Research output per year
Research output per year
Personal profile
Personal profile
Dr. Fang conducts data-driven research and examines how digital information environments—such as recommender systems, influencer content, and AI-mediated interfaces—shape consumer search, engagement, and decision making. Methodologically, he works with large-scale digital trace data, econometric modeling, and computational text analysis. Alongside his research, he teaches Marketing Management, Strategic Marketing, and Marketing Analytics, and Data Visualization.
He is Fellow of the Higher Education Academy (FHEA), Advance HE
Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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Too Many or Too Few? Information Cues in Recommender Systems and Consequences for Search and Purchase Behavior
Fang, X., Kim, S. & Chintagunta, P., Jan 2026, In: Journal of Marketing. 90, 1, p. 9-28 20 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile92 Downloads (Pure) -
Enhancing Social Media Engagement: Speech Act Strategies Across Influencer Types
Fang, X., Shin, S. K. & Huang, X., May 2025, In: Journal of Retailing and Consumer Services. 84, 11 p., 104258.Research output: Contribution to journal › Article › peer-review
2 Downloads (Pure) -
The Impact of Live Chat Availability and Usage on Customer Purchase Behavior
Fang, X., 2025, AMA Summer Academic Conference.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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What Is in a Number: Recommender Impact on the Customer Journey
Fang, X., 2024, University of New South Wales Marketing Camp.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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Consumer brand engagement on social media in the COVID-19 pandemic: the roles of country-of-origin and consumer animosity
Wang, T., Limbu, Y. B. & Fang, X., 24 Feb 2022, In: Journal of Research in Interactive Marketing. 16, 1, p. 45-63 19 p.Research output: Contribution to journal › Article › peer-review
Projects
- 1 Active
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Shanghai Theatre Academy - Structuring Unstructured Data: Enhancing AI Predictions for Movies and TV Shows
Fang, X. (PI)
Arts & Humanities Research Council AHRC
1/01/26 → 30/04/26
Project: Research