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Show Results For
- All HBS Web
(11,284)
- People (74)
- News (2,860)
- Research (3,812)
- Events (45)
- Multimedia (240)
- Faculty Publications (2,375)
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- March 2025
- Article
Novice Risk Work: How Juniors Coaching Seniors on Emerging Technologies Such as Generative AI Can Lead to Learning Failures
By: Katherine C. Kellogg, Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon and Karim R. Lakhani
The literature on communities of practice demonstrates that a proven way for senior professionals to upskill
themselves in the use of new technologies that undermine existing expertise is to learn from junior
professionals. It notes that juniors may be better able... View Details
Keywords: Rank and Position; Competency and Skills; Technology Adoption; Experience and Expertise; AI and Machine Learning
Kellogg, Katherine C., Hila Lifshitz-Assaf, Steven Randazzo, Ethan Mollick, Fabrizio Dell'Acqua, Edward McFowland III, François Candelon, and Karim R. Lakhani. "Novice Risk Work: How Juniors Coaching Seniors on Emerging Technologies Such as Generative AI Can Lead to Learning Failures." Art. 100559. Information and Organization 35, no. 1 (March 2025).
- 2021
- Working Paper
Who Closed the Schools?
By: Joshua D. Coval
This paper examines the differences in characteristics between U.S. public schools that opted for virtual instruction because of COVID-19, and schools that did not. Much of the variation can be explained by measures of the degree to which districts favored teachers... View Details
Keywords: Public Education; COVID-19; Virtual Learning; Education; Health Pandemics; Teaching; Internet and the Web; Policy; Outcome or Result; United States
Coval, Joshua D. "Who Closed the Schools?" Harvard Business School Working Paper, No. 21-127, June 2021.
- Article
What We Can Learn from Five Naturalistic Field Experiments that Failed to Shift Commuter Behaviour
By: Ariella S. Kristal and A.V. Whillans
Across five field experiments with employees of a large organization (n = 68,915), we examined whether standard behavioural interventions (“nudges”) successfully reduced single-occupancy vehicle commutes. In Studies 1 and 2, we sent letters and emails with nudges... View Details
Kristal, Ariella S., and A.V. Whillans. "What We Can Learn from Five Naturalistic Field Experiments That Failed to Shift Commuter Behaviour." Nature Human Behaviour 4, no. 2 (February 2020): 169–176. (This article was featured on the cover as the lead article.)
- August 2013 (Revised November 2020)
- Case
Tesla Motors
In mid-2013, Tesla Motors was riding a wave of success: It had launched its first really mass-produced car—the model S—to rave reviews; had recently raised first-year production targets; and had started taking orders for its next car, the Model X. Tesla seemed to be on... View Details
Keywords: Barriers To Entry; Economic Analysis; Learning Curve; Economies Of Scale; Innovation; Market Entry; Sustainable Competitive Advantage; Vision; Strategy And Leadership; Strategy; Competitive Strategy; Market Entry and Exit; Competitive Advantage; Technological Innovation; Leadership; Learning; Economics; Analysis; Auto Industry
Van den Steen, Eric. "Tesla Motors." Harvard Business School Case 714-413, August 2013. (Revised November 2020.)
- 31 Aug 2020
- Working Paper Summaries
The Pass-Through of Uncertainty Shocks to Households
- 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.
- December 1, 2021
- Article
Do You Know How Your Teams Get Work Done?
By: Rohan Narayana Murty, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna and Kartik Hosanagar
In a research study at four Fortune 500 companies, when managers were asked about their teams’ work, on average they either did not know or could not remember 60% of the work their teams do. This is a major problem because it can lead to unrealistic digital... View Details
Keywords: Leading Teams; Work Recall Gap; Machine Learning; Algorithms; Groups and Teams; Management; Technological Innovation
Murty, Rohan Narayana, Rajath B. Das, Scott Duke Kominers, Arjun Narayan, Suraj Srinivasan, Tarun Khanna, and Kartik Hosanagar. "Do You Know How Your Teams Get Work Done?" Harvard Business Review Digital Articles (December 1, 2021).
- 22 Aug 2005
- Research & Ideas
The Hard Work of Failure Analysis
It hardly needs to be said that organizations cannot learn from failures if people do not discuss and analyze them. Yet this remains an important insight. The learning that is potentially available may not... View Details
Keywords: by Amy Edmondson & Mark D. Cannon
- October 2023
- Article
Product Variety, the Cost of Living, and Welfare Across Countries
By: Alberto Cavallo, Robert C. Feenstra and Robert Inklaar
We use the structure of the Melitz (2003) model to compute the cost of living and welfare across 47 countries, and compare these to conventional measures of prices and real consumption from the International Comparisons Project (ICP). The cost of living is inferred... View Details
Cavallo, Alberto, Robert C. Feenstra, and Robert Inklaar. "Product Variety, the Cost of Living, and Welfare Across Countries." American Economic Journal: Macroeconomics 15, no. 4 (October 2023): 40–66.
- December 2020
- Supplement
VIA Science (B)
By: Juan Alcácer, Rembrand Koning, Annelena Lobb and Kerry Herman
Via (a) captures the early days of the data analytics startup as founders Gounden and Ravanis considered which markets offer the right opportunities for their firm and what kinds of experiments will help them narrow their choice. Supplement Via (b) reveals the... View Details
Keywords: Data Analytics; Machine Learning; Artificial Intelligence; Strategy; Business Startups; AI and Machine Learning; Telecommunications Industry; Utilities Industry; United States; Japan
Alcácer, Juan, Rembrand Koning, Annelena Lobb, and Kerry Herman. "VIA Science (B)." Harvard Business School Supplement 721-368, December 2020.
- 04 Feb 2018
- Working Paper Summaries
The Long-Run Dynamics of Electricity Demand: Evidence from Municipal Aggregation
- October 2023 (Revised June 2024)
- Case
ReUp Education: Can AI Help Learners Return to College?
By: Kris Ferreira, Christopher Thomas Ryan and Sarah Mehta
Founded in 2015, ReUp Education helps “stopped out students”—learners who have stopped making progress towards graduation—achieve their college completion goals. The company relies on a team of success coaches to engage with learners and help them reenroll. In 2019,... View Details
Keywords: AI; Algorithms; Machine Learning; Edtech; Education Technology; Analysis; Higher Education; AI and Machine Learning; Customization and Personalization; Failure; Education Industry; Technology Industry; United States
Ferreira, Kris, Christopher Thomas Ryan, and Sarah Mehta. "ReUp Education: Can AI Help Learners Return to College?" Harvard Business School Case 624-007, October 2023. (Revised June 2024.)
- September 2002 (Revised October 2002)
- Case
Bank of America (A)
By: Stefan H. Thomke and Ashok Nimgade
Describes how Bank of America is creating a system for product and service innovation in its retail banking business. Emphasis is placed on the role of experimentation in some two-dozen real-life "laboratories" that serve as fully operating banking branches and as... View Details
Keywords: Motivation and Incentives; Problems and Challenges; Innovation and Management; Risk and Uncertainty; Change; Failure; Banks and Banking; Learning; Banking Industry
Thomke, Stefan H., and Ashok Nimgade. "Bank of America (A)." Harvard Business School Case 603-022, September 2002. (Revised October 2002.)
- 19 Mar 2012
- HBS Case
HBS Cases: Overcoming the Stress of ‘Englishnization’
seeking out English speakers in their groups. Helping Employees Learn There's a number of techniques companies can employ to reassure and help workers with this transition. First, it's crucial for CEOs and... View Details
Keywords: by Kim Girard
- Article
Towards Robust and Reliable Algorithmic Recourse
By: Sohini Upadhyay, Shalmali Joshi and Himabindu Lakkaraju
As predictive models are increasingly being deployed in high-stakes decision making (e.g., loan
approvals), there has been growing interest in post-hoc techniques which provide recourse to affected
individuals. These techniques generate recourses under the assumption... View Details
Keywords: Machine Learning Models; Algorithmic Recourse; Decision Making; Forecasting and Prediction
Upadhyay, Sohini, Shalmali Joshi, and Himabindu Lakkaraju. "Towards Robust and Reliable Algorithmic Recourse." Advances in Neural Information Processing Systems (NeurIPS) 34 (2021).
- January 2014 (Revised May 2015)
- Case
Open English
By: Jeffrey J. Bussgang and Lisa Mazzanti
Open English, a Miami-based startup offering online English language learning services, had more than 30,000 active students across Latin America in 2012. The company had just closed a $43 million financing round in order to rapidly scale its service to the next level.... View Details
Keywords: Technology Strategy; Product Management; Startup; Online Learning; Digital Platforms; Entrepreneurship; Business Startups; Technology Industry; Miami; Venezuela
Bussgang, Jeffrey J., and Lisa Mazzanti. "Open English." Harvard Business School Case 814-020, January 2014. (Revised May 2015.)
- 14 Aug 2017
- Conference Presentation
A Convex Framework for Fair Regression
By: Richard Berk, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel and Aaron Roth
We introduce a flexible family of fairness regularizers for (linear and logistic) regression problems. These regularizers all enjoy convexity, permitting fast optimization, and they span the range from notions of group fairness to strong individual fairness. By varying... View Details
Berk, Richard, Hoda Heidari, Shahin Jabbari, Matthew Joseph, Michael J. Kearns, Jamie Morgenstern, Seth Neel, and Aaron Roth. "A Convex Framework for Fair Regression." Paper presented at the 4th Workshop on Fairness, Accountability, and Transparency in Machine Learning, Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD), August 14, 2017.
- 2023
- Book
Right Kind of Wrong: The Science of Failing Well
By: Amy Edmondson
A revolutionary guide that will transform your relationship with failure, from the pioneering researcher of psychological safety and award-winning Harvard Business School professor Amy Edmondson.
We used to think of failure as the opposite of success. Now,... View Details
We used to think of failure as the opposite of success. Now,... View Details
Edmondson, Amy. Right Kind of Wrong: The Science of Failing Well. New York, NY: Atria Books, 2023.
- August 2018 (Revised October 2019)
- Case
C3.ai—Driven to Succeed
By: Robert Simons and George Gonzalez
CEO Tom Siebel navigates his artificial intelligence (ai) startup through a series of pivots, market expansions, and even an elephant attack to become a leading platform ad service provider. The case describes his unusual management approach emphasizing employee... View Details
Keywords: Strategy Execution; Performance Measurement; Critical Performance Variables; Strategic Boundaries; Internet Of Things; Artificial Intelligence; Software Development; Big Data; Machine Learning; Business Startups; Management Style; Business Strategy; Performance; Measurement and Metrics; Organizational Culture; AI and Machine Learning; Digital Transformation; Applications and Software; Digital Marketing; Analytics and Data Science; Technology Industry; United States; California
Simons, Robert, and George Gonzalez. "C3.ai—Driven to Succeed." Harvard Business School Case 119-004, August 2018. (Revised October 2019.)
- 2023
- Working Paper
The Complexity of Economic Decisions
By: Xavier Gabaix and Thomas Graeber
We propose a theory of the complexity of economic decisions. Leveraging a macroeconomic framework of production functions, we conceptualize the mind as a cognitive economy, where a task’s complexity is determined by its composition of cognitive operations. Complexity... View Details
Gabaix, Xavier, and Thomas Graeber. "The Complexity of Economic Decisions." Harvard Business School Working Paper, No. 24-049, February 2024.