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(1,216)
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- Faculty Publications (582)
Show Results For
- All HBS Web
(1,216)
- People (1)
- News (245)
- Research (698)
- Events (17)
- Multimedia (9)
- Faculty Publications (582)
- October 14, 2019
- Article
How Artificial Intelligence Is Changing Health Care Delivery
By: Samantha F. Sanders, Mats Terwiesch, William J. Gordon and Ariel Dora Stern
The development of intelligent machines holds great promise for making health care delivery more accurate, efficient, and accessible, but challenges remain for incorporating AI into clinical and administrative settings. View Details
Keywords: Artificial Intelligence; Health Care and Treatment; Service Delivery; Technological Innovation; AI and Machine Learning
Sanders, Samantha F., Mats Terwiesch, William J. Gordon, and Ariel Dora Stern. "How Artificial Intelligence Is Changing Health Care Delivery." NEJM Catalyst (October 17, 2019).
- Research Summary
Overview
By: Isamar Troncoso
Professor Troncoso's research explores problems related to digital marketplaces and AI applications in marketing, and combines toolkits from econometrics, causal inference, and machine learning. She has studied how different platform design choices can lead to... View Details
- June 2025
- Article
Ideation with Generative AI—In Consumer Research and Beyond
By: Julian De Freitas, G. Nave and Stefano Puntoni
The use of large language models (LLMs) in consumer research is rapidly evolving, with applications including synthetic data generation, data analysis, and more. However, their role in creative ideation—a cornerstone of consumer research—remains underexplored. Drawing... View Details
De Freitas, Julian, G. Nave, and Stefano Puntoni. "Ideation with Generative AI—In Consumer Research and Beyond." Journal of Consumer Research 51, no. 1 (June 2025): 18–31.
- 2021
- Article
To Thine Own Self Be True? Incentive Problems in Personalized Law
By: Jordan M. Barry, John William Hatfield and Scott Duke Kominers
Recent years have seen an explosion of scholarship on “personalized law.” Commentators foresee a world in which regulators armed with big data and machine learning techniques determine the optimal legal rule for every regulated party, then instantaneously disseminate... View Details
Keywords: Personalized Law; Regulation; Regulatory Avoidance; Regulatory Arbitrage; Law And Economics; Law And Technology; Law And Artificial Intelligence; Futurism; Moral Hazard; Elicitation; Signaling; Privacy; Law; Governing Rules, Regulations, and Reforms; Information Technology; AI and Machine Learning
Barry, Jordan M., John William Hatfield, and Scott Duke Kominers. "To Thine Own Self Be True? Incentive Problems in Personalized Law." Art. 2. William & Mary Law Review 62, no. 3 (2021).
- July 2024
- Case
Knowledge-Enabled Financial Advice: Digital Transformation at Edward Jones
By: Lauren Cohen, Richard Ryffel, Grace Headinger and Sophia Pan
Edward Jones, a wealth management advisory firm that prided itself on its interpersonal connections and face-to-face interactions, was eager to augment their services with AI capabilities. Built on 1-to-1 close-knit relationships, the firm had more than 15,000 offices... View Details
Keywords: Fintech; Innovation And Strategy; Financial Advisors; Big Data; Artificial Intelligence; Digitization; Financial Institutions; Business Strategy; Competitive Advantage; Technology Adoption; Business Plan; Technological Innovation; Interpersonal Communication; Communication Intention and Meaning; Communication Strategy; Transformation; Employee Stock Ownership Plan; Disruptive Innovation; Innovation Strategy; Innovation and Management; Innovation Leadership; Knowledge Acquisition; Knowledge Use and Leverage; Customer Relationship Management; AI and Machine Learning; Digital Strategy; Financial Services Industry; St. Louis; Missouri; United States; Canada
Cohen, Lauren, Richard Ryffel, Grace Headinger, and Sophia Pan. "Knowledge-Enabled Financial Advice: Digital Transformation at Edward Jones." Harvard Business School Case 225-009, July 2024.
- June 2024
- Article
Oral History and Business History in Emerging Markets
By: Geoffrey Jones
This article describes the motivation, structure and use of the Creating Emerging Markets (CEM) oral history-based project at the Harvard Business School. The project consists of lengthy interviews with business leaders from emerging markets. By June 2024 183... View Details
Jones, Geoffrey. "Oral History and Business History in Emerging Markets." Investigaciones de historia económica 20, no. 2 (June 2024): 1–4.
- 12 PM – 1 PM EDT, 22 Sep 2023
- Webinars: Career
Tech in the Job Search: ChatGPT for Job-Seekers
Join CPD and a former LinkedIn insider for an enlightening webinar and learn to harness the potential of AI tools like ChatGPT to revolutionize your job search experience. View Details
Himabindu Lakkaraju
Himabindu "Hima" Lakkaraju is an Assistant Professor of Business Administration at Harvard Business School. She is also a faculty affiliate in the Department of Computer Science at Harvard University, the Harvard Data Science Initiative, Center for Research on... View Details
- 23 May 2023
- Research & Ideas
Face Value: Do Certain Physical Features Help People Get Ahead?
empirically predicted with a machine learning model, suggests work by Shunyuan Zhang, an assistant professor at Harvard Business School, and collaborators. “Our research... View Details
Keywords: by Kara Baskin
- Research Summary
Overview
Paul is primarily interested in studying explainable machine learning (ML), digital transformation, and data science operations. He works on research that explores how stakeholders within organizations can use machine learning to make better decisions. In particular,... View Details
- 2023
- Chapter
Marketing Through the Machine’s Eyes: Image Analytics and Interpretability
By: Shunyuan Zhang, Flora Feng and Kannan Srinivasan
he growth of social media and the sharing economy is generating abundant unstructured image and video data. Computer vision techniques can derive rich insights from unstructured data and can inform recommendations for increasing profits and consumer utility—if only the... View Details
Zhang, Shunyuan, Flora Feng, and Kannan Srinivasan. "Marketing Through the Machine’s Eyes: Image Analytics and Interpretability." Chap. 8 in Artificial Intelligence in Marketing. 20, edited by Naresh K. Malhotra, K. Sudhir, and Olivier Toubia, 217–238. Review of Marketing Research. Emerald Publishing Limited, 2023.
Magie Cheng
Mengjie (Magie) Cheng is a Ph.D. student in Marketing at Harvard Business School. She received her B.S. in Finance from Chu Kochen Honors College at Zhejiang University and M.S. in Management Science and... View Details
- 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... View Details
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).
- February 2018 (Revised October 2019)
- Case
HubSpot and Motion AI: Chatbot-Enabled CRM
By: Jill Avery and Thomas Steenburgh
HubSpot, an inbound marketing, sales, and customer relationship management (CRM) software provider, announced that it had acquired Motion AI, a software platform that enabled companies to easily build and deploy chatbots, fueled by artificial intelligence, to interact... View Details
Keywords: CRM; Sales Management; Customer Service; Artificial Intelligence; B2B Vs. B2C; Business Marketing; SaaS; Marketing; Marketing Strategy; Brands and Branding; Customer Focus and Relationships; Sales; Salesforce Management; Technological Innovation; Applications and Software; Customer Relationship Management; AI and Machine Learning; Technology Industry; Service Industry; United States; North America
Avery, Jill, and Thomas Steenburgh. "HubSpot and Motion AI: Chatbot-Enabled CRM." Harvard Business School Case 518-067, February 2018. (Revised October 2019.)
- 19 Feb 2019
- First Look
New Research and Ideas, February 19, 2019
forthcoming Journal of Political Economy CEO Behavior and Firm Performance By: Bandiera, Oriana, Stephen Hansen, Andrea Prat, and Raffaella Sadun Abstract— We measure the behavior of 1,114 CEOs in six... View Details
Keywords: Sean Silverthorne
- 2021
- Working Paper
An Empirical Study of Time Allotment and Delays in E-commerce Delivery
By: M. Balakrishnan, MoonSoo Choi and Natalie Epstein
Problem definition: We study how having more time allotted to deliver an order affects the speed of the delivery process. Furthermore, we seek to predict orders that are likely to be delayed early in the delivery process so that actions can be taken to avoid delays.... View Details
Keywords: Logistics; E-commerce; Mathematical Methods; AI and Machine Learning; Performance Productivity
Balakrishnan, M., MoonSoo Choi, and Natalie Epstein. "An Empirical Study of Time Allotment and Delays in E-commerce Delivery." Working Paper, December 2021.
- 12 Apr 2022
- Research & Ideas
Swiping Right: How Data Helped This Online Dating Site Make More Matches
some estimates, with players such as Bumble, Tinder, and OKCupid vying to help people find love. While McFowland is not a dating expert, his work in machine learning View Details
Keywords: by Kara Baskin
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details
Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. "Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse." Proceedings of the International Conference on Learning Representations (ICLR) (2023).