Barry Wray

772 total citations
18 papers, 569 citations indexed

About

Barry Wray is a scholar working on Information Systems, Artificial Intelligence and Industrial and Manufacturing Engineering. According to data from OpenAlex, Barry Wray has authored 18 papers receiving a total of 569 indexed citations (citations by other indexed papers that have themselves been cited), including 6 papers in Information Systems, 6 papers in Artificial Intelligence and 5 papers in Industrial and Manufacturing Engineering. Recurrent topics in Barry Wray's work include Software Engineering Research (3 papers), Open Source Software Innovations (3 papers) and Data Mining Algorithms and Applications (3 papers). Barry Wray is often cited by papers focused on Software Engineering Research (3 papers), Open Source Software Innovations (3 papers) and Data Mining Algorithms and Applications (3 papers). Barry Wray collaborates with scholars based in United States and United Kingdom. Barry Wray's co-authors include David Bejou, Thomas N. Ingram, Adrian Palmer, Richard G. Mathieu, Ina S. Markham, Terry R. Rakes, Loren Paul Rees, Peter Schuhmann, Robert T. Burrus and Stephen Hill and has published in prestigious journals such as Journal of Business Research, European Journal of Marketing and Journal of the Association for Information Systems.

In The Last Decade

Barry Wray

18 papers receiving 488 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Barry Wray United States	7	336	216	126	100	91	18	569
Zeynab Soltani Iran	6	196 0.6×	145 0.7×	118 0.9×	178 1.8×	98 1.1×	6	529
Xinping Shi Hong Kong	14	148 0.4×	161 0.7×	213 1.7×	182 1.8×	140 1.5×	29	548
Suresh K. Tadisina United States	11	264 0.8×	198 0.9×	232 1.8×	308 3.1×	164 1.8×	30	691
Yannis Politis Greece	9	211 0.6×	125 0.6×	59 0.5×	99 1.0×	83 0.9×	16	429
Kevin Kuan‐Shun Chiu Taiwan	8	116 0.3×	132 0.6×	110 0.9×	125 1.3×	219 2.4×	18	650
Leif B. Methlie Norway	12	175 0.5×	134 0.6×	230 1.8×	237 2.4×	174 1.9×	24	657
Cristiane Drebes Pedron Brazil	12	127 0.4×	135 0.6×	172 1.4×	100 1.0×	124 1.4×	69	523
Katrin Jonsson Sweden	9	158 0.5×	273 1.3×	122 1.0×	38 0.4×	176 1.9×	21	564
Dimitris Kardaras Greece	10	131 0.4×	81 0.4×	127 1.0×	143 1.4×	96 1.1×	22	483
Ankit Mehrotra India	16	94 0.3×	217 1.0×	225 1.8×	210 2.1×	113 1.2×	61	708

Countries citing papers authored by Barry Wray

Since Specialization

Citations

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

Fields of papers citing papers by Barry Wray

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Barry Wray

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

All Works

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18 of 18 papers shown

Lee, Lorraine, et al.. (2020). Preparing for Audit Data Analytics with the AICPA General Ledger Audit Data Standards. Journal of Emerging Technologies in Accounting. 18(1). 131–157. 3 indexed citations

Wray, Barry, et al.. (2019). Validating the effectiveness of a locally developed assurance of learning assessment. Journal of Education for Business. 95(4). 216–226. 5 indexed citations

Hill, Stephen, Barry Wray, & Christopher Sibona. (2018). Minimalism in Data Visualization: Perceptions of Beauty, Clarity, Effectiveness, and Simplicity. 11(1). 34. 6 indexed citations

Wray, Barry, et al.. (2015). Determining the Propensity for Academic Dishonesty Using Decision Tree Analysis. Ethics & Behavior. 26(6). 470–487. 7 indexed citations

Glew, David J., et al.. (2011). Evaluating and Improving the Assessment of Undergraduate Student Writing in a School of Business at a Large Regional University.. 11(2). 55–74. 3 indexed citations

Wray, Barry, et al.. (2009). Identifying how determinants impact security-based open source software project success using rule induction. International Journal of Electronic Marketing and Retailing. 2(4). 352–352. 4 indexed citations

Wray, Barry & Richard G. Mathieu. (2008). Evaluating the performance of open source software projects using data envelopment analysis. Information Management & Computer Security. 16(5). 449–462. 13 indexed citations

Mathieu, Richard G. & Barry Wray. (2007). The Application of DEA to Measure the Efficiency of Open Source Security Tool Production. Journal of the Association for Information Systems. 118. 1 indexed citations

Markham, Steven E., Ina S. Markham, & Barry Wray. (2006). Improving the prediction of employee productivity: a comparison of ordinary least squares versus genetic algorithms coupled with artificial neural networks. International Journal of Productivity and Quality Management. 1(4). 379–379. 3 indexed citations

10.

Wray, Barry, Ina S. Markham, & Richard G. Mathieu. (2003). An Artificial Neural Network Approach to Learning from Factory Performance in a Kanban-Based System. CSUSB ScholarWorks (California State University, San Bernardino). 12(2). 7. 3 indexed citations

11.

Wray, Barry, et al.. (2003). Artificial neural networks for predicting the optimal number of kanbans in a JIT manufacturing environment. 5. 3435–3437. 1 indexed citations

12.

Mathieu, Richard G., Barry Wray, & Ina S. Markham. (2002). An approach to learning from both good and poor factory performance in a Kanban-based just-in-time production system. Production Planning & Control. 13(8). 715–724. 4 indexed citations

13.

Markham, Ina S., Richard G. Mathieu, & Barry Wray. (2000). Kanban setting through artificial intelligence: a comparative study of artificial neural networks and decision trees. Integrated Manufacturing Systems. 11(4). 239–246. 37 indexed citations

14.

Markham, Ina S., Richard G. Mathieu, & Barry Wray. (1998). A rule induction approach for determining the number of kanbans in A just-in-time production system. Computers & Industrial Engineering. 34(4). 717–727. 23 indexed citations

15.

Mathieu, Richard G., Christopher L. Huntley, & Barry Wray. (1998). A Method for Incorporating Negative Data into Rule Induction. Journal of the Association for Information Systems. 1 indexed citations

16.

Wray, Barry, Terry R. Rakes, & Loren Paul Rees. (1997). Neural network identification of critical factors in a dynamic just-in-time kanban environment. Journal of Intelligent Manufacturing. 8(2). 83–96. 11 indexed citations

17.

Bejou, David, Barry Wray, & Thomas N. Ingram. (1996). Determinants of relationship quality: An artificial neural network analysis. Journal of Business Research. 36(2). 137–143. 225 indexed citations

18.

Wray, Barry, Adrian Palmer, & David Bejou. (1994). Using Neural Network Analysis to Evaluate Buyer‐Seller Relationships. European Journal of Marketing. 28(10). 32–48. 219 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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