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.
Designing Ranking Systems for Hotels on Travel Search Engines by Mining User-Generated and Crowdsourced Content
2012482 citationsAnindya Ghose, Panagiotis G. Ipeirotis et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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This map shows the geographic impact of Beibei Li'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 Beibei Li with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Beibei Li more than expected).
This network shows the impact of papers produced by Beibei Li. 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 Beibei Li. The network helps show where Beibei Li may publish in the future.
Co-authorship network of co-authors of Beibei Li
This figure shows the co-authorship network connecting the top 25 collaborators of Beibei Li.
A scholar is included among the top collaborators of Beibei Li 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 Beibei Li. Beibei Li is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Ghose, Anindya, et al.. (2020). Trading Privacy for Social Good: Did America Unite During COVID-19?. Journal of the Association for Information Systems.2 indexed citations
9.
Lu, Tian, et al.. (2020). Personalizing debt collections: Combining reinforcement learning and field experiment. International Conference on Information Systems. 2350.1 indexed citations
Lu, Tian, Yingjie Zhang, & Beibei Li. (2019). The Value of Alternative Data in Credit Risk Prediction: Evidence from a Large Field Experiment.. Journal of the Association for Information Systems.11 indexed citations
Wang, Le, et al.. (2018). An empirical investigation of sales cheating effect in E-commerce. Journal of the Association for Information Systems.2 indexed citations
14.
Liu, Jun, Vibhanshu Abhishek, & Beibei Li. (2016). The Impact of Mobile Adoption on Customer Omni-Channel Banking Behavior. International Conference on Information Systems.6 indexed citations
15.
Li, Beibei, et al.. (2016). Modeling User Engagement in Mobile Content Consumption with Tapstream Data. International Conference on Information Systems.1 indexed citations
16.
Li, Beibei, et al.. (2016). Using TB-Sized Data to Understand Multi-Device Advertising.. International Conference on Information Systems.2 indexed citations
17.
Li, Beibei, et al.. (2015). A Tangled Web: Evaluating the Impact of Displaying Fraudulent Reviews. International Conference on Information Systems.5 indexed citations
18.
Li, Beibei, Param Vir Singh, & Quan Wang. (2014). Zoom in iOS Clones: Examining the Antecedents and Consequences of Mobile App Copycats. Journal of the Association for Information Systems.3 indexed citations
19.
Ghose, Anindya, Panagiotis G. Ipeirotis, & Beibei Li. (2012). Search Less, Find More? Examining Limited Consumer Search with Social Media and Product Search Engines. International Conference on Information Systems.7 indexed citations
20.
Li, Beibei, Anindya Ghose, & Panagiotis G. Ipeirotis. (2008). Stay Elsewhere? Improving Local Search for Hotels Using Econometric Modeling and Image Classification.6 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.