William Remus

2.9k citations
66 papers · 2.4k · h-index 23

Impact in

Papers in

William Remus

63 papers receiving 2.1k citations

Peers

William Remus
Comparison fields: 5 of 141
  • General Decision Sciences 252
  • Management Science and Operations Research 1.0k
  • Information Systems and Management 278
  • Management Information Systems 209
  • Artificial Intelligence 534
Replace Fred Collopy with:
Fred Collopy United States
John Kidd United Kingdom
Marcus O’Connor Australia
Rudolf Vetschera Austria
C. West Churchman United States
Leonard Adelman United States
James C. Hershauer United States
Dilek Önkal United Kingdom
Magne Jørgensen Norway
Andrzej P. Wierzbicki Poland
William Remus relative to Fred Collopy United States Fred Collopy's profile →
Citations per field
00.5×2.5×
Fred Collopy · 1×
Citations per year

Countries citing papers authored by William Remus

Since Specialization
Citations

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

Fields of papers citing papers by William Remus

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 16 scholars most cited alongside William Remus, 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 William Remus Line = papers co-authored together William Remus links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 66 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1996380
2 1994363
3 1986204
4 1999144
5 1984128
6 201492
7 199391
8 199473
9 199657
10 198756
11 199756
12 198652
13 200146
14 200445
15 199443
16 198932
17 200232
18 199631
19 199529
20 197825

About William Remus

William Remus is a scholar working on Management Science and Operations Research, Artificial Intelligence, General Decision Sciences, Sociology and Political Science and Management Information Systems, having authored 66 papers that have together received 2.4k indexed citations. Recurring topics across this work include Forecasting Techniques and Applications (20 papers), Decision-Making and Behavioral Economics (11 papers), Neural Networks and Applications (7 papers), Stock Market Forecasting Methods (6 papers), Digital Marketing and Social Media (6 papers), Complex Systems and Decision Making (5 papers), Experimental Behavioral Economics Studies (5 papers) and Technology Adoption and User Behaviour (5 papers). The work is most often cited by research in General Decision Sciences (252 citations), Management Science and Operations Research (1.0k citations), Information Systems and Management (278 citations), Management Information Systems (209 citations) and Artificial Intelligence (534 citations). William Remus has collaborated with scholars based in United States, Australia and Hong Kong. Frequent co-authors include Marcus O’Connor, Tim Hill, Leorey Marquez, Jeffrey E Kottemann, Margaret Meiling Luo, Kenneth Griggs, Fred D. Davis, Kai H. Lim, Clara Wong and Reginald Worthley. Their work appears in journals such as International Journal of Forecasting, Management Science, Journal of Forecasting, MIS Quarterly and American Psychologist.

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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