John D. Johnson

1.2k citations
31 papers · 930 · h-index 10

Impact in

Papers in

John D. Johnson

30 papers receiving 847 citations

Peers

John D. Johnson
Comparison fields: 5 of 112
  • Management Science and Operations Research 197
  • Artificial Intelligence 454
  • Accounting 86
  • Industrial and Manufacturing Engineering 56
  • Safety Research 40
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Paul H. Kvam United States
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Countries citing papers authored by John D. Johnson

Since Specialization
Citations

This map shows the geographic impact of John D. Johnson'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. Johnson 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. Johnson more than expected).

Fields of papers citing papers by John D. Johnson

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by John D. Johnson. 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. Johnson. The network helps show where John D. Johnson may publish in the future.

Co-authors

The 22 scholars most cited alongside John D. Johnson, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with John D. Johnson Line = papers co-authored together John D. Johnson links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 31 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1998209
2 1999190
3 1998125
4 1990100
5 199883
6 199935
7
Advances in artificial intelligence in economics, finance, and management
199432
8 199932
9 199427
10 199416
11 200610
12 20129
13
Expansion Caches For Superscalar Processors
19949
14 19978
15 19837
16 19997
17 20245
18 19835
19 20243
20
TO STOP A THIEF
20002

About John D. Johnson

John D. Johnson is a scholar working on Artificial Intelligence, Management Science and Operations Research, Hardware and Architecture, Computational Theory and Mathematics and Industrial and Manufacturing Engineering, having authored 31 papers that have together received 930 indexed citations. Recurring topics across this work include Neural Networks and Applications (10 papers), Metaheuristic Optimization Algorithms Research (8 papers), Stock Market Forecasting Methods (5 papers), Parallel Computing and Optimization Techniques (5 papers), Numerical Methods and Algorithms (3 papers), Embedded Systems Design Techniques (3 papers), Advanced Data Storage Technologies (2 papers) and Evolutionary Algorithms and Applications (2 papers). The work is most often cited by research in Management Science and Operations Research (197 citations), Artificial Intelligence (454 citations), Accounting (86 citations), Industrial and Manufacturing Engineering (56 citations) and Safety Research (40 citations). John D. Johnson has collaborated with scholars based in United States and South Korea. Frequent co-authors include Robert E. Dorsey, Randall S. Sexton, Bahram Alidaee, Andrew B. Whinston, José B. Torres, V. Scott H. Solberg, Oliver Finley Graves, Mark Van Boening, Reza Barkhi and Hyeseon Lee. Their work appears in journals such as Decision Support Systems, European Journal of Operational Research, The Career Development Quarterly, International Journal of Sports Marketing and Sponsorship and Computational Economics.

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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