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Skip to Main Content Cold Call A podcast featuring faculty discussing cases they've written and the lessons they impart. Subscribe on iTunes 29 Sep...
- 2021
- Working Paper
Time Dependency, Data Flow, and Competitive Advantage
Data is fundamental to machine learning-based products and services and is considered strategic due to its externalities for businesses, governments, non-profits, and more generally for society. It is renowned that the value of organizations (businesses, government...
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Valavi, Ehsan, Joel Hestness, Marco Iansiti, Newsha Ardalani, Feng Zhu, and Karim R. Lakhani. "Time Dependency, Data Flow, and Competitive Advantage." Harvard Business School Working Paper, No. 21-099, March 2021.
- 2021
- Working Paper
Extension Request Avoidance Increases Time Stress among Women
By: Ashley V. Whillans, Jaewon Yoon, Aurora Turek and Grant E. Donnelly
In eight studies using archival data, surveys, and experiments, we identify a novel factor that predicts gender differences in time stress and burnout. Across academic and professional settings, women are less likely to ask for more time when working under adjustable...
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Whillans, Ashley V., Jaewon Yoon, Aurora Turek, and Grant E. Donnelly. "Extension Request Avoidance Increases Time Stress among Women." Harvard Business School Working Paper, No. 21-103, March 2021.
- 2021
- Article
Leisure Beliefs and the Subjective Well-being of Nations
By: A.V. Whillans and Lucia Macchia
Here, we construct a data set of 79 countries (N = 220,000) and explore whether differences in the prioritization of time (leisure) vs. money (work) explain cross-country differences in happiness. Consistent with our predictions, countries whose citizens value leisure...
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Keywords:
Leisure;
Work;
Subjective Well-being;
Public Policy;
Employment;
Happiness;
Governance;
Policy
Whillans, A.V., and Lucia Macchia. "Leisure Beliefs and the Subjective Well-being of Nations." Journal of Positive Psychology 16, no. 2 (2021): 198–206. (Shared Authorship.)
- March 2021
- Article
The Customer May Not Always Be Right: Customer Compatibility and Service Performance
This paper investigates the impact of customer compatibility – the degree of fit between the needs of customers and the capabilities of the operations serving them – on customer experiences and firm performance. We use a variance decomposition analysis to quantify the...
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Keywords:
Customer Compatibility;
Satisfaction;
Profitability;
Service Operations;
Customer Relationship Management;
Customer Satisfaction;
Performance
Buell, Ryan W., Dennis Campbell, and Frances X. Frei. "The Customer May Not Always Be Right: Customer Compatibility and Service Performance." Management Science 67, no. 3 (March 2021): 1468–1488.
- February 2021
- Case
SafeGraph: Selling Data as a Service
By: Ramana Nanda, Abhishek Nagaraj and Allison Ciechanover
Set in January 2021, the CEO of SafeGraph, a four-year-old startup that sold Data as a Service, looked to the future. His aim was to become the most trusted source for data about a physical place. The company provided points of interest (POI) and foot traffic data on...
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- February 2021
- Tutorial
Assessing Prediction Accuracy of Machine Learning Models
By: Michael Toffel and Natalie Epstein
This video describes how to assess the accuracy of machine learning prediction models, primarily in the context of machine learning models that predict binary outcomes, such as logistic regression, random forest, or nearest neighbor models. After introducing and...
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- February 2021
- Tutorial
Getting Started in R Cloud Studio
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...
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- February 2021
- Article
Assessment of Electronic Health Record Use Between U.S. and Non-U.S. Health Systems
By: A Jay Holmgren, Lance Downing, David W. Bates, Tait D. Shanafelt, Arnold Milstein, Christopher Sharp, David Cutler, Robert S. Huckman and Kevin A. Schulman
Importance: Understanding how the electronic health record (EHR) system changes clinician work, productivity, and well-being is critical. Little is known regarding global variation in patterns of use.
Objective: To provide insights into which EHR... View Details
Objective: To provide insights into which EHR... View Details
Keywords:
Electronic Health Records;
Health Care and Treatment;
Online Technology;
Health Industry;
Information Technology Industry
Holmgren, A Jay, Lance Downing, David W. Bates, Tait D. Shanafelt, Arnold Milstein, Christopher Sharp, David Cutler, Robert S. Huckman, and Kevin A. Schulman. "Assessment of Electronic Health Record Use Between U.S. and Non-U.S. Health Systems." JAMA Internal Medicine 181, no. 2 (February 2021): 251–259.
- 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).
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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;
Data and Data Sets;
Analysis;
Customer Value and Value Chain;
Marketing Channels;
Retail Industry;
Consumer Products Industry;
United States
- January 2021 (Revised March 2021)
- Case
THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)
By: Jill Avery, Ayelet Israeli and Emma von Maur
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...
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Keywords:
Data;
Data Analytics;
Artificial Intelligence;
Ai;
Ai Algorithms;
Ai Creativity;
Fashion;
Retail;
Retail Analytics;
Digital Marketing;
E-commerce;
E-commerce Strategy;
Platform;
Platforms;
Big Data;
Preference Elicitation;
Preference Prediction;
Predictive Analytics;
App Development;
"marketing Analytics";
Advertising;
Mobile App;
Mobile Marketing;
Apparel;
Referral Rewards;
Referrals;
Female Ceo;
Female Entrepreneur;
Female Protagonist;
Data and Data Sets;
Analysis;
Creativity;
Marketing Strategy;
Brands and Branding;
Consumer Behavior;
Demand and Consumers;
Forecasting and Prediction;
Marketing Channels;
Online Advertising;
Online Technology;
Mobile Technology;
Fashion Industry;
Retail Industry;
Apparel and Accessories Industry;
Consumer Products Industry;
United States
Avery, Jill, Ayelet Israeli, and Emma von Maur. "THE YES: Reimagining the Future of E-Commerce with Artificial Intelligence (AI)." Harvard Business School Case 521-070, January 2021. (Revised March 2021.)
- January 2021 (Revised April 2021)
- Case
Amazon Shopper Panel: Paying Customers for Their Data
By: Eva Ascarza and Ayelet Israeli
This case introduces a new Amazon program that has consumers upload their receipts from transactions outside of Amazon, in exchange for money. Through the discussion, the case aims to explore issues in customers’ privacy in the digital age, the value of customers’ own...
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Keywords:
Data Analytics;
Data Privacy;
Data Management;
"marketing Analytics";
Marketing Communication;
Marketing Research;
Data-driven Management;
E-commerce;
E-commerce Strategy;
Ethical Decision Making;
Crm;
Consumer Protection;
Targeted Advertising;
Targeted Policies;
Data Ownership;
Marketing;
Research;
Marketing Communications;
Data and Data Sets;
Management;
Customer Relationship Management;
Ethics;
Retail Industry;
Technology Industry;
United States
Ascarza, Eva, and Ayelet Israeli. "Amazon Shopper Panel: Paying Customers for Their Data." Harvard Business School Case 521-058, January 2021. (Revised April 2021.)
- January 2021 (Revised March 2021)
- Exercise
E-Commerce Analytics for CPG Firms (A): Estimating Sales
By: Ayelet Israeli and Fedor (Ted) Lisitsyn
The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for...
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Keywords:
Data Analysis;
Data Analytics;
Cpg;
Consumer Packaged Goods (cpg);
Estimation;
Online Channel;
Retail Analytics;
Retail;
Retailing Industry;
Data;
Data Sharing;
Bricks And Mortar;
Ecommerce;
Direct-to-consumer;
Dtc;
Data and Data Sets;
Sales;
Retail Industry;
Consumer Products Industry;
United States
Israeli, Ayelet, and Fedor (Ted) Lisitsyn. "E-Commerce Analytics for CPG Firms (A): Estimating Sales." Harvard Business School Exercise 521-078, January 2021. (Revised March 2021.)
- January 2021 (Revised March 2021)
- Supplement
E-Commerce Analytics for CPG Firms (A): Estimating Sales
By: Ayelet Israeli and Fedor (Ted) Lisitsyn
The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for...
View Details
Keywords:
Data Analysis;
Data Analytics;
Cpg;
Consumer Packaged Goods (cpg);
Estimation;
Online Channel;
Retail Analytics;
Retail;
Retailing Industry;
Data;
Data Sharing;
Bricks And Mortar;
Ecommerce;
Data and Data Sets;
Analysis;
Sales;
Goods and Commodities;
Retail Industry;
Consumer Products Industry;
United States
- January 2021
- Exercise
E-Commerce Analytics for CPG Firms (B): Optimizing Assortment for a New Retailer
By: Ayelet Israeli and Fedor (Ted) Lisitsyn
The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for...
View Details
Keywords:
Data Analysis;
Data Analytics;
Cpg;
Consumer Packaged Goods (cpg);
Online Channel;
Retail Analytics;
Retail;
Retailing Industry;
Data;
Data Sharing;
Ecommerce;
Crm;
Loyalty Management;
Assortment Planning;
Assortment Optimization;
Lifetime Value (ltv);
Data and Data Sets;
Analysis;
Retention;
Retail Industry;
Consumer Products Industry;
United States
Israeli, Ayelet, and Fedor (Ted) Lisitsyn. "E-Commerce Analytics for CPG Firms (B): Optimizing Assortment for a New Retailer." Harvard Business School Exercise 521-079, January 2021.
- January 2021
- Supplement
E-Commerce Analytics for CPG Firms (B): Optimizing Assortment for a New Retailer
By: Ayelet Israeli and Fedor (Ted) Lisitsyn
The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for...
View Details
Keywords:
Data Analysis;
Data Analytics;
Cpg;
Consumer Packaged Goods (cpg);
Online Channel;
Retail;
Retail Analytics;
Retailing Industry;
Data;
Data Sharing;
Ecommerce;
Assortment Optimization;
Assortment Planning;
Data and Data Sets;
Retention;
Retail Industry;
Consumer Products Industry;
United States
- January 2021 (Revised March 2021)
- Exercise
E-Commerce Analytics for CPG Firms (C): Free Delivery Terms
By: Ayelet Israeli and Fedor (Ted) Lisitsyn
The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for...
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;
Grocery Delivery;
Margins;
Data and Data Sets;
Retention;
Retail Industry;
Consumer Products Industry;
United States
Israeli, Ayelet, and Fedor (Ted) Lisitsyn. "E-Commerce Analytics for CPG Firms (C): Free Delivery Terms." Harvard Business School Exercise 521-080, January 2021. (Revised March 2021.)
- January 2021 (Revised March 2021)
- Supplement
E-Commerce Analytics for CPG Firms (C): Free Delivery Terms
By: Ayelet Israeli and Fedor (Ted) Lisitsyn
The E-Commerce Analytics group at the traditional CPG firm was in charge of compiling various online sales reports, as well as making data-driven recommendations for sales and marketing tactics. In a series of exercises, students address different data challenges for...
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;
Grocery Delivery;
Margins;
Data and Data Sets;
Retention;
Retail Industry;
Consumer Products Industry;
United States
- 2021
- Working Paper
Pricing of Climate Risk Insurance: Regulatory Frictions and Cross-Subsidies
By: Ishita Sen and Ana-Maria Tenekedjieva
Homeowners’ insurance provides households financial protection from climate losses. To improve access and affordability, state regulators impose price controls on insurance companies. Using novel data, we construct a new measure of rate setting frictions for individual...
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Keywords:
Climate Risk;
Homeowners' Insurance;
Price Controls;
Financial Regulation;
Cross-subsidization;
Climate Change;
Household;
Insurance;
Price;
Governance Controls;
Financial Institutions;
United States
Sen, Ishita, and Ana-Maria Tenekedjieva. "Pricing of Climate Risk Insurance: Regulatory Frictions and Cross-Subsidies." Working Paper, January 2021.
- January 2021
- Case
Biobot Analytics
In 2017, Newsha Ghaeli and Mariana Matus were deciding whether to leave their labs at the Massachusetts Institute of Technology, put other job opportunities aside, and dive full-time into founding a wastewater analysis start-up, Biobot. Ghaeli, an architect, and Matus,...
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- January 2021
- Article
Using Models to Persuade
By: Joshua Schwartzstein and Adi Sunderam
We present a framework where "model persuaders" influence receivers’ beliefs by proposing models that organize past data to make predictions. Receivers are assumed to find models more compelling when they better explain the data, fixing receivers’ prior beliefs. Model...
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Keywords:
Model Persuasion;
Data and Data Sets;
Forecasting and Prediction;
Mathematical Methods;
Framework
Schwartzstein, Joshua, and Adi Sunderam. "Using Models to Persuade." American Economic Review 111, no. 1 (January 2021): 276–323.
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Skip to Main Content Cold Call A podcast featuring faculty discussing cases they've written and the lessons they impart. Subscribe on iTunes 29 Sep...