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Publications

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      • 2022
      • Article

      Values and Inequality: Prosocial Jobs and the College Wage Premium

      By: Nathan Wilmers and Letian Zhang
      Employers often recruit workers by invoking corporate social responsibility, organizational purpose, or other claims to a prosocial mission. In an era of substantial labor market inequality, commentators typically dismiss these claims as hypocritical: prosocial... View Details
      Keywords: Corporate Social Responsibility and Impact; Equality and Inequality; Wages; Recruitment
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      Wilmers, Nathan, and Letian Zhang. "Values and Inequality: Prosocial Jobs and the College Wage Premium." American Sociological Review 87, no. 3 (2022): 415–442.
      • 2022
      • Working Paper

      Small Campaign Donors

      By: Laurent Bouton, Julia Cagé, Edgard Dewitte and Vincent Pons
      In this paper, we study the characteristics and behavior of small donors, and compare them to those of large donors. We first build a novel dataset including all the 340 million individual contributions reported to the U.S. Federal Election Commission between 2005 and... View Details
      Keywords: Campaign Finance; Campaign Contributions; Small Donations; ActBlue; WinRed; TV Advertising; Political Elections; Finance; Demographics; Advertising; Analysis; Analytics and Data Science
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      Bouton, Laurent, Julia Cagé, Edgard Dewitte, and Vincent Pons. "Small Campaign Donors." NBER Working Paper Series, No. 30050, May 2022.
      • 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

      Keywords: Algorithm Bias; Personalization; Targeting; Generalized Random Forests (GRF); Discrimination; Customization and Personalization; Decision Making; Fairness; Mathematical Methods
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      Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
      • 2022
      • Working Paper

      Can a Website Bring Unemployment Down? Experimental Evidence from France

      By: Aïcha Ben Dhia, Bruno Crépon, Esther Mbih, Louise Paul-Delvaux, Bertille Picard and Vincent Pons
      We evaluate the impact of an online platform giving job seekers tips to improve their search and recommendations of new occupations and locations to target, based on their personal data and labor market data. Our experiment used an encouragement design and was... View Details
      Keywords: Online Platform; Digital Platform; Unemployment; Encouragement Design; Job Search; Jobs and Positions; Internet and the Web; Well-being; Outcome or Result; Digital Platforms; France
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      Ben Dhia, Aïcha, Bruno Crépon, Esther Mbih, Louise Paul-Delvaux, Bertille Picard, and Vincent Pons. "Can a Website Bring Unemployment Down? Experimental Evidence from France." NBER Working Paper Series, No. 29914, April 2022.
      • March 2022
      • Article

      Learning to Rank an Assortment of Products

      By: Kris Ferreira, Sunanda Parthasarathy and Shreyas Sekar
      We consider the product ranking challenge that online retailers face when their customers typically behave as “window shoppers”: they form an impression of the assortment after browsing products ranked in the initial positions and then decide whether to continue... View Details
      Keywords: Online Learning; Product Ranking; Assortment Optimization; Learning; Internet and the Web; Product Marketing; Consumer Behavior; E-commerce
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      Ferreira, Kris, Sunanda Parthasarathy, and Shreyas Sekar. "Learning to Rank an Assortment of Products." Management Science 68, no. 3 (March 2022): 1828–1848.
      • 2022
      • Working Paper

      The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective

      By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju
      As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how... View Details
      Keywords: AI and Machine Learning; Analytics and Data Science; Mathematical Methods
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      Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Working Paper, 2022.
      • 2022
      • Working Paper

      E-commerce During COVID: Stylized Facts from 47 Economies

      By: Joel Alcedo, Alberto Cavallo, Bricklin Dwyer, Prachi Mishra and Antonio Spilimbergo
      We study e-commerce across 47 economies and 26 industries during the COVID-19 pandemic using aggregated and anonymized transaction-level data from Mastercard, scaled to represent total consumer spending. The share of online transactions in total consumption increased... View Details
      Keywords: COVID-19 Pandemic; Health Pandemics; Spending; Internet and the Web; Global Range; Analysis; E-commerce
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      Alcedo, Joel, Alberto Cavallo, Bricklin Dwyer, Prachi Mishra, and Antonio Spilimbergo. "E-commerce During COVID: Stylized Facts from 47 Economies." NBER Working Paper Series, No. 29729, February 2022.
      • November 2021 (Revised December 2021)
      • Supplement

      PittaRosso (B): Human and Machine Learning

      By: Ayelet Israeli
      This case supplements the "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion" case, and provides major highlights on what happened at the company since the first case. View Details
      Keywords: Artificial Intelligence; Pricing; Pricing Algorithm; Pricing Decisions; Pricing Strategy; Pricing Structure; Promotion; Promotions; Online Marketing; Data-driven Decision-making; Data-driven Management; Retail; Retail Analytics; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Marketing; Transformation; Decision Making; AI and Machine Learning; Retail Industry; Italy
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      Israeli, Ayelet. "PittaRosso (B): Human and Machine Learning." Harvard Business School Supplement 522-047, November 2021. (Revised December 2021.)
      • October 2021 (Revised March 2022)
      • Supplement

      PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

      By: Ayelet Israeli and Fabrizio Fantini
      PittaRosso, a traditional Italian shoe retailer, is implementing an AI system to provide pricing and promotion recommendations. The system allows them to implement changes that would affect both the top of funnel and bottom of funnel activities for the company: once... View Details
      Keywords: Artificial Intelligence; Pricing; Pricing Algorithm; Pricing Decisions; Pricing Strategy; Pricing Structure; Promotion; Promotions; Online Marketing; Data-driven Decision-making; Data-driven Management; Retail; Retail Analytics; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Marketing; Transformation; Decision Making; Retail Industry; Italy
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      Israeli, Ayelet, and Fabrizio Fantini. "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion." Harvard Business School Spreadsheet Supplement 522-710, October 2021. (Revised March 2022.)
      • October 2021 (Revised June 2022)
      • Case

      PittaRosso: Artificial Intelligence-Driven Pricing and Promotion

      By: Ayelet Israeli
      PittaRosso, a traditional Italian shoe retailer, is implementing an AI system to provide pricing and promotion recommendations. The system allows them to implement changes that would affect both the top of funnel and bottom of funnel activities for the company: once... View Details
      Keywords: Artificial Intelligence; Pricing; Pricing Algorithm; Pricing Decisions; Pricing Strategy; Pricing Structure; Promotion; Promotions; Online Marketing; Data-driven Decision-making; Data-driven Management; Retail; Retail Analytics; AI; Price; Advertising Campaigns; Analytics and Data Science; Analysis; Digital Marketing; Budgets and Budgeting; Marketing Strategy; Marketing; Transformation; Decision Making; AI and Machine Learning; Retail Industry; Italy
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      Israeli, Ayelet. "PittaRosso: Artificial Intelligence-Driven Pricing and Promotion." Harvard Business School Case 522-046, October 2021. (Revised June 2022.)
      • September 2021
      • Article

      Joint Problem-solving Orientation in Fluid Cross-boundary Teams

      By: Michaela J. Kerrissey, Anna T. Mayo and Amy C. Edmondson
      Using interviews, a national field survey, and an online laboratory study, we have examined teamwork in fluid cross-boundary teams. Across three studies, we qualitatively discovered and quantitatively explored "joint problem-solving orientation" as a new team factor.... View Details
      Keywords: Problem Solving; Cross-boundary Teams; Groups and Teams; Problems and Challenges; Performance
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      Kerrissey, Michaela J., Anna T. Mayo, and Amy C. Edmondson. "Joint Problem-solving Orientation in Fluid Cross-boundary Teams." Academy of Management Discoveries 7, no. 3 (September 2021): 381–405.
      • 2021
      • Working Paper

      The Value of Data and Its Impact on Competition

      By: Marco Iansiti
      Common regulatory perspective on the relationship between data, value, and competition in online platforms has increasingly centered on the volume of data accumulated by incumbent firms. This view posits the existence of "data network effects," where more data leads to... View Details
      Keywords: Online Platforms; Data Network Effects; Analytics and Data Science; Value; Competition; Digital Platforms
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      Iansiti, Marco. "The Value of Data and Its Impact on Competition." Harvard Business School Working Paper, No. 22-002, July 2021.
      • July 2021
      • Article

      Outsourcing Tasks Online: Matching Supply and Demand on Peer-to-Peer Internet Platforms

      By: Zoë Cullen and Chiara Farronato
      We study the growth of online peer-to-peer markets. Using data from TaskRabbit, an expanding marketplace for domestic tasks at the time of our study, we show that growth varies considerably across cities. To disentangle the potential drivers of growth, we look... View Details
      Keywords: Two-sided Market; Two-sided Platforms; Peer-to-peer Markets; Platform Strategy; Sharing Economy; Platform Growth; Internet and the Web; Digital Platforms; Strategy; Market Design; Network Effects
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      Cullen, Zoë, and Chiara Farronato. "Outsourcing Tasks Online: Matching Supply and Demand on Peer-to-Peer Internet Platforms." Management Science 67, no. 7 (July 2021): 3985–4003.
      • May 2021
      • Simulation

      Customer Compatibility Exercise Application

      By: Ryan W. Buell
      Customers impose considerable variability on the operating systems of service organizations. They show up when they wish (arrival variability), they ask for different things (request variability), they vary in their willingness and ability to help themselves (effort... View Details
      Keywords: Customer Compatibility; Customer Relationship Management; Strategy; Service Operations; Service Delivery; Performance Efficiency; Analysis; Consumer Behavior; Analytics and Data Science
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      Buell, Ryan W. "Customer Compatibility Exercise Application." Harvard Business School Simulation 620-707, May 2021.
      • May 2021 (Revised February 2024)
      • Teaching Note

      THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)

      By: Ayelet Israeli and Jill Avery
      THE YES, a multi-brand shopping app launched in May 2020 offered a new type of buying experience for women’s fashion, driven by a sophisticated algorithm that used data science and machine learning to create and deliver a personalized store for every shopper, based on... View Details
      Keywords: Data; Data Analytics; Artificial Intelligence; AI; AI Algorithms; AI Creativity; Fashion; Retail; Retail Analytics; E-Commerce Strategy; Platform; Platforms; Big Data; Preference Elicitation; Predictive Analytics; App Development; "Marketing Analytics"; Advertising; Mobile App; Mobile Marketing; Apparel; Online Advertising; Referral Rewards; Referrals; Female Ceo; Female Entrepreneur; Female Protagonist; Analytics and Data Science; Analysis; Creativity; Marketing Strategy; Brands and Branding; Consumer Behavior; Demand and Consumers; Forecasting and Prediction; Marketing Channels; Digital Marketing; Internet and the Web; Mobile and Wireless Technology; AI and Machine Learning; E-commerce; Digital Platforms; Fashion Industry; Retail Industry; Apparel and Accessories Industry; Consumer Products Industry; United States
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      Israeli, Ayelet, and Jill Avery. "THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)." Harvard Business School Teaching Note 521-097, May 2021. (Revised February 2024.)
      • April 2021 (Revised July 2021)
      • Case

      StockX: The Stock Market of Things (Abridged)

      By: Chiara Farronato, John J. Horton, Annelena Lobb and Julia Kelley
      Founded in 2015 by Dan Gilbert, Josh Luber, and Greg Schwartz, StockX was an online platform where users could buy and sell unworn luxury and limited-edition sneakers. Sneaker resale prices often fluctuated over time based on supply and demand, creating a robust... View Details
      Keywords: Markets; Auctions; Bids and Bidding; Demand and Consumers; Consumer Behavior; Analytics and Data Science; Market Design; Digital Platforms; Market Transactions; Marketplace Matching; Supply and Industry; Analysis; Price; Product Marketing; Product Launch; Apparel and Accessories Industry; Fashion Industry; North and Central America; United States; Michigan; Detroit
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      Farronato, Chiara, John J. Horton, Annelena Lobb, and Julia Kelley. "StockX: The Stock Market of Things (Abridged)." Harvard Business School Case 621-107, April 2021. (Revised July 2021.)
      • 2021
      • Article

      Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring

      By: Tom Sühr, Sophie Hilgard and Himabindu Lakkaraju
      Ranking algorithms are being widely employed in various online hiring platforms including LinkedIn, TaskRabbit, and Fiverr. Prior research has demonstrated that ranking algorithms employed by these platforms are prone to a variety of undesirable biases, leading to the... View Details
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      Sühr, Tom, Sophie Hilgard, and Himabindu Lakkaraju. "Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society 4th (2021).
      • March 17, 2021
      • Other Article

      Beyond Pajamas: Sizing Up the Pandemic Shopper

      By: Ayelet Israeli, Eva Ascarza and Laura Castrillo
      A first look at how the COVID-19 pandemic impacted e-commerce apparel shopping in the US and the UK. Extensive analysis and interactive graphics utilizing millions of transactions.
      While the pandemic is still playing out, our preliminary investigations... View Details
      Keywords: Retail; Retail Analytics; Consumer; Pandemic; COVID; COVID-19; Apparel; Ecommerce; Online Shopping; Online Apparel; Online Sales; Returns; CRM; Customer Retention; Customer Experience; Customer Value; Digital; Customer Focus and Relationships; Customers; Health Pandemics; Consumer Behavior; Customer Relationship Management; Internet and the Web; Behavior; E-commerce; Retail Industry; Apparel and Accessories Industry; Technology Industry; United States; United Kingdom
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      Israeli, Ayelet, Eva Ascarza, and Laura Castrillo. "Beyond Pajamas: Sizing Up the Pandemic Shopper." Harvard Business School Working Knowledge (March 17, 2021).
      • February 2021
      • Tutorial

      Getting Started in RStudio Cloud

      By: Chiara Farronato and Caleb Kwon
      This video provides an introduction to the free programming language R using an online cloud version of RStudio, which is the most popular editor and interface for writing and executing R code. The video begins by providing a brief background of R and RStudio and... View Details
      Keywords: Data Analysis; Data Analytics; Experiment; Experimental Design; Analytics and Data Science; Analysis; Applications and Software
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      Farronato, Chiara, and Caleb Kwon. Getting Started in RStudio Cloud. Harvard Business School Tutorial 621-708, February 2021.
      • February 2021 (Revised February 2021)
      • Background Note

      eGrocery and the Role of Data for CPG Firms

      By: Ayelet Israeli, Fedor (Ted) Lisitsyn and Mark A. Irwin
      This notes provides information about the eGrocery industry and how traditional CPG companies handle this channel and potential data. It is recommended to use together with a series of exercises entitled: "E-Commerce Analytics for CPG Firms (A), (B), and (C)." View Details
      Keywords: Data; Data Analysis; Data Analytics; Data Sharing; CPG; Consumer Packaged Goods (CPG); Delivery Planning; Customer Lifetime Value; Online Channel; Retail; Retail Analytics; Retailing Industry; Ecommerce; Grocery; Optimization; Analytics and Data Science; Analysis; Customer Value and Value Chain; Marketing Channels; E-commerce; Retail Industry; Consumer Products Industry; United States
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      Israeli, Ayelet, Fedor (Ted) Lisitsyn, and Mark A. Irwin. "eGrocery and the Role of Data for CPG Firms." Harvard Business School Background Note 521-077, February 2021. (Revised February 2021.)
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