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- All HBS Web
(120,115)
- Faculty Publications (37,197)
- December 2023
- Other Article
Introduction to the Special Section on Business and Climate Change
By: Rajesh Chandy, Glen Dowell, Colin Mayer, Erica Plambeck, George Serafeim, Michael W. Toffel, L. Beril Toktay and Elke Weber
Keywords: Climate Change; Adaptation; Policy; Corporate Social Responsibility and Impact; Innovation and Invention; Forecasting and Prediction
Chandy, Rajesh, Glen Dowell, Colin Mayer, Erica Plambeck, George Serafeim, Michael W. Toffel, L. Beril Toktay, and Elke Weber. "Introduction to the Special Section on Business and Climate Change." Management Science 69, no. 12 (December 2023): 7347–7351.
- November 2023
- Article
Knowledge About the Source of Emotion Predicts Emotion-Regulation Attempts, Strategies, and Perceived Emotion-Regulation Success
By: Yael Millgram, Matthew K. Nock, David D. Bailey and Amit Goldenberg
People’s ability to regulate emotions is crucial to healthy emotional functioning. One overlooked aspect in emotion-regulation research is that knowledge about the source of emotions can vary across situations and individuals, which could impact people’s ability to... View Details
Millgram, Yael, Matthew K. Nock, David D. Bailey, and Amit Goldenberg. "Knowledge About the Source of Emotion Predicts Emotion-Regulation Attempts, Strategies, and Perceived Emotion-Regulation Success." Psychological Science 34, no. 11 (November 2023): 1244–1255.
- 2023
- Article
M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models
By: Himabindu Lakkaraju, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai and Haoyi Xiong
While Explainable Artificial Intelligence (XAI) techniques have been widely studied to explain predictions made by deep neural networks, the way to evaluate the faithfulness of explanation results remains challenging, due to the heterogeneity of explanations for... View Details
Keywords: AI and Machine Learning
Lakkaraju, Himabindu, Xuhong Li, Mengnan Du, Jiamin Chen, Yekun Chai, and Haoyi Xiong. "M4: A Unified XAI Benchmark for Faithfulness Evaluation of Feature Attribution Methods across Metrics, Modalities, and Models." Advances in Neural Information Processing Systems (NeurIPS) (2023).
- 2023
- Chapter
Malleability Interventions in Intergroup Relations
By: Smadar Cohen-Chen, Amit Goldenberg, James J. Gross and Eran Halperin
One important characteristic of intergroup relations and conflicts is the fact that toxic or violent intergroup relations are often associated with fixed and stable perceptions of various entities, including the ingroup (stable and positive), the outgroup (stable and... View Details
Cohen-Chen, Smadar, Amit Goldenberg, James J. Gross, and Eran Halperin. "Malleability Interventions in Intergroup Relations." Chap. 7 in Psychological Intergroup Interventions: Evidence-based Approaches to Improve Intergroup Relations, by Eran Halperin, Boaz Hameiri, and Rebecca Littman. Routledge, 2023.
- 2023
- Working Paper
Market Design and Maintenance
By: Alvin E. Roth
Because no marketplace operates in isolation from the larger world, marketplace designs may need to adapt to changes in the larger environments. I discuss such changes in connection with the labor markets for new doctors, new Ph.D. economists, and for kidney exchange... View Details
Keywords: Market Design
Roth, Alvin E. "Market Design and Maintenance." NBER Working Paper Series, No. 31947, December 2023.
- 2023
- Article
MoPe: Model Perturbation-based Privacy Attacks on Language Models
By: Marvin Li, Jason Wang, Jeffrey Wang and Seth Neel
Recent work has shown that Large Language Models (LLMs) can unintentionally leak sensitive information present in their training data. In this paper, we present Model Perturbations (MoPe), a new method to identify with high confidence if a given text is in the training... View Details
Li, Marvin, Jason Wang, Jeffrey Wang, and Seth Neel. "MoPe: Model Perturbation-based Privacy Attacks on Language Models." Proceedings of the Conference on Empirical Methods in Natural Language Processing (2023): 13647–13660.
- 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... View Details
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).
- December 2023
- Article
Recover, Explore, Practice: The Transformative Potential of Sabbaticals
By: Kira Schabram, Matt Bloom and DJ DiDonna
Sabbaticals have seen an exponential growth in adoption over the last two decades and are ascribed extensive benefits by employers and employees alike. Little is known, however, about how individuals spend their time or how their experiences impact them after they... View Details
Schabram, Kira, Matt Bloom, and DJ DiDonna. "Recover, Explore, Practice: The Transformative Potential of Sabbaticals." Academy of Management Discoveries 9, no. 4 (December 2023): 441–468.
- December 2023
- Article
Save More Today or Tomorrow: The Role of Urgency in Precommitment Design
By: Joseph Reiff, Hengchen Dai, John Beshears, Katherine L. Milkman and Shlomo Benartzi
To encourage farsighted behaviors, past research suggests that marketers may be wise to invite consumers to pre-commit to adopt them “later.” However, the authors propose that people will draw different inferences from different types of pre-commitment offers, and that... View Details
Reiff, Joseph, Hengchen Dai, John Beshears, Katherine L. Milkman, and Shlomo Benartzi. "Save More Today or Tomorrow: The Role of Urgency in Precommitment Design." Journal of Marketing Research (JMR) 60, no. 6 (December 2023): 1095–1113.
- December 2023
- Article
Self-Orienting in Human and Machine Learning
By: Julian De Freitas, Ahmet Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum and T. Ullman
A current proposal for a computational notion of self is a representation of one’s body in a specific time and place, which includes the recognition of that representation as the agent. This turns self-representation into a process of self-orientation, a challenging... View Details
De Freitas, Julian, Ahmet Uğuralp, Zeliha Uğuralp, Laurie Paul, Joshua B. Tenenbaum, and T. Ullman. "Self-Orienting in Human and Machine Learning." Nature Human Behaviour 7, no. 12 (December 2023): 2126–2139.
- November–December 2023
- Article
Tax-Loss Harvesting with Cryptocurrencies
By: Lin William Cong, Wayne Landsman, Edward Maydew and Daniel Rabetti
We describe the taxation landscape in the cryptocurrency markets, especially concerning U.S. taxpayers, and examine how recent increases in tax scrutiny have led to changes in crypto investors' trading behavior. We argue conceptually and then empirically document that... View Details
Cong, Lin William, Wayne Landsman, Edward Maydew, and Daniel Rabetti. "Tax-Loss Harvesting with Cryptocurrencies." Art. 101607. Journal of Accounting & Economics 76, nos. 2-3 (November–December 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... View Details
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 Impact of Unionization on Consumer Perceptions of Service Quality: Evidence from Starbucks
By: Isamar Troncoso, Minkyung Kim, Ishita Chakraborty and SooHyun Kim
The US has seen a rise in union movements, but their effects on service industry marketing outcomes like customer satisfaction and perceptions of service quality remain understudied. In this paper, we empirically study the impact on customer satisfaction and... View Details
Keywords: Labor Unions; Customer Satisfaction; Perception; Public Opinion; Employees; Food and Beverage Industry
Troncoso, Isamar, Minkyung Kim, Ishita Chakraborty, and SooHyun Kim. "The Impact of Unionization on Consumer Perceptions of Service Quality: Evidence from Starbucks." Working Paper, 2023.
- 2023
- Chapter
The Platform Organization
By: Annabelle Gawer, Michael A. Cusumano and David B. Yoffie
The industry platform is a distinct organizational form that has grown into importance over the past 20 years. We focus on the digital platform, which, along with its associated ecosystem, is uniquely positioned to create and capture value in the digital economy. View Details
Keywords: Platform; Organization; Organizations; Internet and the Web; Value Creation; Digital Platforms
Gawer, Annabelle, Michael A. Cusumano, and David B. Yoffie. "The Platform Organization." Chap. 5 in Designing Adaptive Organizations, edited by Charles Snow and Øystein D. Fjeldstad, 86–103. Cambridge Companions to Management. Cambridge University Press, 2023.
- December 2023
- Teaching Note
The Rise and Fall of FTX
Teaching Note for HBS Case No. 124-014. View Details
- 2024
- Working Paper
The Uneven Impact of Generative AI on Entrepreneurial Performance
By: Nicholas G. Otis, Rowan Clarke, Solène Delecourt, David Holtz and Rembrand Koning
Scalable and low-cost AI assistance has the potential to improve firm decision-making and economic performance. However, running a business involves a myriad of open-ended problems, making it difficult to know whether recent AI advances can help business owners make... View Details
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.
- November 22, 2023
- Article
Unifying Your Company Around a Moral Goal
By: Ranjay Gulati
In turbulent times, companies need a reliable anchor to guide decision-making. When organizations become moral communities, underpinned by purpose, they provide that stability for stakeholders as well as a reassuring sense of hope, solidarity, agency, and meaning.... View Details
Gulati, Ranjay. "Unifying Your Company Around a Moral Goal." Harvard Business Review Digital Articles (November 22, 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... 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).
- December 2023
- Article
What Can Stockouts Tell Us About Inflation? Evidence from Online Micro Data
By: Alberto Cavallo and Oleksiy Kryvtsov
We use a detailed micro dataset on product availability and stockouts to construct a direct high-frequency measure of consumer product shortages during the 2020-2022 pandemic. We document a widespread multi-fold rise in stockouts in nearly all sectors early in the... View Details
Keywords: Prices; Stockouts; Inventories; Supply Disruptions; COVID-19 Pandemic; Supply Chain; Product; Demand and Consumers
Cavallo, Alberto, and Oleksiy Kryvtsov. "What Can Stockouts Tell Us About Inflation? Evidence from Online Micro Data." Journal of International Economics 146 (December 2023).
- 2023
- Working Paper
What Makes Managers’ Private Disclosures Informative? Evidence from Professional Investors
By: Michael Durney, Hoyoun Kyung, Jihwon Park and Eugene F. Soltes