Suyuan Luo

1.3k total citations · 1 hit paper
17 papers, 977 citations indexed

About

Suyuan Luo is a scholar working on Management Information Systems, Strategy and Management and Information Systems. According to data from OpenAlex, Suyuan Luo has authored 17 papers receiving a total of 977 indexed citations (citations by other indexed papers that have themselves been cited), including 9 papers in Management Information Systems, 7 papers in Strategy and Management and 7 papers in Information Systems. Recurrent topics in Suyuan Luo's work include Supply Chain and Inventory Management (7 papers), Blockchain Technology Applications and Security (6 papers) and Sustainable Supply Chain Management (4 papers). Suyuan Luo is often cited by papers focused on Supply Chain and Inventory Management (7 papers), Blockchain Technology Applications and Security (6 papers) and Sustainable Supply Chain Management (4 papers). Suyuan Luo collaborates with scholars based in China, Hong Kong and United Kingdom. Suyuan Luo's co-authors include Tsan‐Ming Choi, Shu Guo, Qigang Liu, Dongmei Han, Xiutian Shi, Lipan Feng, Chunfa Li, Xudong Lin, Yuhang Jia and Yu Zhou and has published in prestigious journals such as Journal of Cleaner Production, IEEE Access and International Journal of Production Research.

In The Last Decade

Suyuan Luo

17 papers receiving 950 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.

Data quality challenges for sustainable fashion supply chain operations in emerging markets: Roles of blockchain, government sponsors and environment taxes

2019 312 citations Tsan‐Ming Choi, Suyuan Luo Transportation Research Part E Logistics and Transportation Review profile →

Peers

Suyuan Luo

MIS

MARKE

Mohammad Reza Gholamian Iran

Truong Van Nguyen United Kingdom

Ramanath Subramanyam United States

Mariangela Rosano Italy

Rainer Lasch Germany

Henrik Sternberg Sweden

Yanfei Lan China

Bo Yan China

Yeu‐Shiang Huang Taiwan

David Swanson United States

Mohammad Reza Gholamian Iran

Suyuan Luo

353 ×0.7

151 ×0.4

277 ×0.7

MIS

287 ×1.6

MARKE

188 ×1.2

Citations per year, relative to Suyuan Luo Suyuan Luo (= 1×) peers Mohammad Reza Gholamian

Countries citing papers authored by Suyuan Luo

Since Specialization

Citations

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

Fields of papers citing papers by Suyuan Luo

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Suyuan Luo

This figure shows the co-authorship network connecting the top 25 collaborators of Suyuan Luo. A scholar is included among the top collaborators of Suyuan Luo 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 Suyuan Luo. Suyuan Luo 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:

17 of 17 papers shown

Yi, Zelong, et al.. (2025). Agricultural supply chain finance considering interest or direct subsidy by government. Transportation Research Part E Logistics and Transportation Review. 195. 103992–103992. 4 indexed citations

Wang, Yingjia, Tsan‐Ming Choi, & Suyuan Luo. (2024). Sustainability versus exclusivity in the sharing economy: Should leftover luxury fashion products be destroyed?. Transportation Research Part E Logistics and Transportation Review. 192. 103759–103759. 1 indexed citations

Wang, Yingjia, et al.. (2022). Consumer-to-consumer product exchanges for original fashion brands in the sharing economy: Good or bad for fashion knockoffs?. Transportation Research Part E Logistics and Transportation Review. 158. 102599–102599. 8 indexed citations

Zhou, Yu, et al.. (2022). Anti-Counterfeiting in a retail Platform: A Game-Theoretic approach. Transportation Research Part E Logistics and Transportation Review. 165. 102839–102839. 39 indexed citations

Luo, Suyuan & Tsan‐Ming Choi. (2022). E‐commerce supply chains with considerations of cyber‐security: Should governments play a role?. Production and Operations Management. 31(5). 2107–2126. 86 indexed citations

Sun, Xuting, Tsan‐Ming Choi, & Suyuan Luo. (2021). Logistics Capacity Balancing Platforms in the Sharing Economy: When Will Simple Rules Be Optimal?. IEEE Transactions on Systems Man and Cybernetics Systems. 52(4). 2637–2650. 2 indexed citations

Liu, Qigang, et al.. (2021). A multi-task based deep learning approach for intrusion detection. Knowledge-Based Systems. 238. 107852–107852. 46 indexed citations

Shi, Xiutian, et al.. (2021). Innovative platform operations with the use of technologies in the blockchain era. International Journal of Production Research. 61(11). 3651–3669. 71 indexed citations

Luo, Suyuan & Tsan‐Ming Choi. (2021). Great partners: how deep learning and blockchain help improve business operations together. Annals of Operations Research. 339(1-2). 53–78. 30 indexed citations

10.

Luo, Suyuan & Tsan‐Ming Choi. (2020). Operational Research for Technology-Driven Supply Chains in the Industry 4.0 Era: Recent Development and Future Studies. Asia Pacific Journal of Operational Research. 39(1). 12 indexed citations

11.

Choi, Tsan‐Ming, Shu Guo, & Suyuan Luo. (2020). When blockchain meets social-media: Will the result benefit social media analytics for supply chain operations management?. Transportation Research Part E Logistics and Transportation Review. 135. 101860–101860. 133 indexed citations

12.

Luo, Suyuan & Shaohua Wan. (2019). Leveraging Product Characteristics for Online Collusive Detection in Big Data Transactions. IEEE Access. 7. 40154–40164. 7 indexed citations

13.

Luo, Suyuan, et al.. (2019). A novel CNN-DDPG based AI-trader: Performance and roles in business operations. Transportation Research Part E Logistics and Transportation Review. 131. 68–79. 52 indexed citations

14.

Li, Chunfa, Lipan Feng, & Suyuan Luo. (2019). Strategic introduction of an online recycling channel in the reverse supply chain with a random demand. Journal of Cleaner Production. 236. 117683–117683. 63 indexed citations

15.

Choi, Tsan‐Ming & Suyuan Luo. (2019). Data quality challenges for sustainable fashion supply chain operations in emerging markets: Roles of blockchain, government sponsors and environment taxes. Transportation Research Part E Logistics and Transportation Review. 131. 139–152. 312 indexed citations breakdown →

16.

Han, Dongmei, et al.. (2018). A Deep Learning Approach for Credit Scoring of Peer-to-Peer Lending Using Attention Mechanism LSTM. IEEE Access. 7. 2161–2168. 103 indexed citations

17.

Luo, Suyuan, et al.. (2017). Competition, cooperation, and performance: an empirical investigation of Chinese online sellers. Information Systems and e-Business Management. 16(4). 743–760. 8 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