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All HBS Web
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- Faculty Publications (1,949)
- 2023
- Working Paper
New Facts and Data about Professors and Their Research
By: Kyle Myers, Wei Yang Tham, Jerry Thursby, Marie Thursby, Nina Cohodes, Karim R. Lakhani, Rachel Mural and Yilun Xu
We introduce a new survey of professors at roughly 150 of the most research-intensive institutions of higher education in the US. We document seven new features of how research-active professors are compensated, how they spend their time, and how they perceive their...
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Keywords:
Research;
Higher Education;
Compensation and Benefits;
Measurement and Metrics;
Equality and Inequality;
Performance Productivity
Myers, Kyle, Wei Yang Tham, Jerry Thursby, Marie Thursby, Nina Cohodes, Karim R. Lakhani, Rachel Mural, and Yilun Xu. "New Facts and Data about Professors and Their Research." Harvard Business School Working Paper, No. 24-036, December 2023.
- 2023
- Working Paper
Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach
By: Ta-Wei Huang and Eva Ascarza
Data-driven targeted interventions have become a powerful tool for organizations to optimize business outcomes
by utilizing individual-level data from experiments. A key element of this process is the estimation
of Conditional Average Treatment Effects (CATE), which...
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Huang, Ta-Wei, and Eva Ascarza. "Debiasing Treatment Effect Estimation for Privacy-Protected Data: A Model Auditing and Calibration Approach." Harvard Business School Working Paper, No. 24-034, December 2023.
- 2023
- Article
Balancing Risk and Reward: An Automated Phased Release Strategy
By: Yufan Li, Jialiang Mao and Iavor Bojinov
Phased releases are a common strategy in the technology industry for gradually releasing new products or updates through a sequence of A/B tests in which the number of treated units gradually grows until full deployment or deprecation. Performing phased releases in a...
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Li, Yufan, Jialiang Mao, and Iavor Bojinov. "Balancing Risk and Reward: An Automated Phased Release Strategy." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Article
Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset
By: Junling Liu, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu and Michael Lingzhi Li
Recent advancements in large language models (LLMs) have transformed the field of question answering (QA). However, evaluating LLMs in the medical field is challenging due to the lack of standardized and comprehensive datasets. To address this gap, we introduce CMExam,...
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Keywords:
Large Language Model;
AI and Machine Learning;
Analytics and Data Science;
Health Industry
Liu, Junling, Peilin Zhou, Yining Hua, Dading Chong, Zhongyu Tian, Andrew Liu, Helin Wang, Chenyu You, Zhenhua Guo, Lei Zhu, and Michael Lingzhi Li. "Benchmarking Large Language Models on CMExam—A Comprehensive Chinese Medical Exam Dataset." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 2023
- Article
Post Hoc Explanations of Language Models Can Improve Language Models
By: Satyapriya Krishna, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh and Himabindu Lakkaraju
Large Language Models (LLMs) have demonstrated remarkable capabilities in performing complex tasks. Moreover, recent research has shown that incorporating human-annotated rationales (e.g., Chain-of-Thought prompting) during in-context learning can significantly enhance...
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Krishna, Satyapriya, Jiaqi Ma, Dylan Slack, Asma Ghandeharioun, Sameer Singh, and Himabindu Lakkaraju. "Post Hoc Explanations of Language Models Can Improve Language Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Other Article
The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications
By: Mirac Suzgun, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers and Stuart Shieber
Innovation is a major driver of economic and social development, and information about many kinds of innovation is embedded in semi-structured data from patents and patent applications. Though the impact and novelty of innovations expressed in patent data are difficult...
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Keywords:
USPTO;
Natural Language Processing;
Classification;
Summarization;
Patent Novelty;
Patent Trolls;
Patent Enforceability;
Patents;
Innovation and Invention;
Intellectual Property;
AI and Machine Learning;
Analytics and Data Science
Suzgun, Mirac, Luke Melas-Kyriazi, Suproteem K. Sarkar, Scott Duke Kominers, and Stuart Shieber. "The Harvard USPTO Patent Dataset: A Large-Scale, Well-Structured, and Multi-Purpose Corpus of Patent Applications." Conference on Neural Information Processing Systems (NeurIPS), Datasets and Benchmarks Track 36 (2023).
- 2023
- Working Paper
The Uneven Impact of Generative AI on Entrepreneurial Performance
By: Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning
There is a growing belief that scalable and low-cost AI assistance can improve firm
decision-making and economic performance. However, running a business involves
a myriad of open-ended problems, making it hard to generalize from recent studies
showing that...
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Keywords:
AI and Machine Learning;
Performance Improvement;
Small Business;
Decision Choices and Conditions;
Kenya
Otis, Nicholas G., Rowan Clarke, Solène Delecourt, David Holtz, and Rembrand Koning. "The Uneven Impact of Generative AI on Entrepreneurial Performance." Harvard Business School Working Paper, No. 24-042, December 2023.
- 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).
- November–December 2023
- Article
Iterative Coordination and Innovation: Prioritizing Value over Novelty
By: Sourobh Ghosh and Andy Wu
An innovating organization faces the challenge of how to prioritize distinct goals of novelty and value, both of which underlie innovation. Popular practitioner frameworks like Agile management suggest that organizations can adopt an iterative approach of frequent...
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Keywords:
Innovation;
Novelty;
Goals;
Specialization;
Coordination;
Field Experiment;
Software Development;
Agile;
Scrum;
Iteration;
Iterative;
Organizations;
Innovation and Invention;
Value;
Goals and Objectives;
Integration;
Applications and Software
Ghosh, Sourobh, and Andy Wu. "Iterative Coordination and Innovation: Prioritizing Value over Novelty." Organization Science 34, no. 6 (November–December 2023): 2182–2206.
- 2023
- Working Paper
The Optimal Stock Valuation Ratio
By: Sebastian Hillenbrand and Odhrain McCarthy
Trailing price ratios, such as the price-dividend and the price-earnings ratio, scale prices by trailing cash flow measures. They theoretically contain expected returns, yet, their performance in predicting stock market returns is poor. This is because of an omitted...
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Keywords:
Price;
Investment Return;
AI and Machine Learning;
Valuation;
Cash Flow;
Forecasting and Prediction
Hillenbrand, Sebastian, and Odhrain McCarthy. "The Optimal Stock Valuation Ratio." Working Paper, November 2023.
- October 2023
- Case
Making Progress at Progress Software (A)
By: Katherine Coffman, Hannah Riley Bowles and Alexis Lefort
In this case, the Human Capital team at Progress Software has identified that some employees have a hard time understanding how to advance within Progress. This realization leads the team to develop several major people-process innovations: the introduction of...
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- October 2023
- Supplement
Making Progress at Progress Software (B)
By: Katherine Coffman, Hannah Riley Bowles and Alexis Lefort
In this case, the Human Capital team at Progress Software has identified that some employees have a hard time understanding how to advance within Progress. This realization leads the team to develop several major people-process innovations: the introduction of...
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- October 2023 (Revised February 2024)
- Case
Loris
By: Shunyuan Zhang, Das Narayandas, Stacy Straaberg and David Lane
In December 2022, Loris’s executive team considered their go-to-market strategy. Loris was an artificial intelligence (AI) software startup for the customer service industry with two products on the market: 1) Agent Assist which provided customer service agents (CSAs)...
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- 2023
- Working Paper
How Private Investors Value 'Platformness': An Exploratory Study of Unicorns
By: David B. Yoffie, Michael A. Cusumano, Annabelle Gawer, Sarah von Bargen and Kwesi Acquay
This paper explores the premium investors have been willing to pay for private companies (unicorns) that are digital platforms vs. traditional companies.
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- 2023
- Working Paper
In-Context Unlearning: Language Models as Few Shot Unlearners
By: Martin Pawelczyk, Seth Neel and Himabindu Lakkaraju
Machine unlearning, the study of efficiently removing the impact of specific training points on the
trained model, has garnered increased attention of late, driven by the need to comply with privacy
regulations like the Right to be Forgotten. Although unlearning is...
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Pawelczyk, Martin, Seth Neel, and Himabindu Lakkaraju. "In-Context Unlearning: Language Models as Few Shot Unlearners." Working Paper, October 2023.
- 2023
- Book
Move Fast and Fix Things: The Trusted Leader's Guide to Solving Hard Problems
By: Frances X. Frei and Anne Morriss
Speed has gotten a bad name in business, much of it deserved. When Facebook made "Move fast and break things" an informal company motto, it fueled a widely held belief that we can either make progress or take care of people, one or the other. That a certain amount of...
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Keywords:
Leading Change;
Performance Improvement;
Problems and Challenges;
Organizational Change and Adaptation;
Organizational Culture
Frei, Frances X., and Anne Morriss. Move Fast and Fix Things: The Trusted Leader's Guide to Solving Hard Problems. Harvard Business Review Press, 2023.
- 2023
- Working Paper
The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling
Are the inputs used by your AI tool correct and up to date? In this paper, we show that the answer to this question: (i) is frequently a “no” in real business contexts, and (ii) has significant implications on the performance of AI tools. In the context of algorithmic...
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Kwon, Caleb, Antonio Moreno, and Ananth Raman. "The Impact of Input Inaccuracy on Leveraging AI Tools: Evidence from Algorithmic Labor Scheduling." Working Paper, October 2023.
- September 2023
- Teaching Note
Roche: ESG and Access to Healthcare
By: George Serafeim
Teaching Note for HBS Case No. 123-075. In May 2022, Roche Group, one of the largest healthcare companies in the world, hosted its first ESG investor event focused exclusively on its efforts to impact access to healthcare. While Roche had recently set an ambitious goal...
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- September 2023 (Revised January 2024)
- Case
Icahn Enterprises: Ponzi Scheme or Sound Investment
By: Aiyesha Dey, Jonas Heese and James Weber
Icahn Enterprises, a publicly traded limited partnership founded and operated by famed activist investor Carl Icahn, had earned above market returns for over a decade. Between 2018 and early 2023, it had a compound annual return of 31%. Icahn invested in undervalued...
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Dey, Aiyesha, Jonas Heese, and James Weber. "Icahn Enterprises: Ponzi Scheme or Sound Investment." Harvard Business School Case 124-013, September 2023. (Revised January 2024.)
- 2023
- Working Paper
Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality
By: Fabrizio Dell'Acqua, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon and Karim R. Lakhani
The public release of Large Language Models (LLMs) has sparked tremendous interest in how humans will use Artificial Intelligence (AI) to accomplish a variety of tasks. In our study conducted with Boston Consulting Group, a global management consulting firm, we examine...
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Keywords:
Large Language Model;
AI and Machine Learning;
Performance Efficiency;
Performance Improvement
Dell'Acqua, Fabrizio, Edward McFowland III, Ethan Mollick, Hila Lifshitz-Assaf, Katherine C. Kellogg, Saran Rajendran, Lisa Krayer, François Candelon, and Karim R. Lakhani. "Navigating the Jagged Technological Frontier: Field Experimental Evidence of the Effects of AI on Knowledge Worker Productivity and Quality." Harvard Business School Working Paper, No. 24-013, September 2023.