STRATEGIES FOR BI ACCEPTANCE: CHALLENGES AND SOLUTIONS

Yıl 2018, Cilt: 7 Sayı: 1, 237 - 240, 01.09.2018

Naciye Guliz Ugur , Aykut Hamit Turan

https://doi.org/10.17261/Pressacademia.2018.888

Öz

Purpose- The purpose of this study is to propose recommendations for organizations on creating strategies for BI adoption at both the organizational and individual level to help maximize the value of the BI investment.

Methodology- A significant literature review is conducted to identify key success factors. Thereafter, a model is introduced that will help to construct a strategy for BI adoption at both the organizational and the individual level.

Findings- The solution model presented in this work is separated into three sections: people, process, and technology. Working with a BI team and collaborating with department leadership, the Analytics Advisor creates and successfully implements a BI solution through an iterative and agile process.

Conclusion- This paper has demonstrated how an organization can increase its BI adoption rates by developing solutions for one department at a time. The solution enables the departments to implement a BI program that will provide value and a competitive advantage by improving the timeliness and quality of the decision-making process.

Anahtar Kelimeler

Technology acceptance, business intelligence, BI adoption, critical success factors, solution model

Kaynakça

• Boyton, J., Ayscough, P., Kaveri, D., Chiong, R. (2015). Suboptimal business intelligence implementations: understanding and addressing the problems. Journal of Systems & Information Technology, 17(3), 307-320.
• Hobek, R., Ariyachandra, T. R., Frolick, M. N. (2009). The importance of soft skills in business intelligence implementations. Business Intelligence Journal, 14(1), 28-36.
• Hou, C. (2012). Examining the effect of user satisfaction on system usage and individual performance with business intelligence systems: an empirical study of Taiwan’s electronics industry. International Journal of Information Management, 32, 560-573.
• Isik, O., Jones, M. C., Sidorova, A. (2012). Business intelligence (BI) success and the role of BI capabilities. Intelligent Systems in Accounting, Finance & Management, 18(4), 161- 176.
• LaValle, S., Lesser, E., Shockley, R., Hopkins, M. S., Kruschwitz, N. (2011). Big data, analytics and the path from insights to value. MIT Sloan Management Review, 52(2), 21-31.
• Mettler, T., Vimarlund, V. (2009). Understanding business intelligence in the context of healthcare. Health Informatics Journal, 15(3), 254– 264.
• Perez, A. (2015). Seven best practices to make BI adoption pervasive. Business Intelligence Journal, 20(3), 43-58.
• Stangarone, J. (2015). 7 practical ways to improve BI user adoption [Web log post]. Retrieved from http://www.mrc-productivity.com/blog/2015/03/7-practical-ways-to-improve-bi- user-adoption/
• Williams, S., Williams, N. (2003). The business value of business intelligence. Business Intelligence Journal, 8(4), 30-39.
• Wixom, B. H., Watson, H. J., Werner, T. (2011). Developing an enterprise business intelligence capability: the Norfolk Southern journey. MIS Quarterly Executive, 10(2), 61-71.
• Wixom, B. H., Yen, B., Relich, M. (2013). Maximizing value from business analytics. MIS Quarterly Executive, 12(2), 111-123.
• Woodside, J. (2011). Business intelligence best practices for success. Proceedings of the International Conference on Information management & Evaluation, 556-562.
• 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.
• Yeoh, W., Koronios, A. (2010). Critical success factors for business intelligence systems. Journal of Computer Information Systems, 50(Pt. 3), 23-32.
• Yogev, N., Even, A., Fink, L. (2013). How business intelligence creates value: an empirical investigation. International Journal of Business Intelligence Research, 4(3), 16-31.
• Yoon, T. E., Ghosh, B., Jeong, B. (2014). User acceptance of business intelligence (BI) application: technology, individual difference, social influence, and situational constraints. Hawaii International Conference on System Sciences, 53758-3766.