William Remus

2.9k citations
65 papers · 2.2k · h-index 22

Impact in

Papers in

William Remus

62 papers receiving 1.9k citations

Peers

William Remus
Comparison fields: 5 of 140
  • General Decision Sciences 245
  • Management Science and Operations Research 922
  • Information Systems and Management 264
  • Management Information Systems 194
  • Artificial Intelligence 467
Replace John Kidd with:
John Kidd United Kingdom
Fred Collopy United States
Marcus O’Connor Australia
Kenneth R. MacCrimmon Canada
Dilek Önkal Türkiye
C. West Churchman United States
Rudolf Vetschera Austria
James C. Hershauer United States
Magne Jørgensen Norway
Leonard Adelman United States
William Remus relative to John Kidd United Kingdom John Kidd's profile →
Citations per field
00.5×3.8×
John Kidd · 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 15 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 65 papers — load more, or switch the sort, to bring in the rest.

#Work
1 1996344
2 1994330
3 1986196
4 1999124
5 1984121
6 199391
7 201485
8 199468
9 199751
10 198751
11 198649
12 199649
13 199442
14 200440
15 200231
16 198930
17 199528
18 199627
19 197824
20 198723

About William Remus

William Remus is a scholar working on Management Science and Operations Research, General Decision Sciences, Artificial Intelligence, Sociology and Political Science and Management Information Systems, having authored 65 papers that have together received 2.2k indexed citations. Recurring topics across this work include Forecasting Techniques and Applications (19 papers), Decision-Making and Behavioral Economics (11 papers), Digital Marketing and Social Media (6 papers), Neural Networks and Applications (6 papers), Stock Market Forecasting Methods (5 papers), Technology Adoption and User Behaviour (5 papers), Complex Systems and Decision Making (5 papers) and Experimental Behavioral Economics Studies (5 papers). The work is most often cited by research in General Decision Sciences (245 citations), Management Science and Operations Research (922 citations), Information Systems and Management (264 citations), Management Information Systems (194 citations) and Artificial Intelligence (467 citations). William Remus has collaborated with scholars based in United States, Australia and Taiwan. 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, Reginald Worthley and Pauline J. Sheldon. Their work appears in journals such as International Journal of Forecasting, Management Science, MIS Quarterly, Journal of Forecasting and INFORMS Journal on Applied Analytics.

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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