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.
Online Product Reviews: Implications for Retailers and Competing Manufacturers
2014309 citationsYoung Kwark, Jianqing Chen et al.Information Systems Researchprofile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
hero ref
This map shows the geographic impact of Young Kwark'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 Young Kwark with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Young Kwark more than expected).
This network shows the impact of papers produced by Young Kwark. 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 Young Kwark. The network helps show where Young Kwark may publish in the future.
Co-authorship network of co-authors of Young Kwark
This figure shows the co-authorship network connecting the top 25 collaborators of Young Kwark.
A scholar is included among the top collaborators of Young Kwark 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 Young Kwark. Young Kwark is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Pu, Jingchuan, et al.. (2021). Uncertainty-reduction or reciprocity? Understanding the effects of a platform-initiated reviewer incentive program on regular review generation. OpenBU (Boston University).1 indexed citations
3.
Pu, Jingchuan, Young Kwark, Sang Pil Han, Bin Gu, & Qiang Ye. (2017). The Effects of a Platform-Initiated Reviewer Incentive Program on Regular Review Generation. SSRN Electronic Journal.3 indexed citations
4.
Pu, Jingchuan, et al.. (2017). The Double-Edged Sword of Expert Reviewer Programs: The Effects of Offering Expert Reviewer Status on Review Generation.. Journal of the Association for Information Systems.5 indexed citations
Kwark, Young, Gene Moo Lee, Paul A. Pavlou, & Liangfei Qiu. (2016). The Spillover Effects of User-Generated Online Product Reviews on Purchases: Evidence from Clickstream Data. International Conference on Information Systems.4 indexed citations
Burghartz, Joachim N., et al.. (1995). Opportunities for Standard Silicon Technology in RF&Microwave Applications. European Solid-State Device Research Conference. 363–366.3 indexed citations
Yablonovitch, Eli, R.M. Swanson, & Young Kwark. (1984). An n-SIPOS: p-SIPOS homojunction and a SIPOS-Si-SIPOS double heterostructure. Photovoltaic Specialists Conference. 1146–1148.5 indexed citations
20.
Kwark, Young & R.M. Swanson. (1982). Optical Absorption of Thin SIPOS Films. Journal of The Electrochemical Society. 129(1). 197–201.8 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.