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- News (73)
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- Faculty Publications (147)
Show Results For
-
All HBS Web
(374)
- News (73)
- Research (251)
- Events (6)
- Multimedia (1)
- Faculty Publications (147)
- Article
Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy
By: Edward Glaeser, Andrew Hillis, Scott Duke Kominers and Michael Luca
The proliferation of big data makes it possible to better target city services like hygiene inspections, but city governments rarely have the in-house talent needed for developing prediction algorithms. Cities could hire consultants, but a cheaper alternative is to...
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Keywords:
User-generated Content;
Operations;
Tournaments;
Policy-making;
Machine Learning;
Online Platforms;
Analytics and Data Science;
Mathematical Methods;
City;
Infrastructure;
Business Processes;
Government and Politics
Glaeser, Edward, Andrew Hillis, Scott Duke Kominers, and Michael Luca. "Crowdsourcing City Government: Using Tournaments to Improve Inspection Accuracy." American Economic Review: Papers and Proceedings 106, no. 5 (May 2016): 114–118.
- September 2019 (Revised September 2019)
- Case
Facebook Fake News in the Post-Truth World
By: John R. Wells, Carole A. Winkler and Benjamin Weinstock
In August 2019, Mark Zuckerberg, founder and CEO of Facebook, was surrounded by controversy. The first major storm of protest followed the surprise election of Donald Trump as President of the United States on November 8, 2016; many put the blame at the door of fake...
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Keywords:
Facebook;
Fake News;
Mark Zuckerberg;
Donald Trump;
Algorithms;
Social Networks;
Partisanship;
Social Media;
App Development;
Instagram;
WhatsApp;
Smartphone;
Silicon Valley;
Office Space;
Digital Strategy;
Democracy;
Entry Barriers;
Online Platforms;
Controversy;
Tencent;
Agility;
Social Networking;
Gaming;
Gaming Industry;
Computer Games;
Mobile Gaming;
Messaging;
Monetization Strategy;
Advertising;
Digital Marketing;
Business Ventures;
Acquisition;
Mergers and Acquisitions;
Business Growth and Maturation;
Business Headquarters;
Business Organization;
For-Profit Firms;
Trends;
Communication;
Communication Technology;
Forms of Communication;
Interactive Communication;
Interpersonal Communication;
Talent and Talent Management;
Crime and Corruption;
Voting;
Demographics;
Entertainment;
Games, Gaming, and Gambling;
Moral Sensibility;
Values and Beliefs;
Initial Public Offering;
Profit;
Revenue;
Geography;
Geographic Location;
Global Range;
Local Range;
Country;
Cross-Cultural and Cross-Border Issues;
Globalized Firms and Management;
Globalized Markets and Industries;
Governing Rules, Regulations, and Reforms;
Government and Politics;
International Relations;
National Security;
Political Elections;
Business History;
Recruitment;
Selection and Staffing;
Information Management;
Information Publishing;
News;
Newspapers;
Innovation and Management;
Innovation Strategy;
Technological Innovation;
Knowledge Dissemination;
Human Capital;
Law;
Leadership Development;
Leadership Style;
Leading Change;
Business or Company Management;
Crisis Management;
Goals and Objectives;
Growth and Development Strategy;
Growth Management;
Management Practices and Processes;
Management Style;
Management Systems;
Management Teams;
Managerial Roles;
Marketing Channels;
Social Marketing;
Network Effects;
Market Entry and Exit;
Digital Platforms;
Marketplace Matching;
Industry Growth;
Industry Structures;
Monopoly;
Media;
Product Development;
Service Delivery;
Corporate Social Responsibility and Impact;
Mission and Purpose;
Organizational Change and Adaptation;
Organizational Culture;
Organizational Structure;
Public Ownership;
Problems and Challenges;
Business and Community Relations;
Business and Government Relations;
Groups and Teams;
Networks;
Rank and Position;
Opportunities;
Behavior;
Emotions;
Identity;
Power and Influence;
Prejudice and Bias;
Reputation;
Social and Collaborative Networks;
Status and Position;
Trust;
Society;
Civil Society or Community;
Culture;
Public Opinion;
Social Issues;
Societal Protocols;
Strategy;
Adaptation;
Business Strategy;
Commercialization;
Competition;
Competitive Advantage;
Competitive Strategy;
Corporate Strategy;
Customization and Personalization;
Diversification;
Expansion;
Horizontal Integration;
Segmentation;
Information Technology;
Internet and the Web;
Mobile and Wireless Technology;
Applications and Software;
Information Infrastructure;
Valuation;
Advertising Industry;
Communications Industry;
Entertainment and Recreation Industry;
Information Industry;
Information Technology Industry;
Journalism and News Industry;
Media and Broadcasting Industry;
Service Industry;
Technology Industry;
Telecommunications Industry;
Video Game Industry;
United States;
California;
Sunnyvale;
Russia
Wells, John R., Carole A. Winkler, and Benjamin Weinstock. "Facebook Fake News in the Post-Truth World." Harvard Business School Case 720-373, September 2019. (Revised September 2019.)
- April 2020
- Article
CEO Behavior and Firm Performance
By: Oriana Bandiera, Stephen Hansen, Andrea Prat and Raffaella Sadun
We measure the behavior of 1,114 CEOs in six countries parsing granular CEO diary data through an unsupervised machine learning algorithm. The algorithm uncovers two distinct behavioral types: "leaders" and "managers." Leaders focus on multi-function, high-level...
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Bandiera, Oriana, Stephen Hansen, Andrea Prat, and Raffaella Sadun. "CEO Behavior and Firm Performance." Journal of Political Economy 128, no. 4 (April 2020): 1325–1369.
Edward McFowland III
Edward McFowland III is an Assistant Professor in the Technology and Operations Management Unit at Harvard Business School. He teaches the first-year TOM course in the required curriculum.
Professor McFowland’s research interests – which lie at the... View Details
Ayelet Israeli
Ayelet Israeli is the Marvin Bower Associate Professor of Business Administration at the Harvard Business School Marketing Unit. She is the co-founder of the Customer Intelligence Lab at the Digital Data Design (D^3) Institute at Harvard Business School. She teaches...
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- 2024
- Working Paper
Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift
By: Matthew DosSantos DiSorbo and Kris Ferreira
Problem definition: While artificial intelligence (AI) algorithms may perform well on data that are representative of the training set (inliers), they may err when extrapolating on non-representative data (outliers). These outliers often originate from covariate shift,...
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DosSantos DiSorbo, Matthew, and Kris Ferreira. "Warnings and Endorsements: Improving Human-AI Collaboration Under Covariate Shift." Working Paper, February 2024.
- February 2021
- Article
Topic Classification of Electric Vehicle Consumer Experiences with Transformer-Based Deep Learning
By: Sooji Ha, Daniel J Marchetto, Sameer Dharur and Omar Isaac Asensio
The transportation sector is a major contributor to greenhouse gas (GHG) emissions and is a driver of adverse health effects globally. Increasingly, government policies have promoted the adoption of electric vehicles (EVs) as a solution to mitigate GHG emissions....
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Keywords:
Natural Language Processing;
Analytics and Data Science;
Environmental Sustainability;
Infrastructure;
Transportation;
Policy
Ha, Sooji, Daniel J Marchetto, Sameer Dharur, and Omar Isaac Asensio. "Topic Classification of Electric Vehicle Consumer Experiences with Transformer-Based Deep Learning." Art. 100195. Patterns 2, no. 2 (February 2021).
- September–October 2023
- Article
Interpretable Matrix Completion: A Discrete Optimization Approach
By: Dimitris Bertsimas and Michael Lingzhi Li
We consider the problem of matrix completion on an n × m matrix. We introduce the problem of interpretable matrix completion that aims to provide meaningful insights for the low-rank matrix using side information. We show that the problem can be...
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Keywords:
Mathematical Methods
Bertsimas, Dimitris, and Michael Lingzhi Li. "Interpretable Matrix Completion: A Discrete Optimization Approach." INFORMS Journal on Computing 35, no. 5 (September–October 2023): 952–965.
- February 2023 (Revised March 2024)
- Supplement
Shanty Real Estate: Teaching Note Supplement
By: Michael Luca and Jesse M. Shapiro
Shanty is a simulation in which students inhabit the role of either a traditional home buyer or an iBuyer, both bidding on the same condo. The traditional home buyer has access to a “comp sheet” of similar properties that have recently sold, and has done a walkthrough....
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- 08 May 2018
- First Look
First Look at New Research and Ideas, May 8, 2018
manner that increases reimbursement or avoids financial penalties. Identifying upcoding in claims data is challenging due to unobservable confounders (e.g., patient risk). We leverage state-level variations in adverse event reporting...
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Keywords:
Sean Silverthorne
- 2021
- Working Paper
The Demand for Executive Skills
By: Stephen Hansen, Raffaella Sadun, Tejas Ramdas and Joseph B. Fuller
We use a unique corpus of job descriptions for C-suite positions to document skills requirements in top managerial occupations across a large sample of firms. A novel algorithm maps the text of each executive search into six separate skill clusters reflecting...
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Keywords:
C-Suite;
Jobs and Positions;
Competency and Skills;
Management Skills;
Job Search;
Job Design and Levels
Hansen, Stephen, Raffaella Sadun, Tejas Ramdas, and Joseph B. Fuller. "The Demand for Executive Skills." Harvard Business School Working Paper, No. 21-133, June 2021.
- 09 Nov 2020
- News
Best Business Books 2020: Technology & innovation
- 2021
- Working Paper
The Demand for Executive Skills
By: Stephen Hansen, Tejas Ramdas, Raffaella Sadun and Joseph B. Fuller
We use a unique corpus of job descriptions for C-suite positions to document skills requirements in top managerial occupations across a large sample of firms. A novel algorithm maps the text of each executive search into six separate skill clusters reflecting...
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Hansen, Stephen, Tejas Ramdas, Raffaella Sadun, and Joseph B. Fuller. "The Demand for Executive Skills." NBER Working Paper Series, No. 28959, June 2021.
- 2023
- Working Paper
PRIMO: Private Regression in Multiple Outcomes
By: Seth Neel
We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates...
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Neel, Seth. "PRIMO: Private Regression in Multiple Outcomes." Working Paper, March 2023.
Eliminating unintended bias in personalized policies using Bias Eliminating Adapted Trees (BEAT) - PNAS
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... View Details
- Spring 2016
- Article
Performance Responses to Competition Across Skill-Levels in Rank Order Tournaments: Field Evidence and Implications for Tournament Design
By: Kevin J. Boudreau, Karim R. Lakhani and Michael E. Menietti
Tournaments are widely used in the economy to organize production and innovation. We study individual contestant-level data from 2,796 contestants in 774 software algorithm design contests with random assignment. Precisely conforming to theory predictions, the...
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Boudreau, Kevin J., Karim R. Lakhani, and Michael E. Menietti. "Performance Responses to Competition Across Skill-Levels in Rank Order Tournaments: Field Evidence and Implications for Tournament Design." RAND Journal of Economics 47, no. 1 (Spring 2016): 140–165.
- Article
Robust and Stable Black Box Explanations
By: Himabindu Lakkaraju, Nino Arsov and Osbert Bastani
As machine learning black boxes are increasingly being deployed in real-world applications, there
has been a growing interest in developing post hoc explanations that summarize the behaviors
of these black boxes. However, existing algorithms for generating such...
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Lakkaraju, Himabindu, Nino Arsov, and Osbert Bastani. "Robust and Stable Black Box Explanations." Proceedings of the International Conference on Machine Learning (ICML) 37th (2020): 5628–5638. (Published in PMLR, Vol. 119.)
- 03 Mar 2022
- HBS Seminar
Daniela Saban, Stanford
- 22 Jan 2015
- News
Food Safety in Numbers
- January 2024 (Revised February 2024)
- Course Overview Note
Managing Customers for Growth: Course Overview for Students
By: Eva Ascarza
Managing Customers for Growth (MCG) is a 14-session elective course for second-year MBA students at Harvard Business School. It is designed for business professionals engaged in roles centered on customer-driven growth activities. The course explores the dynamics of...
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Keywords:
Customer Relationship Management;
Decision Making;
Analytics and Data Science;
Growth Management;
Telecommunications Industry;
Technology Industry;
Financial Services Industry;
Education Industry;
Travel Industry
Ascarza, Eva. "Managing Customers for Growth: Course Overview for Students." Harvard Business School Course Overview Note 524-032, January 2024. (Revised February 2024.)