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      • December 2020
      • Supplement

      France Télécom (B): A Wave of Staff Suicides

      By: Cynthia A. Montgomery and Ashley V. Whillans
      In the B case we learn that at least 19 France Telecom employees took their own lives between 2006 and 2009, 12 others attempted suicide, and eight suffered from serious depression for reasons reportedly related to work. Some of these deaths occurred in public places,... View Details
      Keywords: Mental Health; Change; Crime and Corruption; Ethics; Health; Human Capital; Human Resources; Labor and Management Relations; Labor Unions; Law; Social Psychology; Strategy; Leadership Style; Organizations; Problems and Challenges; Relationships; Crisis Management; Employees; Well-being; Telecommunications Industry; Europe; European Union
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      Montgomery, Cynthia A., and Ashley V. Whillans. "France Télécom (B): A Wave of Staff Suicides." Harvard Business School Supplement 721-421, December 2020.
      • December 2020
      • Article

      Monetary Policy and Global Banking

      By: Falk Bräuning and Victoria Ivashina
      When central banks adjust interest rates, the opportunity cost of lending in local currency changes, but—in absence of frictions—there is no spillover effect to lending in other currencies. However, when equity capital is limited, global banks must benchmark domestic... View Details
      Keywords: Global Banks; Monetary Policy Transmission; Cross-border Lending; Banks and Banking; Financial Markets; Global Range
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      Bräuning, Falk, and Victoria Ivashina. "Monetary Policy and Global Banking." Journal of Finance 75, no. 6 (December 2020): 3055–3095.
      • Article

      Soul and Machine (Learning)

      By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
      Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to... View Details
      Keywords: Machine Learning; Marketing Applications; Knowledge; Technological Innovation; Core Relationships; Marketing; Applications and Software
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      Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Marketing Letters 31, no. 4 (December 2020): 393–404.
      • 2023
      • Working Paper

      The Market for Healthcare in Low Income Countries

      By: Abhijit Banerjee, Abhijit Chowdhury, Jishnu Das, Jeffrey Hammer, Reshmaan Hussam and Aakash Mohpal
      Patient trust is an important driver of the demand for healthcare. But it may also impact supply: doctors who realize that patients may not trust them may adjust their behavior in response. We assemble a large dataset that assesses clinical performance using... View Details
      Keywords: Health Care and Treatment; Quality; Developing Countries and Economies; Trust
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      Banerjee, Abhijit, Abhijit Chowdhury, Jishnu Das, Jeffrey Hammer, Reshmaan Hussam, and Aakash Mohpal. "The Market for Healthcare in Low Income Countries." Working Paper, July 2023.
      • 2020
      • Working Paper

      Party-State Capitalism in China

      By: Margaret Pearson, Meg Rithmire and Kellee Tsai
      The “state capitalism” model, in which the state retains a dominant role as owner or investor-shareholder amidst the presence of markets and private firms, has received increasing attention, with China cited as the main exemplar. Yet as models evolve, so has China’s... View Details
      Keywords: Capitalism; State Capitalism; Economic Systems; China
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      Pearson, Margaret, Meg Rithmire, and Kellee Tsai. "Party-State Capitalism in China." Harvard Business School Working Paper, No. 21-065, November 2020.
      • November 2020 (Revised April 2021)
      • Case

      Roll-Ups and Surprise Billing: Collisions at the Intersection of Private Equity and Patient Care

      By: Trevor Fetter and Kira Seiger
      This case describes the increasing investment by private equity (PE) firms in patient care and other healthcare services. The case focuses on investments in physician staffing firms and roll-up strategy investments in physician practice management (PPM). Included in... View Details
      Keywords: Business Ventures; Acquisition; Mergers and Acquisitions; Business Model; Change; Disruption; Fluctuation; Trends; Customers; Customer Value and Value Chain; Ethics; Fairness; Finance; Equity; Insurance; Private Equity; Geography; Geographic Scope; Health; Health Care and Treatment; Markets; Demand and Consumers; Supply and Industry; Industry Structures; Ownership; Ownership Type; Private Ownership; Relationships; Agency Theory; Business and Community Relations; Business and Shareholder Relations; Business and Stakeholder Relations; Networks; Strategy; Competition; Consolidation; Expansion; Integration; Horizontal Integration; Vertical Integration; Value; Value Creation; Health Industry; Insurance Industry; United States
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      Fetter, Trevor, and Kira Seiger. "Roll-Ups and Surprise Billing: Collisions at the Intersection of Private Equity and Patient Care." Harvard Business School Case 321-049, November 2020. (Revised April 2021.)
      • 2020
      • Working Paper

      Determinants of Early-Stage Startup Performance: Survey Results

      By: Thomas R. Eisenmann
      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... View Details
      Keywords: Startups; Survey Research; Performance Analysis; Entrepreneurship; Performance; Analysis; Business Startups; Failure; Surveys
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      Eisenmann, Thomas R. "Determinants of Early-Stage Startup Performance: Survey Results." Harvard Business School Working Paper, No. 21-057, October 2020.
      • October 2020 (Revised December 2020)
      • Case

      AfricInvest: A Pan-African Investment Platform

      By: Victoria Ivashina and Youssef Abdel Aal
      The case is set in December 2018, when Ziad Oueslati, co-managing director and co-founder of AfricInvest, a leading pan-African private equity firm headquartered in Tunisia, was reflecting on the future direction of his firm. AfricInvest started as a traditional small... View Details
      Keywords: Finance; Private Equity; Venture Capital; Strategy; Governance; Financial Services Industry; Tunisia; Africa; Middle East
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      Ivashina, Victoria, and Youssef Abdel Aal. "AfricInvest: A Pan-African Investment Platform." Harvard Business School Case 221-037, October 2020. (Revised December 2020.)
      • October 2020 (Revised March 2024)
      • Case

      Experimentation at Yelp

      By: Iavor Bojinov and Karim R. Lakhani
      Over the last decade, experimentation has become integral to the research and development processes of technology companies—including Yelp—for understanding customer preferences and mitigating innovation risks. The case describes Yelp's journey with experimentation,... View Details
      Keywords: Customer Relationship Management; Collaborative Innovation and Invention; Risk Management; Advertising; Research and Development; Technology Industry
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      Bojinov, Iavor, and Karim R. Lakhani. "Experimentation at Yelp." Harvard Business School Case 621-064, October 2020. (Revised March 2024.)
      • 2022
      • Working Paper

      The Stock Market Value of Human Capital Creation

      By: Matthias Regier and Ethan Rouen
      We develop a measure of firm-year-specific human capital investment from publicly disclosed personnel expenses (PE) and examine the stock market valuation of this investment. Measuring the future value of PE (PEFV) based on the relation between lagged... View Details
      Keywords: Intangibles; Market Valuation; Human Capital; Stocks; Financial Markets; Valuation
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      Regier, Matthias, and Ethan Rouen. "The Stock Market Value of Human Capital Creation." Harvard Business School Working Paper, No. 21-047, October 2020. (Revised March 2022.)
      • 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
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      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.)
      • 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
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      Yang, Jeremy, Dean Eckles, Paramveer Dhillon, and Sinan Aral. "Targeting for Long-Term Outcomes." Working Paper, October 2020.
      • 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
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      Israeli, Ayelet, and Eva Ascarza. "Algorithmic Bias in Marketing." Harvard Business School Teaching Note 521-035, September 2020. (Revised July 2022.)
      • 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
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      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
      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
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      Ascarza, Eva, and Ayelet Israeli. "Artea (A), (B), (C), and (D): Designing Targeting Strategies." Harvard Business School Teaching Note 521-041, September 2020. (Revised February 2024.)
      • 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
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      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
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      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
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      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
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      Ascarza, Eva, and Ayelet Israeli. "Artea: Designing Targeting Strategies." Harvard Business School Exercise 521-021, September 2020. (Revised June 2023.)
      • 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
      Keywords: Race; Gender; Marketing; Diversity; Customer Relationship Management; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States
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      Ascarza, Eva, and Ayelet Israeli. Spreadsheet Supplement to "Artea: Designing Targeting Strategies". Harvard Business School Spreadsheet Supplement 521-703, September 2020. (Revised July 2022.)
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