Filter Results:
(4,017)
Show Results For
- All HBS Web
(4,017)
- People (2)
- News (556)
- Research (2,848)
- Events (51)
- Multimedia (21)
- Faculty Publications (2,050)
Show Results For
- All HBS Web
(4,017)
- People (2)
- News (556)
- Research (2,848)
- Events (51)
- Multimedia (21)
- Faculty Publications (2,050)
Page 1 of 4,017
Results →
- 08 Oct 2018
- Working Paper Summaries
Developing Theory Using Machine Learning Methods
- 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
Proserpio, Davide, 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. "Soul and Machine (Learning)." Harvard Business School Working Paper, No. 20-036, September 2019.
- Article
Adaptive Machine Unlearning
By: Varun Gupta, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Chris Waites
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for sequences of deletions, most prior work in the non-convex setting gives valid guarantees... View Details
Gupta, Varun, Christopher Jung, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Chris Waites. "Adaptive Machine Unlearning." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- Article
Soul and Machine (Learning)
By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, medical diagnoses, and language translation, but how can machine learning help marketers improve marketing decisions? Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to... View Details
Keywords: Machine Learning; Marketing Applications; Knowledge; Technological Innovation; Core Relationships; Marketing; Applications and Software
Proserpio, Davide, John R. Hauser, Xiao Liu, Tomomichi Amano, Burnap Alex, Tong Guo, Dokyun (DK) Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu, and Hema Yoganarasimhan. "Soul and Machine (Learning)." Marketing Letters 31, no. 4 (December 2020): 393–404.
- March 2022 (Revised January 2025)
- Technical Note
Prediction & Machine Learning
This note provides an introduction to machine learning for an introductory data science course. The note begins with a description of supervised, unsupervised, and reinforcement learning. Then, the note provides a brief explanation of the difference between traditional... View Details
Keywords: Machine Learning; Data Science; Learning; Analytics and Data Science; Performance Evaluation; AI and Machine Learning
Bojinov, Iavor I., Michael Parzen, and Paul Hamilton. "Prediction & Machine Learning." Harvard Business School Technical Note 622-101, March 2022. (Revised January 2025.)
- March–April 2015
- Article
Why We Think We Can't Dance: Theory of Mind and Children's Desire to Perform
By: Lan Nguyen Chaplin and Michael I. Norton
Theory of Mind (ToM) allows children to achieve success in the social world by understanding others' minds. A study with 3–12 year olds, however, demonstrates that gains in ToM are linked to decreases in children's desire to engage in performative behaviors associated... View Details
Chaplin, Lan Nguyen, and Michael I. Norton. "Why We Think We Can't Dance: Theory of Mind and Children's Desire to Perform." Child Development 86, no. 2 (March–April 2015): 651–658.
- September–October 2021
- Article
Internalization of Advertising Services: Testing a Theory of the Firm
By: Alvin J. Silk, Birger Wernerfelt and Shuyi Yu
In 1956, a group of trade associations representing publishers and independent advertising agencies signed a consent decree aimed at ending a set of trade practices that for half a century effectively precluded advertisers from owning and operating in-house agencies.... View Details
Keywords: Internationalization; Specialization; Theory Of The Firm; Advertising Agencies; Advertising; Organizational Structure; Theory
Silk, Alvin J., Birger Wernerfelt, and Shuyi Yu. "Internalization of Advertising Services: Testing a Theory of the Firm." Marketing Science 40, no. 5 (September–October 2021): 946–963.
- 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
Keywords: Machine Learning; Statistics; Econometric Analyses; Experimental Methods; Data Analysis; Data Analytics; Forecasting and Prediction; Analytics and Data Science; Analysis; Mathematical Methods
Toffel, Michael W., Natalie Epstein, Kris Ferreira, and Yael Grushka-Cockayne. "Assessing Prediction Accuracy of Machine Learning Models." Harvard Business School Technical Note 621-045, August 2020. (Revised September 2020.)
- Teaching Interest
Big Data Analytics and Machine Learning
Big data in the context of marketing, management, and innovation strategy. Machine Learning algorithms and tools.
View Details
View Details
- 2019
- Article
An Empirical Study of Rich Subgroup Fairness for Machine Learning
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.
- 18 Nov 2016
- Conference Presentation
Rawlsian Fairness for Machine Learning
By: Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Leon Roth
Motivated by concerns that automated decision-making procedures can unintentionally lead to discriminatory behavior, we study a technical definition of fairness modeled after John Rawls' notion of "fair equality of opportunity". In the context of a simple model of... View Details
Joseph, Matthew, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Leon Roth. "Rawlsian Fairness for Machine Learning." Paper presented at the 3rd Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), November 18, 2016.
- November 2021
- Article
Corporate Strategy and the Theory of the Firm in the Digital Age
By: Markus Menz, Sven Kunisch, Julian Birkinshaw, David J. Collis, Nicolai J. Foss, Robert E. Hoskisson and John Prescott
The purpose of this article is to reinvigorate research in the intersection of corporate strategy and the theory of the firm in light of the rapid advancement of digital technologies. Using the theory of the firm as an interpretive lens, we focus our analysis on the... View Details
Keywords: Digitalization; Multi-business Firm; Scale And Scope; Theory Of The Firm; Corporate Strategy; Technological Innovation; Competitive Advantage; Organizational Design; Theory; Research; Digital Transformation
Menz, Markus, Sven Kunisch, Julian Birkinshaw, David J. Collis, Nicolai J. Foss, Robert E. Hoskisson, and John Prescott. "Corporate Strategy and the Theory of the Firm in the Digital Age." Journal of Management Studies 58, no. 7 (November 2021): 1695–1720.
- 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
Keywords: Building Theory; Grounded Theory; Theories-in-use; Theory Construction; Theory Development; Marketing; Knowledge; Theory
Zaltman, G., Valarie A. Zeithaml, Bernard Jaworski, Ajay K. Kohli, Kapil R. Tuli, and Wolfgang Ulaga. "A Theories-in-Use Approach to Building Marketing Theory." Journal of Marketing 84, no. 1 (January 2020): 32–51.
- 2020
- Working Paper
Machine Learning for Pattern Discovery in Management Research
Supervised machine learning (ML) methods are a powerful toolkit for discovering robust patterns in quantitative data. The patterns identified by ML could be used as an observation for further inductive or abductive research, but should not be treated as the result of a... View Details
Keywords: Machine Learning; Theory Building; Induction; Decision Trees; Random Forests; K-nearest Neighbors; Neural Network; P-hacking; Analytics and Data Science; Analysis
Choudhury, Prithwiraj, Ryan Allen, and Michael G. Endres. "Machine Learning for Pattern Discovery in Management Research." Harvard Business School Working Paper, No. 19-032, September 2018. (Revised June 2020.)
- 2022
- Working Paper
Machine Learning Models for Prediction of Scope 3 Carbon Emissions
By: George Serafeim and Gladys Vélez Caicedo
For most organizations, the vast amount of carbon emissions occur in their supply chain and in the post-sale processing, usage, and end of life treatment of a product, collectively labelled scope 3 emissions. In this paper, we train machine learning algorithms on 15... View Details
Keywords: Carbon Emissions; Climate Change; Environment; Carbon Accounting; Machine Learning; Artificial Intelligence; Digital; Data Science; Environmental Sustainability; Environmental Management; Environmental Accounting
Serafeim, George, and Gladys Vélez Caicedo. "Machine Learning Models for Prediction of Scope 3 Carbon Emissions." Harvard Business School Working Paper, No. 22-080, June 2022.
- 2017
- Working Paper
Machine Learning Methods for Strategy Research
By: Mike Horia Teodorescu
Numerous applications of machine learning have gained acceptance in the field of strategy and management research only during the last few years. Established uses span such diverse problems as strategic foreign investments, strategic resource allocation, systemic risk... View Details
Keywords: Machine Learning; Natural Language Processing; Classification; Decision Trees; Strategic Decisions; Strategy; Research; Information Technology
Teodorescu, Mike Horia. "Machine Learning Methods for Strategy Research." Harvard Business School Working Paper, No. 18-011, August 2017. (Revised October 2017.)
- 22 May 2012
- Working Paper Summaries
Componential Theory of Creativity
Keywords: by Teresa M. Amabile
- Mar 2021
- Conference Presentation
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
By: Seth Neel, Aaron Leon Roth and Saeed Sharifi-Malvajerdi
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both... View Details
Neel, Seth, Aaron Leon Roth, and Saeed Sharifi-Malvajerdi. "Descent-to-Delete: Gradient-Based Methods for Machine Unlearning." Paper presented at the 32nd Algorithmic Learning Theory Conference, March 2021.
- 2012
- Working Paper
Componential Theory of Creativity
The componential theory of creativity is a comprehensive model of the social and psychological components necessary for an individual to produce creative work. The theory is grounded in a definition of creativity as the production of ideas or outcomes that are both... View Details
Amabile, Teresa M. "Componential Theory of Creativity." Harvard Business School Working Paper, No. 12-096, April 2012.
- 2017
- Chapter
Institutional Theory and the Natural Environment: Building Research Through Tensions and Paradox
By: P. Devereaux Jennings and Andrew J. Hoffman
The focus of institutional theory is directed towards an understanding of situations where context is strong and binding, yet subtly experienced; where agency is often diffuse, embodied in an arrangement or system of actors rather than in an individual; and where... View Details
Jennings, P. Devereaux, and Andrew J. Hoffman. "Institutional Theory and the Natural Environment: Building Research Through Tensions and Paradox." Chap. 29 in The SAGE Handbook of Organizational Institutionalism. 2nd ed. Edited by Royston Greenwood, Christine Oliver, Thomas B. Lawrence, and Renate E. Meyer, 759–785. Thousand Oaks, CA: SAGE Publications, 2017.