Filter Results:
(178)
Show Results For
- All HBS Web (228)
- Faculty Publications (102)
Show Results For
- All HBS Web (228)
- Faculty Publications (102)
Sort by
- October 2014
- Supplement
Quiet Logistics (B)
By: Robert Simons and Natalie Kindred
This two-part case focuses on how to identify and manage strategic uncertainties in an innovative, entrepreneurial start-up company. In the (A) case, students learn about Quiet Logistics, an e-commerce fulfillment company working with high-end apparel retailers such as... View Details
Keywords: Strategy Execution; Strategic Uncertainties; Managing Growth; Disruptive Change; Robotics; Disruptive Technologies; Managing Start-ups; Management Control Systems; Performance Measurement; Business Growth and Maturation; Disruption; Entrepreneurship; Disruptive Innovation; Crisis Management; Risk Management; Organizational Change and Adaptation; Business Strategy; Competitive Strategy; E-commerce; Distribution Industry; Technology Industry; United States
Simons, Robert, and Natalie Kindred. "Quiet Logistics (B)." Harvard Business School Supplement 115-003, October 2014.
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.
- 2020
- Working Paper
Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion
By: Ryan Allen and Prithwiraj Choudhury
Past research offers mixed perspectives on whether domain experience helps or hurts algorithm-augmented work performance. To reconcile these perspectives, we theorize that domain experience affects algorithm-augmented performance via two distinct countervailing... View Details
Keywords: Automation; Domain Experience; Algorithmic Aversion; Experts; Algorithms; Machine Learning; Decision-making; Future Of Work; Employees; Experience and Expertise; Decision Making; Performance
Allen, Ryan, and Prithwiraj Choudhury. "Algorithm-Augmented Work and Domain Experience: The Countervailing Forces of Ability and Aversion." Harvard Business School Working Paper, No. 21-073, October 2020. (Revised September 2021.)
- 2018
- Working Paper
How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections
By: Maria Ibanez and Michael W. Toffel
Many production processes are subject to inspection to ensure they meet quality, safety, and environmental standards imposed by companies and regulators. Inspection accuracy is critical to inspections being a useful input to assessing risks, allocating quality... View Details
Keywords: Assessment; Bias; Inspection; Scheduling; Econometric Analysis; Empirical Research; Regulation; Health; Food; Safety; Quality; Performance Consistency; Performance Evaluation; Food and Beverage Industry; Service Industry
Ibanez, Maria, and Michael W. Toffel. "How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections." Harvard Business School Working Paper, No. 17-090, April 2017. (Revised October 2018. Formerly titled "Assessing the Quality of Quality Assessment: The Role of Scheduling". Featured in Forbes, Food Safety Magazine, and Food Safety News.)
- Article
Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting
By: Raymond H. Mak, Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani and Eva C. Guinan
Importance: Radiation therapy (RT) is a critical cancer treatment, but the existing radiation oncologist work force does not meet growing global demand. One key physician task in RT planning involves tumor segmentation for targeting, which requires substantial... View Details
Keywords: Crowdsourcing; AI Algorithms; Health Care and Treatment; Collaborative Innovation and Invention; AI and Machine Learning
Mak, Raymond H., Michael G. Endres, Jin Hyun Paik, Rinat A. Sergeev, Hugo Aerts, Christopher L. Williams, Karim R. Lakhani, and Eva C. Guinan. "Use of Crowd Innovation to Develop an Artificial Intelligence-Based Solution for Radiation Therapy Targeting." JAMA Oncology 5, no. 5 (May 2019): 654–661.
- Research Summary
Incorporating Price and Inventory Endogeneity in Firm-Level Sales Forecasting.
Forecasting firm-level sales is a key activity in top-down planning in most organizations. In the retailing industry, firms can use inventory and price to stimulate demand. Hence, standard time series methods for sales forecasting can be improved by incorporating... View Details
- Working Paper
Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application
By: Flora Feng, Charis Li and Shunyuan Zhang
Peer-to-peer (P2P) marketplaces have seen exponential growth in recent years featured by unique offerings from individual providers. Despite the perceived value of uniqueness, scalable quantification of visual uniqueness in P2P platforms like Airbnb has been largely... View Details
Keywords: Peer-to-peer Markets; Marketplace Matching; AI and Machine Learning; Demand and Consumers; Digital Platforms; Marketing
Feng, Flora, Charis Li, and Shunyuan Zhang. "Visual Uniqueness in Peer-to-Peer Marketplaces: Machine Learning Model Development, Validation, and Application." SSRN Working Paper Series, No. 4665286, February 2024.
- January 2008 (Revised July 2009)
- Case
Forecasting the Great Depression
What is proper role of professional economic forecasting in financial decision making? The case presents excerpts from three leading economic forecasters on the eve of, and just after, the stock market crash of October 1929. The first set of excerpts is from Roger... View Details
Keywords: History; Mathematical Methods; Personal Development and Career; Forecasting and Prediction; Financial Crisis
Friedman, Walter A. "Forecasting the Great Depression." Harvard Business School Case 708-046, January 2008. (Revised July 2009.)
- 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).
- 21 Aug 2018
- First Look
New Research and Ideas, August 21, 2018
accuracy of daily sales forecasts. We collaborated with an online apparel retailer to assemble a dataset that combines (1) detailed internal operational information, including data on sales, advertising, and promotions, as well as (2)... View Details
Keywords: Dina Gerdeman
- June 2020
- Article
How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections
By: Maria Ibanez and Michael W. Toffel
Accuracy and consistency are critical for inspections to be an effective, fair, and useful tool for assessing risks, quality, and suppliers—and for making decisions based on those assessments. We examine how inspector schedules could introduce bias that erodes... View Details
Keywords: Assessment; Bias; Inspection; Scheduling; Econometric Analysis; Empirical Research; Regulation; Health; Food; Safety; Quality; Performance Consistency; Governing Rules, Regulations, and Reforms
Ibanez, Maria, and Michael W. Toffel. "How Scheduling Can Bias Quality Assessment: Evidence from Food Safety Inspections." Management Science 66, no. 6 (June 2020): 2396–2416. (Revised February 2019. Featured in Harvard Business Review, Forbes, Food Safety Magazine, Food Safety News, and KelloggInsight. (2020 MSOM Responsible Research Finalist.))
- 2021
- Working Paper
'Just Letting You Know…': Underestimating Others' Desire for Constructive Feedback
By: Nicole Abi-Esber, Jennifer Abel, Juliana Schroeder and Francesca Gino
People often avoid giving feedback to others even when it would help fix a problem immediately. Indeed, in a pilot field study (N=155), only 2.6% of individuals provided feedback to survey administrators that the administrators had food or marker on their faces.... View Details
Keywords: Feedback; Helping; Prosocial Behavior; Relationships; Social Psychology; Theory; Perception
Abi-Esber, Nicole, Jennifer Abel, Juliana Schroeder, and Francesca Gino. "'Just Letting You Know…': Underestimating Others' Desire for Constructive Feedback." Harvard Business School Working Paper, No. 22-009, August 2021.
- 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).
- Mar 2020
- Conference Presentation
A New Analysis of Differential Privacy's Generalization Guarantees
By: Christopher Jung, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi and Moshe Shenfeld
We give a new proof of the "transfer theorem" underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and... View Details
Jung, Christopher, Katrina Ligett, Seth Neel, Aaron Roth, Saeed Sharifi-Malvajerdi, and Moshe Shenfeld. "A New Analysis of Differential Privacy's Generalization Guarantees." Paper presented at the 11th Innovations in Theoretical Computer Science Conference, Seattle, March 2020.
- 2023
- Working Paper
PRIMO: Private Regression in Multiple Outcomes
By: Seth Neel
We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates... View Details
Neel, Seth. "PRIMO: Private Regression in Multiple Outcomes." Working Paper, March 2023.
- 2019
- Article
An Empirical Study of Rich Subgroup Fairness for Machine Learning
By: Michael J Kearns, Seth Neel, Aaron Leon Roth and Zhiwei Steven Wu
Kearns et al. [2018] recently proposed a notion of rich subgroup fairness intended to bridge the gap between statistical and individual notions of fairness. Rich subgroup fairness picks a statistical fairness constraint (say, equalizing false positive rates across... View Details
Kearns, Michael J., Seth Neel, Aaron Leon Roth, and Zhiwei Steven Wu. "An Empirical Study of Rich Subgroup Fairness for Machine Learning." Proceedings of the Conference on Fairness, Accountability, and Transparency (2019): 100–109.
- 05 Dec 2023
- Research & Ideas
Lessons in Decision-Making: Confident People Aren't Always Correct (Except When They Are)
vote for the accuracy of their own solution to each task. These decisions served to find out whether the aggregate outcomes that emerge from people self-selecting into participating more or less aggressively in an auction, market, or... View Details
Keywords: by Kara Baskin
- Teaching Interest
Overview
Paul is primarily interested in teaching data science to management students through the case method. This includes technical topics (programming and statistics) as well as higher-level management issues (digital transformation, data governance, etc.) As a research... View Details
Keywords: A/B Testing; AI; AI Algorithms; AI Creativity; Algorithm; Algorithm Bias; Algorithmic Bias; Algorithmic Fairness; Algorithms; Analytics; Application Program Interface; Artificial Intelligence; Causality; Causal Inference; Computing; Computers; Data Analysis; Data Analytics; Data Architecture; Data As A Service; Data Centers; Data Governance; Data Labeling; Data Management; Data Manipulation; Data Mining; Data Ownership; Data Privacy; Data Protection; Data Science; Data Science And Analytics Management; Data Scientists; Data Security; Data Sharing; Data Strategy; Data Visualization; Database; Data-driven Decision-making; Data-driven Management; Data-driven Operations; Datathon; Economics Of AI; Economics Of Innovation; Economics Of Information System; Economics Of Science; Forecast; Forecast Accuracy; Forecasting; Forecasting And Prediction; Information Technology; Machine Learning; Machine Learning Models; Prediction; Prediction Error; Predictive Analytics; Predictive Models; Analysis; AI and Machine Learning; Analytics and Data Science; Applications and Software; Digital Transformation; Information Management; Digital Strategy; Technology Adoption
- 22 Feb 2024
- Research & Ideas
How to Make AI 'Forget' All the Private Data It Shouldn't Have
income of any one person. Even if you subtract the two numbers, there's a margin of error now where it gives you the guarantee [that] there's no way someone could really figure out with a certain degree of accuracy what your income is.... View Details
- August 2019
- Article
When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation
By: Yicheng Song, Nachiketa Sahoo and Elie Ofek
Sometimes we desire change, a break from the same or an opportunity to fulfill different aspects of our needs. Noting that consumers seek variety, several approaches have been developed to diversify items recommended by personalized recommender systems. However,... View Details
Keywords: Recommender Systems; Personalization; Recommendation Diversity; Variety Seeking; Collaborative Filtering; Consumer Utility Models; Digital Media; Clickstream Analysis; Learning-to-rank; Consumer Behavior; Media; Customization and Personalization; Strategy; Mathematical Methods
Song, Yicheng, Nachiketa Sahoo, and Elie Ofek. "When and How to Diversify—A Multicategory Utility Model for Personalized Content Recommendation." Management Science 65, no. 8 (August 2019): 3737–3757.