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2018 | vol. 28/2, 4/2018 | 439--446
Tytuł artykułu

The Analytic Hierarchy Process AHP for Business Intelligence System Evaluation

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The aim of the article is a presentation of the report of the latest studies carried out at the Faculty of Management and Economics of Services (University of Szczecin, Poland) on analyzing the quality level of two business intelligence system (Power BI and Tableau Public). Analysis was based on the multi-criteria analytic hierarchy process AHP method, a tool that is used for determining the main criteria of BI system evaluation. By structuring the problem based on the hierarchy, it is possible to better understand the level of quality, the criteria to be used and the alternatives to be evaluated. The proposed research concept can be used to analyze business intelligence problems within the framework of specific subjects, such as system quality, quality of analysis, data cleaning and data connectors, visualization etc. Assuming a proper (the best description of the nature of BI systems) selection of descriptive characteristics and transforming them into real determinants, the AHP concept can be used to improve decisions on quality issues in BI system evaluation.(author's abstract)
Rocznik
Strony
439--446
Opis fizyczny
Twórcy
autor
  • Uniwersytet Szczeciński
Bibliografia
  • Cai, S., Jun, M., Pham, L. (2007). End-user computing satisfaction and its key dimensions: An exploratory study. Southwest Decision Sciences Institute, 2007, 725-734.
  • Cronin, J., Taylor, S. (1994). SERPVERF Versus SERVQUAL: Reconciling Performance-Based and Perceptions - Minus-Expectations Measurement of Service quality. Journal of Marketing, 58 (1), 23-24.
  • DeLone, W.H., McLean, E.R. (2003). The DeLone and McLean model of information systems success: A ten-year update. Management Information Systems, 19 (4), 9-30.
  • Diech, W., Korbicz, J., Rutkowski, L., Tadeusiewicz, R. (2000). Sieci neuronowe. Warszawa: Akademicka Oficyna Wydawnicza.
  • Doll, W.J., Torkzadeh, G. (1988). The Measurement of End-User Computing Satisfaction. MIS Quarterly, 12 (2), 259-274.
  • Hashmi, N. (2006). Business Information Warehouse for SAP. Roseville: Prima Publishing
  • Muntean, M. (2012). Business intellignece approches. Iasi, WSEAS Conference on Mathematics and Computers in Business and Economics. Retrieved from: https://mpra.ub.uni-muenchen.de/41139/1/MPRA_paper_41139.pdf.
  • Nedelcu, B. (2015). Business intelligence systems. Database Systems Journal, 1/12.
  • Popper, K. (2002). Logika odkrycia naukowego. Warszawa: Wydawnictwo Naukowe PWN
  • Ranjan, J. (2009). Business intelligence, cocepts, components, techiques and benefits. Journal of Theoretical and Applied Information Technology. Retrieved from: http://www.jatit.org/volumes/research-papers/Vol9No1/9Vol9No1.pdf.
  • Saaty, R. (2002). Decision Making in Complex Environments: The Analytic Network Process (ANP) for Dependence and Feedback; a manual for the ANP Software SuperDecisions. Creative Decisions Foundation.
  • Stecyk, A. (2016). Doskonalenie jakości usług edukacyjnych w szkolnictwie wyższym. Podejście metodyczne. Szczecin: Uniwersytet Szczeciński.
  • Wells, D. (2008). Business Analytics - Getting the Point. Retrieved from: http://b-eye-network.com/view/7133.
Typ dokumentu
Bibliografia
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