Ankara Hacı Bayram Veli Üniversitesi İktisadi ve İdari Bilimler FakültesiDergisi (Online), vol.24, no.2, pp.1-15, 2022 (Peer-Reviewed Journal)
A Decision Support System with Business Intelligence: Iranian and Turkish Researcher collaborate enough?
The advancement of information and communication technologies demands the employment of cutting-edge technological tools in many sectors, including higher education. These tools assist managers in performing their management tasks more effectively and in continuing their operations by enabling them to make informed judgments. Among these tools, business intelligence technology has risen to prominence in recent years as a critical strategic management tool. Feeding from many different systems, BI is a digital tool that can be used at different decision levels at the operational, tactical, and strategic levels. It may be used to organize and monitor scientific research, as well as to track its efficacy over time. Bibliometric data can be an important source for this important technology at this point. The study examines the province's, Iran's, and Turkey's scientific productivity between 2010 and 2020 using bibliometric data from Web of Science. A decision support system is modeled in order to make this query more effective and parametric for decision-makers. The scientific productivity of the two countries is analyzed at the macro level through the relevant bibliometric data source, and at the micro level, the publications jointly produced by the researchers in the two countries are detailed in the research areas, researchers, institutions, works produced and citations received, journals published together, funds. Scientific production is measured in terms of institutions, regional location, and collaboration with other nations. The two nations collaborated on 6.723 publications over the relevant time (5.915 articles). Although both countries are neighbors to each other, they are in eighth place in the list of collaborating countries in terms of research intensity. Among the countries with the most intense cooperation for both countries are the USA and England. The top three institutions working together most intensively in both countries are Islamic Azad University, Middle East Technical University, and Istanbul Technical University. Physics, engineering, chemistry, mathematics, and material science are the most intensely collaborative research areas. The developed model is seen as a valuable tool for university library services or scientific productivity monitoring, which is different from packaged software, and provides the opportunity to go into detail, for the evaluation of scientific productivity at the level of countries.
Iran, Turkey, collaboration, scientific productivity, business intelligence, bibliometric data management
- Abramo, G., & D’Angelo, C. A. (2020). A novel methodology to assess the scientific standing of nations at field level. Journal of Informetrics, 14(1), 1-13. https://doi.org/10.1016/j.joi.2019.100986
- Abramo, G., D’Angelo, C. A., & Di Costa, F. (2009). Research collaboration and productivity: is there correlation?. Higher education, 57(2), 155-171. https://doi.org/10.1007/s10734-008-9139-z
- Ardanuy, J. (2012). Scientific collaboration in Library and Information Science viewed through the Web of Knowledge: The Spanish case. Scientometrics, 90(3), 877-890. https://doi.org/10.1007/s11192-011-0552-1
- Benckendorff, P. (2010). Exploring the limits of tourism research collaboration: A social network analysis of co-authorship patterns in Australian and New Zealand tourism research. CAUTHE 2010: Tourism and Hospitality: Challenge the Limits, 151. Hobart, Australia, 8-11 February 2010. Hobart, Australia: University of Tasmania.
- Chen, K., Zhang, Y., & Fu, X. (2019). International research collaboration: An emerging domain of innovation studies? Research Policy, 48(1), 149-168. https://doi.org/10.1016/j.respol.2018.08.005
- Chung, W., Chen, H., & Reid, E. (2009). Business stakeholder analyzer: An experiment of classifying stakeholders on the Web. Journal of the American Society for Information Science and Technology, 60(1), 59-74. https://doi.org/10.1002/asi.20948
- Cox, B. L., & Jantti, M. (2012). Capturing business intelligence required for targeted marketing, demonstrating value, and driving process improvement. Library & information science research, 34(4), 308-316. http://dx.doi.org/10.1016/j.lisr.2012.06.002
- Damar, M. (2021). Endüstri 4.0 Çağında Yükseköğretim Kurulumları İçin Tedarik Zinciri Yönetiminde Bir İş Zekâsı Karar Destek Sistemi Uygulaması. İzmir Sosyal Bilimler Dergisi, 3(2), 144-158. https://doi.org/10.47899/ijss.20213204
- Damar, M., Özdağoğlu, G., & Özdağoğlu, A. (2018). İş zekasını ve ilgili teknolojileri konu alan araştırmalara küresel ölçekte bilimetrik bakış. Bilgi Ekonomisi ve Yönetimi Dergisi, 13(2), 197-217.
- Daniel, B. (2015). Big data and analytics in higher education: Opportunities and challenges. British Journal of Educational Technology, 46(5), 904-920. https://doi.org/10.1111/bjet.12230
- DataWorldBank, (2022). The World Bank Data, Population Counts. Accessed Date: 17/02/2022, https://data.worldbank.org/indicator/SP.POP.TOTL?locations=IR
- Dunning, J. (2000). Regions, Globalisation & the Knowledge-Based Economy. Oxford: Oxford University Press.
- Guster, D., & Brown, C. G. (2012). The application of business intelligence to higher education: Technical and managerial perspectives. Journal of Information Technology Management, 23(2), 42-62.
- Hamad, F., Al-Aamr, R., Jabbar, S. A., & Fakhuri, H. (2021). Business intelligence in academic libraries in Jordan: Opportunities and challenges. IFLA journal, 47(1), 37-50. https://doi.org/10.1177/0340035220931882
- Hartley, K., & Seymour, L.F.(2010). Towards a framework for the adoption of business intelligence in public sector organisations: the case of South Africa. The Proceedings of the SAICSIT 11, October 3-5, 2010, Cape Town, South Africa.
- Inzelt, A., Schubert, A., & Schubert, M. (2009). Incremental citation impact due to international co-authorship in Hungarian higher education institutions. Scientometrics,78(1), 37-43. https://doi.org/10.1007/s11192-007-1957-8
- Lariviere, V., Gingras, Y., & Archambault, É. (2006). Canadian collaboration networks: A comparative analysis of the natural sciences, social sciences, and the humanities. Scientometrics,68(3), 519-533. https://doi.org/10.1007/s11192-006-0127-8
- Leydesdorff, L., & Wagner, C.S. (2008). International collaboration in science and the formation of a core group. Journal of Informetrics, 2(4), 317-325. https://doi.org/10.1016/j.joi.2008.07.003
- Miller, G. (2011). Social scientists wade into the Tweet stream. Science, 333(2011), 1814-1815. https://doi.org/10.1126/science.333.6051.1814
- MSRT (2022). Islamic Republic of Iran, Ministry of Science Research and Technology, Statistics-2019, Erişim Tarihi: 10/02/2022, https://www.msrt.ir/en/page/20/statistics-2019#us2015
- Nikzad, M., Jamali, H. R., & Hariri, N. (2011). Patterns of Iranian co-authorship networks in social sciences: A comparative study. Library & Information Science Research, 33(4), 313-319. http://dx.doi.org/10.1016/j.lisr.2011.01.005
- Olmeda‐Gómez, C., Perianes‐Rodriguez, A., Ovalle‐Perandones, M. A., Guerrero‐Bote, V. P., & de Moya Anegón, F. (2009, January). Visualization of scientific co‐authorship in Spanish universities. In Aslib Proceedings, 61(1), 83-100. https://doi.org/10.1108/00012530910932302
- Schmidt, J. (2007). Knowledge politics of interdisciplinarity. Specifying the type of interdisciplinarity in the NSF’s NBIC scenario. Innovation: The European Journal of Social Science Research, 20(4), 313-328. https://doi.org/10.1080/13511610701760721
- Scholtz, B., Calitz, A., & Haupt, R. (2018). A business intelligence framework for sustainability information management in higher education. International Journal of Sustainability in Higher Education,19(2), 266-290. https://doi.org/10.1108/IJSHE-06-2016-0118
- Skolnikoff, E.B. (1993). The elusive transformation: Science, technology and the evolution of international politics. Princeton, NJ: Princeton University Press.
- Stehr, N. (2005). Knowledge politics: Governing the consequences of science and technology. New York: Routledge.
- Tang, L., & Shapira, P. (2011). China-US scientific collaboration in nanotechnology: patterns and dynamics. Scientometrics, 88(1), 1-16. https://doi.org/10.1007/s11192-011-0376-z
- Tešendić, D., & Krstićev, D. B. (2019). Business intelligence in the service of libraries. Information Technology and Libraries, 38(4), 98-113. http://dx.doi.org/10.6017/ital.v38i4.10599
- Wagner, C. S., & Leydesdorff, L. (2005). Mapping the network of global science: comparing international co-authorships from 1990 to 2000. International Journal of Technology and Globalisation, 1(2), 185-208. http://dx.doi.org/10.1504/IJTG.2005.007050
- Webometrics (2022). Ranking Web of Universities. Countries arranged by Number of Universities in Top Ranks. Accessed Date: 17/02/2022. https://www.webometrics.info/en/distribution_by_country
- Zeng, D., Chen, H., Castillo-Chavez, C. Lynch, W.B., & Thurmond, M. (2011). Infectious Disease Informatics and Biosurveillance. 27, Boston: Springer. https://doi.org/10.1007/978-1-4419-6892-0