Thomas E. McKee

1.2k total citations
28 papers, 874 citations indexed

About

Thomas E. McKee is a scholar working on Accounting, Artificial Intelligence and Management Information Systems. According to data from OpenAlex, Thomas E. McKee has authored 28 papers receiving a total of 874 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Accounting, 8 papers in Artificial Intelligence and 4 papers in Management Information Systems. Recurrent topics in Thomas E. McKee's work include Financial Distress and Bankruptcy Prediction (11 papers), Auditing, Earnings Management, Governance (9 papers) and Imbalanced Data Classification Techniques (8 papers). Thomas E. McKee is often cited by papers focused on Financial Distress and Bankruptcy Prediction (11 papers), Auditing, Earnings Management, Governance (9 papers) and Imbalanced Data Classification Techniques (8 papers). Thomas E. McKee collaborates with scholars based in United States and Norway. Thomas E. McKee's co-authors include Terje Lensberg, Aasmund Eilifsen, Marilyn Greenstein, William F. Messier, Theodore J. Mock, Reiner Quick and Leila Hashemi-Beni and has published in prestigious journals such as SHILAP Revista de lepidopterología, European Journal of Operational Research and Journal of the Association for Information Systems.

In The Last Decade

Thomas E. McKee

26 papers receiving 791 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Thomas E. McKee United States	12	650	330	140	135	117	28	874
Pervaiz Alam United States	18	701 1.1×	234 0.7×	197 1.4×	138 1.0×	237 2.0×	58	1.0k
Ram S. Sriram United States	15	344 0.5×	150 0.5×	76 0.5×	99 0.7×	150 1.3×	34	615
Mark Cecchini United States	8	362 0.6×	234 0.7×	82 0.6×	120 0.9×	61 0.5×	12	556
Kurt Fanning United States	8	389 0.6×	397 1.2×	50 0.4×	168 1.2×	80 0.7×	19	931
James A. Gentry United States	16	893 1.4×	244 0.7×	328 2.3×	127 0.9×	329 2.8×	50	1.3k
John Pointon United Kingdom	15	594 0.9×	345 1.0×	272 1.9×	81 0.6×	230 2.0×	40	991
D.K. Malhotra United States	13	579 0.9×	291 0.9×	472 3.4×	141 1.0×	65 0.6×	75	925
María Jesús Segovia Vargas Spain	11	282 0.4×	175 0.5×	89 0.6×	69 0.5×	71 0.6×	49	569
Der‐Jang Chi Taiwan	11	207 0.3×	154 0.5×	45 0.3×	115 0.9×	93 0.8×	15	483
Lili Sun United States	16	925 1.4×	110 0.3×	234 1.7×	70 0.5×	370 3.2×	44	1.1k

Countries citing papers authored by Thomas E. McKee

Since Specialization

Citations

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

Fields of papers citing papers by Thomas E. McKee

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Thomas E. McKee. 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 Thomas E. McKee. The network helps show where Thomas E. McKee may publish in the future.

Co-authorship network of co-authors of Thomas E. McKee

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

All Works

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20 of 20 papers shown

1.

Eilifsen, Aasmund, et al.. (2020). An Exploratory Study into the Use of Audit Data Analytics on Audit Engagements. Accounting Horizons. 34(4). 75–103. 67 indexed citations

2.

McKee, Thomas E. & Leila Hashemi-Beni. (2020). DEVELOPING GEOSPATIAL SCIENTISTS – USING STUDENTS AS PARTNERS IN DRONE RESEARCH. SHILAP Revista de lepidopterología. XLIV-M-2-2020. 69–72. 1 indexed citations

3.

McKee, Thomas E.. (2020). Analyzing An Audit Population Via Either Excel Pivot Tables and/or R Language Cluster Analysis. Current Issues in Auditing. 15(1). I1–I14. 3 indexed citations

4.

Eilifsen, Aasmund, et al.. (2019). An Exploratory Study into the Use of Audit Data Analytics on Audit Engagements. SSRN Electronic Journal. 9 indexed citations

5.

McKee, Thomas E.. (2009). A Meta-Learning Approach to Predicting Financial Statement Fraud. Journal of Emerging Technologies in Accounting. 6(1). 5–26. 10 indexed citations

6.

McKee, Thomas E., et al.. (2008). A Comparison of the Information Technology Knowledge of United States and German Auditors. Repositorio Institucional de la Universidad de Huelva (Universidad de Huelva). 8(14). 45–77. 17 indexed citations

7.

McKee, Thomas E.. (2007). Altman's 1968 Bankruptcy Prediction Model Revisited via Genetic Programming: New Wine from an Old Bottle or a Better Fermentation Process?. Journal of Emerging Technologies in Accounting. 4(1). 87–101. 9 indexed citations

8.

McKee, Thomas E.. (2005). Earnings Management: An Executive Perspective. Medical Entomology and Zoology. 72 indexed citations

9.

Lensberg, Terje, Aasmund Eilifsen, & Thomas E. McKee. (2004). Bankruptcy theory development and classification via genetic programming. European Journal of Operational Research. 169(2). 677–697. 118 indexed citations

10.

McKee, Thomas E.. (2003). Rough sets bankruptcy prediction models versus auditor signalling rates. Journal of Forecasting. 22(8). 569–586. 76 indexed citations

11.

McKee, Thomas E.. (2000). Developing a bankruptcy prediction model via rough sets theory. Intelligent Systems in Accounting Finance & Management. 9(3). 159–173. 12 indexed citations

12.

McKee, Thomas E.. (2000). Developing a bankruptcy prediction model via rough sets theory. Intelligent Systems in Accounting Finance & Management. 9(3). 159–173. 106 indexed citations

13.

McKee, Thomas E. & Marilyn Greenstein. (2000). Predicting bankruptcy using recursive partitioning and a realistically proportioned data set. Journal of Forecasting. 19(3). 219–230. 9 indexed citations

14.

McKee, Thomas E. & Marilyn Greenstein. (2000). Predicting bankruptcy using recursive partitioning and a realistically proportioned data set. Journal of Forecasting. 19(3). 219–230. 56 indexed citations

15.

McKee, Thomas E.. (1995). Predicting Bankruptcy via Induction. Journal of the Association for Information Systems. 10(1). 26–36. 8 indexed citations

16.

McKee, Thomas E.. (1992). An Audit Framework for Expert Systems. Intelligent Systems in Accounting Finance & Management. 1(4). 261–273.

17.

McKee, Thomas E., et al.. (1992). A comparison of Norwegian and United States accounting students' learning style preferences. Accounting Education. 1(4). 321–341. 23 indexed citations

18.

McKee, Thomas E.. (1989). Modern Analytical Auditing: Practical Guidance for Auditors and Accountants. Medical Entomology and Zoology.

19.

McKee, Thomas E., et al.. (1988). A graphical approach to teaching the relationship between the evaluation of internal accounting controls and substantive audit testing. Journal of Accounting Education. 6(1). 123–130. 2 indexed citations

20.

McKee, Thomas E.. (1979). AN 1870 CORPORATE AUDIT COMMITTEE. Accounting Historians Journal. 6(2). 61–68. 9 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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