outcome is observed or relying on simple short-term proxies for the long-term outcome. Here we... View Details
Keywords: Targeted Marketing; Optimization; Churn Management; Marketing; Customer Relationship Management; Policy; Learning; Outcome or Result
Yang, Jeremy, Dean Eckles, Paramveer Dhillon, and Sinan Aral. "Targeting for Long-Term Outcomes." Working Paper, October 2020.
- September 2020 (Revised January 2021)
- Case
Comviva: Exploring New Frontiers (A)
By: Dante Roscini and Mahima Rao-Kachroo
Comviva, a mobile solutions provider active in India and 94 other countries, has had a rich history and been successful across many emerging and complex markets: Latin America, South-East Asia, Africa. What are the lessons learnt from expansion, cultural fits, and... View Details
Keywords: Internet and the Web; Acquisition; Emerging Markets; Cross-Cultural and Cross-Border Issues; Mobile and Wireless Technology; Growth and Development Strategy; Telecommunications Industry; India; South Asia
Roscini, Dante, and Mahima Rao-Kachroo. "Comviva: Exploring New Frontiers (A)." Harvard Business School Case 721-006, September 2020. (Revised January 2021.)
- September 2020 (Revised July 2022)
- Teaching Note
Algorithmic Bias in Marketing
By: Ayelet Israeli and Eva Ascarza
Teaching Note for HBS No. 521-020. 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... View Details
- 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... View Details
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... View Details
- 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... View Details
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... View Details
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... View Details
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... View Details
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 December 2021)
- Case
Building India's 2.0: PayNearby
By: Lauren Cohen and Spencer C. N. Hagist
Headquartered in Mumbai, India, FinTech startup Nearby Technologies has seen its flagship brand, PayNearby, rapidly flourish across most of its target market within just four years. The unprecedented success of its payment app, which allows users to access banking... View Details
Keywords: Fintech; Developing Markets; Payments; Financial Inclusion; Finance; Entrepreneurship; Emerging Markets; Competitive Strategy; Banking Industry; India
Cohen, Lauren, and Spencer C. N. Hagist. "Building India's 2.0: PayNearby." Harvard Business School Case 221-027, September 2020. (Revised December 2021.)
- September 2020 (Revised July 2022)
- Supplement
Spreadsheet Supplement to "Artea: Designing Targeting Strategies"
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to "Artea: Designing Targeting Strategies" (521-021). View Details
- September 2020 (Revised July 2022)
- Supplement
Spreadsheet Supplement to Artea (B) and (C)
By: Eva Ascarza and Ayelet Israeli
Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting" View Details
- 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... View Details