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- 2023
- Article
On Minimizing the Impact of Dataset Shifts on Actionable Explanations
By: Anna P. Meyer, Dan Ley, Suraj Srinivas and Himabindu Lakkaraju
The Right to Explanation is an important regulatory principle that allows individuals to request actionable explanations for algorithmic decisions. However, several technical challenges arise when providing such actionable explanations in practice. For instance, models... View Details
Meyer, Anna P., Dan Ley, Suraj Srinivas, and Himabindu Lakkaraju. "On Minimizing the Impact of Dataset Shifts on Actionable Explanations." Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI) 39th (2023): 1434–1444.
- 2023
- Article
On the Impact of Actionable Explanations on Social Segregation
By: Ruijiang Gao and Himabindu Lakkaraju
As predictive models seep into several real-world applications, it has become critical to ensure that individuals who are negatively impacted by the outcomes of these models are provided with a means for recourse. To this end, there has been a growing body of research... View Details
Gao, Ruijiang, and Himabindu Lakkaraju. "On the Impact of Actionable Explanations on Social Segregation." Proceedings of the International Conference on Machine Learning (ICML) 40th (2023): 10727–10743.
- August 2023 (Revised March 2024)
- Case
Arla Foods: Data-Driven Decarbonization (A)
By: Michael Parzen, Michael W. Toffel, Susan Pinckney and Amram Migdal
The case describes Arla’s history, in particular its climate change mitigation efforts, and how it implemented a price incentive system to motivate individual farms to implement scope 1 greenhouse gas emissions mitigation measures and receive a higher milk price. The... View Details
Keywords: Dairy Industry; Business Earnings; Agribusiness; Animal-Based Agribusiness; Acquisition; Mergers and Acquisitions; Decision Making; Decisions; Voting; Environmental Management; Climate Change; Environmental Regulation; Environmental Sustainability; Green Technology; Pollution; Moral Sensibility; Values and Beliefs; Financial Strategy; Price; Profit; Revenue; Food; Geopolitical Units; Global Strategy; Ownership Type; Cooperative Ownership; Performance Efficiency; Performance Evaluation; Problems and Challenges; Natural Environment; Science-Based Business; Business Strategy; Commercialization; Cooperation; Corporate Strategy; Food and Beverage Industry; Agriculture and Agribusiness Industry; Europe; United Kingdom; European Union; Germany; Denmark; Sweden; Luxembourg; Belgium
Parzen, Michael, Michael W. Toffel, Susan Pinckney, and Amram Migdal. "Arla Foods: Data-Driven Decarbonization (A)." Harvard Business School Case 624-003, August 2023. (Revised March 2024.)
- August 2023 (Revised January 2024)
- Supplement
Arla Foods: Data-Driven Decarbonization (B)
By: Michael Parzen, Michael W. Toffel, Susan Pinckney and Amram Migdal
The case describes Arla’s history, in particular its climate change mitigation efforts, and how it implemented a price incentive system to motivate individual farms to implement scope 1 greenhouse gas emissions mitigation measures and receive a higher milk price. The... View Details
Keywords: Dairy Industry; Earnings Management; Environmental Accounting; Animal-Based Agribusiness; Mergers and Acquisitions; Decisions; Voting; Climate Change; Environmental Regulation; Environmental Sustainability; Green Technology; Pollution; Moral Sensibility; Values and Beliefs; Financial Strategy; Price; Profit; Revenue; Food; Geopolitical Units; Cross-Cultural and Cross-Border Issues; Global Strategy; Cooperative Ownership; Performance Efficiency; Performance Evaluation; Problems and Challenges; Natural Environment; Science-Based Business; Business Strategy; Commercial Banking; Cooperation; Corporate Strategy; Motivation and Incentives; Food and Beverage Industry; Agriculture and Agribusiness Industry; Europe; United Kingdom; European Union; Denmark; Sweden; Luxembourg; Belgium
Parzen, Michael, Michael W. Toffel, Susan Pinckney, and Amram Migdal. "Arla Foods: Data-Driven Decarbonization (B)." Harvard Business School Supplement 624-036, August 2023. (Revised January 2024.)
- August 2023
- Case
The Ethical Tightrope: When to Disclose the AI Shortcut
By: David G. Fubini, William Fubini and Patrick Sanguineti
In this short vignette on ethics in consulting, John Child, a new Associate at a prestigious firm who is eager to impress, decides to use an AI tool to expedite his analysis and craft his presentation due to a short project timeframe. Feeling uneasy about his decision... View Details
Fubini, David G., William Fubini, and Patrick Sanguineti. "The Ethical Tightrope: When to Disclose the AI Shortcut." Harvard Business School Case 424-011, August 2023.
- July–August 2023
- Article
Demand Learning and Pricing for Varying Assortments
By: Kris Ferreira and Emily Mower
Problem Definition: We consider the problem of demand learning and pricing for retailers who offer assortments of substitutable products that change frequently, e.g., due to limited inventory, perishable or time-sensitive products, or the retailer’s desire to... View Details
Keywords: Experiments; Pricing And Revenue Management; Retailing; Demand Estimation; Pricing Algorithm; Marketing; Price; Demand and Consumers; Mathematical Methods
Ferreira, Kris, and Emily Mower. "Demand Learning and Pricing for Varying Assortments." Manufacturing & Service Operations Management 25, no. 4 (July–August 2023): 1227–1244. (Finalist, Practice-Based Research Competition, MSOM (2021) and Finalist, Revenue Management & Pricing Section Practice Award, INFORMS (2019).)
- July 2023
- Article
Design and Analysis of Switchback Experiments
By: Iavor I Bojinov, David Simchi-Levi and Jinglong Zhao
In switchback experiments, a firm sequentially exposes an experimental unit to a random treatment, measures its response, and repeats the procedure for several periods to determine which treatment leads to the best outcome. Although practitioners have widely adopted... View Details
Bojinov, Iavor I., David Simchi-Levi, and Jinglong Zhao. "Design and Analysis of Switchback Experiments." Management Science 69, no. 7 (July 2023): 3759–3777.
- June 2023
- Simulation
Artea Dashboard and Targeting Policy Evaluation
By: Ayelet Israeli and Eva Ascarza
Companies deploy A/B experiments to gain valuable insights about their customers in order to answer strategic business problems. In marketing, A/B tests are often used to evaluate marketing interventions intended to generate incremental outcomes for the firm. The Artea... View Details
Keywords: Algorithm Bias; Algorithmic Data; Race And Ethnicity; Experimentation; Promotion; Marketing And Society; Big Data; Privacy; Data-driven Management; Data Analysis; Data Analytics; E-Commerce Strategy; Discrimination; Targeted Advertising; Targeted Policies; Pricing Algorithms; A/B Testing; Ethical Decision Making; Customer Base Analysis; Customer Heterogeneity; Coupons; Marketing; Race; Gender; Diversity; Customer Relationship Management; Marketing Communications; Advertising; Decision Making; Ethics; E-commerce; Analytics and Data Science; Retail Industry; Apparel and Accessories Industry; United States
- June 2023
- Case
Investing in the Climate Transition at Neuberger Berman
By: George Serafeim and Benjamin Maletta
By mid-2023, Neuberger Berman (NB), an active asset manager, had grown its assets under management to about half a trillion dollars and took pride in its client centricity and innovative spirit. Responding to client demand for investment products that integrated... View Details
Keywords: Carbon Emissions; Sustainability; Decarbonization; Performance; Risk Assessment; Opportunities; Environmental Sustainability; Carbon Footprint; Business Analysis; Investing; Regulation; Asset Management; Investment Strategy; Climate Change; Transition; Analysis; Product Positioning; Strategy; Investment Portfolio; Financial Services Industry; Energy Industry
Serafeim, George, and Benjamin Maletta. "Investing in the Climate Transition at Neuberger Berman." Harvard Business School Case 123-092, June 2023.
- 2023
- Working Paper
Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation
By: Dae Woong Ham, Michael Lindon, Martin Tingley and Iavor Bojinov
Randomized experiments have become the standard method for companies to evaluate the performance of new products or services. In addition to augmenting managers’ decision-making, experimentation mitigates risk by limiting the proportion of customers exposed to... View Details
Keywords: Performance Evaluation; Research and Development; Analytics and Data Science; Consumer Behavior
Ham, Dae Woong, Michael Lindon, Martin Tingley, and Iavor Bojinov. "Design-Based Confidence Sequences: A General Approach to Risk Mitigation in Online Experimentation." Harvard Business School Working Paper, No. 23-070, May 2023.
- 2023
- Article
Provable Detection of Propagating Sampling Bias in Prediction Models
By: Pavan Ravishankar, Qingyu Mo, Edward McFowland III and Daniel B. Neill
With an increased focus on incorporating fairness in machine learning models, it becomes imperative not only to assess and mitigate bias at each stage of the machine learning pipeline but also to understand the downstream impacts of bias across stages. Here we consider... View Details
Ravishankar, Pavan, Qingyu Mo, Edward McFowland III, and Daniel B. Neill. "Provable Detection of Propagating Sampling Bias in Prediction Models." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 8 (2023): 9562–9569. (Presented at the 37th AAAI Conference on Artificial Intelligence (2/7/23-2/14/23) in Washington, DC.)
- January 2023
- Article
The Dark Side of Machiavellian Rhetoric: Signaling in Reward-Based Crowdfunding Performance
By: Goran Calic, Rene Arseneault and Maryam Ghasemaghaei
In this study, we explore the impact of Machiavellian rhetoric on fundraising within the increasingly important context of online crowdfunding. The “all-or-nothing” funding model used by the world’s largest crowdfunding platform, Kickstarter, may be an attractive... View Details
Calic, Goran, Rene Arseneault, and Maryam Ghasemaghaei. "The Dark Side of Machiavellian Rhetoric: Signaling in Reward-Based Crowdfunding Performance." Journal of Business Ethics 182, no. 3 (January 2023): 875–896.
- 2023
- Article
Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators
By: Benjamin Jakubowski, Siram Somanchi, Edward McFowland III and Daniel B. Neill
Regression discontinuity (RD) designs are widely used to estimate causal effects in the absence of a randomized experiment. However, standard approaches to RD analysis face two significant limitations. First, they require a priori knowledge of discontinuities in... View Details
Jakubowski, Benjamin, Siram Somanchi, Edward McFowland III, and Daniel B. Neill. "Exploiting Discovered Regression Discontinuities to Debias Conditioned-on-observable Estimators." Journal of Machine Learning Research 24, no. 133 (2023): 1–57.
- 2023
- Working Paper
Detecting Structural Breaks in Inflation Trends: A High-Frequency Approach
By: Alberto Cavallo and Gaston Garcia Zavaleta
We combine standard structural-break methods with high-frequency data to identify shifts in inflation trends. We use this approach to study the inflation dynamics of 25 countries from January 2022 to April 2023 and find evidence of a broad-based slowdown in about half... View Details
Cavallo, Alberto, and Gaston Garcia Zavaleta. "Detecting Structural Breaks in Inflation Trends: A High-Frequency Approach." Working Paper, May 2023. (Preliminary draft.)
- May–June 2023
- Article
Unmasking Behaviors During the Pandemic with Video Analytics
By: Shunyuan Zhang, Kaiquan Xu and Kannan Srinivasan
In 2020, as the novel coronavirus spread globally, face masks were recommended in public settings to protect against and slow down viral transmission. People complied to varying extents, and their reactions may have been driven by a variety of psychological factors.... View Details
Zhang, Shunyuan, Kaiquan Xu, and Kannan Srinivasan. "Unmasking Behaviors During the Pandemic with Video Analytics." Marketing Science 42, no. 3 (May–June 2023): 440–450.
- 2024
- Working Paper
Using LLMs for Market Research
By: James Brand, Ayelet Israeli and Donald Ngwe
Large language models (LLMs) have rapidly gained popularity as labor-augmenting
tools for programming, writing, and many other processes that benefit from quick text
generation. In this paper we explore the uses and benefits of LLMs for researchers and
practitioners... View Details
Keywords: Large Language Model; Research; AI and Machine Learning; Analysis; Customers; Consumer Behavior; Technology Industry; Information Technology Industry
Brand, James, Ayelet Israeli, and Donald Ngwe. "Using LLMs for Market Research." Harvard Business School Working Paper, No. 23-062, April 2023. (Revised July 2024.)
- April 2023
- Article
Perceptions Related to Engaging in Non-driving Activities in an Automated Vehicle While Commuting: A Text Mining Approach
By: Yilun Xing, Linda Ng Boyle, Raffaella Sadun, John D. Lee, Orit Shaer and Andrew Kun
Automated vehicles (AVs) offer human operators the opportunity to participate in non-driving activities while on the move. In this study, we examined and compared drivers' perception of non-driving activities in two driving modes: highly AVs in the future and current... View Details
Xing, Yilun, Linda Ng Boyle, Raffaella Sadun, John D. Lee, Orit Shaer, and Andrew Kun. "Perceptions Related to Engaging in Non-driving Activities in an Automated Vehicle While Commuting: A Text Mining Approach." Transportation Research Part F: Traffic Psychology and Behaviour 94 (April 2023): 305–320.
- March 2023
- Module Note
The Advisor's Approach to Strategy Analysis
By: David G. Fubini and Patrick Sanguineti
A module note for the Mastering Consulting and Advisory Skills (MCAS) course, "The Advisor's Approach to Strategy Analysis" introduces the options-led approach to strategy as well as how this valuable piece of an advisor's toolbelt differs from that of an operator. View Details
Keywords: Strategy
Fubini, David G., and Patrick Sanguineti. "The Advisor's Approach to Strategy Analysis." Harvard Business School Module Note 423-080, March 2023.
- 2023
- Article
Comparison of COVID-19 Hospitalization Costs across Care Pathways: A Patient-level Time-driven Activity-based Costing Analysis in a Brazilian Hospital
By: Ricardo Bertoglio Cardoso, Miriam Allein Zago Marcolino, Milena Soriano Marcolino, Camila Felix Fortis, Leila Beltrami Moreira, Ana Paula Coutinho, Nadine Oliveira Clausell, Junaid Nabi, Robert S. Kaplan, Ana Paula Beck da Silva Etges and Carisi Anne Polanczyk
The COVID-19 pandemic raised awareness of the need to better understand where and how patient-level costs are incurred in health care organizations. This study used time-driven activity-based costing to estimate COVID-19 patient-level hospital costs in a Brazilian... View Details
Cardoso, Ricardo Bertoglio, Miriam Allein Zago Marcolino, Milena Soriano Marcolino, Camila Felix Fortis, Leila Beltrami Moreira, Ana Paula Coutinho, Nadine Oliveira Clausell, Junaid Nabi, Robert S. Kaplan, Ana Paula Beck da Silva Etges, and Carisi Anne Polanczyk. "Comparison of COVID-19 Hospitalization Costs across Care Pathways: A Patient-level Time-driven Activity-based Costing Analysis in a Brazilian Hospital." BMC Health Services Research 23, no. 198 (2023).
- March–April 2023
- Article
Market Segmentation Trees
By: Ali Aouad, Adam Elmachtoub, Kris J. Ferreira and Ryan McNellis
Problem definition: We seek to provide an interpretable framework for segmenting users in a population for personalized decision making. Methodology/results: We propose a general methodology, market segmentation trees (MSTs), for learning market... View Details
Keywords: Decision Trees; Computational Advertising; Market Segmentation; Analytics and Data Science; E-commerce; Consumer Behavior; Marketplace Matching; Marketing Channels; Digital Marketing
Aouad, Ali, Adam Elmachtoub, Kris J. Ferreira, and Ryan McNellis. "Market Segmentation Trees." Manufacturing & Service Operations Management 25, no. 2 (March–April 2023): 648–667.