Filter Results:
(967)
Show Results For
- All HBS Web
(2,917)
- Faculty Publications (967)
Show Results For
- All HBS Web
(2,917)
- Faculty Publications (967)
- May 2023 (Revised November 2023)
- Case
Arcos Dorados: Decarbonizing McDonald’s in Latin America
By: George Serafeim, Michael W. Toffel, Jenyfeer Martinez Buitrago and Mariana Cal
This case describes the decarbonization strategy of Arcos Dorados—McDonald’s largest independent franchisee, operating in 20 countries and territories in Latin America and the Caribbean—and how the company measured its greenhouse gas (GHG) emissions, including those... View Details
Keywords: Environmental Accounting; Animal-Based Agribusiness; Plant-Based Agribusiness; Change Management; Forecasting and Prediction; Environmental Sustainability; Food; Growth Management; Supply Chain; Corporate Social Responsibility and Impact; Strategy; Agriculture and Agribusiness Industry; Food and Beverage Industry; Green Technology Industry; Consumer Products Industry; Latin America; North and Central America; South America
Serafeim, George, Michael W. Toffel, Jenyfeer Martinez Buitrago, and Mariana Cal. "Arcos Dorados: Decarbonizing McDonald’s in Latin America." Harvard Business School Case 623-017, May 2023. (Revised November 2023.)
- May 11, 2020
- Article
Steer Your Family Businesses Through an Unplanned Transition
By: Josh Baron and Nick Di Loreto
In a perfect world, family businesses will transition leadership from one generation to the next along a predictable and well-planned process — whether that’s determined within the business, the ownership group, or the family itself — passing the baton after years of... View Details
Baron, Josh, and Nick Di Loreto. "Steer Your Family Businesses Through an Unplanned Transition." Harvard Business Review (website) (May 11, 2020).
- 2023
- Article
Conduit Incentives: Eliciting Cooperation from Workers Outside of Managers' Control
By: Susanna Gallani
Can managers use monetary incentives to elicit cooperation from workers they cannot reward for their efforts? I study “conduit incentives,” an innovative incentive design, whereby managers influence bonus-ineligible workers’ effort by offering bonus-eligible employees... View Details
Keywords: Organizational Behavior Modification; Peer Monitoring; Persistence Of Performance Improvements; Crowding Out; Implicit Incentives; Compensation; Healthcare; Social Pressure; Image Motivation; Incentives; Motivation; Performance; Behavior; Motivation and Incentives; Compensation and Benefits; Governing Rules, Regulations, and Reforms; Organizational Culture; Health Industry; California
Gallani, Susanna. "Conduit Incentives: Eliciting Cooperation from Workers Outside of Managers' Control." Accounting Review 93, no. 3 (2023): 1–28.
- May 2023
- Article
Equilibrium Effects of Pay Transparency
By: Zoë B. Cullen and Bobak Pakzad-Hurson
The public discourse around pay transparency has focused on the direct effect: how workers seek
to rectify newly-disclosed pay inequities through renegotiations. The question of how wage-setting
and hiring practices of the firm respond in equilibrium has received... View Details
Keywords: Pay Transparency; Online Labor Market; Privacy; Wage Gap; Corporate Disclosure; Wages; Negotiation
Cullen, Zoë B., and Bobak Pakzad-Hurson. "Equilibrium Effects of Pay Transparency." Econometrica 91, no. 3 (May 2023): 765–802. (Lead Article.)
- May 2023
- Article
Incentive Effects of Subjective Allocations of Rewards and Penalties
By: Wei Cai, Susanna Gallani and Jee-Eun Shin
We examine the incentive effects of subjectivity in allocating tournament-based rewards and punishments. We use data from a company where reward and punishment decisions are based on a combination of objective metrics and subjective performance assessments. Rankings... View Details
Keywords: Subjectivity; Tournament-based Incentives; Rewards; Penalties; Expectancy Theory; Employees; Compensation and Benefits; Management; Decisions; Performance; Measurement and Metrics
Cai, Wei, Susanna Gallani, and Jee-Eun Shin. "Incentive Effects of Subjective Allocations of Rewards and Penalties." Management Science 69, no. 5 (May 2023): 3121–3139.
- 2023
- Article
Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse
By: Martin Pawelczyk, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci and Himabindu Lakkaraju
As machine learning models are increasingly being employed to make consequential decisions in real-world settings, it becomes critical to ensure that individuals who are adversely impacted (e.g., loan denied) by the predictions of these models are provided with a means... View Details
Pawelczyk, Martin, Teresa Datta, Johannes van-den-Heuvel, Gjergji Kasneci, and Himabindu Lakkaraju. "Probabilistically Robust Recourse: Navigating the Trade-offs between Costs and Robustness in Algorithmic Recourse." Proceedings of the International Conference on Learning Representations (ICLR) (2023).
- April 21, 2023
- Article
When Scenario Planning Fails
By: Kalle Heikkinen, William R. Kerr, Mika Malin, Panu Routila and Eemil Rupponen
How can organizations perform scenario planning when they are hit by shocks outside of leaders’ field of vision? Interviews with Nordic executives, who experienced both the Covid-19 pandemic and were in close proximity to Russia as the country invaded Ukraine, can... View Details
Keywords: Planning; Crisis Management; Organizational Structure; Forecasting and Prediction; System Shocks; Organizational Change and Adaptation
Heikkinen, Kalle, William R. Kerr, Mika Malin, Panu Routila, and Eemil Rupponen. "When Scenario Planning Fails." Harvard Business Review Digital Articles (April 21, 2023).
- April 12, 2023
- Article
Using AI to Adjust Your Marketing and Sales in a Volatile World
By: Das Narayandas and Arijit Sengupta
Why are some firms better and faster than others at adapting their use of customer data to respond to changing or uncertain marketing conditions? A common thread across faster-acting firms is the use of AI models to predict outcomes at various stages of the customer... View Details
Keywords: Forecasting and Prediction; AI and Machine Learning; Consumer Behavior; Technology Adoption; Competitive Advantage
Narayandas, Das, and Arijit Sengupta. "Using AI to Adjust Your Marketing and Sales in a Volatile World." Harvard Business Review Digital Articles (April 12, 2023).
- 2023
- Working Paper
Feature Importance Disparities for Data Bias Investigations
By: Peter W. Chang, Leor Fishman and Seth Neel
It is widely held that one cause of downstream bias in classifiers is bias present in the training data. Rectifying such biases may involve context-dependent interventions such as training separate models on subgroups, removing features with bias in the collection... View Details
Chang, Peter W., Leor Fishman, and Seth Neel. "Feature Importance Disparities for Data Bias Investigations." Working Paper, March 2023.
- April 2023
- Article
Learning Down to Train Up: Mentors Are More Effective When They Value Insights from Below
By: Ting Zhang, Dan Wang and Adam D. Galinsky
Although mentorship is vital for individual success, potential mentors often view it as a costly burden. To understand what motivates mentors to overcome this barrier and more fully engage with their mentees, we introduce a new construct, learning direction, which... View Details
Keywords: Mentoring; Learning Direction; Interpersonal Communication; Learning; Leadership Development
Zhang, Ting, Dan Wang, and Adam D. Galinsky. "Learning Down to Train Up: Mentors Are More Effective When They Value Insights from Below." Academy of Management Journal 66, no. 2 (April 2023): 604–637.
- April 2023
- Article
On the Privacy Risks of Algorithmic Recourse
By: Martin Pawelczyk, Himabindu Lakkaraju and Seth Neel
As predictive models are increasingly being employed to make consequential decisions, there is a growing emphasis on developing techniques that can provide algorithmic recourse to affected individuals. While such recourses can be immensely beneficial to affected... View Details
Pawelczyk, Martin, Himabindu Lakkaraju, and Seth Neel. "On the Privacy Risks of Algorithmic Recourse." Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS) 206 (April 2023).
- 2023
- Working Paper
PRIMO: Private Regression in Multiple Outcomes
By: Seth Neel
We introduce a new differentially private regression setting we call Private Regression in Multiple Outcomes (PRIMO), inspired the common situation where a data analyst wants to perform a set of l regressions while preserving privacy, where the covariates... View Details
Neel, Seth. "PRIMO: Private Regression in Multiple Outcomes." Working Paper, March 2023.
- 2023
- Working Paper
The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities
By: David S. Scharfstein and Sergey Chernenko
We show that the use of algorithms to predict race has significant limitations in measuring and understanding the sources of racial disparities in finance, economics, and other contexts. First, we derive theoretically the direction and magnitude of measurement bias in... View Details
Keywords: Racial Disparity; Paycheck Protection Program; Measurement Error; AI and Machine Learning; Race; Measurement and Metrics; Equality and Inequality; Prejudice and Bias; Forecasting and Prediction; Outcome or Result
Scharfstein, David S., and Sergey Chernenko. "The Limits of Algorithmic Measures of Race in Studies of Outcome Disparities." Working Paper, April 2023.
- April 2023
- Article
The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences
By: Armin Falk, Anke Becker, Thomas Dohmen, David B. Huffman and Uwe Sunde
Incentivized choice experiments are a key approach to measuring preferences in economics but are also costly. Survey measures are a low-cost alternative but can suffer from additional forms of measurement error due to their hypothetical nature. This paper seeks to... View Details
Keywords: Survey Validation; Experiment; Preference Measurement; Surveys; Economics; Behavior; Measurement and Metrics
Falk, Armin, Anke Becker, Thomas Dohmen, David B. Huffman, and Uwe Sunde. "The Preference Survey Module: A Validated Instrument for Measuring Risk, Time, and Social Preferences." Management Science 69, no. 4 (April 2023): 1935–1950.
- 2023
- Working Paper
Organizational Responses to Product Cycles
By: Achyuta Adhvaryu, Vittorio Bassi, Anant Nyshadham, Jorge Tamayo and Nicolas Torres
Product cycles entail the mass production of new—and often increasingly complex—products on a regular basis. How do firms manage these changes? We use granular daily data from a leading automobile manufacturer to study the organizational impacts of introducing new... View Details
Keywords: Training; Organizational Change and Adaptation; Knowledge Management; Production; Product; Organizational Structure; Auto Industry; Argentina
Adhvaryu, Achyuta, Vittorio Bassi, Anant Nyshadham, Jorge Tamayo, and Nicolas Torres. "Organizational Responses to Product Cycles." Harvard Business School Working Paper, No. 23-061, March 2023. (Revise & Resubmit Journal of Political Economy.)
- 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... View Details
Keywords: Insurance And Reinsurance; Natural Disasters; Turkey; Insurance; Climate Change; Analytics and Data Science; Insurance Industry; Financial Services Industry; Turkey
Macomber, John D., and Fares Khrais. "Allianz Türkiye (C): Managing the 2017 Hail Storm." Harvard Business School Supplement 223-084, March 2023.
- March 2023 (Revised April 2024)
- 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... View Details
Keywords: Insurance And Reinsurance; Natural Disasters; Turkey; Insurance; Climate Change; Analytics and Data Science; Insurance Industry; Financial Services Industry; Turkey
Macomber, John D., and Fares Khrais. "Allianz Türkiye: Adapting to Climate Change." Harvard Business School Case 223-074, March 2023. (Revised April 2024.)
- 2023
- Working Paper
Complexity and Time
By: Benjamin Enke, Thomas Graeber and Ryan Oprea
We provide experimental evidence that core intertemporal choice anomalies -- including extreme short-run impatience, structural estimates of present bias, hyperbolicity and transitivity violations -- are driven by complexity rather than time or risk preferences. First,... View Details
Enke, Benjamin, Thomas Graeber, and Ryan Oprea. "Complexity and Time." NBER Working Paper Series, No. 31047, March 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.
- 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... View Details
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.