Kai Zhu
Assistant Professor
Biografia
Kai Zhu è Assistant Professor presso il Dipartimento di Marketing dell’Università Bocconi. Ha conseguito un PhD in Sistemi Informativi dall'Università di Boston.
La sua ricerca esplora come le tecnologie digitali trasformano mercati, media, politica e società, con un interesse particolare per la trasformazione digitale dei mercati culturali – come notizie, libri, cinema e musica. Utilizza strumenti computazionali come machine learning, natural language processing, causal inference e network analysis per analizzare grandi moli di dati strutturati e non strutturati, con l’obiettivo di comprendere il comportamento umano e le dinamiche sistemiche.
In Bocconi insegna corsi come Data Mining for Marketing, Business, and Society e Large Language Models for Market Research.
Tra le sue pubblicazioni figurano studi sulla crescita dei contenuti e le dinamiche dell’attenzione nelle reti informative, sulle narrazioni mediatiche in tempi di crisi e sugli effetti del feedback tra pari nei contenuti generati dagli utenti su piattaforme digitali.
Pubblicazioni recenti
- 2025
Monetizing Platforms: An Empirical Analysis of Supply and Demand Responses to Entry Costs in Two-Sided Markets
ZHU, K., Q. SHI, S. BANERJEE, "Monetizing Platforms: An Empirical Analysis of Supply and Demand Responses to Entry Costs in Two-Sided Markets", Management Science, 2025 - 2024
Negative Peer Feedback and User Content Generation: Evidence From a Restaurant Review Platform
ZHU, K., W. KHERN-AM-NUAI, Y. YU, "Negative Peer Feedback and User Content Generation: Evidence From a Restaurant Review Platform", Production and Operations Management, 2024 - 2023
Platform Monetization and Unintended Consequences for Digital Cultural Markets: Evidence from a Two-sided Market for Book Promotions
ZHU, K., Q. SHI, S. BANERJEE, "Platform Monetization and Unintended Consequences for Digital Cultural Markets: Evidence from a Two-sided Market for Book Promotions" in Hawaii International Conference on System Sciences 2023 (HICSS-56), January 3-6, 2023, Maui, Hawaii, United States of America - 2022
Platform Monetization and Unintended Consequences on its Ecosystem: Evidence from a Two-sided Market for Books
ZHU, K., Q. SHI, S. BANERJEE, "Platform Monetization and Unintended Consequences on its Ecosystem: Evidence from a Two-sided Market for Books" in EMAC Annual Conference, May 24-27, 2022, Budapest, Hungary - 2022
If a Tree Falls in the Forest: Presidential Press Conferences and Early Media Narratives about the COVID-19 Crisis
KRUPENKIN, M., K. ZHU, D. WALKER, D. ROTHSCHILD, "If a Tree Falls in the Forest: Presidential Press Conferences and Early Media Narratives about the COVID-19 Crisis", Journal of Quantitative Description: Digital Media, 2022, vol. 2, pp. 1-72 - 2021
Platform Monetization in a Two-sided Market: A Natural Experiment on Goodreads.com
ZHU, K., Q. SHI, S. BANERJEE, "Platform Monetization in a Two-sided Market: A Natural Experiment on Goodreads.com" in 2021 Conference on Digital Experimentation @ MIT (CODE@MIT), November 4-5, 2021, (online), United States of America - 2020
Content Growth and Attention Contagion in Information Networks: Addressing Information Poverty on Wikipedia
ZHU, K., D. WALKER, L. MUCHNIK, "Content Growth and Attention Contagion in Information Networks: Addressing Information Poverty on Wikipedia", Information Systems Research, 2020, vol. 31, no. 2, pp. 491-509 - 2015
Attribute reduction approaches for general relation decision systems
LIU, G., L. LI, J. YANG, Y. FENG, K. ZHU, "Attribute reduction approaches for general relation decision systems", Pattern Recognition Letters, 2015, vol. 65, pp. 81-87 - 2014
The relationship among three types of rough approximation pairs
LIU, G., K. ZHU, "The relationship among three types of rough approximation pairs", Knowledge-Based Systems, 2014, vol. 60, pp. 28-34
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