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- May 2022
- Article
When Harry Fired Sally: The Double Standard in Punishing Misconduct
By: Mark Egan, Gregor Matvos and Amit Seru
We examine gender differences in misconduct punishment in the financial advisory industry. We find evidence of a “gender punishment gap”: following an incident of misconduct, female advisers are 20% more likely to lose their jobs and 30% less likely to find new jobs...
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- April 2022
- Case
Marsha Simms: Trailblazer in Corporate Law
By: Robin Ely, Boris Groysberg, Colleen Ammerman and Olivia Hull
Follows the journey of lawyer Marsha Simms from her childhood in racially-segregated St. Louis to the upper echelons of the New York legal community. Describes her education, career choices, accomplishments, and setbacks. Highlights significant moments such as her...
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Keywords:
Leadership;
Career;
Career Management;
Diversity;
Inclusion;
Equity;
Gender;
Race;
Corporate Finance;
Law;
Leadership Development;
Personal Development and Career;
Relationships;
Power and Influence;
Social and Collaborative Networks;
Status and Position;
Social Issues;
Legal Services Industry;
United States;
New York (state, US)
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
- September 2021
- Case
Francoise Brougher (A)
By: Edward H. Chang, Nour Kteily and Kathleen McGinn
Francoise Brougher was a high-powered technology executive in Silicon Valley. After successful stints at Google (where she rose to lead a $16 billion ad sales business) and Square (which she helped take public), she joined Pinterest as its first Chief Operating Officer...
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- August 2021
- Case
Zoom Video Communications: Building a Culture of Diversity, Equity, & Inclusion During COVID-19
By: Karen G. Mills, Scott Duke Kominers, Christopher Stanton, Andy Wu, George Gonzalez and Gabriella Elanbeck
Keywords:
Diversity Management;
Diversity Training;
Cultural Change;
Cultural Diversity;
Inclusion;
Inclusive Growth;
Inclusive Hiring;
Hiring;
Hiring Of Employees;
Recruiting;
Performance Management;
Change Leadership;
Race And Ethnicity;
Racial Bias;
Racial Disparity;
Racial Injustice;
Racial Tensions;
Racism;
Organization;
Organization Process;
Organization Structure;
Structural/institutional Racism;
Leadership And Change Management;
Leadership And Managing People;
Leading;
Gender Bias;
Discrimination;
Inequalities;
Inequality;
Social Change;
Employee Attitude Development And Empowerment;
Employee Bonding;
Employee Empowerment;
Employee Engagement;
Employee Fairness;
Employee Morale;
Employee Performance Management;
Employee Relations;
Company Culture;
Company Values;
Values;
COVID-19 Pandemic;
Demographics;
Diversity;
Age;
Ethnicity;
Gender;
Business Processes;
Change Management;
Change;
Race;
Human Capital;
Human Resources;
Compensation and Benefits;
Employees;
Employee Relationship Management;
Recruitment;
Retention;
Selection and Staffing;
Jobs and Positions;
Job Interviews;
Leadership;
Leading Change;
Management;
Management Teams;
Business or Company Management;
Crisis Management;
Goals and Objectives;
Growth and Development;
Growth and Development Strategy;
Growth Management;
Management Style;
Organizational Change and Adaptation;
Organizational Culture;
Organizational Design;
Organizational Structure;
Organizations;
Mission and Purpose;
Culture;
Happiness;
Prejudice and Bias;
Satisfaction;
Equity;
Identity;
Leadership Style;
Values and Beliefs;
Technology Industry;
United States
- June 2021
- Article
The Role of Beliefs in Driving Gender Discrimination
By: Katherine B. Coffman, Christine L. Exley and Muriel Niederle
While there is ample evidence of discrimination against women in the workplace, it can be difficult to understand what factors contribute to discriminatory behavior. We use an experiment to both document discrimination and unpack its sources. First, we show that, on...
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- May–June 2021
- Article
How to Close the Gender Gap
By: Colleen Ammerman and Boris Groysberg
Most companies say they’re committed to advancing women into leadership roles. What they may fail to recognize, though, is that systemic barriers are holding women back. As a result, women remain disadvantaged at every stage of their employment and underrepresented in...
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- 2021
- Working Paper
Venture Capital's 'Me Too' Moment
By: Sophie Calder-Wang, Paul A. Gompers and Patrick Sweeney
In this paper, we document the historically low rate of hiring of women in the venture capital sector. We find that the high-profile Ellen Pao v. Kleiner Perkins gender discrimination trial had dramatic treatment effects. In difference-in-differences regressions, we...
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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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Keywords:
Targeted Advertising;
Targeting;
Algorithmic Data;
Bias;
A/B Testing;
Experiment;
Advertising;
Gender;
Race;
Diversity;
Marketing;
Customer Relationship Management;
Prejudice and Bias;
Analytics and Data Science;
Retail Industry;
Apparel and Accessories Industry;
Technology Industry;
United States
- March 2021
- Teaching Plan
The Black New Venture Competition
Black entrepreneurs encounter many unique obstacles when raising capital to start and grow a business, some stemming from deep systemic discrimination. During their second year at Harvard Business School (HBS), MBA students Kimberly Foster and Tyler Simpson decided to...
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Keywords:
Analytics;
Startup;
Start-up;
Startup Financing;
Financing;
Startups;
Start-ups;
Business And Community;
Business And Society;
Business Growth;
Discrimination;
Women;
Women-owned Businesses;
African Americans;
African-american Entrepreneurs;
African-american Investors;
African-American Protagonist;
African-American Women;
Early Stage Funding;
Early Stage;
Innovation ＆ Entrepreneurship;
Innovation Competitions;
Entrepreneurial Financing;
Business Plan;
Business Startups;
Diversity;
Gender;
Race;
Entrepreneurship;
Venture Capital;
Small Business;
Leadership;
Information Technology;
Competition
- November–December 2020
- Article
Does Gender Matter? The Effect of Management Responses on Reviewing Behavior
By: Davide Proserpio, Isamar Troncoso and Francesca Valsesia
We study the effect of management responses on the reviewing behavior of self-identified female and male reviewers. Using data from Tripadvisor, we show that after hotels begin to respond to reviews, the probability that a negative review comes from a self-identified...
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- September 2020
- Case
The Black New Venture Competition
Black entrepreneurs encounter many unique obstacles when raising capital to start and grow a business. During their second year at Harvard Business School (HBS), MBA students Kimberly Foster and Tyler Simpson decided to do something to make a difference for...
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Keywords:
Startup;
Start-up;
Startup Financing;
Startups;
Start-ups;
African-American Protagonist;
African-american Entrepreneurs;
African-american Investors;
African-Americans;
African-American Women;
Black Leadership;
Black Inventors;
Black Entrepreneurs;
Harvard Business School;
Harvard;
Business And Society;
Early Stage Funding;
Early Stage Finance;
Technology Entrepreneurship;
Discrimination;
Technology Ventures;
Entrepreneurial Finance;
Entrepreneurial Financing;
Business Plan;
Business Startups;
Business Ventures;
Financing and Loans;
Business Growth and Maturation;
Diversity;
Gender;
Race;
Entrepreneurship;
Venture Capital;
Small Business;
Leadership;
Information Technology;
Competition;
Technology Industry
- 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
- September 2020 (Revised July 2022)
- 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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- 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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- 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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- 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...
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;
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
- 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"
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- September 2020 (Revised July 2022)
- 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