Utilization of Business Intelligence Tools among Business Intelligence Users





The study was an investigation of the impact of perceived usefulness and perceived ease of use of business intelligence (BI) tools among users. The relationship between and among the dependent variable (utilization of BI tools) and the independent variables (perceived usefulness and perceived ease of use) was investigated through the lenses of technology acceptance model (TAM). Other objectives for the current research were to build a model to predict users’ utilization of the independent variables, and to generalize the results of the research to the IT population. Data for the current research was collected utilizing a survey questionnaire, designed by the researcher, with a 5-point Likert scale to interpret responses to the survey questions. The analysis consisted of descriptive statistics and multiple regressions models. A prediction model was structured using generalized linear models. The result of the study was the development of a prediction model for BI tools utilization through the lenses of a technology acceptance model (TAM). The model highlighted the importance of up-to-date information provided by current BI tools, ability of BI tools to provide users with more analytical tools to accomplish their jobs, the degree to which BI tools allow users to present convincing arguments, the ability of BI tools to provide users with more possible solutions, the ability of BI tools to reduce the time required to accomplish jobs, and the ability of BI tools to help users make relevant business predictions.


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Author Biography

Emad Ahmed, Arab Open University

Assistant Professor


Agarwal, R. & Prasad, J. (1998).A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215. DOI: https://doi.org/10.1287/isre.9.2.204

Alter, A. (2004). A work system view of DSS in its fourth decade.Decision Support Systems, 38 (3), 319-327. DOI: https://doi.org/10.1016/j.dss.2003.04.001

Biere, M. (2010).The New Era of Enterprise Business Intelligence: Using Analytics to Achieve a Global Competitive Advantage.

Burton, B. and Hostmann, B. (2005).Findings from Sydney symposium: Perceptions of business intelligence. Retrieved from Gartner database

Chasalow, L. C. (2009). A model of organizational competencies for business intelligence success. Virginia Commonwealth University). ProQuest Dissertations and Theses, 191-n/a. Retrieved from http://search.proquest.com/docview/305174488?accountid=34899.(305174488).

Chung W., Zhang, Y., Huang, Z., Wang, G., Ong, T. and Chen H. (2004). Internet searching and browsing in a multilingual world: An experiment on the Chinese business intelligence portal (CbizPort). Journal of the American Society for Information Science andTechnology, 55 (9), 818-831. DOI: https://doi.org/10.1002/asi.20025

Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of DOI: https://doi.org/10.2307/249008

information technology. MIS Quarterly, 13(3), 319–340.

Davis, F. D., Bagozzi, R. P., &Warshaw, P. R. (1989).User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. DOI: https://doi.org/10.1287/mnsc.35.8.982

Dedrick, J., Gurbaxani, V., & Kraemer, K.L (2003). “Information technology and economic performance: A critical review of the empirical evidence.” ACM Computing Surveys. 35(1), 1-28. DOI: https://doi.org/10.1145/641865.641866

DeLone, W. H., & McLean, E. R. (1992).Information system success—the quest for the DOI: https://doi.org/10.1287/isre.3.1.60

dependent variable. Information Systems Research, 3, 60–95

DeLone, W.H. and McLean, E.R. (2003). The DeLone and McLean model of information system success: A ten-year update. Journal of Management Information Systems, 19 (4), 9–30. DOI: https://doi.org/10.1080/07421222.2003.11045748

Erickson, W. (2003). Smart companies in the 21st century: The secrets of creating successful business intelligence solutions. TDWI The Data Warehousing Institute Report Series, 1-35.

Fink, Arlene (1995). How to Sample in Surveys.Thousand Oaks, Calif: Sage Publications.

Gable, G., Sedera, D., and Chan, T. (2003). “Enterprise Systems Success: A Measurement Model.” International Conference on Information Systems, Seattle, WA.

Gartner Research Group (2011). Research Note: Key Issues for Business Intelligence and Performance Management Initiatives.

Jourdan, Z., Rainer, R. K. and Marshall, T. E. (2008). Business intelligence: An analysis of the literature. Information Systems Management, 25 (2), 121–131. DOI: https://doi.org/10.1080/10580530801941512

Moss, L. T. and Atre, S. (2003).Business intelligence roadmap: The complete project lifecycle for decision-support applications. Boston, MA: Addison-Wesley.

Negash, S. (2004).Business intelligence. Communications of the Association for Information Systems, 13, 177-195. DOI: https://doi.org/10.17705/1CAIS.01315

Niemi, T., Toivonen, S., Niinmaki, M., &Nummenmaa, J. (2007).Ontologisms with semantic Web/Grid in data integration for OLAP. International Journal on Semantic Web and Information Systems, 3(4), 25-49. DOI: https://doi.org/10.4018/jswis.2007100102

Olszak, C. M. and Ziemba, E. (2003). Business intelligence as a key to management of an DOI: https://doi.org/10.28945/2672

enterprise. Proceedings of Informing Science And IT Education. Santa Rosa, CA.

Pisello, T. &Strassmann, P. (2003). IT Value Chain Management- Maximizing the ROI from IT investment. New Canaan: The Information Economic Press.

Power, D. J. (2003).A brief history of decision support systems [Web log post]. Retrieved From http://dssresources.com/history/dsshistory.html

Ranjan, J. (2008). Business Intelligence: concepts, components, techniques, and benefits.Journal of Theoretical and Applied Information Technology. 3(4), 60-70.

Shroff, R. H. &Deneen, C. C., & Ng, E. M. (2011).Analysis of the technology acceptance model in examining students' behavioral intention to use an e-portfolio system. Australasian Journal of Educational Technology, 27(4), 600-618. Retrieved from http://search.proquest.com/docview/964174838?accountid=34899 DOI: https://doi.org/10.14742/ajet.940

Thong, J. (1999).An integrated model of information systems adoption in small businesses. Journal of Management Information Systems, 15(4), 187-214. DOI: https://doi.org/10.1080/07421222.1999.11518227

Weier, M.H. (2007). QUERY: What's next in BI? Information Week, 1128, 27-29.

White, C. (2004, September). Now is the right time for real-time BI. Information Management Magazine. Retrieved from: http://www.dmreview.com

Williams, S. and Williams, N. (2007). The profit impact of business intelligence, San Francisco, CA: Morgan Kaufmann. DOI: https://doi.org/10.1016/B978-012372499-1/50004-1

Wixom, B. & Watson, H. (2001).An Empirical Investigation of the Factors Affecting Data Warehousing Success. MIS Quarterly (25:1), 17-41. DOI: https://doi.org/10.2307/3250957




How to Cite

Ahmed, E. (2021). Utilization of Business Intelligence Tools among Business Intelligence Users. International Journal for Innovation Education and Research, 9(6), 237–253. https://doi.org/10.31686/ijier.vol9.iss6.3172