Paul Byrnes

875 total citations · 1 hit paper
7 papers, 478 citations indexed

About

Paul Byrnes is a scholar working on Management Information Systems, Information Systems and Artificial Intelligence. According to data from OpenAlex, Paul Byrnes has authored 7 papers receiving a total of 478 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Management Information Systems, 2 papers in Information Systems and 2 papers in Artificial Intelligence. Recurrent topics in Paul Byrnes's work include Big Data and Business Intelligence (3 papers), Imbalanced Data Classification Techniques (2 papers) and Anomaly Detection Techniques and Applications (2 papers). Paul Byrnes is often cited by papers focused on Big Data and Business Intelligence (3 papers), Imbalanced Data Classification Techniques (2 papers) and Anomaly Detection Techniques and Applications (2 papers). Paul Byrnes collaborates with scholars based in United States, United Kingdom and Kuwait. Paul Byrnes's co-authors include J. Donald Warren, Kevin Moffitt, Gary P. Schneider, Elizabeth A. Payne, Diane J. Janvrin, Mary B. Curtis, Thomas E. Nelson, Miklos A. Vasarhelyi, Jun Dai and Qi Liu and has published in prestigious journals such as Journal of Small Business Management, Accounting Horizons and Journal of Information Systems.

In The Last Decade

Paul Byrnes

7 papers receiving 426 citations

Hit Papers

align trajectories

What are hit papers?

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.

How Big Data Will Change Accounting

2015 369 citations J. Donald Warren, Kevin Moffitt et al. Accounting Horizons profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Paul Byrnes United States	5	269	180	106	99	70	7	478
J. Donald Warren United States	6	246 0.9×	192 1.1×	102 1.0×	86 0.9×	73 1.0×	16	453
Danielle R. Lombardi United States	8	207 0.8×	194 1.1×	115 1.1×	62 0.6×	47 0.7×	19	404
Roman Chychyla United States	7	210 0.8×	359 2.0×	88 0.8×	64 0.6×	119 1.7×	15	557
Amer Qasim United Arab Emirates	12	169 0.6×	223 1.2×	49 0.5×	87 0.9×	78 1.1×	40	470
Anna Samsonova‐Taddei United Kingdom	10	180 0.7×	224 1.2×	58 0.5×	44 0.4×	145 2.1×	23	484
Gary P. Schneider United States	9	188 0.7×	148 0.8×	44 0.4×	70 0.7×	41 0.6×	47	368
Marcia Weidenmier Watson United States	12	274 1.0×	344 1.9×	31 0.3×	153 1.5×	108 1.5×	23	600
Najoua Elommal France	5	87 0.3×	113 0.6×	56 0.5×	66 0.7×	82 1.2×	15	346
Joshua G. Coyne United States	7	199 0.7×	145 0.8×	38 0.4×	207 2.1×	62 0.9×	20	435
Mohammed Muneerali Thottoli Oman	12	115 0.4×	217 1.2×	60 0.6×	61 0.6×	92 1.3×	44	508

Countries citing papers authored by Paul Byrnes

Since Specialization

Citations

This map shows the geographic impact of Paul Byrnes'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 Paul Byrnes with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Paul Byrnes more than expected).

Fields of papers citing papers by Paul Byrnes

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Paul Byrnes. 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 Paul Byrnes. The network helps show where Paul Byrnes may publish in the future.

Co-authorship network of co-authors of Paul Byrnes

This figure shows the co-authorship network connecting the top 25 collaborators of Paul Byrnes. A scholar is included among the top collaborators of Paul Byrnes 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 Paul Byrnes. Paul Byrnes is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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7 of 7 papers shown

1.

Nelson, Thomas E., et al.. (2020). The use of cluster analysis in entrepreneurship research: Review of past research and future directions. Journal of Small Business Management. 60(4). 961–1000. 23 indexed citations

2.

Dai, Jun, et al.. (2019). Rutgers Studies in Accounting Analytics: Audit Analytics in the Financial Industry. 1 indexed citations

3.

Byrnes, Paul. (2019). Automated Clustering for Data Analytics. Journal of Emerging Technologies in Accounting. 16(2). 43–58. 12 indexed citations

4.

Byrnes, Paul, et al.. (2019). Clustering University Programs in Accounting to Enhance Selection Productivity: Precursor to Recommendation System Development. Journal of Emerging Technologies in Accounting. 16(1). 65–79. 1 indexed citations

5.

Byrnes, Paul. (2015). Developing automated applications for clustering and outlier detection. Rutgers University Community Repository (Rutgers University). 15 indexed citations

6.

Warren, J. Donald, Kevin Moffitt, & Paul Byrnes. (2015). How Big Data Will Change Accounting. Accounting Horizons. 29(2). 397–407. 369 indexed citations breakdown →

7.

Janvrin, Diane J., Elizabeth A. Payne, Paul Byrnes, Gary P. Schneider, & Mary B. Curtis. (2012). The Updated COSO Internal Control—Integrated Framework: Recommendations and Opportunities for Future Research. Journal of Information Systems. 26(2). 189–213. 57 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.

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