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Show Results For
-
All HBS Web
(2,678)
- People (14)
- News (610)
- Research (1,393)
- Events (10)
- Multimedia (10)
- Faculty Publications (670)
- Teaching Interest
Overview
Paul is primarily interested in teaching data science to management students through the case method. This includes technical topics (programming and statistics) as well as higher-level management issues (digital transformation, data governance, etc.) As a research...
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Keywords:
A/B Testing;
AI;
AI Algorithms;
AI Creativity;
Algorithm;
Algorithm Bias;
Algorithmic Bias;
Algorithmic Fairness;
Algorithms;
Analytics;
Application Program Interface;
Artificial Intelligence;
Causality;
Causal Inference;
Computing;
Computers;
Data Analysis;
Data Analytics;
Data Architecture;
Data As A Service;
Data Centers;
Data Governance;
Data Labeling;
Data Management;
Data Manipulation;
Data Mining;
Data Ownership;
Data Privacy;
Data Protection;
Data Science;
Data Science And Analytics Management;
Data Scientists;
Data Security;
Data Sharing;
Data Strategy;
Data Visualization;
Database;
Data-driven Decision-making;
Data-driven Management;
Data-driven Operations;
Datathon;
Economics Of AI;
Economics Of Innovation;
Economics Of Information System;
Economics Of Science;
Forecast;
Forecast Accuracy;
Forecasting;
Forecasting And Prediction;
Information Technology;
Machine Learning;
Machine Learning Models;
Prediction;
Prediction Error;
Predictive Analytics;
Predictive Models;
Analysis;
AI and Machine Learning;
Analytics and Data Science;
Applications and Software;
Digital Transformation;
Information Management;
Digital Strategy;
Technology Adoption
- 05 Oct 2015
- Working Paper Summaries
Online Network Revenue Management Using Thompson Sampling
- 2022
- Working Paper
Rethinking Explainability as a Dialogue: A Practitioner's Perspective
By: Himabindu Lakkaraju, Dylan Slack, Yuxin Chen, Chenhao Tan and Sameer Singh
As practitioners increasingly deploy machine learning models in critical domains such as healthcare, finance, and policy, it becomes vital to ensure that domain experts function effectively alongside these models. Explainability is one way to bridge the gap between...
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Keywords:
Natural Language Conversations;
AI and Machine Learning;
Experience and Expertise;
Interactive Communication;
Business and Stakeholder Relations
Lakkaraju, Himabindu, Dylan Slack, Yuxin Chen, Chenhao Tan, and Sameer Singh. "Rethinking Explainability as a Dialogue: A Practitioner's Perspective." Working Paper, 2022.
- February 2018
- Case
Amazon, Google, and Apple: Smart Speakers and the Battle for the Connected Home
By: Rajiv Lal and Scott Johnson
Amazon, Google, and Apple all offer their own smart speaker. The devices represent each firm's entry point into the connected home market. All three companies come into the space with their own strengths and weaknesses. Who will win?
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Keywords:
Apple;
Apple Inc.;
Google;
Amazon;
Amazon.com;
Google Home;
Homepod;
Echo;
Smart Home;
Connected Home;
Voice;
Artificial Intelligence;
Machine Learning;
Internet Of Things;
Smart Speaker;
Connected Speaker;
Intelligent Assistants;
Virtual Assistants;
Voice Assistants;
Alexa;
Google Assistant;
Siri;
Technological Innovation;
Disruptive Innovation;
Competitive Strategy;
Business Strategy;
Adoption;
Information Infrastructure;
Information Technology;
Internet and the Web;
Mobile and Wireless Technology;
Applications and Software;
Technology Adoption;
Digital Platforms;
Household;
AI and Machine Learning;
Electronics Industry;
Technology Industry;
United States
Lal, Rajiv, and Scott Johnson. "Amazon, Google, and Apple: Smart Speakers and the Battle for the Connected Home." Harvard Business School Case 518-035, February 2018.
- December 2014
- Case
Groupon: A New CEO Takes Charge
By: Lynda M. Applegate and Arnold B. Peinado
On August 7, 2013, Eric Lefkofsky, the chairman and largest shareholder of Groupon was named CEO, replacing founder Andrew Mason, who had run the company since its inception in 2009. When Groupon had its initial public offering (IPO) in November 2011, the company's...
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- 2023
- Working Paper
Translating Information into Action: A Public Health Experiment in Bangladesh
By: Reshmaan Hussam, Kailash Pandey, Abu Shonchoy and Chikako Yamauchi
While models of technology adoption posit learning as the basis of behavior change, information campaigns in public health frequently fail to change behavior. We design an information campaign embedding hand-hygiene edutainment within popular dramas using mobile...
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Hussam, Reshmaan, Kailash Pandey, Abu Shonchoy, and Chikako Yamauchi. "Translating Information into Action: A Public Health Experiment in Bangladesh." Working Paper, February 2023.
Ta-Wei Huang
Ta-Wei (David) Huang is a PhD candidate in Quantitative Marketing at Harvard Business School. His research integrates causal inference and machine learning to address methodological challenges and unintended consequences in targeting, personalization, and online...
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- December 1984
- Case
Expense Tracking System at Tiger Creek
By: Shoshana Zuboff
Mill manager Carl Adelman learns that a group of senior managers is soon to visit the Tiger Creek mill to learn more about the success of the newly implemented Expense Tracking System. The System had been installed on two paper machines to give workers real time cost...
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Zuboff, Shoshana. "Expense Tracking System at Tiger Creek." Harvard Business School Case 485-057, December 1984.
- December 2018 (Revised March 2021)
- Background Note
Modern Automation (A): Artificial Intelligence
By: William R. Kerr and James Palano
This primer is meant to be a field guide to the late 2010s' surge in business use of "Artificial Intelligence" (AI), or enterprise software based in machine learning. First, it provides an overview of the key trends—digitization, connectivity, the continuation of...
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Keywords:
Artificial Intelligence;
Digitization;
Connectivity;
Computing;
Future Of Work;
Automation;
AI and Machine Learning
Kerr, William R., and James Palano. "Modern Automation (A): Artificial Intelligence." Harvard Business School Background Note 819-084, December 2018. (Revised March 2021.)
- April 2023
- Case
Fizzy Fusion: When Data-Driven Decision Making Failed
By: Michael Parzen, Eddie Lin, Douglas Ng and Jessie Li
This is a case about a fictional New York beverage company called Fizzy Fusion. The business is facing supply chain and inventory management challenges with its new product, SparklingSip. Despite seeking help from a data science consulting firm, the machine learning...
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Keywords:
Supply Chain Management;
Production;
Risk and Uncertainty;
Analytics and Data Science;
Food and Beverage Industry
Parzen, Michael, Eddie Lin, Douglas Ng, and Jessie Li. "Fizzy Fusion: When Data-Driven Decision Making Failed." Harvard Business School Case 623-071, April 2023.
Himabindu Lakkaraju
Himabindu "Hima" Lakkaraju is an Assistant Professor of Business Administration at Harvard Business School. She is also a faculty affiliate in the Department of Computer Science at Harvard University, the Harvard Data Science Initiative, Center for Research on... View Details
- 2023
- Article
Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability
By: Usha Bhalla, Suraj Srinivas and Himabindu Lakkaraju
With the increased deployment of machine learning models in various real-world applications, researchers and practitioners alike have emphasized the need for explanations of model behaviour. To this end, two broad strategies have been outlined in prior literature to...
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Bhalla, Usha, Suraj Srinivas, and Himabindu Lakkaraju. "Verifiable Feature Attributions: A Bridge between Post Hoc Explainability and Inherent Interpretability." Advances in Neural Information Processing Systems (NeurIPS) (2023).
Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem
Combining machine learning with econometric analysis is becoming increasingly prevalent in both research and practice. A common empirical strategy involves the application of predictive modeling techniques to "mine" variables of interest from available data,...
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- 2023
- Working Paper
The Customer Journey as a Source of Information
By: Nicolas Padilla, Eva Ascarza and Oded Netzer
In the face of heightened data privacy concerns and diminishing third-party data access,
firms are placing increased emphasis on first-party data (1PD) for marketing decisions.
However, in environments with infrequent purchases, reliance on past purchases 1PD...
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Keywords:
Customer Journey;
Privacy;
Consumer Behavior;
Analytics and Data Science;
AI and Machine Learning;
Customer Focus and Relationships
Padilla, Nicolas, Eva Ascarza, and Oded Netzer. "The Customer Journey as a Source of Information." Harvard Business School Working Paper, No. 24-035, October 2023. (Revised October 2023.)
- April 2022 (Revised May 2022)
- Case
Mastercard Labs (A)
When Ajaypal (Ajay) Banga became the CEO of Mastercard in 2010, he shifted the company’s competitive focus from card networks to cash itself. Mastercard’s new vision of a “World Beyond Cash” distilled into a three-pronged framework: Grow the core business, Diversify...
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Keywords:
Organizational Behavior;
Culture;
Culture Change;
Organizational Adaptation;
Organizational Effectiveness;
Alignment;
Leadership;
Leadership Development;
Innovation;
Innovation Ecosystems;
Ecosystem;
Diversity;
Collaboration;
Co-creation;
Learning Organizations;
Empowerment;
Globalization;
Agility;
Prototype;
Experiment;
Partnerships;
Operating Model;
Risk Management;
Metrics;
Payments;
Financial Inclusion;
Financial Industry;
Ambidexterity;
Corporate Innovation;
Innovation Lab;
Digital Transformation;
Digital Strategy;
Credit Cards;
Innovation Leadership;
Organizational Culture
Hill, Linda A., Sunil Gupta, Emily Tedards, and Julia Kelley. "Mastercard Labs (A)." Harvard Business School Case 422-080, April 2022. (Revised May 2022.)
- March–April 2023
- Article
Pricing for Heterogeneous Products: Analytics for Ticket Reselling
By: Michael Alley, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li and Georgia Perakis
Problem definition: We present a data-driven study of the secondary ticket market. In particular, we are primarily concerned with accurately estimating price sensitivity for listed tickets. In this setting, there are many issues including endogeneity, heterogeneity in...
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Keywords:
Price;
Demand and Consumers;
AI and Machine Learning;
Investment Return;
Entertainment and Recreation Industry;
Sports Industry
Alley, Michael, Max Biggs, Rim Hariss, Charles Herrmann, Michael Lingzhi Li, and Georgia Perakis. "Pricing for Heterogeneous Products: Analytics for Ticket Reselling." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 409–426.
Jeremy Yang
Jeremy Yang is an Assistant Professor of Business Administration in the Marketing Unit at Harvard Business School. He teaches Marketing in the MBA required curriculum. He develops data products for...
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- 13 Sep 2018
- HBS Seminar
Nitin Joglekar, Boston University
- 19 Jan 2023
- Research & Ideas
What Makes Employees Trust (vs. Second-Guess) AI?
the model was accurate,” one interviewee told researchers, according to the paper. That employee confidence has big implications. In Tapestry’s case, revenue rose—and the leftover stock was less common—in stores where allocators used the...
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Keywords:
by Rachel Layne