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- 2023
- Working Paper
Large Shocks Travel Fast
By: Alberto Cavallo, Francesco Lippi and Ken Miyahara
We leverage the inflation upswing of 2022 and various granular datasets to identify robust price-setting patterns following a large supply shock. We show that the frequency of price changes increases dramatically after a large shock. We set up a parsimonious New...
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Keywords:
Macroeconomics;
Analytics and Data Science;
Inflation and Deflation;
Cost;
Mathematical Methods
Cavallo, Alberto, Francesco Lippi, and Ken Miyahara. "Large Shocks Travel Fast." NBER Working Paper Series, No. 31659, September 2023.
- August 2023
- Case
Beamery: Using Skills and AI to Modernize HR
By: Boris Groysberg, Alexis Lefort, Susan Pinckney and Carolina Bartunek
Unicorn human relationships startup Beamery evaluates it growth versus depth strategy as its strategic partners and customers could become future competitors in a quickly changing AI based human resources and talent management industry
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Keywords:
Acquisition;
Business Growth and Maturation;
Business Startups;
Competency and Skills;
Experience and Expertise;
Talent and Talent Management;
Customers;
Nationality;
Learning;
Entrepreneurship;
Corporate Entrepreneurship;
Social Entrepreneurship;
Human Resources;
Employee Relationship Management;
Recruitment;
Retention;
Selection and Staffing;
Values and Beliefs;
Cross-Cultural and Cross-Border Issues;
Analytics and Data Science;
Applications and Software;
Disruptive Innovation;
Technological Innovation;
Job Offer;
Job Search;
Job Design and Levels;
Employment;
Human Capital;
Europe;
United Kingdom;
United States
- July 2023
- Article
So, Who Likes You? Evidence from a Randomized Field Experiment
By: Ravi Bapna, Edward McFowland III, Probal Mojumder, Jui Ramaprasad and Akhmed Umyarov
With one-third of marriages in the United States beginning online, online dating platforms have become important curators of the modern social fabric. Prior work on online dating has elicited two critical frictions in the heterosexual dating market. Women, governed by...
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Keywords:
Online Dating;
Internet and the Web;
Analytics and Data Science;
Gender;
Emotions;
Social and Collaborative Networks
Bapna, Ravi, Edward McFowland III, Probal Mojumder, Jui Ramaprasad, and Akhmed Umyarov. "So, Who Likes You? Evidence from a Randomized Field Experiment." Management Science 69, no. 7 (July 2023): 3939–3957.
- July 2023
- Article
Takahashi-Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations
By: Dawson Beutler, Alex Billias, Sam Holt, Josh Lerner and TzuHwan Seet
In 2001, Dean Takahashi and Seth Alexander of the Yale University Investments Office developed a deterministic model for estimating future cash flows and valuations for the Yale endowment’s private equity portfolio. Their model, which is simple and intuitive, is still...
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Beutler, Dawson, Alex Billias, Sam Holt, Josh Lerner, and TzuHwan Seet. "Takahashi-Alexander Revisited: Modeling Private Equity Portfolio Outcomes Using Historical Simulations." Journal of Portfolio Management 49, no. 7 (July 2023): 144–158.
- 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...
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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
- 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...
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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.
- May–June 2023
- Article
Analytics for Marketers: When to Rely on Algorithms and When to Trust Your Gut
By: Fabrizio Fantini and Das Narayandas
Advanced analytics can help companies solve a host of management problems, including those related to marketing, sales, and supply-chain operations, which can lead to a sustainable competitive advantage. But as more data becomes available and advanced analytics are...
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Fantini, Fabrizio, and Das Narayandas. "Analytics for Marketers: When to Rely on Algorithms and When to Trust Your Gut." Harvard Business Review 101, no. 3 (May–June 2023): 82–91.
- 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...
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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.
- 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....
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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.
- April 2023
- Technical Note
An Art & A Science: How to Apply Design Thinking to Data Science Challenges
By: Michael Parzen, Eddie Lin, Douglas Ng and Jessie Li
We hear it all the time as managers: “what is the data that backs up your decisions?” Even local mom-and-pop shops now have access to complex point-of-sale systems that can closely track sales and customer data. Social media influencers have turned into seven-figure...
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Parzen, Michael, Eddie Lin, Douglas Ng, and Jessie Li. "An Art & A Science: How to Apply Design Thinking to Data Science Challenges." Harvard Business School Technical Note 623-070, April 2023.
- April 2023
- Case
Fizzy Fusion: When Data-Driven Decision Making Failed
By: Michael Parzen, Eddie Lin, Douglas Ng and Jessie Li
This is a case about a fictional New York beverage company called Fizzy Fusion. The business is facing supply chain and inventory management challenges with its new product, SparklingSip. Despite seeking help from a data science consulting firm, the machine learning...
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Keywords:
Supply Chain Management;
Production;
Risk and Uncertainty;
Analytics and Data Science;
Food and Beverage Industry
Parzen, Michael, Eddie Lin, Douglas Ng, and Jessie Li. "Fizzy Fusion: When Data-Driven Decision Making Failed." Harvard Business School Case 623-071, April 2023.
- March 2023
- Supplement
Allianz Türkiye (B): Adapting to a Changing World
By: John D. Macomber and Fares Khrais
- March 2023
- Supplement
Allianz Türkiye (C): Managing the 2017 Hail Storm
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe...
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- March 2023
- Case
Allianz Türkiye: Adapting to Climate Change
By: John D. Macomber and Fares Khrais
Allianz Turkey is a property casualty insurance company operating in a region experiencing increasing losses from natural catastrophe events related to climate change, for example hail, wildfire, and flooding. There are also substantial other natural catastrophe...
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- 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...
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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.
- 2023
- Working Paper
Senior Team Emotional Dynamics and Strategic Decision Making at a Platform Transition
By: Timo O. Vuori and Michael L. Tushman
Based on an inductive case study, we develop an emotional-temporal process model of an incumbent’s
strategic decision making at a platform transition. We describe the senior team’s emotional response to
this transition and the impact of these emotions on their...
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Vuori, Timo O., and Michael L. Tushman. "Senior Team Emotional Dynamics and Strategic Decision Making at a Platform Transition." Harvard Business School Working Paper, No. 23-054, March 2023.
- January–February 2023
- Article
Forecasting COVID-19 and Analyzing the Effect of Government Interventions
By: Michael Lingzhi Li, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis and Dimitris Bertsimas
We developed DELPHI, a novel epidemiological model for predicting detected cases and deaths in the prevaccination era of the COVID-19 pandemic. The model allows for underdetection of infections and effects of government interventions. We have applied DELPHI across more...
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Keywords:
COVID-19 Pandemic;
Epidemics;
Analytics and Data Science;
Health Pandemics;
AI and Machine Learning;
Forecasting and Prediction
Li, Michael Lingzhi, Hamza Tazi Bouardi, Omar Skali Lami, Thomas Trikalinos, Nikolaos Trichakis, and Dimitris Bertsimas. "Forecasting COVID-19 and Analyzing the Effect of Government Interventions." Operations Research 71, no. 1 (January–February 2023): 184–201.
- 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.)
- 2023
- Article
Evaluating Explainability for Graph Neural Networks
By: Chirag Agarwal, Owen Queen, Himabindu Lakkaraju and Marinka Zitnik
As explanations are increasingly used to understand the behavior of graph neural networks (GNNs), evaluating the quality and reliability of GNN explanations is crucial. However, assessing the quality of GNN explanations is challenging as existing graph datasets have no...
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Keywords:
Analytics and Data Science
Agarwal, Chirag, Owen Queen, Himabindu Lakkaraju, and Marinka Zitnik. "Evaluating Explainability for Graph Neural Networks." Art. 114. Scientific Data 10 (2023).
- 2023
- Article
Experimental Evaluation of Individualized Treatment Rules
By: Kosuke Imai and Michael Lingzhi Li
The increasing availability of individual-level data has led to numerous applications of individualized (or personalized) treatment rules (ITRs). Policy makers often wish to empirically evaluate ITRs and compare their relative performance before implementing them in a...
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Keywords:
Causal Inference;
Heterogeneous Treatment Effects;
Precision Medicine;
Uplift Modeling;
Analytics and Data Science;
AI and Machine Learning
Imai, Kosuke, and Michael Lingzhi Li. "Experimental Evaluation of Individualized Treatment Rules." Journal of the American Statistical Association 118, no. 541 (2023): 242–256.