Publications

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

January 2009
Case

Creating The Partnership Solutions Group at Lehman Brothers

By: David A. Thomas and Stephanie Creary

Explores how two senior Wall St. executives created a successful commercial opportunity for Lehman Brothers that focused on building relationships with minority- and women-owned financial services firms. Illustrates how Patricia Miller Zollar and Nadja Fidelia aligned... View Details

Keywords: Diversity; Gender; Partners and Partnerships; Power and Influence; Value Creation; Financial Services Industry

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September 2020 (Revised July 2022)
Supplement

Spreadsheet Supplement to Artea (B) and (C)

Spreadsheet Supplement to "Artea (B): Including Customer-level Demographic Data" and "Artea (C): Potential Discrimination through Algorithmic Targeting" View Details

Keywords: Gender; Race; Diversity; Marketing; Customer Relationship Management; Demographics; Prejudice and Bias; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States

30 Jul 2024
Research & Ideas

Do Social Movements Sway Voters? Not Really, Except for One

researchers measured changes in people’s attitudes before, during, and after protests on a variety of topics, including environmental protection, gender equality, gun control, immigration, national and international politics, and racial... View Details

Keywords: by Sean Silverthorne

14 May 2019
Working Paper Summaries

Do Experts Listen to Other Experts? Field Experimental Evidence from Scientific Peer Review

Keywords: by Misha Teplitskiy, Hardeep Ranu, Gary Gray, Michael Menietti, Eva Guinan, and Karim R. Lakhani

March 2024
Case

Unintended Consequences of Algorithmic Personalization

“Unintended Consequences of Algorithmic Personalization” (HBS No. 524-052) investigates algorithmic bias in marketing through four case studies featuring Apple, Uber, Facebook, and Amazon. Each study presents scenarios where these companies faced public criticism for... View Details

Keywords: Race; Gender; Marketing; Diversity; Customer Relationship Management; Prejudice and Bias; Customization and Personalization; Technology Industry; Retail Industry; United States

Educators

19 Jun 2013
Research & Ideas

Analyzing Institutions to Solve Big Problems

New South Wales. Local Construction of a Global Standard: Foreign Share Ownership and Workplace Gender Diversity in Japan explains that historically, the Japanese corporate structure has made it difficult... View Details

Keywords: by Carmen Nobel & Anna Secino

November 2007
Case

Differences at Work: Allie (A)

By: Sandra J. Sucher and Rachel Gordon

On a business trip, Allie's boss demands that she and a colleague skip planned company meetings and "meet him at the beach in their bikinis." View Details

Keywords: Behavior; Managerial Roles; Ethics; Gender; Diversity; Power and Influence

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29 Jan 2019
First Look

New Research and Ideas, January 29, 2019

establishing the existence of equilibria and other useful properties in diverse settings such as matching, auctions, and exchange economies with indivisible goods. We extend earlier models’ definitions of substitutability to settings in... View Details

Keywords: Dina Gerdeman

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

Keywords: Marketing; Race; Ethnicity; Gender; Diversity; Prejudice and Bias; Decision Making; Ethics; Customer Relationship Management; Retail Industry; Technology Industry; Apparel and Accessories Industry; United States

April 1993
Case

Sumiko Ito

By: David A. Thomas

Describes the life and career of the first Japanese female investment banker at Nomura Securities, Sumiko Ito, who later became a partner at Alex Brown, a U.S. investment bank. Organized around the major life events and career transitions Ms. Ito experienced. Set in... View Details

Keywords: Investment Banking; Personal Development and Career; Gender; Diversity; Cross-Cultural and Cross-Border Issues; Banking Industry; Financial Services Industry; Japan; England; United States

Educators

September 2020
Teaching Plan

Harlem Capital: Changing the Face of Entrepreneurship

By: George Serafeim

Jarrid Tingle and Henri Pierre-Jacques had spent the summer between their first and second years of their MBA program fund raising for their start-up venture capital (VC) firm, Harlem Capital Partners. Harlem Capital was founded upon the principle that addressing the... View Details

Keywords: Venture Capital Firm Compensation; Entrepreneurial Finance; Entrepreneurial Financing; Black Entrepreneurs; Black Leadership; Black Inventors; Inclusion; Minority-owned Businesses; Race And Ethnicity; Race Characteristics; Entrepreneurship; Venture Capital; Diversity; Race; Gender; Mission and Purpose; Social Enterprise; Financial Services Industry; United States

October 2000 (Revised January 2002)
Case

Individual and the Corporation, The: Kathy Levinson and E*TRADE (B)

By: Joseph L. Badaracco Jr. and Susan Harmeling

Supplements the (A) case. View Details

Keywords: Conflict of Interests; Leadership; Managerial Roles; Values and Beliefs; Decision Choices and Conditions; Gender; Diversity; Financial Services Industry

Educators

November 2007 (Revised July 2009)
Case

Differences at Work: Martin (A)

By: Sandra J. Sucher and Rachel Gordon

Martin, a gay man who was not out at his Italian firm, witnesses his division manager deliver a homophobic comment to his boss. He wonders what he should do. View Details

Keywords: Prejudice and Bias; Behavior; Managerial Roles; Ethics; Gender; Diversity; Power and Influence

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09 Sep 2024
HBS Case

McDonald’s and the Post #MeToo Rules of Sex in the Workplace

note: This article was updated to include additional advice related to CEO compensation policies. You Might Also Like: When Managers Set Unrealistic Expectations, Employees Cut Ethical Corners Rapport: The Hidden Advantage That Women Managers Bring to Teams Want to... View Details

Keywords: by Avery Forman; Food & Beverage

June 2023
Simulation

Artea Dashboard and Targeting Policy Evaluation

By: Ayelet Israeli and Eva Ascarza

Companies deploy A/B experiments to gain valuable insights about their customers in order to answer strategic business problems. In marketing, A/B tests are often used to evaluate marketing interventions intended to generate incremental outcomes for the firm. The Artea... View Details

07 Aug 2018
First Look

New Research and Ideas, August 8, 2018

was, at least in part, due to cultural differences between immigrants and natives, suggesting that diversity might be economically beneficial but politically hard to manage. Download working paper:... View Details

Keywords: by Sean Silverthorne

September 2020 (Revised February 2024)
Teaching Note

Artea (A), (B), (C), and (D): Designing Targeting Strategies

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

Keywords: Targeted Advertising; Targeting; Race; Gender; Diversity; Marketing; Customer Relationship Management; Prejudice and Bias; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States

September 2020 (Revised July 2022)
Exercise

Artea (B): Including Customer-Level Demographic Data

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