Dokyun Lee

3.0k total citations · 2 hit papers
42 papers, 1.8k citations indexed

About

Dokyun Lee is a scholar working on Marketing, Sociology and Political Science and Artificial Intelligence. According to data from OpenAlex, Dokyun Lee has authored 42 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Marketing, 18 papers in Sociology and Political Science and 12 papers in Artificial Intelligence. Recurrent topics in Dokyun Lee's work include Digital Marketing and Social Media (18 papers), Consumer Market Behavior and Pricing (11 papers) and FinTech, Crowdfunding, Digital Finance (8 papers). Dokyun Lee is often cited by papers focused on Digital Marketing and Social Media (18 papers), Consumer Market Behavior and Pricing (11 papers) and FinTech, Crowdfunding, Digital Finance (8 papers). Dokyun Lee collaborates with scholars based in United States, Canada and Singapore. Dokyun Lee's co-authors include Kartik Hosanagar, Harikesh S. Nair, Kannan Srinivasan, Shunyuan Zhang, Param Vir Singh, Andreas Buja, Daniel Fleder, Kannan Srinivasan, Xiao Liu and Gordon Burtch and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Management Science and Scientific Reports.

In The Last Decade

Dokyun Lee

37 papers receiving 1.7k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Advertising Content and Consumer Engagement on Social Media: Evidence from Facebook

2018 594 citations Dokyun Lee, Kartik Hosanagar et al. profile →
Generative artificial intelligence, human creativity, and art

2024 131 citations Dokyun Lee et al. PNAS Nexus profile →

Peers

Dokyun Lee

SPS

MARKE

AI

ISM

IS

Seshadri Tirunillai United States

Yung-Ming Li Taiwan

Dawn G. Gregg United States

David Schuff United States

Sanjeev Verma India

Joni Salminen Qatar

Xusen Cheng China

Zhijie Lin China

Arnaud De Bruyn France

Param Vir Singh United States

Seshadri Tirunillai United States

Dokyun Lee

1.1k ×1.0

SPS

851 ×1.1

MARKE

343 ×1.0

AI

261 ×1.0

ISM

96 ×0.4

IS

Citations per year, relative to Dokyun Lee Dokyun Lee (= 1×) peers Seshadri Tirunillai

Countries citing papers authored by Dokyun Lee

Since Specialization

Citations

This map shows the geographic impact of Dokyun Lee's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Dokyun Lee with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dokyun Lee more than expected).

Fields of papers citing papers by Dokyun Lee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Dokyun Lee. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Dokyun Lee. The network helps show where Dokyun Lee may publish in the future.

Co-authorship network of co-authors of Dokyun Lee

This figure shows the co-authorship network connecting the top 25 collaborators of Dokyun Lee. A scholar is included among the top collaborators of Dokyun Lee based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Dokyun Lee. Dokyun Lee is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Lee, Dokyun, et al.. (2025). Who expands the human creative frontier with generative AI: Hive minds or masterminds?. Science Advances. 11(36). eadu5800–eadu5800. 2 indexed citations

2.

Lee, Dokyun, et al.. (2025). Take caution in using LLMs as human surrogates. Proceedings of the National Academy of Sciences. 122(24). e2501660122–e2501660122. 8 indexed citations

3.

Burtch, Gordon, et al.. (2024). The consequences of generative AI for online knowledge communities. Scientific Reports. 14(1). 10413–10413. 16 indexed citations

4.

Lee, Dokyun, et al.. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus. 3(3). pgae052–pgae052. 131 indexed citations breakdown →

5.

Burtch, Gordon, et al.. (2024). Generative AI Degrades Online Communities. Communications of the ACM. 67(3). 40–42. 7 indexed citations

6.

Jin, Ginger Zhe, et al.. (2023). M&A and Innovation: A New Classification of Patents. AEA Papers and Proceedings. 113. 288–293.

7.

Hosanagar, Kartik & Dokyun Lee. (2023). AI in Personalized Product Recommendations. 3(1-2). 24–28.

8.

Lee, Dongwon, Anandasivam Gopal, Dokyun Lee, & D. W. Shin. (2023). Nudging Private Ryan: Mobile Microgiving under Economic Incentives and Audience Effects. MIS Quarterly. 47(3). 1101–1146. 4 indexed citations

9.

Burtch, Gordon, et al.. (2023). The Consequences of Generative AI for UGC and Online Community Engagement. SSRN Electronic Journal. 20 indexed citations

10.

Lee, Dokyun, et al.. (2021). How Do Fast Fashion Copycats Affect the Popularity of Premium Brands? Evidence from Social Media. SSRN Electronic Journal.

11.

Proserpio, Davide, John R. Hauser, Xiao Liu, et al.. (2020). Soul and machine (learning). Marketing Letters. 31(4). 393–404. 21 indexed citations

12.

Burtch, Gordon, et al.. (2020). Peer Awards Retain New Users and Encourage Exploitation in Users’ Production of Creative UGC. SSRN Electronic Journal.

13.

Lee, Dokyun, et al.. (2020). Good Explanation for Algorithmic Transparency. Proceedings of the AAAI/ACM Conference on AI Ethics and Society. 93–93. 11 indexed citations

14.

Lee, Dokyun, et al.. (2018). Focused Concept Miner (FCM): Interpretable Deep Learning for Text Exploration. SSRN Electronic Journal. 4 indexed citations

15.

Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, & Tridas Mukhopadhyay. (2018). Demand Interactions in Sharing Economies: Evidence from a Natural Experiment Involving Airbnb and Uber/Lyft. SSRN Electronic Journal. 9 indexed citations

16.

Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, & Kannan Srinivasan. (2017). How Much Is an Image Worth? Airbnb Property Demand Estimation Leveraging Large Scale Image Analytics. SSRN Electronic Journal. 44 indexed citations

17.

Lee, Dokyun & Kartik Hosanagar. (2017). How Do Recommender Systems Affect Sales Diversity? A Cross-Category Investigation via Randomized Field Experiment. SSRN Electronic Journal. 33 indexed citations

18.

Zhang, Shunyuan, Dokyun Lee, Param Vir Singh, & Kannan Srinivasan. (2016). How Much Is An Image Worth? An Empirical Analysis of Property’s Image Aesthetic Quality on Demand at AirBNB. International Conference on Information Systems. 11 indexed citations

19.

Lee, Dokyun & Kartik Hosanagar. (2014). Impact of Recommender Systems on Sales Volume and Diversity. Journal of the Association for Information Systems. 33 indexed citations

20.

Hosanagar, Kartik, Daniel Fleder, Dokyun Lee, & Andreas Buja. (2013). Will the Global Village Fracture into Tribes? Recommender Systems and their Effects on Consumer Fragmentation. Scholarly Commons (University of Pennsylvania). 1 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

Explore authors with similar magnitude of impact