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- All HBS Web (341)
- Faculty Publications (120)
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- 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
Cui, Ruomeng, Santiago Gallino, Antonio Moreno, and Dennis J. Zhang. "The Operational Value of Social Media Information." Special Issue on Big Data in Supply Chain Management. Production and Operations Management 27, no. 10 (October 2018): 1749–1774.
- Article
Financial Innovation and Endogenous Growth
By: Luc Laeven, Ross Levine and Stelios Michalopoulos
Is financial innovation necessary for sustaining economic growth? To address this question, we build a Schumpeterian model in which entrepreneurs earn profits by inventing better goods, and profit-maximizing financiers arise to screen entrepreneurs. The model has two... View Details
Laeven, Luc, Ross Levine, and Stelios Michalopoulos. "Financial Innovation and Endogenous Growth." Journal of Financial Intermediation 24, no. 1 (January 2015): 1–24.
- March 2022
- Article
Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models
By: Fiammetta Menchetti and Iavor Bojinov
Researchers regularly use synthetic control methods for estimating causal effects when a sub-set of units receive a single persistent treatment, and the rest are unaffected by the change. In many applications, however, units not assigned to treatment are nevertheless... View Details
Keywords: Causal Inference; Partial Interference; Synthetic Controls; Bayesian Structural Time Series; Mathematical Methods
Menchetti, Fiammetta, and Iavor Bojinov. "Estimating the Effectiveness of Permanent Price Reductions for Competing Products Using Multivariate Bayesian Structural Time Series Models." Annals of Applied Statistics 16, no. 1 (March 2022): 414–435.
- 2022
- Article
Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations
By: Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach and Himabindu Lakkaraju
As post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to ensure that the quality of the resulting explanations is consistently high across all subgroups of a population. For instance, it... View Details
Dai, Jessica, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach, and Himabindu Lakkaraju. "Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations." Proceedings of the AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (2022): 203–214.
- 2022
- Working Paper
The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective
By: Satyapriya Krishna, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu and Himabindu Lakkaraju
As various post hoc explanation methods are increasingly being leveraged to explain complex models in high-stakes settings, it becomes critical to develop a deeper understanding of if and when the explanations output by these methods disagree with each other, and how... View Details
Krishna, Satyapriya, Tessa Han, Alex Gu, Javin Pombra, Shahin Jabbari, Steven Wu, and Himabindu Lakkaraju. "The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective." Working Paper, 2022.
- 20 Dec 2013
- Working Paper Summaries
Zooming In: A Practical Manual for Identifying Geographic Clusters
Keywords: by Juan Alcácer & Minyuan Zhao
- 2008
- Working Paper
Allocating Marketing Resources
By: Sunil Gupta and Thomas J. Steenburgh
Marketing is essential for the organic growth of a company. Not surprisingly, firms spend billions of dollars on marketing. Given these large investments, marketing managers have the responsibility to optimally allocate these resources and demonstrate that these... View Details
Keywords: Investment Return; Resource Allocation; Marketing; Demand and Consumers; Mathematical Methods
Gupta, Sunil, and Thomas J. Steenburgh. "Allocating Marketing Resources." Harvard Business School Working Paper, No. 08-069, February 2008.
- Article
Valuation of Bankrupt Firms
By: S. C. Gilson, E. S. Hotchkiss and R. S. Ruback
This study compares the market value of firms that reorganize in bankruptcy with estimates of value based on management's published cash flow projections. We estimate firm values using models that have been shown in other contexts to generate relatively precise... View Details
Gilson, S. C., E. S. Hotchkiss, and R. S. Ruback. "Valuation of Bankrupt Firms." Review of Financial Studies 13, no. 1 (Spring 2000): 43–74. (Abridged version reprinted in The Journal of Corporate Renewal 13, no. 7 (July 2000))
- Research Summary
Markets and Market Design
The topic on which I currently spend the most of my research energy is the study of strategic interaction and reputation systems on eBay and similar markets from an applied, market design perspective. The rise of the Internet allowed a whole new generation of markets... View Details
- 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.
- Forthcoming
- Article
Reputation Burning: Analyzing the Impact of Brand Sponsorship on Social Influencers
By: Mengjie Cheng and Shunyuan Zhang
The growth of the influencer marketing industry warrants an empirical examination of the effect of posting sponsored videos on influencers' reputations. We collected a novel dataset of user-generated YouTube videos created by prominent English-speaking influencers in... View Details
- 2009
- Working Paper
Estimating the Effects of Large Shareholders Using a Geographic Instrument
By: Bo Becker, Henrik Cronqvist and Rudiger Fahlenbrach
Large shareholders may play an important role for firm performance and policies, but identifying this empirically presents a challenge due to the endogeneity of ownership structures. We develop and test an empirical framework which allows us to separate selection from... View Details
Keywords: Business Headquarters; Geographic Location; Corporate Governance; Governance Controls; Performance Effectiveness; Business and Shareholder Relations; Mathematical Methods
Becker, Bo, Henrik Cronqvist, and Rudiger Fahlenbrach. "Estimating the Effects of Large Shareholders Using a Geographic Instrument." Harvard Business School Working Paper, No. 10-028, October 2009. (Revised February 2010.)
- 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.
- Article
Fast Subset Scan for Multivariate Spatial Biosurveillance
By: Daniel B. Neill, Edward McFowland III and Huanian Zheng
We present new subset scan methods for multivariate event detection in massive space-time datasets. We extend the recently proposed 'fast subset scan' framework from univariate to multivariate data, enabling computationally efficient detection of irregular space-time... View Details
Neill, Daniel B., Edward McFowland III, and Huanian Zheng. "Fast Subset Scan for Multivariate Spatial Biosurveillance." Statistics in Medicine 32, no. 13 (June 15, 2013): 2185–2208.
- 2010
- Working Paper
Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans
By: Doug J. Chung, Thomas J. Steenburgh and K. Sudhir
We estimate a dynamic structural model of sales force response to a bonus based compensation plan. The paper has two main methodological innovations: First, we implement empirically the method proposed by Arcidiacono and Miller (2010) to accommodate unobserved latent... View Details
Keywords: Compensation and Benefits; Performance Productivity; Mathematical Methods; Salesforce Management; Motivation and Incentives
Chung, Doug J., Thomas J. Steenburgh, and K. Sudhir. "Do Bonuses Enhance Sales Productivity? A Dynamic Structural Analysis of Bonus-Based Compensation Plans." Harvard Business School Working Paper, No. 11-041, October 2010.
- 2013
- Working Paper
Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific Coefficients, and Estimating Strategy Trade-Offs
By: Juan Alcacer, Wilbur Chung, Ashton Hawk and Goncalo Pacheco-de-Almeida
Although Strategy research aims to understand how firm actions have differential effects on performance, most empirical research estimates the average effects of these actions across firms. This paper promotes Random Coefficients Models (RCMs) as an ideal empirical... View Details
Alcacer, Juan, Wilbur Chung, Ashton Hawk, and Goncalo Pacheco-de-Almeida. "Applying Random Coefficient Models to Strategy Research: Testing for Firm Heterogeneity, Predicting Firm-Specific Coefficients, and Estimating Strategy Trade-Offs." Harvard Business School Working Paper, No. 14-022, September 2013.
- June, 2024
- Book Review
Debunking Immigration Myths: A Review Essay of 'Streets of Gold: America’s Untold Story of Immigrant Success' (PublicAffairs, 2022) by Ran Abramitzky and Leah Boustan
By: Marco Tabellini
This essay reviews Streets of Gold: America’s Untold Story of Immigrant Success by Ran Abramitzky and Leah Boustan. This elegantly written book, highly accessible to both economists and non-economists, is a must-read for anyone interested in the topic of... View Details
Tabellini, Marco. "Debunking Immigration Myths: A Review Essay of 'Streets of Gold: America’s Untold Story of Immigrant Success' (PublicAffairs, 2022) by Ran Abramitzky and Leah Boustan." Journal of Economic Literature 62, no. 2 (June, 2024): 739–760.
- 2020
- Working Paper
Iterative Coordination and Innovation
By: Sourobh Ghosh and Andy Wu
Agile management practices from the software industry continue to transform the way organizations innovate across industries, yet they remain understudied in the organizations literature. We investigate the widespread Agile practice of iterative coordination: frequent... View Details
Keywords: Innovation; Goals; Specialization; Coordination; Field Experiment; Software Development; Organizations; Collaborative Innovation and Invention; Goals and Objectives; Integration; Software
Ghosh, Sourobh, and Andy Wu. "Iterative Coordination and Innovation." Harvard Business School Working Paper, No. 20-121, January 2020.
- 2022
- Working Paper
Slowly Varying Regression under Sparsity
By: Dimitris Bertsimas, Vassilis Digalakis Jr, Michael Lingzhi Li and Omar Skali Lami
We consider the problem of parameter estimation in slowly varying regression models with sparsity constraints. We formulate the problem as a mixed integer optimization problem and demonstrate that it can be reformulated exactly as a binary convex optimization problem... View Details
Keywords: Mathematical Methods
Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression under Sparsity." Working Paper, September 2022.