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.
Emotional expression by artificial intelligence chatbots to improve customer satisfaction: Underlying mechanism and boundary conditions
2023102 citationsJunbo Zhang, Qi Chen et al.Tourism Managementprofile →
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 Luning Liu'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 Luning Liu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Luning Liu more than expected).
This network shows the impact of papers produced by Luning Liu. 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 Luning Liu. The network helps show where Luning Liu may publish in the future.
Co-authorship network of co-authors of Luning Liu
This figure shows the co-authorship network connecting the top 25 collaborators of Luning Liu.
A scholar is included among the top collaborators of Luning Liu 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 Luning Liu. Luning Liu is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Wang, Lingli, et al.. (2020). Effects of Voice-Based AI in Customer Service: Evidence from a Natural Experiment. Journal of the Association for Information Systems.2 indexed citations
Liu, Luning, et al.. (2019). A Study of Factors Influencing Restaurants Sales in Online-to-offline Food Delivery platforms: Differences between High-sales Restaurants and Low-sales Restaurants. Journal of the Association for Information Systems. 128.5 indexed citations
15.
Liu, Luning, et al.. (2018). Designing an O2O Citizen Participation Ecosystem for the Sustainable Governance of Societies. Journal of the Association for Information Systems. 225.3 indexed citations
16.
Wang, Xiao‐Lei, et al.. (2016). How Control Initiatives Affect Quality of G2B E-government Services: A Multi-method Study. WHICEB. 47.1 indexed citations
17.
Liu, Luning, et al.. (2014). COMPREHENSIVE UNDERSTANDING THE INHIBITORS AND ENABLERS OF KNOWLEDGE TRANSFER IN ERP ASSIMILATIONS: A MULTI-CASE STUDY. Journal of the Association for Information Systems. 168.3 indexed citations
18.
Zhang, Guijie, et al.. (2014). UNDERSTANDING THE EVOLUTION OF INFORMATION SYSTEMS RESEARCH FROM THE PERSPECTIVE OF CO-AUTHORSHIP NETWORK: A COMPREHENSIVE DATA ANALYSIS FROM 1993 TO 2012. Journal of the Association for Information Systems. 327.3 indexed citations
19.
Liu, Luning, et al.. (2014). Understanding Individual Level ERP Assimilation from a Social Network Perspective: a Multi-Case Study.. Journal of the Association for Information Systems.3 indexed citations
20.
Yang, Yi, et al.. (2014). The Selection Method of Fuzzy Composite Operators Based on the Clear Field. 12(1).
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.