![Open Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextgreen.png)
![Restricted Access](https://i-scholar.in/lib/pkp/templates/images/icons/fulltextred.png)
Determining Business Intelligence Usage Success
Business intelligence systems are highly complex systems that senior executives use to process vast amounts of information when making decisions. Business intelligence systems are rarely used to their full potential due to a poor understanding of the factors that contribute to system success. Organizations using business intelligence systems frequently find that it is not easy to evaluate the effectiveness of these systems, and researchers have noted that there is limited scholarly and practical understanding of how quality factors affect information use within these systems. This quantitative post positivist research used the information system (IS) success model to analyze how information quality and system quality influence information use in business intelligence systems. This study was also designed to investigate the moderating effects of maturity constructs (i.e., data sources and analytical capabilities) on the relationships between quality factors and information use.
Keywords
Business Intelligence, Information Quality, System Quality, Systems Maturity.
User
Font Size
Information
- Adamala, S., & Cidrin, L. (2011). Key success factors in business intelligence. Journal of Intelligence Studies in Business, 1, 107-127. Retrieved from https://ojs.hh.se/index.php/JISIB/index
- Appelbaum, D., Kogan, A., Vasarhelyi, M., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25(Supplement C), 29-44. doi:/10.1016/j.accinf.2017.03.003
- Arnott, D., Lizama, F., & Song, Y. (2017). Patterns of business intelligence systems use in organizations. Decision Support Systems, 97, 58-68. doi:10.1016/j.dss.2017.03.005
- Bach, M. P., Čeljo, A., & Zoroja, J. (2016). Technology acceptance model for business intelligence systems: Preliminary research. Procedia Computer Science, 100, 995-1001. doi:10.1016/j.procs.2016.09.270
- Baker, E., & Chasalow, L. (2015). Factors contributing to business intelligence success: The impact of dynamic capabilities. In Twenty-first Americas Conference on Information Systems (Vol. 1, pp. 670-682). Red Hook, NY: Curran Associates.
- Castellanos, M., Gupta, C., Wang, S., Dayal, U., & Durazo, M. (2012). A platform for situational awareness in operational BI. Decision Support Systems, 52, 869-883. doi:10.1016/j.dss.2011.11.011
- Chuah, M. H., & Wong, K. L. (2011). A review of business intelligence and its maturity models. African Journal of Business Management, 5, 3424-3428. doi:10.5897/AJBM10.1564
- DeLone, W. H., & McLean, E. R. (2003). The DeLone & McLean model of information systems success: A ten-year update. Journal of Management Information Systems, 19(4), 9-30. doi:10.1080/07421222.2003.11045748
- DeLone, W. H., & McLean, E. R. (2016). Information systems success measurement. Foundations and Trends® in Information Systems, 2(1), 1-116. doi:10.1561/2900000005
- Dooley, P. P., Levy, Y., Hackney, R. A., & Parrish, J. L. (2018). Critical value factors in business intelligence systems implementations. In A. V. Deokar, A. Gupta, L. S. Iyer, & M. C. Jones (Eds.), Analytics and data science: Annals of information systems (pp. 55-78). Cham, Switzerland: Springer.
- Even, A., Parmet, Y., & Erez, L. (2015). Factors that affect customers readiness for internet-based BI services. International Journal of Business Intelligence Research, 6(1), 30-48. doi:10.4018/IJBIR.2015010103
- Eybers, S., & Giannakopoulos, A. (2015). Identifying critical success factors for business intelligence systems. In E. Pimenidis, & M. Odeh (Eds.), Proceedings of the 9th European Conference on IS Management and Evaluation (pp. 77-84). Reading, England: Academic Conferences and Publishing International.
- Farrokhi, V., & Pokoradi, L. (2012). The necessities for building a model to evaluate business intelligence projects-literature review. International Journal of Computer Science & Engineering Survey, 3(2), 1-10. doi:10.5121/ijcses.2012.3201
- Fink, L., Yogev, N., & Even, A. (2017). Business intelligence and organizational learning: An empirical investigation of value creation processes. Information & Management, 54(1), 38-56. doi:10.1016/j.im.2016.03.009
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18, 39-50. doi:10.2307/3151312
- Foshay, N., Taylor, A., & Mukherjee, A. (2014). Winning the hearts and minds of business intelligence users: The role of metadata. Information Systems Management, 31, 167-180. doi:10.1080/10580530.2014.890444
- Gaardboe, R., & Svarre, T. (2017). Critical factors for business intelligence success. Proceedings of the 25th European Conference on Information systems (ECIS), 2017, 471-482. Retrieved from http://aisel.aisnet.org/ecis2017_rp/31
- Grublješič, T., & Jaklič, J. (2015). Business intelligence acceptance: The prominence of organizational factors. Information Systems Management, 32, 299-315. doi:10.1080/10580530.2015.1080000
- Hackney, R., Dooley, P., Levvy, Y., & Parrish, J. (2015). Critical value factors in business intelligence systems implementation success: An empirical analysis of system and information quality. Paper presented at the 2015 International Conference on Information Systems, Ft. Worth, Texas. Retrieved from http://bura.brunel.ac.uk/handle/2438/11970
- Hair, J. F., Jr. (2016). A primer on partial least squares structural equation modeling (2nd ed.). Retrieved from https://bookshelf.vitalsource.com/books/9781483377469
- Keith, T. Z. (2015). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling (2nd ed.). New York, NY: Routledge.
- Kowalczyk, M., & Gerlach, J. P. (2015). Business intelligence & analytics and decision quality – Insights on analytics specialization and information processing modes. ECIS 2015 Completed Research Papers, Paper 110. doi:10.18151/7217398
- Kowalczyk, M., & Buxmann, P. (2014). Big data and information processing in organizational decision processes. Business & Information Systems Engineering, 6, 267-278. doi:10.1007/s12599-014-0341-5
- Mardiana, S., Tjakraatmadja, J. H., & Aprianingsih, A. (2015). DeLone-McLean information system success model revisited: The separation of intention to use-use and the integration of technology acceptance models. International Journal of Economics and Financial Issues, 5(1S), 172-182. Retrieved from http://www.econjournals.com/index.php/ijefi/index
- Olszak, C. M. (2016). Toward better understanding and use of business intelligence in organizations. Information Systems Management, 33, 105-123. doi:10.1080/10580530.2016.1155946
- Peters, T., Işık, Ö., Tona, O., & Popovič, A. (2016). How system quality influences mobile BI use: The mediating role of engagement. International Journal of Information Management, 36, 773-783. doi:10.1016/j.ijinfomgt.2016.05.003
- Popovič, A., Coelho, P. S., & Jaklič, J. (2009). The impact of business intelligence system maturity on information quality. Information Research, 14(4), Article 417. Retrieved from http://www.informationr.net/ir/index.html
- Popovič, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54, 729-739. doi:10.1016/j.dss.2012.08.017
- Popovič, A., Hackney, R., Coelho, P. S., & Jaklič, J. (2014). How information-sharing values influence the use of information systems: An investigation in the business intelligence systems context. The Journal of Strategic Information Systems, 23, 270-283. doi:10.1016/j.jsis.2014.08.003
- Rouhani, S., Ashrafi, A., Zare Ravasan, A., & Afshari, S. (2016). The impact model of business intelligence on decision support and organizational benefits. Journal of Enterprise Information Management, 29, 19-50. doi:10.1108/JEIM-12-2014-0126
- Seddon, P. B., Constantinidis, D., Tamm, T., & Dod, H. (2017). How does business analytics contribute to business value? Information Systems Journal, 27, 237-269. doi:10.1111/isj.12101
- Torres, R., Sidorova, A., & Jones, M. C. (2018). Enabling firm performance through business intelligence and analytics: A dynamic capabilities perspective. Information & Management, 55, 822-839. doi:10.1016/j.im.2018.03.010
- Trieu, V. H. (2017). Getting value from business intelligence systems: A review and research agenda. Decision Support Systems, 93, 111-124. doi:10.1016/j.dss.2016.09.019
- Vallurupalli, V., & Bose, I. (2018). Rabbit or tortoise? Rethinking customer acquisition at Dravya Bank. Communications of the Association for Information Systems, 43, Article 22. doi:10.17705/1CAIS.04322
- Visinescu, L. L., Jones, M. C., & Sidorova, A. (2016). Improving decision quality: The role of business intelligence. The Journal of Computer Information Systems, 57, 58-66. doi:10.1080/08874417.2016.1181494
- Wixom, B., & Todd, P. A. (2005). A theoretical integration of user satisfaction and technology acceptance. Information Systems Research, 16(1), 85-102. doi:10.1287/isre.1050.0042
- Yeoh, W., & Koronios, A. (2010). Critical success factors for business intelligence systems. The Journal of Computer Information Systems, 50(3), 23-32. Retrieved from https://www.tandfonline.com/loi/ucis20
- Yeoh, W., Koronios, A., & Gao, J. (2008). Managing the implementation of business intelligence systems: A critical success factors framework. International Journal of Enterprise Information Systems, 4(3), 79-94. doi:10.4018/jeis.2008070106
- Yeoh, W., & Popovič, A. (2016). Extending the understanding of critical success factors for implementing business intelligence systems. Journal of the Association for Information Science & Technology, 67(1), 134-147. doi:10.1002/asi.23366
![](https://i-scholar.in/public/site/images/abstractview.png)
Abstract Views: 253
![](https://i-scholar.in/public/site/images/pdfview.png)
PDF Views: 125