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Show Results For
-
All HBS Web
(11,772)
- People (75)
- News (2,847)
- Research (3,652)
- Events (31)
- Multimedia (331)
- Faculty Publications (2,322)
- 25 Apr 2016
- News
Learn to Love Networking
- 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...
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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.
- October 2018
- Case
Learning How to Honnold
By: Eugene F. Soltes, Sara Hess and Dutch Leonard
Alex Honnold is the world’s most accomplished free climber. To many, climbing sheer vertical faces of rock—like the famed El Capitan—without a rope is viewed as not simply risky but reckless. Honnold contrasts this sentiment by presenting his perspective on risk taking...
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Soltes, Eugene F., Sara Hess, and Dutch Leonard. "Learning How to Honnold." Harvard Business School Case 119-043, October 2018.
- 2011
- Chapter
The Contribution of Teams to Organizational Learning
By: Kathryn S. Roloff, Anita W. Woolley and Amy C. Edmondson
Organizational learning theorists have proposed that teams play a critical role in organizational learning (Senge, 1990; Edmondson, 2002). Indeed, as organizations become increasingly more global, teams are formed to leverage knowledge, to increase efficiency, and to...
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Roloff, Kathryn S., Anita W. Woolley, and Amy C. Edmondson. "The Contribution of Teams to Organizational Learning." In Handbook of Organizational Learning and Knowledge Management. 2nd ed. Edited by M. Easterby-Smith and M. Lyles, 249–272. London: John Wiley & Sons, 2011.
- 13 Nov 2020
- News
Countdown To Remote Learning
members to teach online, and get 1,900 students—who had scattered around the world—ready to learn online,” says Srikant Datar. “It was daunting.” The School quickly convened a...
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- Web
Lifelong Learning - Alumni
HBS for Life As a lifelong member of the HBS community, you have access to insights, training, expertise, and support from faculty and fellow alumni that will help you navigate opportunities and challenges throughout your life and journey. Alumni Forums RecenHBS Alumni...
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- 16 Mar 2011
- News
Learn From Failure
- 2018
- Working Paper
Learning to Become a Taste Expert
By: Kathryn A. Latour and John A. Deighton
Evidence suggests that consumers seek to become more expert about hedonic products to enhance their enjoyment of future consumption occasions. Current approaches to becoming an expert center on cultivating an analytic mindset. In the present research the authors...
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Keywords:
Hedonic;
Wine;
Expertise;
Holistic;
Analytic;
Sensory;
Taste;
Learning;
Experience and Expertise;
Analysis;
Perception
Latour, Kathryn A., and John A. Deighton. "Learning to Become a Taste Expert." Harvard Business School Working Paper, No. 18-107, June 2018.
- May 2012 (Revised February 2014)
- Teaching Note
Learning About Reducing Hospital Mortality at Kaiser Permanente
By: Anita Carson Tucker
- 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...
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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.)
CSML: Leading Learning
Leading Learning is organized into four modules focused on developing the school structures, systems, supports, and culture that lead to excellent teaching and learning in every classroom, for every student.
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- 13 Jun 2018
- Working Paper Summaries
Learning to Become a Taste Expert
- Sep 03 2020
- Testimonial
Discovering Different Ways to Learn and Engage
- 05 Jul 2006
- Working Paper Summaries
Failing to Learn and Learning to Fail (Intelligently): How Great Organizations Put Failure to Work to Improve and Innovate
Keywords:
by Mark D. Cannon & Amy C. Edmondson
- 05 Aug 2016
- Video
Learn New Skills. Learn About Yourself.
- Article
Learning by Thinking: The Role of Reflection in Individual Learning
By: Giada Di Stefano, Francesca Gino, Gary P. Pisano and Bradley R. Staats
It is common wisdom that practice makes perfect. And, in fact, we find evidence that when given a choice between practicing a task and reflecting on their previously accumulated practice, most people opt for the former. We argue in this paper that this preference is...
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- Sep 03 2020
- Testimonial
Learning in Uncertain Times
- 13 Oct 2015
- News
Why Organizations Don’t Learn
- 06 Mar 2021
- News