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.
Examining the Relationship Between Reviews and Sales: The Role of Reviewer Identity Disclosure in Electronic Markets
Citations per year, relative to Anindya Ghose Anindya Ghose (= 1×)
peers
P.K. Kannan
Countries citing papers authored by Anindya Ghose
Since
Specialization
Citations
This map shows the geographic impact of Anindya Ghose'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 Anindya Ghose with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Anindya Ghose more than expected).
This network shows the impact of papers produced by Anindya Ghose. 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 Anindya Ghose. The network helps show where Anindya Ghose may publish in the future.
Co-authorship network of co-authors of Anindya Ghose
This figure shows the co-authorship network connecting the top 25 collaborators of Anindya Ghose.
A scholar is included among the top collaborators of Anindya Ghose 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 Anindya Ghose. Anindya Ghose 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
3.
Adamopoulos, Panagiotis, et al.. (2020). Predicting Stages in the Consumer Path-Purchase Journey: An Omnichannel Deep-Learning Model. SSRN Electronic Journal.1 indexed citations
4.
Spann, Martin, et al.. (2018). Measuring The Effectiveness of Location-Based Mobile Push vs. Pull Targeting. Journal of the Association for Information Systems.3 indexed citations
5.
Ghose, Anindya, Param Vir Singh, & Vilma Todri. (2017). Got Annoyed? Examining the Advertising Effectiveness and Annoyance Dynamics. Journal of the Association for Information Systems.8 indexed citations
6.
Burtch, Gordon, Anindya Ghose, & Sunil Wattal. (2016). Secret Admirers: An Empirical Examination of Information Hiding and Contribution Dynamics in Online Crowdfunding. SSRN Electronic Journal.51 indexed citations
7.
Ghose, Anindya & Sung-Hyuk Park. (2013). The Negative Impact of Mobile Devices on Niche Product Consumption. International Conference on Information Systems.5 indexed citations
8.
Wang, Jing, Anindya Ghose, & Panagiotis G. Ipeirotis. (2012). Bonus, Disclosure, and Choice: What Motivates the Creation of High-Quality Paid Reviews?. Rare & Special e-Zone (The Hong Kong University of Science and Technology).35 indexed citations
9.
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
10.
Ghose, Anindya & Sang Pil Han. (2012). ESTIMATING DEMAND FOR MOBILE APPLICATIONS IN THE NEW MOBILE ECONOMY. International Conference on Information Systems. 455–474.3 indexed citations
11.
Burtch, Gordon, Anindya Ghose, & Sunil Wattal. (2011). AN EMPIRICAL EXAMINATION OF THE ANTECEDENTS OF CONTRIBUTION PATTERNS IN CROWDFUNDED MARKETS. Journal of the Association for Information Systems. 559–575.11 indexed citations
12.
Han, Sang Pil, Anindya Ghose, & Raghuram Iyengar. (2011). NETWORK STABILITY AND SOCIAL CONTAGION ON THE MOBILE INTERNET. International Conference on Information Systems. 3330–3348.4 indexed citations
13.
Ghose, Anindya & Sang Pil Han. (2009). AN EMPIRICAL ANALYSIS OF USER CONTENT GENERATION AND USAGE BEHAVIOR IN MOBILE DIGITAL MEDIA. Journal of the Association for Information Systems. 190.12 indexed citations
14.
Balakrishnan, Karthik, Anindya Ghose, & Panagiotis G. Ipeirotis. (2008). The Impact of Information Disclosure on Stock Market Returns: The Sarbanes-Oxley Act and the Role of Media as an Information Intermediary..5 indexed citations
15.
Ghose, Anindya & Bin Gu. (2008). Market Frictions, Demand Structure and Price Competition in Online Markets. Journal of the Association for Information Systems. 139.3 indexed citations
16.
Ghose, Anindya & Uday Rajan. (2006). The Economic Impact of Regulatory Information Disclosure on Information Security Investments, Competition, and Social Welfare..17 indexed citations
17.
Ghose, Anindya & Arun Sundararajan. (2005). Pricing Security Software: Theory and Evidence. The Faculty Digital Archive (New York University).3 indexed citations
18.
Ghose, Anindya & Arun Sundararajan. (2005). Software Versioning and Quality Degradation? An Exploratory Study of the Evidence. Journal of the Association for Information Systems.28 indexed citations
19.
Ghose, Anindya, Michael D. Smith, & Rahul Telang. (2004). PRICE ELASTICITIES AND SOCIAL WELFARE IN SECONDARY ELECTRONIC MARKETS. Journal of the Association for Information Systems. 611–614.1 indexed citations
20.
Ghose, Anindya, Rahul Telang, & Ramayya Krishnan. (2003). Durable Goods Competition in Secondary Electronic Markets.. International Conference on Information Systems. 341–352.3 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.