Business intelligence - publication analysis using the R language

• Autorzy: Wyskwarski Marcin

Identyfikatory

DOI

10.29119/1641-3466.2019.137.17

• Języki publikacji: EN

Abstrakty

EN

The purpose of the work was to analyse publications in the area of Business Intelligence. Only bibliometric data was used in the analysis. The analysis was performed using the R programming language. An attempt was made to determine whether by analysing bibliometric data, it is possible to obtain information on Business Intelligence systems. Aiming at achieving the adopted goal, in the second point of the work, selected information on Business Intelligence systems was presented. The third point presents the manner of collecting data. Further stages of the analysis were also presented. The fourth point contains the results of the conducted research. Among others, the number of publications in individual years and the most common words in titles, abstracts and keywords were presented. Using two topic modelling algorithms, topics were generated that can also be used to identify information related to Business Intelligence systems.

Słowa kluczowe

EN

Business Intelligence; bibliometrics; text mining; R language

PL

Business Intelligence; bibliometria; eksploracja tekstu; język R

• Wydawca: Wydawnictwo Politechniki Śląskiej
• Czasopismo: Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Śląska
• Rocznik: 2019
• Tom: z. 137
• Strony: 263--274
• Opis fizyczny: Bibliogr. 22 poz.

Twórcy

autor

Wyskwarski Marcin

• Department of Economy and Informatics, Faculty of Organization and Management of Silesian University of Technology

Bibliografia

• 1. Addor, N., & Melsen, L.A. (2019). Legacy, Rather Than Adequacy, Drives the Selection of Hydrological Models. Water Resources Research, 55(1), 378-390. https://doi.org/10.1029/ 2018WR022958.
• 2. Araujo, C. (2006). Bibliometria: Evolução histórica e questões atuais. Em Questão, 12, 11-32.
• 3. Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/ j.joi.2017.08.007
• 4. Aria, M., Cuccurullo, C., & Sarto, F. (2015). Exploring healthcare governance literature: Systematic review and paths for future research. MECOSAN, 23, 61-80. https://doi.org/ 10.3280/MESA2014-091004.
• 5. Blei, D.M., & Lafferty, J.D. (2005). Correlated Topic Models. Proceedings of the 18th International Conference on Neural Information Processing Systems, 147-154. Retrieved from http://dl.acm.org/citation.cfm?id=2976248.2976267.
• 6. Blei, D., Ng, A., & Jordan, M. (2003). Latent Dirichlet Allocation Michael I. Jordan. Journal of Machine Learning Research, 3. Retrieved from http://www.jmlr.org/papers/ volume3/blei03a/blei03a.pdf.
• 7. Briner, R.B., & Denyer, D. (2012). Systematic review and evidence synthesis as a practice and scholarship tool. In D. Rousseau (Ed.), The Oxford Handbook of Evidence-Based Management (pp. 112-129). United Kingdom: Oxford University Press.
• 8. Cheng, L., & Cheng, P. (2011). Integration: Knowledge Management and Business Intelligence, 307-310. https://doi.org/10.1109/BIFE.2011.172.
• 9. Cuccurullo, C., Aria, M., & Sarto, F. (2016). Foundations and trends in performance management. A twenty-five years bibliometric analysis in business and public administration domains. Scientometrics, 108(2), 595-611. https://doi.org/10.1007/s11192-016-1948-8.
• 10. Ellegaard, O., & Wallin, J.A. (2015). The bibliometric analysis of scholarly production: How great is the impact? Scientometrics, 105(3), 1809-1831. https://doi.org/10.1007/ s11192-015-1645-z.
• 11. Feinerer, I. (2008a). A Text Mining Framework in {R} and Its Applications (Department of Statistics and Mathematics, Vienna University of Economics and Business Administration). Retrieved from http://epub.wu-wien.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_e09.
• 12. Feinerer, I. (2008b). An Introduction to Text Mining. R News, 8(2), 19-22. Retrieved from http://cran.r-project.org/doc/Rnews/.
• 13. Feinerer, I., Hornik, K., & Meyer, D. (2008). Text Mining Infrastructure. Journal of Statistical Software, 25(5), 1-54. Retrieved from http://www.jstatsoft.org/v25/i05.
• 14. Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information and Management, 54(1), 38-56. https://doi.org/10.1016/j.im.2016.03.009.
• 15. Hannula, M., & Pirttimäki, V. (2003). Business Intelligence Empirical Study on the top 50 Finnish Companies. The Journal of American Academy of Business, Cambridge, 2(2), 593-599.
• 16. Hornik, K., & Grün, B. (2011). Topicmodels: An R Package for Fitting Topic Models. Journal of Statistical Software, 40. https://doi.org/10.18637/jss.v040.i13.
• 17. Kimball, R., & Ross, M. (2013). The data warehouse toolkit: the definitive guide to dimensional modeling. Wiley.
• 18. Luhn, H.P. (1958). A Business Intelligence System. IBM J. Res. Dev., 2(4), 314-319. https://doi.org/10.1147/rd.24.0314.
• 19. Negash, S., & Gray, P. (2008). Business Intelligence. Handbook on Decision Support Systems, 2, 175-193. https://doi.org/10.1007/978-3-540-48716-6_9.
• 20. Turban, E., Aronson, J.E., & Liang, T.-P. (2005). Decision support systems and intelligent systems. Pearson/Prentice Hall.
• 21. Wixom, B., & Watson, H. (2010). The BI-Based Organization. International Journal of Business Intelligence Research, 1(1), 13-28. https://doi.org/10.4018/jbir.2010071702.
• 22. Yoon, T., Ghosh, B., & Jeong, B. (2014). User Acceptance of Business Intelligence (BI) Application: Technology, Individual Difference, Social Influence, and Situational Constraints. Proceedings of the Annual Hawaii International Conference on System Sciences, 3758-3766. https://doi.org/10.1109/HICSS.2014.467.