Publications
Publications
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- All HBS Web
(3,248)
- Faculty Publications (323)
- 2025
- Working Paper
Combining Complements: Theory and Evidence from Cancer Treatment Innovation
By: Rebekah Dix and Todd A. Lensman
Innovations often combine several components to achieve outcomes greater than the
“sum of the parts.” We argue that such combination innovations can introduce an understudied
inefficiency—a positive market expansion externality that benefits the owners of
the... View Details
- January–February 2025
- Article
Want Your Company to Get Better at Experimentation?: Learn Fast by Democratizing Testing
By: Iavor Bojinov, David Holtz, Ramesh Johari, Sven Schmit and Martin Tingley
For years, online experimentation has fueled the innovations of leading tech companies, enabling them to rapidly test and refine new ideas, optimize product features, personalize user experiences, and maintain a competitive edge. The widespread availability and lower... View Details
- 2025
- Working Paper
Policy-Aware Experimentation: Strategic Sampling for Optimized Targeting Policies
By: Yi-Wen Chen, Eva Ascarza and Oded Netzer
Firms often rely on randomized experiments to estimate customer-level treatment effects for targeting policies. Standard "test-then-learn" approaches typically sample customers uniformly to optimize estimation accuracy but ignore economic objectives, leading to... View Details
- October 2024
- Article
Sampling Bias in Entrepreneurial Experiments
By: Ruiqing Cao, Rembrand Koning and Ramana Nanda
Using data from a prominent online platform for launching new digital products, we document that ‘sampling bias’—defined as the difference between a startup’s target customer base and the actual sample on which early ‘beta tests’ are conducted—has a systematic and... View Details
- October 2024
- Supplement
Skills-First Hiring at IBM
By: Boris Groysberg and Sarah Mehta
A video supplement to accompany "Skills-First Hiring at IBM" (case no. 422-013) View Details
Keywords: Competency and Skills; Experience and Expertise; Talent and Talent Management; Human Resources; Employees; Recruitment; Retention; Selection and Staffing; Jobs and Positions; Job Design and Levels; Job Interviews; Society; Social Issues; Technology Industry; United States; New York (state, US)
- September–October 2024
- Article
How AI Can Power Brand Management
By: Julian De Freitas and Elie Ofek
Marketers have begun experimenting with AI to improve their brand-management efforts. But unlike other marketing tasks, brand management involves more than just repeatedly executing one specialized function. Long considered the exclusive domain of creative talent, it... View Details
- August 2024 (Revised September 2024)
- Case
Dishoom: From Bombay with Love
By: Anjali M. Bhatt and Thomas J. DeLong
Shamil and Kavi Thakrar, co-founders of Dishoom, faced critical decisions as they looked to expand the UK-based restaurant group. Shamil, the CEO, was confident in Dishoom's potential for growth but he was concerned about preserving the culture and values centered... View Details
- July 2024
- Article
Chatbots and Mental Health: Insights into the Safety of Generative AI
By: Julian De Freitas, Ahmet Kaan Uğuralp, Zeliha Uğuralp and Stefano Puntoni
Chatbots are now able to engage in sophisticated conversations with consumers. Due to the ‘black box’ nature of the algorithms, it is impossible to predict in advance how these conversations will unfold. Behavioral research provides little insight into potential safety... View Details
- July–August 2024
- Article
Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals
By: Ta-Wei Huang and Eva Ascarza
Firms are increasingly interested in developing targeted interventions for customers with the best response,
which requires identifying differences in customer sensitivity, typically through the conditional average treatment
effect (CATE) estimation. In theory, to... View Details
- July 2024
- Article
How Artificial Intelligence Constrains Human Experience
By: A. Valenzuela, S. Puntoni, D. Hoffman, N. Castelo, J. De Freitas, B. Dietvorst, C. Hildebrand, Y.E. Huh, R. Meyer, M. Sweeney, S. Talaifar, G. Tomaino and K. Wertenbroch
Many consumption decisions and experiences are digitally mediated. As a consequence, consumer behavior is increasingly the joint product of human psychology and ubiquitous algorithms (Braun et al. 2024; cf. Melumad et al. 2020). The coming of age of Large Language... View Details
- 2024
- Working Paper
Navigating Software Vulnerabilities: Eighteen Years of Evidence from Medium and Large U.S. Organizations
By: Raviv Murciano-Goroff, Ran Zhuo and Shane Greenstein
How prevalent are severe software vulnerabilities, how fast do software users respond to the availability of secure versions, and what determines the variance in the installation distribution? Using the largest dataset ever assembled on user updates, tracking server... View Details
- 2024
- Working Paper
Webmunk: A New Tool for Studying Online Behavior and Digital Platforms
By: Chiara Farronato, Andrey Fradkin and Chris Karr
Understanding the behavior of users online is important for researchers, policymakers, and private companies alike. But observing online behavior and conducting experiments is difficult without direct access to the user base and software of technology companies. We... View Details
- June 2024
- Teaching Note
Skills-First Hiring at IBM
By: Boris Groysberg and Sarah Mehta
Teaching note for “Skills-First Hiring at IBM,” case no. 422-013. View Details
Keywords: Competency and Skills; Experience and Expertise; Talent and Talent Management; Human Resources; Human Capital; Employees; Recruitment; Retention; Selection and Staffing; Jobs and Positions; Job Design and Levels; Job Interviews; Society; Societal Protocols; Technology Industry; United States; New York (state, US)
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
By: Ta-Wei Huang, Eva Ascarza and Ayelet Israeli
This paper introduces Incrementality Representation Learning (IRL), a novel multitask representation learning framework that predicts heterogeneous causal effects of marketing interventions. By leveraging past experiments, IRL efficiently designs and targets... View Details
- 2025
- Working Paper
Evaluations Amid Measurement Error: Determining the Optimal Timing for Workplace Interventions
By: Matthew DosSantos DiSorbo, Iavor I. Bojinov and Fiammetta Menchetti
Researchers have embraced factorial experiments to simultaneously evaluate multiple treatments, each with different levels. Typically, in large-scale factorial experiments, the primary objective is identifying the treatment with the largest causal effect, especially... View Details
- June 2024
- Article
Defining Who You Are by Whom You Serve? Strategies for Prosocial–Professional Identity Integration with Clients
By: Lakshmi Ramarajan and Julie Yen
Many professionals want to both achieve professional success and contribute to society. Yet, in some professional contexts, these aims are in tension because serving elite clients is considered the pinnacle of professional success, but professionals themselves may view... View Details
- 2023
- Working Paper
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
By: Biyonka Liang and Iavor I. Bojinov
Typically, multi-armed bandit (MAB) experiments are analyzed at the end of the study and thus require the analyst to specify a fixed sample size in advance. However, in many online learning applications, it is advantageous to continuously produce inference on the... View Details
- 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
- 2024
- Working Paper
Design of Panel Experiments with Spatial and Temporal Interference
By: Tu Ni, Iavor Bojinov and Jinglong Zhao
One of the main practical challenges companies face when running experiments (or A/B tests) over a panel is interference, the setting where one experimental unit's treatment assignment at one time period impacts another's outcomes, possibly at the following time... View Details
Keywords: Research
- 2021
- Working Paper
Quantifying the Value of Iterative Experimentation
By: Iavor I Bojinov and Jialiang Mao
Over the past decade, most technology companies and a growing number of conventional firms have adopted online experimentation (or A/B testing) into their product development process. Initially, A/B testing was deployed as a static procedure in which an experiment was... View Details