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- All HBS Web
(2,447)
- Faculty Publications (347)
- March 2024
- Article
Investigation of Divergent Thinking among Surgeons and Surgeon Trainees in Canada (IDEAS): A Mixed-methods Study
By: Alex Thabane, Tyler McKechnie, Vikram Arora, Goran Calic, Jason W Busse, Ranil Sonnadara and Mohit Bhandari
Objective: To assess the creative potential of surgeons and surgeon trainees, as measured by divergent thinking. The secondary objectives were to identify factors associated with divergent thinking, assess confidence in creative problem-solving and the perceived effect... View Details
Thabane, Alex, Tyler McKechnie, Vikram Arora, Goran Calic, Jason W Busse, Ranil Sonnadara, and Mohit Bhandari. "Investigation of Divergent Thinking among Surgeons and Surgeon Trainees in Canada (IDEAS): A Mixed-methods Study." BMJ Open 14, no. 3 (March 2024).
- February 2024 (Revised July 2024)
- Case
Taffi: Entrepreneurship in Saudi Arabia
By: Paul A. Gompers and Fares Khrais
Taffi was a tech-enabled fashion styling startup founded by Shahad Geoffrey in Saudi Arabia in 2020. Within three years of operating, Geoffrey had pivoted the business multiple times. In 2023, Geoffrey was attempting the business’s most ambitious pivot yet, shifting... View Details
Keywords: Business Startups; Disruption; Entrepreneurship; Venture Capital; Investment; Growth and Development Strategy; Business Strategy; AI and Machine Learning; Fashion Industry; Technology Industry; Saudi Arabia; Arabian Peninsula
Gompers, Paul A., and Fares Khrais. "Taffi: Entrepreneurship in Saudi Arabia." Harvard Business School Case 224-052, February 2024. (Revised July 2024.)
- February 2024
- Teaching Note
TimeCredit
By: Emanuele Colonnelli, Raymond Kluender and Shai Benjamin Bernstein
Teaching Note for HBS Case No. 824-139. TimeCredit is an artificial intelligence (AI) startup that is developing large language models (LLMs) to generate accounting memos. The case follows Ndonga Sagnia, a Gambian Harvard Business School MBA student with an accounting... View Details
- February 2024 (Revised September 2024)
- Case
TimeCredit
By: Emanuele Colonnelli, Raymond Kluender and Shai Benjamin Bernstein
TimeCredit is an artificial intelligence (AI) startup that is developing large language models (LLMs) to generate accounting memos. The case follows Ndonga Sagnia, a Gambian Harvard Business School MBA student with an accounting background, as she decides how much... View Details
Keywords: Accounting; Business Startups; Entrepreneurship; Financing and Loans; AI and Machine Learning; Entrepreneurial Finance; Identity; Technology Industry
Colonnelli, Emanuele, Raymond Kluender, and Shai Benjamin Bernstein. "TimeCredit." Harvard Business School Case 824-139, February 2024. (Revised September 2024.)
- February 2024
- Article
Pricing Power in Advertising Markets: Theory and Evidence
By: Matthew Gentzkow, Jesse M. Shapiro, Frank Yang and Ali Yurukoglu
Existing theories of media competition imply that advertisers will pay a lower price in equilibrium to reach consumers who multi-home across competing outlets. We generalize, extend, and test this prediction. We find that television outlets whose viewers watch more... View Details
Gentzkow, Matthew, Jesse M. Shapiro, Frank Yang, and Ali Yurukoglu. "Pricing Power in Advertising Markets: Theory and Evidence." American Economic Review 114, no. 2 (February 2024): 500–533.
- February 2024
- Article
Representation and Extrapolation: Evidence from Clinical Trials
By: Marcella Alsan, Maya Durvasula, Harsh Gupta, Joshua Schwartzstein and Heidi L. Williams
This article examines the consequences and causes of low enrollment of Black patients in clinical
trials. We develop a simple model of similarity-based extrapolation that predicts that evidence is
more relevant for decision-making by physicians and patients when it... View Details
Keywords: Representation; Racial Disparity; Health Testing and Trials; Race; Equality and Inequality; Innovation and Invention; Pharmaceutical Industry
Alsan, Marcella, Maya Durvasula, Harsh Gupta, Joshua Schwartzstein, and Heidi L. Williams. "Representation and Extrapolation: Evidence from Clinical Trials." Quarterly Journal of Economics 139, no. 1 (February 2024): 575–635.
- 2024
- Working Paper
Who Values Democracy?
By: Max Miller
This paper tests the conventional view that redistribution is central to the democratization process using data from stock markets. Consistent with this view, democratizations have a large, negative impact on asset valuations driven by a rise in redistribution risk.... View Details
Keywords: Government and Politics; Risk and Uncertainty; Financial Crisis; Macroeconomics; Financial Markets; Valuation
Miller, Max. "Who Values Democracy?" Working Paper, February 2024. (Revise and Resubmit, Journal of Political Economy.)
- 2024
- Conference Paper
Quantifying Uncertainty in Natural Language Explanations of Large Language Models
By: Himabindu Lakkaraju, Sree Harsha Tanneru and Chirag Agarwal
Large Language Models (LLMs) are increasingly used as powerful tools for several
high-stakes natural language processing (NLP) applications. Recent prompting
works claim to elicit intermediate reasoning steps and key tokens that serve as
proxy explanations for LLM... View Details
Lakkaraju, Himabindu, Sree Harsha Tanneru, and Chirag Agarwal. "Quantifying Uncertainty in Natural Language Explanations of Large Language Models." Paper presented at the Society for Artificial Intelligence and Statistics, 2024.
- 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,... View Details
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).
- December 2023
- Article
When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments
By: Christian Kaps, Simone Marinesi and Serguei Netessine
Globally, 1.5 billion people live off the grid, their only access to electricity often limited to operationally-expensive fossil fuel generators. Solar power has risen as a sustainable and less costly option, but its generation is variable during the day and... View Details
Kaps, Christian, Simone Marinesi, and Serguei Netessine. "When Should the Off-Grid Sun Shine at Night? Optimum Renewable Generation and Energy Storage Investments." Management Science 69, no. 12 (December 2023): 7633–7650.
- 2023
- Article
Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness
By: Suraj Srinivas, Sebastian Bordt and Himabindu Lakkaraju
One of the remarkable properties of robust computer vision models is that their input-gradients are often aligned with human perception, referred to in the literature as perceptually-aligned gradients (PAGs). Despite only being trained for classification, PAGs cause... View Details
Srinivas, Suraj, Sebastian Bordt, and Himabindu Lakkaraju. "Which Models Have Perceptually-Aligned Gradients? An Explanation via Off-Manifold Robustness." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- November–December 2023
- Article
Keep Your AI Projects on Track
By: Iavor Bojinov
AI—and especially its newest star, generative AI—is today a central theme in corporate boardrooms, leadership discussions, and casual exchanges among employees eager to supercharge their productivity. Sadly, beneath the aspirational headlines and tantalizing potential... View Details
Keywords: Generative Models; AI and Machine Learning; Success; Failure; Product Development; Technology Adoption
Bojinov, Iavor. "Keep Your AI Projects on Track." Harvard Business Review 101, no. 6 (November–December 2023): 53–59.
- 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)... View Details
- October 2023 (Revised November 2023)
- Case
Recycle & Re-Match: The Future of Soccer Turfs
By: George Serafeim, Lena Duchene and Carlota Moniz
By August 2023, Re-Match, an artificial turf waste-to-value company, had operations in Denmark and the Netherlands and had recycled over 160,000 tons of waste and plastic fiber. With recent capital injection from the VC firm Verdane and a dual revenue business model,... View Details
Keywords: Carbon Emissions; Carbon Abatement; Sustainability; Recycling; Waste Management; Technology; Entrepreneurial Management; Business Growth and Maturation; Business Model; Decisions; Energy Conservation; Investment Return; Profit; Technological Innovation; Patents; Growth and Development Strategy; Market Entry and Exit; Digital Platforms; Wastes and Waste Processing; Business Strategy; Competition; Expansion; Technology Adoption; Sports; Environmental Sustainability; Entrepreneurship; Green Technology Industry; Service Industry; Manufacturing Industry; Rubber Industry; Sports Industry; Denmark; Netherlands; France; United States; Pennsylvania; Europe
Serafeim, George, Lena Duchene, and Carlota Moniz. "Recycle & Re-Match: The Future of Soccer Turfs." Harvard Business School Case 124-032, October 2023. (Revised November 2023.)
- 2023
- Working Paper
Black-box Training Data Identification in GANs via Detector Networks
By: Lukman Olagoke, Salil Vadhan and Seth Neel
Since their inception Generative Adversarial Networks (GANs) have been popular generative models across images, audio, video, and tabular data. In this paper we study whether given access to a trained GAN, as well as fresh samples from the underlying distribution, if... View Details
Olagoke, Lukman, Salil Vadhan, and Seth Neel. "Black-box Training Data Identification in GANs via Detector Networks." Working Paper, October 2023.
- 2023
- Working Paper
Causal Interpretation of Structural IV Estimands
By: Isaiah Andrews, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan and Jesse M. Shapiro
We study the causal interpretation of instrumental variables (IV) estimands of nonlinear, multivariate structural models with respect to rich forms of model misspecification. We focus on guaranteeing that the researcher's estimator is sharp zero consistent, meaning... View Details
Keywords: Mathematical Methods
Andrews, Isaiah, Nano Barahona, Matthew Gentzkow, Ashesh Rambachan, and Jesse M. Shapiro. "Causal Interpretation of Structural IV Estimands." NBER Working Paper Series, No. 31799, October 2023.
- October 2023
- Article
Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA
By: Matthew S. Johnson, David I. Levine and Michael W. Toffel
We study how a regulator can best target inspections. Our case study is a U.S. Occupational Safety and Health Administration (OSHA) program that randomly allocated some inspections. On average, each inspection averted 2.4 serious injuries (9%) over the next five years.... View Details
Keywords: Safety Regulations; Regulations; Regulatory Enforcement; Machine Learning Models; Safety; Operations; Service Operations; Production; Forecasting and Prediction; Decisions; United States
Johnson, Matthew S., David I. Levine, and Michael W. Toffel. "Improving Regulatory Effectiveness Through Better Targeting: Evidence from OSHA." American Economic Journal: Applied Economics 15, no. 4 (October 2023): 30–67. (Profiled in the Regulatory Review.)
- September 2023 (Revised September 2024)
- Case
IBJ, Inc. (A): Seeking Matrimony in Japan
By: Ramon Casadesus-Masanell and Akiko Saito
In March 2020, Shigeru Ishizaka, founder and CEO of IBJ, Inc., Japan's largest marriage matching service provider, faced a critical decision regarding the company’s planned ¥3.5 billion (US$32.8 million) acquisition of competitor ZWEI Co., Ltd. IBJ, founded in 2006,... View Details
Casadesus-Masanell, Ramon, and Akiko Saito. "IBJ, Inc. (A): Seeking Matrimony in Japan." Harvard Business School Case 724-356, September 2023. (Revised September 2024.)
- September 2023 (Revised January 2024)
- Case
AI21 Labs in 2023: Strategy for Generative AI
By: David Yoffie, Orna Dan and Elena Corsi
Israeli generative artificial intelligence company AI21 Labs was founded in 2017 to realize the vision of true machine intelligence. It sought to reinvent writing and reading and in 2020 it launched Wordtune, an app using GenAI software to offer alternate text... View Details
Keywords: Decision Making; AI and Machine Learning; Innovation Strategy; Growth and Development Strategy; Applications and Software; Competitive Strategy; Technology Industry; Israel
Yoffie, David, Orna Dan, and Elena Corsi. "AI21 Labs in 2023: Strategy for Generative AI." Harvard Business School Case 724-383, September 2023. (Revised January 2024.)
- September 2023 (Revised March 2024)
- Case
ReMo Energy: Sizing Up Investors
By: Jeffrey J. Bussgang and Tom Quinn
In 2023, executives with ReMo Energy (founded 2020) were deciding which size ammonia plant to build as their first project. Their innovative model produced ammonia—useful for making fertilizer and for energy storage—from renewable energy, and they had received funding... View Details
Keywords: Factories, Labs, and Plants; Business Startups; Cost vs Benefits; Design; Energy Conservation; Energy Generation; Renewable Energy; Venture Capital; Investment Return; Goods and Commodities; Size; Infrastructure; Risk and Uncertainty; Science-Based Business; Commercialization; Technological Innovation; Chemical Industry; Energy Industry; Green Technology Industry; United States; Boston
Bussgang, Jeffrey J., and Tom Quinn. "ReMo Energy: Sizing Up Investors." Harvard Business School Case 824-027, September 2023. (Revised March 2024.)