Mochen Yang

522 total citations
26 papers, 299 citations indexed

About

Mochen Yang is a scholar working on Artificial Intelligence, Management Science and Operations Research and Sociology and Political Science. According to data from OpenAlex, Mochen Yang has authored 26 papers receiving a total of 299 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Artificial Intelligence, 9 papers in Management Science and Operations Research and 8 papers in Sociology and Political Science. Recurrent topics in Mochen Yang's work include Digital Marketing and Social Media (6 papers), Auction Theory and Applications (5 papers) and Consumer Market Behavior and Pricing (4 papers). Mochen Yang is often cited by papers focused on Digital Marketing and Social Media (6 papers), Auction Theory and Applications (5 papers) and Consumer Market Behavior and Pricing (4 papers). Mochen Yang collaborates with scholars based in United States, China and Canada. Mochen Yang's co-authors include Gediminas Adomavičius, Yuqing Ren, Gordon Burtch, Alok Gupta, Ming‐Hui Wen, Xuan Bi, Antino Kim, Jingjing Zhang, Xunhua Guo and Edward McFowland and has published in prestigious journals such as Management Science, MIS Quarterly and Information Systems Research.

In The Last Decade

Mochen Yang

24 papers receiving 286 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Mochen Yang United States 7 165 79 74 53 45 26 299
Ashish Kumar Rathore India 7 252 1.5× 91 1.2× 92 1.2× 74 1.4× 75 1.7× 13 384
Shun‐Yang Lee United States 7 206 1.2× 63 0.8× 77 1.0× 66 1.2× 67 1.5× 15 304
Mingyue Zhang China 10 163 1.0× 74 0.9× 91 1.2× 63 1.2× 37 0.8× 24 362
Hossein Ghasemkhani United States 7 161 1.0× 38 0.5× 81 1.1× 51 1.0× 46 1.0× 17 296
Yingda Lu United States 8 169 1.0× 36 0.5× 87 1.2× 63 1.2× 76 1.7× 31 298
Shu He United States 9 176 1.1× 69 0.9× 137 1.9× 39 0.7× 42 0.9× 24 391
Alexander Pelaez United States 9 130 0.8× 88 1.1× 68 0.9× 85 1.6× 17 0.4× 17 316
Vilma Todri United States 8 228 1.4× 66 0.8× 162 2.2× 80 1.5× 35 0.8× 14 377

Countries citing papers authored by Mochen Yang

Since Specialization
Citations

This map shows the geographic impact of Mochen Yang'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 Mochen Yang with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mochen Yang more than expected).

Fields of papers citing papers by Mochen Yang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Mochen Yang. 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 Mochen Yang. The network helps show where Mochen Yang may publish in the future.

Co-authorship network of co-authors of Mochen Yang

This figure shows the co-authorship network connecting the top 25 collaborators of Mochen Yang. A scholar is included among the top collaborators of Mochen Yang 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 Mochen Yang. Mochen Yang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Shi, Bowen, Xiaojie Mao, Mochen Yang, & Bo Li. (2025). What, Why, and How: An Empiricist’s Guide to Double/Debiased Machine Learning. Information Systems Research.
2.
Bi, Xuan, et al.. (2024). Welfare and fairness dynamics in federated learning: a client selection perspective. Statistics and Its Interface. 17(3). 383–395. 1 indexed citations
3.
Song, Xiaowei, et al.. (2024). CloudNet: Building a Data-Plane for Anonymous Communication Network Based on Cloud Service. 575–582. 1 indexed citations
4.
Bala, Hillol, et al.. (2024). Engaging Users on Social Media Business Pages: The Roles of User Comments and Firm Responses. MIS Quarterly. 48(2). 731–748. 2 indexed citations
5.
Wang, Wen, Mochen Yang, & Tianshu Sun. (2023). Human-AI Co-Creation in Product Ideation: the Dual View of Quality and Diversity. SSRN Electronic Journal. 1 indexed citations
6.
Bi, Xuan, Mochen Yang, & Gediminas Adomavičius. (2023). Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations. Information Systems Research. 35(1). 339–362. 3 indexed citations
7.
Bi, Xuan, Alok Gupta, & Mochen Yang. (2023). Understanding Partnership Formation and Repeated Contributions in Federated Learning: An Analytical Investigation. Management Science. 70(8). 4974–4994. 8 indexed citations
8.
Yang, Mochen, et al.. (2023). Judge me on my losers: Do robo‐advisors outperform human investors during the COVID‐19 financial market crash?. Production and Operations Management. 32(10). 3174–3192. 13 indexed citations
9.
Adomavičius, Gediminas & Mochen Yang. (2022). Integrating Behavioral, Economic, and Technical Insights to Understand and Address Algorithmic Bias: A Human-Centric Perspective. ACM Transactions on Management Information Systems. 13(3). 1–27. 7 indexed citations
10.
Adomavičius, Gediminas, Alok Gupta, & Mochen Yang. (2022). Bidder Support in Multi-item Multi-unit Continuous Combinatorial Auctions: A Unifying Theoretical Framework. Information Systems Research. 33(4). 1174–1195. 4 indexed citations
11.
Yang, Mochen, Edward McFowland, Gordon Burtch, & Gediminas Adomavičius. (2022). Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem. RePEc: Research Papers in Economics. 1(2). 138–155. 1 indexed citations
12.
13.
Bi, Xuan, Mochen Yang, & Gediminas Adomavičius. (2020). Consumer Acquisition for Recommender Systems: A Theoretical Framework and Empirical Evaluations. SSRN Electronic Journal. 2 indexed citations
14.
Kim, Antino, Mochen Yang, & Jingjing Zhang. (2020). When Algorithms Err: Differential Impact of Early vs. Late Errors on Users' Reliance on Algorithms. SSRN Electronic Journal. 3 indexed citations
15.
Yang, Mochen, Gediminas Adomavičius, & Alok Gupta. (2019). Efficient Computational Strategies for Dynamic Inventory Liquidation. Information Systems Research. 30(2). 595–615. 1 indexed citations
16.
Yang, Mochen, Edward McFowland, Gordon Burtch, & Gediminas Adomavičius. (2019). Achieving Reliable Causal Inference with Data-Mined Variables: A Random Forest Approach to the Measurement Error Problem. SSRN Electronic Journal. 1 indexed citations
17.
Yang, Mochen, Yuqing Ren, & Gediminas Adomavičius. (2018). Understanding User-Generated Content and Customer Engagement on Facebook Business Pages. SSRN Electronic Journal. 1 indexed citations
18.
Yang, Mochen, Gediminas Adomavičius, Gordon Burtch, & Yuqing Ren. (2018). Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining. Information Systems Research. 29(1). 4–24. 69 indexed citations
19.
Yang, Mochen, Gediminas Adomavičius, Gordon Burtch, & Yuqing Ren. (2017). Mind the Gap: Accounting for Measurement Error and Misclassification in Variables Generated via Data Mining. SSRN Electronic Journal. 1 indexed citations
20.
Yang, Mochen & Xunhua Guo. (2013). Relationship Between Online and Offline Social Capital: Evidence from a Social Network Site in China. WHICEB. 86. 4 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.

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