Publications
Publications
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- All HBS Web
(1,027)
- Faculty Publications (217)
- February 2020
- Article
Being 'Good' or 'Good Enough': Prosocial Risk and the Structure of Moral Self-regard
By: Julian Zlatev, Daniella M. Kupor, Kristin Laurin and Dale T. Miller
The motivation to feel moral powerfully guides people’s prosocial behavior. We propose that people’s efforts to preserve their moral self-regard conform to a moral threshold model. This model predicts that people are primarily concerned with whether their... View Details
- 2020
- Conference Presentation
A Performance-optimized Limb Detection Model Selectively Predicts Behavioral Responses Based on Movement Similarity
By: X. Zhao, J. De Freitas, L. Tarhan and G. A. Alvarez
- 2019
- Working Paper
Soul and Machine (Learning)
By: Davide Proserpio, John R. Hauser, Xiao Liu, Tomomichi Amano, Alex Burnap, Tong Guo, Dokyun Lee, Randall Lewis, Kanishka Misra, Eric Schwarz, Artem Timoshenko, Lilei Xu and Hema Yoganarasimhan
Machine learning is bringing us self-driving cars, improved medical diagnostics, and machine translation, but can it improve marketing decisions? It can. Machine learning models predict extremely well, are scalable to “big data,” and are a natural fit to rich media... View Details
- Article
Valuing Time Over Money Predicts Happiness After a Major Life Transition: A Preregistered Longitudinal Study of Graduating Students
By: A.V. Whillans, Lucia Macchia and Elizabeth Dunn
How does prioritizing time or money shape major life decisions and subsequent well-being? In a preregistered longitudinal study of approximately 1000 graduating university students, respondents who valued time over money chose more intrinsically rewarding activities... View Details
- July 2019
- Case
Autonomous Vehicles: Smooth or Bumpy Ride Ahead?
By: Elie Ofek and Akhil Waghmare
In early 2019, transportation was set to undergo a major transformation with the advent of autonomous vehicles (AVs), also referred to as driverless cars, which were nearing completion from an R&D and testing phase. Yet many questions remained open regarding exactly... View Details
- February 2019 (Revised August 2019)
- Case
KangaTech
By: Karim R. Lakhani, Patrick J. Ferguson, Sarah Fleischer, Jin Hyun Paik and Steven Randazzo
On a warm January afternoon in 2019, Steve Saunders, Dave Scerri, Carl Dilena, and Nick Haslam (see Exhibit 1 for biographies), co-founders of KangaTech, wrapped up the latest round of discussions about the future direction of their sports-technology start-up. Focused... View Details
- 2020
- Working Paper
Overcoming the Cold Start Problem of CRM Using a Probabilistic Machine Learning Approach
By: Eva Ascarza
The success of Customer Relationship Management (CRM) programs ultimately depends on the firm's ability to understand consumers' preferences and precisely capture how these preferences may differ across customers. Only by understanding customer heterogeneity, firms can... View Details
- 2019
- Article
Fair Algorithms for Learning in Allocation Problems
By: Hadi Elzayn, Shahin Jabbari, Christopher Jung, Michael J Kearns, Seth Neel, Aaron Leon Roth and Zachary Schutzman
Settings such as lending and policing can be modeled by a centralized agent allocating a scarce resource (e.g. loans or police officers) amongst several groups, in order to maximize some objective (e.g. loans given that are repaid, or criminals that are apprehended).... View Details
- Article
Faithful and Customizable Explanations of Black Box Models
By: Himabindu Lakkaraju, Ece Kamar, Rich Caruana and Jure Leskovec
As predictive models increasingly assist human experts (e.g., doctors) in day-to-day decision making, it is crucial for experts to be able to explore and understand how such models behave in different feature subspaces in order to know if and when to trust them. To... View Details
- January 2019
- Article
Making Moves Matter: Experimental Evidence on Incentivizing Bureaucrats Through Performance-Based Postings
By: Adnan Q. Khan, Asim Ijaz Khwaja and Benjamin A. Olken
Bureaucracies often post staff to better or worse locations, ostensibly to provide incentives. Yet we know little about whether this works, with heterogeneity in preferences over postings impacting effectiveness. We propose a performance-ranked serial dictatorship... View Details
- October 2018
- Article
The Operational Value of Social Media Information
By: Ruomeng Cui, Santiago Gallino, Antonio Moreno and Dennis J. Zhang
While the value of using social media information has been established in multiple business contexts, the field of operations and supply chain management have not yet explored the possibilities it offers in improving firms' operational decisions. This study attempts to... View Details
- Article
Applying Random Coefficient Models to Strategy Research: Identifying and Exploring Firm Heterogeneous Effects
By: Juan Alcácer, Wilbur Chung, Ashton Hawk and Gonçalo Pacheco-de-Almeida
Strategy aims at understanding the differential effects of firms’ actions on performance. However, standard regression models estimate only the average effects of these actions across firms. Our paper discusses how random coefficient models (RCMs) may generate new... View Details
- September 26, 2018
- Article
Ownership and Power Structure: Together at Last
By: Laura Alfaro, Nicholas Bloom, Paola Conconi, Harald Fadinger, Patrick Legros, Andrew Newman, Raffaella Sadun and John Van Reenen
Economists have largely ignored the deep interdependency between integration and delegation. This column describes a new theory of integration and delegation choices aimed at shedding light on how these distinct elements of organizational design interact. Contrary to... View Details
- August 2018 (Revised September 2018)
- Supplement
Predicting Purchasing Behavior at PriceMart (B)
By: Srikant M. Datar and Caitlin N. Bowler
Supplements the (A) case. In this case, Wehunt and Morse are concerned about the logistic regression model overfitting to the training data, so they explore two methods for reducing the sensitivity of the model to the data by regularizing the coefficients of the... View Details
- August 2018 (Revised April 2019)
- Supplement
Chateau Winery (B): Supervised Learning
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on “Chateau Winery (A).” In this case, Bill Booth, marketing manager of a regional wine distributor, shifts to supervised learning techniques to try to predict which deals he should offer to customers based on the purchasing behavior of those... View Details
- August 2018 (Revised September 2018)
- Case
LendingClub (A): Data Analytic Thinking (Abridged)
By: Srikant M. Datar and Caitlin N. Bowler
LendingClub was founded in 2006 as an alternative, peer-to-peer lending model to connect individual borrowers to individual investor-lenders through an online platform. Since 2014 the company has worked with institutional investors at scale. While the company assigns... View Details
- August 2018 (Revised September 2018)
- Supplement
LendingClub (B): Decision Trees & Random Forests
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) case. In this case students follow Emily Figel as she builds two tree-based models using historical LendingClub data to predict, with some probability, whether borrower will repay or default on his loan.
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- August 2018 (Revised September 2018)
- Supplement
LendingClub (C): Gradient Boosting & Payoff Matrix
By: Srikant M. Datar and Caitlin N. Bowler
This case builds directly on the LendingClub (A) and (B) cases. In this case students follow Emily Figel as she builds an even more sophisticated model using the gradient boosted tree method to predict, with some probability, whether a borrower would repay or default... View Details
- August 2018
- Article
Extrapolation and Bubbles
By: Nicholas Barberis, Robin Greenwood, Lawrence Jin and Andrei Shleifer
We present an extrapolative model of bubbles. In the model, many investors form their demand for a risky asset by weighing two signals: an average of the asset’s past price changes and the asset’s degree of overvaluation. The two signals are in conflict, and investors... View Details
- June 2018
- Article
The Power of Workplace Rewards: Using Self-Determination Theory to Understand Why Reward Satisfaction Matters for Workers Around the World
By: Anais Thibault Landry and A.V. Whillans
How can workplace rewards promote employee well-being and engagement? To answer these questions, we utilized self-determination theory to examine whether reward satisfaction predicted employee well-being, job satisfaction, intrinsic motivation, and affective... View Details
Keywords: Workplace; Rewards; Motivation; Employees; Satisfaction; Motivation and Incentives; Welfare