Publications
Publications
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- All HBS Web
(1,184)
- Faculty Publications (344)
- June 2021
- Case
Akira Fukabori and Kevin Kajitani at avatarin (A) (Abridged)
By: Linda A. Hill and Emily Tedards
In 2016, Akira Fukabori and Kevin Kajitani, aeronautical engineers at All Nippon Airways Co., Ltd., began to wonder why, in a world of accelerating globalization and digital connectivity, those who lived in far-remote villages or impoverished urban areas could not... View Details
Keywords: Agility; Ecosystem; Innovation Ecosystems; Innovation; Crowdsourcing; XPRIZE; Open Innovation; Partnership; Government; Collaboration; Co-creation; Purpose; Impact; Social Impact; Movement; Organizational Behavior; Organizational Ambidexterity; Ambidexterity; Culture; Culture Change; Global Teams; Experimentation; Space; Space Industry; Airline Industry; Start-up; Platform Business; Platform Strategy; Platform; Digital; Robotics; Robots; Avatar; Telepresence; Innovation Lab; Mobility; COVID-19; Intrapreneurship; Public-private Partnership; Innovation and Invention; Technological Innovation; Partners and Partnerships; Collaborative Innovation and Invention; Alignment; Leadership; Leading Change; Diversity; Organizational Culture; Change Management; Strategy; Entrepreneurship; Digital Platforms; Transportation Industry; Aerospace Industry; Japan
- Article
Large-Scale Field Experiment Shows Null Effects of Team Demographic Diversity on Outsiders' Willingness to Support the Team
By: Edward H. Chang, Erika L. Kirgios and Rosanna K. Smith
Demographic diversity in the United States is rising, and increasingly, work is conducted in teams. These co-occurring phenomena suggest that it might be increasingly common for work to be conducted by demographically diverse teams. But to date, in spite of copious... View Details
- 2021
- Working Paper
Population Interference in Panel Experiments
By: Iavor I Bojinov, Kevin Wu Han and Guillaume Basse
The phenomenon of population interference, where a treatment assigned to one experimental unit affects another experimental unit's outcome, has received considerable attention in standard randomized experiments. The complications produced by population interference in... View Details
- March 2021
- Case
VideaHealth: Building the AI Factory
By: Karim R. Lakhani and Amy Klopfenstein
Florian Hillen, co-founder and CEO of VideaHealth, a startup that used artificial intelligence (AI) to detect dental conditions on x-rays, spent the early years of his company laying the groundwork for an AI factory. A process for quickly building and iterating on new... View Details
Keywords: Artificial Intelligence; Innovation and Invention; Disruptive Innovation; Technological Innovation; Information Technology; Applications and Software; Technology Adoption; Digital Platforms; Entrepreneurship; AI and Machine Learning; Technology Industry; Medical Devices and Supplies Industry; North and Central America; United States; Massachusetts; Cambridge
- March 2021
- Article
Bayesian Signatures of Confidence and Central Tendency in Perceptual Judgment
By: Yang Xiang, Thomas Graeber, Benjamin Enke and Samuel Gershman
This paper theoretically and empirically investigates the role of Bayesian noisy cognition in perceptual judgment, focusing on the central tendency effect: the well-known empirical regularity that perceptual judgments are biased towards the center of the... View Details
- 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
- February 2021
- Tutorial
T-tests: Theory and Practice
This video provides an introduction to hypothesis testing, sampling, t-tests, and p-values. It provides examples of A/B testing and t-testing to assess whether difference between two groups are statistically significant. This video can be assigned in conjunction with... View Details
- Article
Towards Robust and Reliable Algorithmic Recourse
By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan
approvals), there has been growing interest in post-hoc techniques which provide recourse to affected
individuals. These techniques generate recourses under the assumption... View Details
- December 2020 (Revised March 2024)
- Supplement
Experimentation at Yelp
By: Iavor Bojinov and Karim R. Lakhani
- Article
Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
By: Kaivalya Rawal and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision-making, there has been a lot of interest in developing algorithms which can provide recourses to affected individuals. While developing such tools is important, it is even more critical to... View Details
- 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... View Details
- October 2020 (Revised March 2024)
- Case
Experimentation at Yelp
By: Iavor Bojinov and Karim R. Lakhani
Over the last decade, experimentation has become integral to the research and development processes of technology companies—including Yelp—for understanding customer preferences and mitigating innovation risks. The case describes Yelp's journey with experimentation,... View Details
- 2022
- Working Paper
Heterogeneity of Gain-Loss Attitudes and Expectations-Based Reference Points
By: Pol Campos-Mercade, Lorenz Goette, Thomas Graeber, Alex Kellogg and Charles Sprenger
Existing tests of reference-dependent preferences assume universal loss aversion. This paper examines heterogeneity in gain-loss attitudes, and explores its implications for identifying models of the reference point. In two experimental settings we measure gain-loss... View Details
- 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
- 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
- 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
- August 2020
- Technical Note
Comparing Two Groups: Sampling and t-Testing
This note describes sampling and t-tests, two fundamental statistical concepts. View Details
- August 2020
- Article
Machine Learning and Human Capital Complementarities: Experimental Evidence on Bias Mitigation
By: Prithwiraj Choudhury, Evan Starr and Rajshree Agarwal
The use of machine learning (ML) for productivity in the knowledge economy requires considerations of important biases that may arise from ML predictions. We define a new source of bias related to incompleteness in real time inputs, which may result from strategic... View Details
- 2020
- Working Paper
What Can Economics Say About Alzheimer's Disease?
By: Amitabh Chandra, Courtney Coile and Corina Mommaerts
Alzheimer’s Disease (AD) affects one in ten people aged 65 or older and is the most expensive disease in the United States. We describe the central economic questions raised by AD. While there is overlap with the economics of aging, the defining features of the... View Details
- Article
The Impact of COVID-19 on Small Business Outcomes and Expectations
By: Alexander Bartik, Marianne Bertrand, Zoë B. Cullen, Edward L. Glaeser, Michael Luca and Christopher Stanton
To explore the impact of COVID on small businesses, we conducted a survey of more than 5,800 small businesses between March 28 and April 4, 2020. Several themes emerged. First, mass layoffs and closures had already occurred—just a few weeks into the crisis. Second, the... View Details