Randomized Controlled Experiments - Publications - Faculty & Research - Harvard Business School

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March 2016
Case

Evive Health and Workplace Influenza Vaccinations

By: John Beshears

Evive Health is a company that manages communication campaigns on behalf of health insurance plans and large employers. Using big data techniques and insights from behavioral economics, Evive deploys targeted and effective messages that improve individuals' health... View Details

Keywords: Vaccination; Influenza; Flu Shot; Preventive Care; Health Care; Behavioral Economics; Choice Architecture; Nudge; Experimental Design; Randomized Controlled Trial; RCT; Causal Inference; Consumer Behavior; Health Care and Treatment; Health Testing and Trials; Communication Strategy; Health Industry

spring 2008
Article

Cost Reductions, Cost Padding and Stock Market Prices: The Chilean Experience with Price Cap Regulation

By: Rafael Di Tella and Alexander Dyck

Keywords: Cost; Price; Stocks; Markets; Governing Rules, Regulations, and Reforms; Chile

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September 2018
Article

Discretionary Task Ordering: Queue Management in Radiological Services

By: Maria Ibanez, Jonathan R. Clark, Robert S. Huckman and Bradley R. Staats

Work-scheduling research typically prescribes task sequences implemented by managers. Yet employees often have discretion to deviate from their prescribed sequence. Using data from 2.4 million radiological diagnoses, we find that doctors prioritize similar tasks... View Details

Keywords: Discretion; Scheduling; Queue; Healthcare; Learning; Experience; Decentralization; Operations; Service Operations; Service Delivery; Performance; Performance Effectiveness; Performance Efficiency; Performance Improvement; Performance Productivity; Decisions; Time Management; Cost vs Benefits; Health Industry

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2017
Working Paper

Seeking to Belong: How the Words of Internal and External Beneficiaries Influence Performance

By: Paul Green, Francesca Gino and Bradley R. Staats

In this paper, we examine how connecting to beneficiaries of one’s work increases performance and argue that beneficiaries internal to an organization (i.e., one’s own colleagues) can serve as an important source of motivation, even in jobs that—on the surface—may seem... View Details

Keywords: Prosocial Motivation; Belongingness; Motivation; Job Design; Field Experiment; Motivation and Incentives; Strategy; Job Design and Levels

Research Summary

Overview

By: Katherine B. Coffman

Professor Coffman studies the sources of gender gaps in economically-important contexts. Her work focuses on the role of beliefs: how do stereotypes bias the beliefs that individuals hold about themselves (and others), and how do these biased beliefs shape... View Details

Keywords: Gender; Stereotypes; Diversity Management; Experiments

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

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May–June 2019
Article

U-Shaped Conformity in Online Social Networks

By: Monic Sun, Michael Zhang and Feng Zhu

We explore how people balance their needs to belong and to be different from their friends by studying their choices of a virtual-house wall color on a leading Chinese social-networking site. The setting enables us to randomize both the popular color and the adoption... View Details

Keywords: Conformity; Normative Social Influence; Social Networks; Field Experiment; Social and Collaborative Networks; Behavior; Attitudes; Social Media

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February 2018
Article

Retention Futility: Targeting High-Risk Customers Might Be Ineffective.

By: Eva Ascarza

Companies in a variety of sectors are increasingly managing customer churn proactively, generally by detecting customers at the highest risk of churning and targeting retention efforts towards them. While there is a vast literature on developing churn prediction models... View Details

Keywords: Retention/churn; Proactive Churn Management; Field Experiments; Heterogeneous Treatment Effect; Machine Learning; Customer Relationship Management; Risk Management

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Research Summary

Overview

The Information Age has introduced well-received opportunities to track performance. Fitbits and Fuelbands allow individuals to track their own performance; companies like Uber and leading hospitals help you choose a driver or a doctor based on how others rated... View Details

Keywords: Management Accounting; Disclosure; Performance Measurement; Incentives; Control; Education; Education Industry; Health Industry; Transportation Industry; Energy Industry; Auto Industry; United States; Japan; India

July 2021
Article

Do Interactions with Candidates Increase Voter Support and Participation? Experimental Evidence from Italy

By: Enrico Cantoni and Vincent Pons

We test whether politicians can use direct contact to reconnect with citizens, increase turnout, and win votes. During the 2014 Italian municipal elections, we randomly assigned 26,000 voters to receive visits from city council candidates, from canvassers supporting... View Details

Keywords: Campaigns; Candidates; Elections; Experiment; Political Parties; Turnout; Voting Behavior; Voting; Political Elections; Behavior; Interpersonal Communication; Italy

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2017
Working Paper

Discretionary Task Ordering: Queue Management in Radiological Services

By: Maria Ibanez, Jonathan R. Clark, Robert S. Huckman and Bradley R. Staats

Work scheduling research typically prescribes task sequences implemented by managers. Yet employees often have discretion to deviate from their prescribed sequence. Using data from 2.4 million radiological diagnoses, we find that doctors prioritize similar tasks... View Details

2023
Working Paper

Effects of Structured Sharing of Best Practices in an Unstructured Information Sharing System

By: Shelley Xin Li and Tatiana Sandino

Unstructured information sharing systems, such as certain enterprise social networks (ESNs), can supplement top-down knowledge transfer with a wide array of ideas through peer-to-peer knowledge sharing. However, the unstructured nature of such systems can also lead... View Details

Keywords: Retail; Best Practices; Enterprise Social Media; Management Accounting And Control Systems; Knowledge Sharing; Networks; Management Systems; Management Practices and Processes; Social Media; Europe

September 2012
Article

The Relationship Between Economic Preferences and Psychological Personality Measures

By: Anke Becker, Thomas Deckers, Thomas Dohmen, Armin Falk and Fabian Kosse

Although both economists and psychologists seek to identify determinants of heterogeneity in behavior, they use different concepts to capture them. In this review, we first analyze the extent to which economic preferences and psychological concepts of personality, such... View Details

Keywords: Risk Preference; Time Preference; Social Preferences; Locus Of Control; Big Five; Economics; Behavior; Personal Characteristics

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March 2022
Article

Sensitivity Analysis of Agent-based Models: A New Protocol

By: Emanuele Borgonovo, Marco Pangallo, Jan Rivkin, Leonardo Rizzo and Nicolaj Siggelkow

Agent-based models (ABMs) are increasingly used in the management sciences. Though useful, ABMs are often critiqued: it is hard to discern why they produce the results they do and whether other assumptions would yield similar results. To help researchers address such... View Details

Keywords: Agent-based Modeling; Sensitivity Analysis; Design Of Experiments; Total Order Sensitivity Indices; Organizations; Behavior; Decision Making; Mathematical Methods

2024
Working Paper

Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference

By: Michael Lindon, Dae Woong Ham, Martin Tingley and Iavor I. Bojinov

Linear regression adjustment is commonly used to analyze randomized controlled experiments due to its efficiency and robustness against model misspecification. Current testing and interval estimation procedures leverage the asymptotic distribution of such estimators to... View Details

Keywords: Mathematical Methods; Analytics and Data Science

April 2020
Article

Designs for Estimating the Treatment Effect in Networks with Interference

By: Ravi Jagadeesan, Natesh S. Pillai and Alexander Volfovsky

In this paper, we introduce new, easily implementable designs for drawing causal inference from randomized experiments on networks with interference. Inspired by the idea of matching in observational studies, we introduce the notion of considering a treatment... View Details

Keywords: Experimental Design; Network Inference; Neyman Estimator; Symmetric Interference Model; Homophily

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Awards

Low-Cost RCT Competition

By: Michael W. Toffel

Winner (with Matthew Johnson and David I. Levine) of the 2014 Coalition for Evidence-Based Policy Grant Competition on the subject of “Demonstrating How Low-Cost Randomized Controlled Trials Can Drive Effective Social Spending.” View Details

April 2019 (Revised January 2022)
Case

Clear Link Technologies, LLC: Driving Sales with Peer Effects

By: Christopher Stanton, Richard Saouma and Olivia Hull

The importance of a good peer or coworker is widely discussed, but understanding the glue that makes coworkers valuable is less understood. This case sheds light on the importance of peers and the practices and environments that make a group greater than the sum of its... View Details

Article

The Importance of Being Causal

By: Iavor I Bojinov, Albert Chen and Min Liu

Causal inference is the study of how actions, interventions, or treatments affect outcomes of interest. The methods that have received the lion’s share of attention in the data science literature for establishing causation are variations of randomized experiments.... View Details

Keywords: Causal Inference; Observational Studies; Cross-sectional Studies; Panel Studies; Interrupted Time-series; Instrumental Variables

Importance of Being Causal

Causal inference is the study of how actions, interventions, or treatments affect outcomes of interest. The methods that have received the lion’s share of attention in the data science literature for establishing causation are variations of randomized... View Details

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