Economics & Sociology
ISSN: 2071-789X eISSN: 2306-3459 DOI: 10.14254/2071-789XIndex PUBMS: f5512f57-a601-11e7-8f0e-080027f4daa0

Title: | Effect of faculty on research cooperation and publication: Employing natural language processing |
Issue: |
Vol. 11, No 4, 2018
Published date: 12-2018 (print) / 12-2018 (online) |
Journal: |
Economics & Sociology
ISSN: 2071-789X, eISSN: 2306-3459 |
Authors: |
Nitza Davidovitch
Ariel University, Israel Eyal Eckhaus Ariel University, Israel |
Keywords: | academic conference, gender, faculty, academia |
DOI: | 10.14254/2071-789X.2018/11-4/11 |
Index PUBMS: | 059d6b10-18e2-11e9-82eb-fa163e6feac6 |
Language: | English |
Pages: | 173-180 (8) |
JEL classification: | M10, M20 |
Website: | https://www.economics-sociology.eu/?627,en_effect-of-faculty-on-research-cooperation-and-publication-employing-natural-language-processing |
Licenses: |
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This study continues a series of studies on the effectiveness of scientific conferences. This topic has not been sufficiently investigated although it receives large funds, assuming that these conferences have added value for staff members' academic-professional development. Predicated on questionnaires filled by 96 academic staff members from 17 different departments, we found that when choosing conferences to attend, the type of faculty affect the search for cooperation. Moreover, staff members from the Faculty of Natural Sciences attribute more significance to conferences that result in publications than staff members from the Faculty of Health. The Faculty of Engineering creates negative mediation in the correlation between gender and cooperation. Namely, the Faculty of Engineering does not urge cooperation and even has a negative effect, but its effect is evident mainly among men. This finding complements prior research findings showing that women are more inclined to cooperation (Eckhaus & Davidovitch, 2018a). The current findings show that the inclination to cooperation is not related only to gender issues rather the faculty has an effect as well. The current findings might have a contribution to the significance of the faculty as an influential factor of conferences on cooperation – and in fact on the professional development of staff members.
1. Brainerd, C. J., Wang, Z., & Reyna, V. F. (2013). Superposition of episodic memories: Overdistribution and quantum models. Topics in Cognitive Science, 5(4), 773-799.
2. Cambria, E., & White, B. (2014). Jumping NLP curves: A review of natural language processing research. IEEE Computational Intelligence Magazine, 9(2), 48-57.
3. Davidovitch, N., & Eckhaus, E. (2018). The influence of birth country on selection of conference destination-employing natural language processing. Higher Education Studies, 8(2), 92-96.
4. Davidovitch, N., Sinuani-Stern Z., & Soen., D. (2014). Performance measures – Compensation for research and teaching outcomes in the higher education system. The case of Israel – A comparative view. American International Journal of Contemporary Research,
5. Davidovitch, N., Soen., D., & Sinuani-Stern Z. (2011). Performance measures of academic faculty: A case study. Journal of Further and Higher Education, 35(3), 355–373
6. Eckhaus, E. (2016). Corporate transformational leadership's effect on financial performance. Journal of Leadership, Accountability and Ethics, 13(1), 90-102.
7. Eckhaus, E. (2017). A shift in leadership. Academy of Strategic Management Journal, 16(1), 19-31.
8. Eckhaus, E., & Sheaffer, Z. (2018). Managerial hubris detection: the case of Enron. Risk Management, Published online at https://doi.org/10.1057/s41283-41018-40037-41280.
9. Eckhaus, E., & Ben-Hador, B. (2017). Gossip and gender differences: a content analysis approach. Journal of Gender Studies, 1-12. Published online at: http://www.tandfonline.com/doi/full/10.1080/09589236.2017.1411789, doi:10.1080/09589236.2017.1411789
10. Eckhaus, E., & Davidovitch, N. (2018a). Impact of gender and conference size on conference preferences: Employing natural language processing. International Journal of Educational Methodology, 4(1), 45-52.
11. Eckhaus, E., & Davidovitch, N. (2018b). Improving academic conferences – criticism and suggestions utilizing natural language processing. European Journal of Educational Research, 7(3), Forthcoming.
12. Harzing, A.-W., & Alakangas, S. (2016). Google Scholar, Scopus and the Web of Science: a longitudinal and cross-disciplinary comparison. Scientometrics, 106(2), 787-804.
13. Majid, N., Ahmad, R. R., Din, U. K. S., Rambely, A. S., Suradi, N. R. M., & Shahabudin, F. A. A. (2012). Academic research process: a review on current practices in school of mathematical sciences. Procedia - Social and Behavioral Sciences, 59, 394-398.
14. Ryu, E. (2014). Model fit evaluation in multilevel structural equation models. Frontiers in psychology, 5, 81.
15. Suzuki, M., Kuriyama, N., Ito, A., & Makino, S. (2008). Automatic clustering of part-of-speech for vocabulary divided PLSA language model. In Peoceedings of The International Conference on Natural Language Processing and Knowledge Engineering, pp. 1-7. .
16. Veloutsou, C., & Chreppas, C. (2015). Training or vacation? The academic conference tourism. Tourismos: An International Multidisciplinary Journal of Tourism, 10(1), 101-130.
17. Wilson, C. S., & Tenopir, C. (2008). Local citation analysis, publishing and reading patterns: Using multiple methods to evaluate faculty use of an academic library's research collection. Journal of the Association for Information Science and Technology,