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- October 2020 (Revised June 2021)
- Case
Francisco Partners Private Credit Opportunity Fund
By: Luis M. Viceira, John D. Dionne, Soracha Prathanrasnikorn and Ari Sunshine
In April 2020, Scott Einsenberg, the Head of Credit at the private equity firm Francisco Partners, is deciding whether to go ahead with extending a private lending agreement to Eventbrite, Inc. (NYSE: EB), a leading global event management and online ticketing... View Details
Viceira, Luis M., John D. Dionne, Soracha Prathanrasnikorn, and Ari Sunshine. "Francisco Partners Private Credit Opportunity Fund." Harvard Business School Case 221-002, October 2020. (Revised June 2021.)
- 2021
- Working Paper
Accounting for Organizational Employment Impact
By: David Freiberg, Katie Panella, George Serafeim and T. Robert Zochowski
Organizations create significant positive and negative impacts through their employment practices. This paper builds on the substantial body of research regarding job quality and impact measurement to present a framework for monetized analysis of employment impact. We... View Details
Keywords: Impact-Weighted Accounts; IWAI; Employment Impact; Employment; Jobs and Positions; Quality; Measurement and Metrics; Analysis; Framework
Freiberg, David, Katie Panella, George Serafeim, and T. Robert Zochowski. "Accounting for Organizational Employment Impact." Harvard Business School Working Paper, No. 21-050, October 2020. (Revised August 2021.)
- 2020
- Working Paper
Fresh Fruit and Vegetable Consumption: The Impact of Access and Value
By: Retsef Levi, Elisabeth Paulson and Georgia Perakis
The goal of this paper is to leverage household-level data to improve food-related policies aimed at increasing the consumption of fruits and vegetables (FVs) among low-income households. Currently, several interventions target areas where residents have limited... View Details
Keywords: Food Deserts; Food Access; Food Policy; Causal Inference; Food; Nutrition; Poverty; Government Administration
Levi, Retsef, Elisabeth Paulson, and Georgia Perakis. "Fresh Fruit and Vegetable Consumption: The Impact of Access and Value." MIT Sloan Research Paper, No. 5389-18, October 2020.
- Fall 2020
- Article
Sizing Up Corporate Restructuring in the COVID Crisis
By: Robin Greenwood, Benjamin Iverson and David Thesmar
In the wake of the COVID-19 pandemic, the financial and legal system will need to deal with a surge of financial distress in the business sector. Some firms will be able to survive, while others will face bankruptcy and thus need to be liquidated or reorganized. Many... View Details
Greenwood, Robin, Benjamin Iverson, and David Thesmar. "Sizing Up Corporate Restructuring in the COVID Crisis." Brookings Papers on Economic Activity (Fall 2020). (Also NBER Working Paper, No. 28104.)
- October 2020
- Article
Why Time Poverty Matters for Individuals, Organisations, and Nations
By: Laura Giurge, Ashley V. Whillans and Colin West
Over the last two decades, global wealth has risen. Yet, material affluence has not translated into time affluence. Instead, most people today report feeling persistently “time poor”—like they have too many things to do and not enough time to do them. This is critical... View Details
Giurge, Laura, Ashley V. Whillans, and Colin West. "Why Time Poverty Matters for Individuals, Organisations, and Nations." Nature Human Behaviour 4, no. 10 (October 2020): 993–1003. (Shared Authorship.)
- 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 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
- 2022
- Working Paper
Where the Cloud Rests: The Location Strategies 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. We examine the market before the pandemic in 2018 and 2019, when supply and demand for data services were geographically stable. We compare with the entry... View Details
Greenstein, Shane, and Tommy Pan Fang. "Where the Cloud Rests: The Location Strategies of Data Centers." Harvard Business School Working Paper, No. 21-042, September 2020. (Revised June 2022.)
- 2020
- Working Paper
Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 7 The Value Structure of Technologies, Part 2: Strategy without Numbers
Functional analysis as set forth in the last chapter decomposes a technical system into functional components that do things to advance the system’s purpose and the goals of its designers. Functional analysis in turn can be used to construct value structure maps... View Details
Keywords: Modularity; Value Structure Mapping; Value Capture; Information Technology; Organizations; Strategy; Value Creation
Baldwin, Carliss Y. "Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 7 The Value Structure of Technologies, Part 2: Strategy without Numbers." Harvard Business School Working Paper, No. 21-040, September 2020.
- 2020
- Working Paper
Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted... View Details
Bojinov, Iavor I., David Simchi-Levi, and Jinglong Zhao. "Design and Analysis of Switchback Experiments." Harvard Business School Working Paper, No. 21-034, September 2020.
- 2020
- Working Paper
Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 2 Transactions in a Task Network
From the 1930s through today, many economists have conceived of large technical systems for the production of goods and services as a series of transactions. This point of view has led eminent economists to assert that transactions are the fundamental unit of analysis... View Details
Baldwin, Carliss Y. "Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 2 Transactions in a Task Network." Harvard Business School Working Paper, No. 21-030, August 2020.
- August 2020 (Revised December 2020)
- Background Note
A Note on Ethical Analysis
By: Nien-hê Hsieh
To engage in ethical analysis is to answer such questions as “What is the right thing to do?” “What does it mean to be a good person?” “How should I live my life?” Ethical analysis, on its own, is often not adequate for doing the right thing or being a good... View Details
Hsieh, Nien-hê. "A Note on Ethical Analysis." Harvard Business School Background Note 321-038, August 2020. (Revised December 2020.)
- August 2020 (Revised September 2020)
- Technical Note
Assessing Prediction Accuracy of Machine Learning Models
The note introduces a variety of methods to assess the accuracy of machine learning prediction models. The note begins by briefly introducing machine learning, overfitting, training versus test datasets, and cross validation. The following accuracy metrics and tools... View Details
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- August 2020
- Technical Note
Comparing Two Groups: Sampling and t-Testing
This note describes sampling and t-tests, two fundamental statistical concepts. View Details
Keywords: Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Analytics and Data Science; Analysis; Surveys; Mathematical Methods
Bojinov, Iavor I., Chiara Farronato, Yael Grushka-Cockayne, Willy C. Shih, and Michael W. Toffel. "Comparing Two Groups: Sampling and t-Testing." Harvard Business School Technical Note 621-044, August 2020.
- August 2020 (Revised June 2021)
- Case
Just Arrived: Integrating Refugees in Sweden
By: Brian Trelstad, Emilie Billaud and Mette Fuglsang Hjortshoej
Just Arrived is an online platform that matches newly-arrived immigrants in Sweden with employment opportunities. As one of several for-profit and non-profit start-ups in Europe that is looking to address the refugee crisis, the case enables a comparative analysis of... View Details
Keywords: Immigration; Refugees; Employment; Integration; Business Model; Social Entrepreneurship; Growth and Development Strategy; Employment Industry; Sweden; Italy; Germany
Trelstad, Brian, Emilie Billaud, and Mette Fuglsang Hjortshoej. "Just Arrived: Integrating Refugees in Sweden." Harvard Business School Case 321-040, August 2020. (Revised June 2021.)