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.
Algorithmic bias in data-driven innovation in the age of AI
2021209 citationsShahriar Akter, John D’Ambra et al.profile →
Author Peers
Peers are selected by citation overlap in the author's most active subfields.
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This map shows the geographic impact of John D’Ambra'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 John D’Ambra with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites John D’Ambra more than expected).
This network shows the impact of papers produced by John D’Ambra. 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 John D’Ambra. The network helps show where John D’Ambra may publish in the future.
Co-authorship network of co-authors of John D’Ambra
This figure shows the co-authorship network connecting the top 25 collaborators of John D’Ambra.
A scholar is included among the top collaborators of John D’Ambra 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 John D’Ambra. John D’Ambra is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
All Works
20 of 20 papers shown
1.
Xiao, Lin, et al.. (2016). DETERMINING MOTIVATIONS FOR ONLINE GROUP BUYING –A USES AND GRATIFICATIONS PERSPECTIVE. Pacific Asia Conference on Information Systems. 361.
2.
Xiao, Lin, Zixiu Guo, & John D’Ambra. (2014). A Typology of Online Group Buyers: Using Means-end Structures for Benefit Segmentation. Journal of the Association for Information Systems.4 indexed citations
3.
Xiao, Lin, et al.. (2014). Understanding Online Group Purchase Decision Making: A Means-End Chain Approach. Journal of the Association for Information Systems. 290.3 indexed citations
4.
Alkhalifah, Ali & John D’Ambra. (2012). Factors Effecting User Adoption Of I Dentity Management Systems: An Empi Rical Study. Pacific Asia Conference on Information Systems. 182.1 indexed citations
5.
Akter, Shahriar, Pradeep Ray, & John D’Ambra. (2011). Viewing systems as services: the role of service quality. Journal of the Association for Information Systems.7 indexed citations
6.
Akter, Shahriar, John D’Ambra, & Pradeep Ray. (2011). An evaluation of PLS based complex models: the roles of power analysis, predictive relevance and GOF index. Journal of the Association for Information Systems.188 indexed citations
7.
Ghobadi, Shahla & John D’Ambra. (2011). Coopetitive Knowledge Sharing: An Analytical Review of Literature. Electronic Journal of Knowledge Management. 9(4). 307–317.23 indexed citations
8.
Xiao, Lin, Zixiu Guo, & John D’Ambra. (2010). An Empirical Study of Multi-dimensional Trust and Eloyalty in E-commerce in China. Americas Conference on Information Systems. 62.6 indexed citations
9.
D’Ambra, John, et al.. (2010). The Influence of Self-Esteem and Locus of Control on Perceived Email Overload. Journal of the Association for Information Systems. 86.9 indexed citations
10.
Abedin, Babak, Farhad Daneshgar, & John D’Ambra. (2010). Underlying factors of sense of community in asynchronous computer supported collaborative learning environments. UTS ePRESS (University of Technology Sydney). 6(3). 585–596.31 indexed citations
11.
Akter, Shahriar, John D’Ambra, & Pradeep Ray. (2010). User Perceived Service Quality of mHealth Services in Developing Countries. Journal of the Association for Information Systems. 134.43 indexed citations
12.
D’Ambra, John, et al.. (2009). Exploring the Relationship Between Personality Traits, eMail Overload and Productivity in the Workplace. Journal of the Association for Information Systems. 233.1 indexed citations
13.
Mistilis, Nina & John D’Ambra. (2007). A Taxonomy of Virtual Information Tasks and e-capability of Visitor Information Centres: An Australian Case Study. Journal of the Association for Information Systems. 473.2 indexed citations
14.
Guo, Zixiu, John D’Ambra, Tim Turner, Huiying Zhang, & Tong Zhang. (2006). Effectiveness of Meeting Outcomes in Virtual vs. Face-to-Face Teams: A Comparison Study in China. Journal of the Association for Information Systems.4 indexed citations
15.
D’Ambra, John, et al.. (2005). The Web and Traditional Information Resources: How Do They Contribute to Overall Satisfaction with an Information Service?. Journal of the Association for Information Systems. 207.1 indexed citations
16.
D’Ambra, John, et al.. (2004). Extending Media Richness Theory: The Influence of a Shared Social Construction. Journal of the Association for Information Systems. 1767–1779.3 indexed citations
17.
Guo, Zixiu & John D’Ambra. (2003). Understanding the Role of National Culture on Communication Media Choice Behavior: A Cross- Cultural Comparison within a Multinational Organizational Setting. Journal of the Association for Information Systems. 96.2 indexed citations
18.
D’Ambra, John. (2001). Evaluating the Utility and Impact of the World Wide Web. Journal of the Association for Information Systems. 887–897.
19.
Guo, Zixiu, et al.. (2001). Understanding Cultural Influence on Media Choice: A Cross-Cultural Study within Multinational Organization Setting. Journal of the Association for Information Systems. 54.2 indexed citations
20.
D’Ambra, John. (1995). Computer-mediated communication and management communication: Transforming corporate Communication.. European Conference on Information Systems. 577–590.
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.