Publications
Publications
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- All HBS Web
(4,157)
- Faculty Publications (1,262)
- January–February 2020
- Article
Competing in the Age of AI
By: Marco Iansiti and Karim R. Lakhani
Today’s markets are being reshaped by a new kind of firm—one in which artificial intelligence (AI) runs the show. This cohort includes giants like Google, Facebook, and Alibaba, and growing businesses such as Wayfair and Ocado. Every time we use their services, the... View Details
- 2021
- Working Paper
Impact Investing: A Theory of Financing Social Enterprises
By: Benjamin N. Roth
I present a model of financing social enterprises to delineate the role of impact investors relative to “pure” philanthropists. I characterize the optimal scale and structure of a social enterprise when financed by grants, and when financed by investments. Impact... View Details
- Article
Fake AI People Won't Fix Online Dating
Computer-generated images may inspire even more distrust and surely won’t lead to the love of a lifetime. View Details
- 2020
- Book
Competing in the Age of AI: Strategy and Leadership When Algorithms and Networks Run the World
By: Marco Iansiti and Karim R. Lakhani
In industry after industry, data, analytics, and AI-driven processes are transforming the nature of work. While we often still treat AI as the domain of a specific skill, business function, or sector, we have entered a new era in which AI is challenging the very... View Details
- Article
A Theories-in-Use Approach to Building Marketing Theory
By: G. Zaltman, Valarie A. Zeithaml, Bernard Jaworski, Ajay K. Kohli, Kapil R. Tuli and Wolfgang Ulaga
This article’s objective is to inspire and provide guidance on the development of marketing knowledge based on the theories-in-use (TIU) approach. The authors begin with a description of the TIU approach and compare it with other inductive and deductive research... View Details
- Article
Detecting Adversarial Attacks via Subset Scanning of Autoencoder Activations and Reconstruction Error
By: Celia Cintas, Skyler Speakman, Victor Akinwande, William Ogallo, Komminist Weldemariam, Srihari Sridharan and Edward McFowland III
Reliably detecting attacks in a given set of inputs is of high practical relevance because of the vulnerability of neural networks to adversarial examples. These altered inputs create a security risk in applications with real-world consequences, such as self-driving... View Details
- May 2020
- Article
Scalable Holistic Linear Regression
By: Dimitris Bertsimas and Michael Lingzhi Li
We propose a new scalable algorithm for holistic linear regression building on Bertsimas & King (2016). Specifically, we develop new theory to model significance and multicollinearity as lazy constraints rather than checking the conditions iteratively. The resulting... View Details
- November 2019
- Supplement
Hapag-Lloyd AG: Complying with IMO 2020
By: Benjamin C. Esty, Mette Fuglsang Hjortshoej and Emer Moloney
A new environmental regulation known as IMO 2020 was creating what one industry analyst called “the biggest shakeup for the oil and shipping industries in decades.” According to the new regulation, all ocean-going ships would have to limit their sulfur emissions by... View Details
- November 2019
- Case
Hapag-Lloyd AG: Complying with IMO 2020
By: Benjamin C. Esty, Mette Fuglsang Hjortshoej and Emer Moloney
A new environmental regulation known as IMO 2020 was creating what one industry analyst called “the biggest shakeup for the oil and shipping industries in decades.” According to the new regulation, all ocean-going ships would have to limit their sulfur emissions by... View Details
- 2020
- Working Paper
Tech Clusters
By: William R. Kerr and Frederic Robert-Nicoud
Tech clusters like Silicon Valley play a central role for modern innovation, business competitiveness, and economic performance. This paper reviews what constitutes a tech cluster, how they function internally, and the degree to which policy makers can purposefully... View Details
- Article
How to Use Heuristics for Differential Privacy
By: Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
We develop theory for using heuristics to solve computationally hard problems in differential privacy. Heuristic approaches have enjoyed tremendous success in machine learning, for which performance can be empirically evaluated. However, privacy guarantees cannot be... View Details
- 2019
- Working Paper
Design Rules, Volume 2: How Technology Shapes Organizations: Chapter 17 The Wintel Standards-based Platform
The purpose of this chapter is to use the theory of bottlenecks laid out in previous chapters to better understand the dynamics of an open standards-based platform. I describe how the Wintel platform evolved from 1990 through 2000 under joint sponsorship of Intel and... View Details
- 2019
- Working Paper
The Impact of the General Data Protection Regulation on Internet Interconnection
By: Ran Zhuo, Bradley Huffaker, KC Claffy and Shane Greenstein
The Internet comprises thousands of independently operated networks, where bilaterally negotiated interconnection agreements determine the flow of data between networks. The European Union’s General Data Protection Regulation (GDPR) imposes strict restrictions on... View Details
- October 2019
- Case
Feeling Machines: Emotion AI at Affectiva
By: Shane Greenstein and John Masko
In 2016, Affectiva—a Boston-based emotion AI software company with a long track record of building emotion-sensing software for market research—had attempted to expand into new verticals by releasing a mobile software development kit (SDK) that downloaders could adapt... View Details
- Article
Machine Learning Approaches to Facial and Text Analysis: Discovering CEO Oral Communication Styles
By: Prithwiraj Choudhury, Dan Wang, Natalie A. Carlson and Tarun Khanna
We demonstrate how a novel synthesis of three methods—(1) unsupervised topic modeling of text data to generate new measures of textual variance, (2) sentiment analysis of text data, and (3) supervised ML coding of facial images with a cutting-edge convolutional neural... View Details
- October 2019
- Case
Leading Bank Leumi into the Future
By: Joshua D. Margolis, Allison M. Ciechanover, Nicole Keller and Danielle Golan
An unlikely but highly effective leader of a traditional bank, Rakefet Russak-Aminoach, simultaneously leads a classic change effort and an unconventional effort to innovate. She focuses her initial energy on making the bank more efficient in the face of industry... View Details
Keywords: Mobile Banking; Digital Banking; Fintech; Startup; Financial Services; Artificial Intelligence; Innovation; Efficiency; Organizational Change; Personal Development; Female Ceo; Banks and Banking; Mobile and Wireless Technology; Leadership; Organizational Change and Adaptation; Innovation and Invention; Disruption; Information Technology; Opportunities; Performance Effectiveness; Personal Development and Career; AI and Machine Learning; Financial Services Industry; Banking Industry; Israel
- October 2019 (Revised March 2021)
- Background Note
Modern Automation (B): Robotics
By: William R. Kerr and James Palano
Driven largely by advances in perception and situational awareness, robots in the 2010s were gaining functionality that allowed them to be applied to fundamentally new types of work. The expanding range of new tasks that could be completed by machines had significant... View Details
- Article
Organizational Innovation in the Multinational Enterprise: Internalization Theory and Business History
By: Teresa da Silva Lopes, Mark Casson and Geoffrey Jones
This article engages in a methodological experiment by using historical evidence to challenge a common misperception about internalization theory. The theory has often been criticized for maintaining that it assumes a hierarchically organized MNE based on knowledge... View Details
- 2019
- Working Paper
Soul and Machine (Learning)
By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, improved medical diagnostics, and machine translation, but can it improve marketing decisions? It can. Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to rich media... View Details
- 2020
- Working Paper
Collusion in Brokered Markets
By: John William Hatfield, Scott Duke Kominers and Richard Lowery
The U.S. residential real estate agency market presents a puzzle for economic theory: commissions on real estate transactions have remained high for decades even though entry is frequent and costs are low. We model the real estate agency market, and other brokered... View Details