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All HBS Web
(2,172)
- Faculty Publications (235)
- December 2022 (Revised February 2023)
- Case
Akooda: Charging Toward Operational Intelligence
By: Christopher T. Stanton and Mel Martin
The Akooda case describes the challenges confronting founder and CEO Yuval Gonczarowski (MBA ‘17) in 2022 as he attempts to boost sales. Launched in November 2020, Akooda was an AI company that mined 20 different sources of digital data, from tools like Slack, Google...
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Keywords:
Data Mining;
Productivity;
Monitoring;
Data Analysis;
AI and Machine Learning;
Knowledge Management;
Operations;
Problems and Challenges;
Employee Relationship Management;
Information Technology Industry;
Technology Industry;
Information Industry;
Boston;
Israel
Stanton, Christopher T., and Mel Martin. "Akooda: Charging Toward Operational Intelligence." Harvard Business School Case 823-018, December 2022. (Revised February 2023.)
- 2023
- Working Paper
Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development
By: Daniel Yue, Paul Hamilton and Iavor Bojinov
Predictive model development is understudied despite its centrality in modern artificial
intelligence and machine learning business applications. Although prior discussions
highlight advances in methods (along the dimensions of data, computing power, and
algorithms)...
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Keywords:
Analytics and Data Science
Yue, Daniel, Paul Hamilton, and Iavor Bojinov. "Nailing Prediction: Experimental Evidence on the Value of Tools in Predictive Model Development." Harvard Business School Working Paper, No. 23-029, December 2022. (Revised April 2023.)
- 2022
- Article
OpenXAI: Towards a Transparent Evaluation of Model Explanations
By: Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik and Himabindu Lakkaraju
While several types of post hoc explanation methods have been proposed in recent literature, there is very little work on systematically benchmarking these methods. Here, we introduce OpenXAI, a comprehensive and extensible opensource framework for evaluating and...
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Agarwal, Chirag, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, and Himabindu Lakkaraju. "OpenXAI: Towards a Transparent Evaluation of Model Explanations." Advances in Neural Information Processing Systems (NeurIPS) (2022).
- December 2022
- Article
The Rise of People Analytics and the Future of Organizational Research
By: Jeff Polzer
Organizations are transforming as they adopt new technologies and use new sources of data, changing the experiences of employees and pushing organizational researchers to respond. As employees perform their daily activities, they generate vast digital data. These data,...
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Keywords:
Organizational Change and Adaptation;
Analytics and Data Science;
Technology Adoption;
Employees
Polzer, Jeff. "The Rise of People Analytics and the Future of Organizational Research." Art. 100181. Research in Organizational Behavior 42 (December 2022). (Supplement.)
- October 2022
- Case
Single.Earth
By: Rembrand Koning and Emer Moloney
Estonian greentech company Single.Earth is launching a nature-backed token that is linked to and funds the protection of a specific plot fo land. The first landowners had been onboarded to the company's Digital Twin, a virtual representation of the planet's natural...
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Keywords:
Alternative Assets;
Business Startups;
Entrepreneurship;
Climate Change;
Environmental Sustainability;
Green Technology;
Natural Resources;
Pollution;
Analytics and Data Science;
Marketing;
Product Marketing;
Product Launch;
Product Positioning;
Markets;
Market Timing;
Strategy;
Green Technology Industry;
Estonia
- 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...
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Keywords:
Mathematical Methods
Bertsimas, Dimitris, Vassilis Digalakis Jr, Michael Lingzhi Li, and Omar Skali Lami. "Slowly Varying Regression under Sparsity." Working Paper, September 2022.
- August 2022
- Supplement
Zalora: Data-Driven Pricing Recommendations
By: Ayelet Israeli
This exercise can be used in conjunction with the main case "Zalora: Data-Driven Pricing" to facilitate class discussion without requiring data analysis from the students. Instead, the exercise presents reports that were created by the data science team to answer the...
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Keywords:
Pricing;
Pricing Algorithms;
Dynamic Pricing;
Ecommerce;
Pricing Strategy;
Pricing And Revenue Management;
Apparel;
Singapore;
Startup;
Demand Estimation;
Data Analysis;
Data Analytics;
Exercise;
Price;
Internet and the Web;
Apparel and Accessories Industry;
Retail Industry;
Fashion Industry;
Singapore
Israeli, Ayelet. "Zalora: Data-Driven Pricing Recommendations." Harvard Business School Supplement 523-032, August 2022.
- August 2022
- Background Note
Retail Media Networks
By: Eva Ascarza, Ayelet Israeli and Celine Chammas
In 2022, retail media was one of the fastest growing segments in digital advertising. A retail media network (RMN) allows a retailer to use its assets for advertising. Retailers set up an advertising business by allowing marketers to buy advertising space across their...
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Keywords:
Advertisers;
Advertising Media;
Media And Broadcasting Industry;
Retail;
Retail Analytics;
Retail Promotion;
Retailing;
Ecommerce;
E-Commerce Strategy;
E-commerce;
Marketing Communication;
Targeting;
Targeted Advertising;
Targeted Marketing;
Advertising;
Marketing;
Marketing Communications;
Marketing Strategy;
Brands and Branding;
Media;
Marketing Channels;
Retail Industry;
Consumer Products Industry;
Advertising Industry;
United States
Ascarza, Eva, Ayelet Israeli, and Celine Chammas. "Retail Media Networks." Harvard Business School Background Note 523-029, August 2022.
- August, 2022
- Article
Changing Ingroup Boundaries: The Effect of Immigration on Race Relations in the United States
By: Vasiliki Fouka and Marco Tabellini
How do social group boundaries evolve? Does the appearance of a new outgroup change the ingroup's perceptions of other outgroups? We introduce a conceptual framework of context-dependent categorization, in which exposure to one minority leads to recategorization of...
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Keywords:
In-group-out-group Relations;
Ingroup-outgroup Relations;
Immigration;
Race;
Relationships;
United States
Fouka, Vasiliki, and Marco Tabellini. "Changing Ingroup Boundaries: The Effect of Immigration on Race Relations in the United States." American Political Science Review 116, no. 3 (August, 2022): 968–984. (Featured in the Boston Globe, Washington Post, and HBS Working Knowledge.)
- 2022
- Article
Data Poisoning Attacks on Off-Policy Evaluation Methods
By: Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin and Himabindu Lakkaraju
Off-policy Evaluation (OPE) methods are a crucial tool for evaluating policies in high-stakes domains such as healthcare, where exploration is often infeasible, unethical, or expensive. However, the extent to which such methods can be trusted under adversarial threats...
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Lobo, Elita, Harvineet Singh, Marek Petrik, Cynthia Rudin, and Himabindu Lakkaraju. "Data Poisoning Attacks on Off-Policy Evaluation Methods." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 38th (2022): 1264–1274.
- 2023
- Working Paper
Dynamic Pricing, Intertemporal Spillovers, and Efficiency
By: Alexander J. MacKay, Dennis Svartbäck and Anders G. Ekholm
Pricing technology that allows firms to rapidly adjust prices has two potential benefits.
Time-varying prices can respond to high-frequency demand shocks to generate greater revenues,
and they can also be used to smooth out demand to reduce costs. Using data...
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MacKay, Alexander J., Dennis Svartbäck, and Anders G. Ekholm. "Dynamic Pricing, Intertemporal Spillovers, and Efficiency." Harvard Business School Working Paper, No. 23-007, July 2022. (Revised December 2023.)
- 2022
- Working Paper
Pricing Power in Advertising Markets: Theory and Evidence
By: Matthew Gentzkow, Jesse M. Shapiro, Frank Yang and Ali Yurukoglu
Existing theories of media competition imply that advertisers will pay a lower price in equilibrium to reach consumers who multi-home across competing outlets. We generalize and extend this theoretical result and test it using data from television and social media...
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Gentzkow, Matthew, Jesse M. Shapiro, Frank Yang, and Ali Yurukoglu. "Pricing Power in Advertising Markets: Theory and Evidence." NBER Working Paper Series, No. 30278, July 2022.
- June 2022 (Revised October 2022)
- Background Note
Digital Commerce and Delivery: Preparing Food and Retail Value Chains for a 50-50 World
By: William R. Kerr, Daniel O'Connor, Paige Boehmcke and Will Ensor
Increasing digitalization of grocery retail and quick commerce reveals insights about managing complex supply chains at scale and shifting revenue streams from product sales to data monetization. How are the roles of retailers changing? What happens if marginal cost...
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Keywords:
Grocery Delivery;
Grocery;
Digitalization;
Fulfillment;
Delivery;
Supply Chain;
Disruption;
Food;
Supply Chain Management;
Market Design;
Trends;
Value Creation;
Goods and Commodities;
Customer Value and Value Chain;
Digital Transformation;
Retail Industry;
Food and Beverage Industry;
United States;
China
Kerr, William R., Daniel O'Connor, Paige Boehmcke, and Will Ensor. "Digital Commerce and Delivery: Preparing Food and Retail Value Chains for a 50-50 World." Harvard Business School Background Note 822-108, June 2022. (Revised October 2022.)
- 2022
- Article
Values and Inequality: Prosocial Jobs and the College Wage Premium
By: Nathan Wilmers and Letian Zhang
Employers often recruit workers by invoking corporate social responsibility, organizational purpose, or other claims to a prosocial mission. In an era of substantial labor
market inequality, commentators typically dismiss these claims as hypocritical: prosocial...
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Wilmers, Nathan, and Letian Zhang. "Values and Inequality: Prosocial Jobs and the College Wage Premium." American Sociological Review 87, no. 3 (2022): 415–442.
- 2022
- Article
Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
By: Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay and Himabindu Lakkaraju
As machine learning (ML) models become more widely deployed in high-stakes applications, counterfactual explanations have emerged as key tools for providing actionable model explanations in practice. Despite the growing popularity of counterfactual explanations, a...
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Keywords:
Machine Learning Models;
Counterfactual Explanations;
Adversarial Examples;
Mathematical Methods
Pawelczyk, Martin, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, and Himabindu Lakkaraju. "Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 25th (2022).
- 2022
- Working Paper
An Anatomy of Performance Monitoring
By: Achyuta Adhvaryu, Anant Nyshadham and Jorge Tamayo
Performance monitoring is a mainstay management tool in most organizations. Yet we still know little about whether—and why—better monitoring yields better performance in practice. To shed light on these questions, we study the introduction of a performance monitoring...
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Keywords:
Performance Monitoring;
Worker Skills;
Skill Depreciation;
Managerial Inattention;
On-the-job Training;
Productivity;
Multitasking;
Quick Serve Restaurants;
Performance Evaluation;
Employees;
Competency and Skills;
Training;
Performance Productivity;
Management;
Information Technology;
Food and Beverage Industry;
Puerto Rico
Adhvaryu, Achyuta, Anant Nyshadham, and Jorge Tamayo. "An Anatomy of Performance Monitoring." Harvard Business School Working Paper, No. 22-066, March 2022. (R&R Journal of Political Economy.)
- Article
Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)
By: Eva Ascarza and Ayelet Israeli
An inherent risk of algorithmic personalization is disproportionate targeting of individuals from certain groups (or demographic characteristics such as gender or race), even when the decision maker does not intend to discriminate based on those “protected”... View Details
Keywords:
Algorithm Bias;
Personalization;
Targeting;
Generalized Random Forests (GRF);
Discrimination;
Customization and Personalization;
Decision Making;
Fairness;
Mathematical Methods
Ascarza, Eva, and Ayelet Israeli. "Eliminating Unintended Bias in Personalized Policies Using Bias-Eliminating Adapted Trees (BEAT)." e2115126119. Proceedings of the National Academy of Sciences 119, no. 11 (March 8, 2022).
- March 8, 2022
- Article
Women Can’t Go Back to the Pre-Pandemic Status Quo
By: Colleen Ammerman and Boris Groysberg
Survey data collected in 2018 and 2019 from Harvard Business School graduates revealed that for women—and especially women of color—well-being at work was suffering long before the pandemic. While 17% of all respondents said that they often or very often experienced...
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Ammerman, Colleen, and Boris Groysberg. "Women Can’t Go Back to the Pre-Pandemic Status Quo." Harvard Business Review (website) (March 8, 2022).
- February 24, 2022
- Article
Want to Prevent the Next Hospital Bed Crisis? Enlist the SEC
By: Regina E. Herzlinger and Richard Boxer
During the initial phases of the COVID-19 pandemic, many U.S. hospitals could not provide enough beds to meet demand. Solving the problem of inadequate capacity is of utmost importance in the “new normal,” which requires recognizing the ongoing need for hospital-based...
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Keywords:
COVID;
COVID-19 Pandemic;
Hospital Capacity;
SEC Regulation;
Health Pandemics;
Crisis Management;
Performance Capacity;
Planning
Herzlinger, Regina E., and Richard Boxer. "Want to Prevent the Next Hospital Bed Crisis? Enlist the SEC." Harvard Business School Working Knowledge (February 24, 2022).
- 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...
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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.