<link>https://www.khaichiong.com/</link></image><item><title>Generative AI and Job Satisfaction: Evidence from Glassdoor Employee Reviews</h1> <article> <h1>Generative AI and Job Satisfaction: Evidence from Glassdoor Employee Reviews</h1> <p>Sun, 26 Apr 2026 00:00:00 +0000</p> <p>Working paper. Invited revision at <em>Journal of Marketing Research</em>.</p> </article> <article> <h1>Getting the Most Out of Online A/B Tests Using the Minimax-Regret Criteria</h1> <p>Sun, 26 Apr 2026 00:00:00 +0000</p> <p>Accepted and forthcoming in <em>Management Science</em>.</p> </article> <article> <h1>Learning Heterogeneity from Unstructured Data: An Application to Chatbot Personalization</h1> <p>Sun, 26 Apr 2026 00:00:00 +0000</p> <p>Working paper.</p> </article> <article> <h1>Mass Shootings and Their Impact on Retail</h1> <p>Sun, 26 Apr 2026 00:00:00 +0000</p> <p>Accepted and forthcoming in <em>Marketing Science</em>.</p> </article> <article> <h1>Stability of Procurement Networks</h1> <p>Sun, 26 Apr 2026 00:00:00 +0000</p> <p>Accepted and forthcoming in <em>Manufacturing & Service Operations Management</em> (M&SOM).</p> </article> <article> <h1>Can AI and AI‑Hybrids Detect Persuasion Skills? Salesforce Hiring with Conversational Video Interviews</h1> <p>Wed, 01 Jan 2025 00:00:00 +0000</p> <p>Chakraborty, Chiong, Dover, and Sudhir explore the use of AI and AI‑human hybrid systems to evaluate persuasion skills in salesforce hiring. Using data from conversational video interviews, they find that AI‑enabled assessments can complement human judgement, offering scalable and equitable hiring practices. The paper received the AMA AI SIG Award for Best AI in Marketing Paper published in 2024.</p> </article> <article> <h1>Combining Choices and Response Times in the Field: A Drift‑Diffusion Model of Mobile Advertisements</h1> <p>Thu, 01 Feb 2024 00:00:00 +0000</p> <p>The authors apply the drift‑diffusion model, a staple of neuroeconomics, to field data from mobile advertising. By jointly modeling consumer choices and the time taken to make those choices, they shed light on underlying cognitive processes and the effectiveness of mobile ad campaigns.</p> </article> <article> <h1>Experience</h1> <p>Tue, 24 Oct 2023 00:00:00 +0000</p> </article> <article> <h1>Estimation of High‑Dimensional Seemingly Unrelated Regression Models</h1> <p>Sun, 01 Aug 2021 00:00:00 +0000</p> <p>Tan, Chiong, and Moon extend the classical seemingly unrelated regression framework to high‑dimensional settings. Their regularization‑based estimator allows researchers to handle many equations simultaneously while capturing dependence across them. The results have implications for macroeconomics, finance, and marketing analytics.</p> </article> <article> <h1>Counterfactual Estimation in Semiparametric Discrete‑Choice Models</h1> <p>Fri, 01 Jan 2021 00:00:00 +0000</p> <p>In this book chapter, Chiong, Hsieh, and Shum examine counterfactual estimation techniques for semiparametric discrete‑choice models. They show how to obtain policy‑relevant counterfactuals when only partial information about preferences is available, illustrating the methods with examples from industrial organization.</p> </article> <article> <h1>Random Projection Estimation of Discrete-Choice Models with Large Choice Sets</h1> <p>Tue, 01 Jan 2019 00:00:00 +0000</p> <p>Chiong and Shum tackle the computational challenges posed by discrete-choice models with large choice sets. Their random projection estimator dramatically reduces dimensionality, allowing researchers to analyze complex choice data efficiently. The approach is particularly useful for modern marketing and transportation applications involving many alternatives.</p> </article> <article> <h1>Estimation of Graphical Models using the ℓ₁,₂ Norm</h1> <p>Mon, 01 Oct 2018 00:00:00 +0000</p> <p>In this article, Chiong and Moon develop estimation techniques for graphical models based on the ℓ₁,₂ norm penalty. The method promotes group sparsity in the inverse covariance matrix, allowing practitioners to uncover network structures in data sets with many variables. The technique has applications in econometrics and machine learning.</p> </article> <article> <h1>Duality in Dynamic Discrete Choice Models</h1> <p>Tue, 01 Mar 2016 00:00:00 +0000</p> <p><em>Duality in Dynamic Discrete Choice Models</em> explores the relationship between choice probabilities and value functions in dynamic discrete choice models. By exploiting convex analysis, the authors derive a dual representation that simplifies computation and facilitates counterfactual analysis. The framework contributes to empirical work in industrial organization and dynamic decision-making.</p> </article> </main></body></html>