Factors to Determine Business Intelligence Implementation in Organizations

Michael Smith Moreno Saavedra, Christian Bach

Abstract


In this study, we deploy review centric research of User Satisfaction, Cost Reducing, and Time Optimization impacting decision making on whether to implement Business Intelligence in organizations. The purpose of this paper is to present a literature review to examine the association between Business Intelligence and these three different factors. The primary goal of this study was to investigate the user’s satisfaction, cost reducing, and time optimization implications in order to choose Business Intelligence as a business tool. A comprehensive literature review of business intelligence created a theoretical foundation for this paper. Using the grounded theory, a model was developed and evaluated based on how User Satisfaction, Cost Reducing and Time Optimization impacts managerial decision-making using Business Intelligence. Using data from JSTOR and Academic Search Premier databases a new model is presented to encapsulate the highly dynamic interaction of user satisfaction, cost reducing, and time optimization with business intelligence to provide elements to consider the implementation of BI in a firm. This model highlights three key aspects that administrators consider in order to determine the possibility of taking Business Intelligence as an instrument in their daily duties. The theoretical model is limited to three factors only, which could be extended in future studies on this topic. Moreover, this model has been discussed using a theoretical perspective whereas practical contributions has been given less attention. This study provides an exciting opportunity to advance our knowledge of business intelligence to efficiently and flexibly help companies make the right decisions in real time, grasp business opportunities, and gain competitive advantage.

Keywords


Business Intelligence; Cost Reducing; Decision Making; Time Optimization; User Satisfaction

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References


Arnott, D. and G. Pervan, The Methodological and Theoretical Foundations of Decision Support Systems Research, in Information Systems Foundations: Theory, Representation and Reality. 2007, ANU Press. p. 247-262.

Bunata, E., Using business intelligence to manage supply costs. hfm (Healthcare Financial Management), 2013. 67(8): p. 44-47.

Cheng, S., Q. Zhang, and Q. Qin, Big data analytics with swarm intelligence. Industrial Management & Data Systems, 2016. 116(4): p. 646-666.

Dod, H.S. and R. Sharma, Competing with Business Analytics Research in progress, in Information Systems Foundations: Theory Building in Information Systems. 2012, ANU Press. p. 239-250.

Hannula, M. and V. Pirttimaki, Business intelligence empirical study on the top 50 Finnish companies. Journal of American Academy of Business, 2003. 2(2): p. 593-599.

Işık, Ö., M.C. Jones, and A. Sidorova, Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 2013. 50(1): p. 13-23.

Karimi, J., T.M. Somers, and A. Bhattacherjee, The Impact of ERP Implementation on Business Process Outcomes: A Factor-Based Study. Journal of Management Information Systems, 2007. 24(1): p. 101-134.

Kulkarni, U.R., J.A. Robles-Flores, and A. Popovič, Business Intelligence Capability: The Effect of Top Management and the Mediating Roles of User Participation and Analytical Decision Making Orientation. Journal of the Association for Information Systems, 2017. 18(7): p. 516-541.

Negash, S., Business intelligence. The communications of the Association for Information Systems, 2004. 13(1): p. 177-195.

Popeanga, J. and I. Lungu, Real-Time Business Intelligence for the Utilities Industry. Database Systems Journal, 2012. III(4): p. 15-24.

Rutz, D., T. Nelakanti, and N. Rahman, Practical Implications of Real Time Business Intelligence. Journal of Computing and Information Technology, 2012. 20(4): p. 257–264.

Seibold, M., D. Jacobs, and A. Kemper, Operational Business Intelligence: Processing Mixed Workloads. IT Professional, 2013. 15(5): p. 16-21.

Thamir, A. and E. Poulis, Business Intelligence Capabilities and Implementation Strategies. International Journal of Global Business, 2015. 8(1): p. 34-45.

Thompson, W.J.J. and J.S. van der Walt, Business intelligence in the cloud. 2010. Vol. 12. 2010.

Tutunea, M.F., Business Intelligence Solutions for Mobile Devices – An Overview. Procedia Economics and Finance, 2015. 27(Supplement C): p. 160-169.

Nedelcu, B., Business Intelligence Systems. Database Systems Journal, 2014(4): p. 12-20.

Hou, C.-K., 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, 2012. 32(6): p. 560-573.

Serumaga-Zake, P.A.E., The role of user satisfaction in implementing a Business Intelligence System. 2017. Vol. 19. 2017.

Trifu, M.R. and M.-L. Ivan, Big Data Components for Business Process Optimization. Informatica Economica, 2016. 20(1): p. 72-78.

LePine, J.A. and A. Wilcox-King, Developing novel theoretical insight from reviews of existing theory and research. Academy of Management Review, 2010. 35(4): p. 506-509.

Chakravarty, A. and S.S. Naware, Cost-effectiveness Analysis for Technology Acquisition. Medical Journal Armed Forces India, 2008. 64(1): p. 46-49.

Crabtree, G., Maximizing Labor Productivity. Financial Executive, 2012. 28(4): p. 21.

Hansen, D., Powering Business Analytics With Big Data and Real-Time Using Data Integration. Database Trends and Applications, 2013. 27(2): p. 40-41.

Kim, S.H. and T. Mukhopadhyay, Determining Optimal CRM Implementation Strategies. Information Systems Research, 2011. 22(3): p. 624-639.

Müller, M.P., et al., Decision Support for IT Investment Projects. Business & Information Systems Engineering, 2016. 58(6): p. 381-396.

Nasirzadeh, F. and P. Nojedehi, Dynamic modeling of labor productivity in construction projects. International Journal of Project Management, 2013. 31(6): p. 903-911.

Sohn, S., A contextual perspective on consumers' perceived usefulness: The case of mobile online shopping. Journal of Retailing and Consumer Services, 2017. 38(Supplement C): p. 22-33.

Sund, R., Contributions of Capital Deeping (IT and Non IT) and Total Factor Productivity in Labor Productivity Growth: Major Asian Countries. Productivity, 2014. 54(4): p. 442-443.

Wei, K. and J. Ram, Perceived usefulness of podcasting in organizational learning: The role of information characteristics. Computers in Human Behavior, 2016. 64(Supplement C): p. 859-870.

Doll, W.J., et al., The Meaning and Measurement of User Satisfaction: A Multigroup Invariance Analysis of the End-User Computing Satisfaction Instrument. Journal of Management Information Systems, 2004. 21(1): p. 227-262.

Sun, Y., et al., User Satisfaction with Information Technology Service Delivery: A Social Capital Perspective. Information Systems Research, 2012. 23(4): p. 1195-1211.

Chang, I.C., et al., Electronic medical record quality and its impact on user satisfaction — Healthcare providers' point of view. Government Information Quarterly, 2012. 29(2): p. 235-242.

Wixom, B.H. and P.A. Todd, A Theoretical Integration of User Satisfaction and Technology Acceptance. Information Systems Research, 2005. 16(1): p. 85-102.

Torkzadeh, G. and J. Lee, Measures of perceived end-user computing skills. Information & Management, 2003. 40(7): p. 607-615.

Goldhammer, F., J. Naumann, and Y. Keßel, Assessing individual differences in basic computer skills: Psychometric characteristics of an interactive performance measure. European Journal of Psychological Assessment, 2013. 29(4): p. 263-275.

Doll, W.J. and G. Torkzadeh, A Discrepancy Model of End-User Computing Involvement. Management Science, 1989. 35(10): p. 1151-1171.

Harrison, A.W. and R. Kelly Rainer, A general measure of user computing satisfaction. Computers in Human Behavior, 1996. 12(1): p. 79-92.

Davis, F.D., Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 1989. 13(3): p. 319-340.

Bhattacherjee, A., Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 2001. 25(3): p. 351-370.

Jafari Navimipour, N. and Z. Soltani, The impact of cost, technology acceptance and employees' satisfaction on the effectiveness of the electronic customer relationship management systems. Computers in Human Behavior, 2016. 55(Part B): p. 1052-1066.

Jan, A.U. and V. Contreras, Technology acceptance model for the use of information technology in universities. Computers in Human Behavior, 2011. 27(2): p. 845-851.

Morgan-Thomas, A. and C. Veloutsou, Beyond technology acceptance: Brand relationships and online brand experience. Journal of Business Research, 2013. 66(1): p. 21-27.

Varma, S. and J.H. Marler, The dual nature of prior computer experience: More is not necessarily better for technology acceptance. Computers in Human Behavior, 2013. 29(4): p. 1475-1482.

Marino, D.J., Using business intelligence to reduce the cost of care. hfm (Healthcare Financial Management), 2014. 68(3): p. 42-46.

Chin, T.A., et al., The Impact of Supply Chain Integration on Operational Capability in Malaysian Manufacturers. Procedia - Social and Behavioral Sciences, 2014. 130(Supplement C): p. 257-265.

Besemer, D., On-Demand Information: The Future Is Now for Pharmaceutical Manufacturing. Pharmaceutical Technology, 2006. 30(2): p. 110.

Wernz, C., H. Zhang, and P. Kongkiti, International study of technology investment decisions at hospitals. Industrial Management & Data Systems, 2014. 114(4): p. 568-582.

Benaroch, M., et al., Option-Based Risk Management: A Field Study of Sequential Information Technology Investment Decisions. Journal of Management Information Systems, 2007. 24(2): p. 103-140.

Demirhan, D., V.S. Jacob, and S. Raghunathan, Information Technology Investment Strategies under Declining Technology Cost. Journal of Management Information Systems, 2005. 22(3): p. 321-350.

Voudouris, I., et al., Effectiveness of technology investment: Impact of internal technological capability, networking and investment's strategic importance. Technovation, 2012. 32(6): p. 400-414.

David, J.S., D. Schuff, and R.S. Louis, Managing your total IT cost of ownership. Commun. ACM, 2002. 45(1): p. 101-106.

Kotlar, J., et al., Technology Acquisition in Family and Nonfamily Firms: A Longitudinal Analysis of Spanish Manufacturing Firms. Journal of Product Innovation Management, 2013. 30(6): p. 1073-1088.

Jones, G.K., A. Lanctot, and H.J. Teegen, Determinants and performance impacts of external technology acquisition. Journal of Business Venturing, 2001. 16(3): p. 255-283.

Khan, F.A., et al., Efficient data access and performance improvement model for virtual data warehouse. Sustainable Cities and Society, 2017. 35(Supplement C): p. 232-240.

Dobrev, K. and M. Hart, Benefits, Justification and Implementation Planning of Real-Time Business Intelligence Systems. Electronic Journal of Information Systems Evaluation, 2015. 18(2): p. 104-118.

Miller, D.J., C.A. Nelson, and D. Oleynikov, Shortened OR time and decreased patient risk through use of a modular surgical instrument with artificial intelligence. Surgical Endoscopy, 2009. 23(5): p. 1099-1105.

Şerbănescu, L. and M. Rădulescu, Optimizing Time in Business with Business Intelligence Solution. Procedia - Social and Behavioral Sciences, 2012. 62: p. 638-648.

Lovrić, L., Information-communication technology impact on labor productivity growth of EU developing countries. Zbornik radova Ekonomskog fakulteta u Rijeci : časopis za ekonomsku teoriju i praksu, 2012. 30(2): p. 223-245.

Gessner, G.H. and L. Volonino, Quick Response Improves Returns on Business Intelligence Investments. Information Systems Management, 2005. 22(3): p. 66-74.

Sheshasaayee, A. and T.A. Swetha Margaret, The Challenges of Business Intelligence in Cloud Computing. Indian Journal of Science and Technology, 2015. 8(36): p. 1-6.

Sandu, D.I., Operational and real-time Business Intelligence. Informatică economică, 2008. XII(3): p. 33-36.

Anonymous, Four Trends Reshaping the Business Intelligence Landscape in 2013. Database Trends and Applications, 2013. 27(2): p. 38-39.




DOI: http://dx.doi.org/10.24018/ejers.2017.2.12.527

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