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- 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)
Groysberg, Boris, and Sarah Mehta. "Skills-First Hiring at IBM." Harvard Business School Multimedia/Video Supplement 425-707, October 2024.
- 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
Keywords: Creativity; AI and Machine Learning; Brands and Branding; Product Positioning; Customer Focus and Relationships
De Freitas, Julian, and Elie Ofek. "How AI Can Power Brand Management." Harvard Business Review 102, no. 5 (September–October 2024): 108–114.
- August 2024 (Revised September 2024)
- Case
Dishoom: From Bombay with Love
By: Anjali 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
Keywords: Autonomy; Chatbots; New Technology; Brand Crises; Mental Health; Large Language Model; AI and Machine Learning; Behavior; Well-being; Technological Innovation; Ethics
De Freitas, Julian, Ahmet Kaan Uğuralp, Zeliha Uğuralp, and Stefano Puntoni. "Chatbots and Mental Health: Insights into the Safety of Generative AI." Journal of Consumer Psychology 34, no. 3 (July 2024): 481–491.
- 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
Keywords: Long-run Targeting; Heterogeneous Treatment Effect; Statistical Surrogacy; Customer Churn; Field Experiments; Consumer Behavior; Customer Focus and Relationships; AI and Machine Learning; Marketing Strategy
Huang, Ta-Wei, and Eva Ascarza. "Doing More with Less: Overcoming Ineffective Long-Term Targeting Using Short-Term Signals." Marketing Science 43, no. 4 (July–August 2024): 863–884.
- 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
Keywords: Large Language Model; User Experience; AI and Machine Learning; Consumer Behavior; Technology Adoption; Risk and Uncertainty; Cost vs Benefits
Valenzuela, A., 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. "How Artificial Intelligence Constrains Human Experience." Journal of the Association for Consumer Research 9, no. 3 (July 2024): 241–256.
- 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
Murciano-Goroff, Raviv, Ran Zhuo, and Shane Greenstein. "Navigating Software Vulnerabilities: Eighteen Years of Evidence from Medium and Large U.S. Organizations." NBER Working Paper Series, No. 32696, July 2024.
- 2024
- Working Paper
Webmunk: A New Tool for Studying Online Behavior and Digital Platforms
By: Chiara Farronato, Audrey 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
Farronato, Chiara, Audrey Fradkin, and Chris Karr. "Webmunk: A New Tool for Studying Online Behavior and Digital Platforms." NBER Working Paper Series, No. 32694, July 2024.
- 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
AI Companions Reduce Loneliness
By: Julian De Freitas, Ahmet K Uguralp, Zeliha O Uguralp and Puntoni Stefano
Chatbots are now able to engage in sophisticated conversations with consumers in the domain of relationships, providing a potential coping solution to widescale societal loneliness. Behavioral research provides little insight into whether these applications are... View Details
De Freitas, Julian, Ahmet K Uguralp, Zeliha O Uguralp, and Puntoni Stefano. "AI Companions Reduce Loneliness." Harvard Business School Working Paper, No. 24-078, June 2024.
- 2024
- Working Paper
Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization
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
Keywords: Heterogeneous Treatment Effect; Multi-task Learning; Representation Learning; Personalization; Promotion; Deep Learning; Field Experiments; Customer Focus and Relationships; Customization and Personalization
Huang, Ta-Wei, Eva Ascarza, and Ayelet Israeli. "Incrementality Representation Learning: Synergizing Past Experiments for Intervention Personalization." Harvard Business School Working Paper, No. 24-076, June 2024.
- 2024
- Working Paper
Winner Take All: Exploiting Asymmetry in Factorial Designs
By: Matthew DosSantos DiSorbo, Iavor I. Bojinov and Fiammetta Menchetti
Researchers and practitioners have embraced factorial experiments to simultaneously test multiple treatments, each with different levels. With the rise of technologies like Generative AI, factorial experimentation has become even more accessible: it is easier than ever... View Details
Keywords: Factorial Designs; Fisher Randomizations; Rank Estimators; Employer Interventions; Causal Inference; Mathematical Methods; Performance Improvement
DosSantos DiSorbo, Matthew, Iavor I. Bojinov, and Fiammetta Menchetti. "Winner Take All: Exploiting Asymmetry in Factorial Designs." Harvard Business School Working Paper, No. 24-075, June 2024.
- 2024
- Working Paper
Personalization and Targeting: How to Experiment, Learn & Optimize
By: Aurelie Lemmens, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Elea McDonnell Feit, Brett Gordon, Ayelet Israeli, Carl F. Mela and Oded Netzer
Personalization has become the heartbeat of modern marketing. Advances in causal inference and machine learning enable companies to understand how the same marketing action can impact the choices of individual customers differently. This article provides an academic... View Details
Keywords: Personalization; Targeting; Experiments; Observational Studies; Policy Implementation; Policy Evaluation; Customization and Personalization; Marketing Strategy; AI and Machine Learning
Lemmens, Aurelie, Jason M.T. Roos, Sebastian Gabel, Eva Ascarza, Hernan Bruno, Elea McDonnell Feit, Brett Gordon, Ayelet Israeli, Carl F. Mela, and Oded Netzer. "Personalization and Targeting: How to Experiment, Learn & Optimize." Working Paper, June 2024.
- 2024
- Working Paper
Business Experiments as Persuasion
By: Orie Shelef, Rebecca Karp and Robert Wuebker
Much of the prior work on experimentation rests upon the assumption that entrepreneurs and managers use—or should optimally adopt—a "scientific approach" to test possible decisions before making them. This paper offers an alternative view of experimental strategy,... View Details
Shelef, Orie, Rebecca Karp, and Robert Wuebker. "Business Experiments as Persuasion." Harvard Business School Working Paper, No. 24-065, March 2024.
- 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
Liang, Biyonka, and Iavor I. Bojinov. "An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits." Harvard Business School Working Paper, No. 24-057, March 2024.
- 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
Lindon, Michael, Dae Woong Ham, Martin Tingley, and Iavor I. Bojinov. "Anytime-Valid Inference in Linear Models and Regression-Adjusted Causal Inference." Harvard Business School Working Paper, No. 24-060, March 2024.
- 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
Ni, Tu, Iavor Bojinov, and Jinglong Zhao. "Design of Panel Experiments with Spatial and Temporal Interference." Harvard Business School Working Paper, No. 24-058, March 2024.
- 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
Bojinov, Iavor I., and Jialiang Mao. "Quantifying the Value of Iterative Experimentation." Harvard Business School Working Paper, No. 24-059, March 2024.
- 2024
- Book
Deals: The Economic Structure of Business Transactions
By: Guhan Subramanian and Michael Klausner
Drawing on real-life cases from a wide range of industries, two acclaimed experts offer a sophisticated but accessible guide to business deals, designed to maximize value for your side.
Business transactions take widely varying forms—from multibillion-dollar... View Details
Business transactions take widely varying forms—from multibillion-dollar... View Details
Subramanian, Guhan, and Michael Klausner. Deals: The Economic Structure of Business Transactions. Harvard University Press, 2024.
- January 11, 2024
- Article
Understanding the Tradeoffs of the Amazon Antitrust Case
By: Chiara Farronato, Andrey Fradkin, Andrei Hagiu and Dionne Lomax
Regulators in the United States and Europe have been taking on Big Tech, challenging what they say are the companies’ anti-competitive and predatory strategies that harm consumers and third-party users of their platforms. This article examines the FTC’s case against... View Details
Keywords: Monopoly; Governing Rules, Regulations, and Reforms; Market Design; Lawsuits and Litigation
Farronato, Chiara, Andrey Fradkin, Andrei Hagiu, and Dionne Lomax. "Understanding the Tradeoffs of the Amazon Antitrust Case." Harvard Business Review Digital Articles (January 11, 2024).