William J. Tastle

675 citations
32 papers · 413 indexed · h-index 11
Topics
Multi-Criteria Decision Making (8 papers)Rough Sets and Fuzzy Logic (5 papers)Teaching and Learning Programming (3 papers)

In The Last Decade

William J. Tastle

29 papers receiving 369 citations

Peers

William J. Tastle
Comparison fields: 5 of 134
  • Management Science and Operations Research 65
  • Sociology and Political Science 58
  • Education 54
  • Artificial Intelligence 53
  • Information Systems 51
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Countries citing papers authored by William J. Tastle

Since Specialization
Citations

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

Fields of papers citing papers by William J. Tastle

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of William J. Tastle

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

All Works

20 of 20 papers shown
#WorkIndexed citations
1
Use of Failure in IS Development Statistics: Lessons for IS Curriculum Design
1
2 3
3 8
4
A Study of the Programming Languages Used in Information Systems and in Computer Science Curricula.
3
5 1
6
Measuring Faculty Instructional Performance
1
7 0
8
Assessing Team Performance in Information Systems Projects
4
9 10
10
A New Tool in IS Management: Geographic Information Systems
3
11 171
12 4
13 36
14 31
15
A new measure to analyze student performance using the likert scale
19
16 15
17 14
18
Analysis and Design: Assessing Actual and Desired Course Content
5
19
Globalization Issues in Information Systems Education: Toward a Collaborative Multischool Systems Analysis Experience
2
20 1

About William J. Tastle

William J. Tastle is a scholar working on Management Science and Operations Research, Computer Science Applications and Management Information Systems, having authored 32 papers that have together received 413 indexed citations. Recurring topics across this work include Multi-Criteria Decision Making (8 papers), Rough Sets and Fuzzy Logic (5 papers) and Teaching and Learning Programming (3 papers). The work is most often cited by research in Management Science and Operations Research (65 citations), Computer Science Applications (28 citations) and Statistics, Probability and Uncertainty (18 citations). William J. Tastle has collaborated with scholars based in United States, Iceland and Switzerland. Frequent co-authors include Mark J. Wierman, Massimo Buscema, Uldarico Rex Dumdum, Eileen P. Kelly, Barbara Russell, Jeffry Babb, John N. Mordeson, Amjad Abdullat and Terry D. Clark. Their work appears in journals such as SHILAP Revista de lepidopterología, Journal of the Association for Information Systems and Behavior Research Methods.

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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