Investigating the Effect of Business Intelligence on Business Value Creation
Business Intelligence (BI), a technology-driven process, is considered as a strategic tool which includes the technologies used by enterprises for the data analysis of business information. The purpose of this study is to present a new model to investigating the effect of business intelligence on business value creation. A comprehensive literature review is conducted to identify the factors for the model development. The case study of this research is one of the research institute in Iran. The statistical population of this study is 90 employees of the institute which have been selected by simple random sampling. The researcher designed a Likert-based questionnaire to collect the data from this sample. Validity of the questionnaire was confirmed by the experts in the field of business. The reliability was confirmed by Cronbach's alpha. The results of regression analysis showed that variables of operational and strategic capabilities of business intelligence are effective in business value creation. In addition, the results indicated that knowledge management has a positive mediating effect on the relationships between operational capabilities of business intelligence and business value creation, and strategic capabilities of business intelligence and business value creation.
Bhatt, G. D. (2001). Knowledge management in organizations: examining the interaction between technologies, techniques, and people. Journal of knowledge management, 5(1), 68-75.
Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS quarterly, 1165-1188.
Cooper, B. L., Watson, H. J., Wixom, B. H., & Goodhue, D. L. (2000). Data warehousing supports corporate strategy at First American Corporation. MIS quarterly, 547-567.
Cooper, L. P., Nash, R. L., Phan, T.-A. T., & Bailey, T. R. (2005). Learning about the Organization via Knowledge Management: The Case of JPL. International Journal of Knowledge Management (IJKM), 1(1), 47-66.
Dzenopoljac, V., Alasadi, R., Zaim, H., & Bontis, N. (2018). Impact of knowledge management processes on business performance: Evidence from Kuwait. Knowledge and Process Management, 25(2), 77-87.
Fan, S., Lau, R. Y., & Zhao, J. L. (2015). Demystifying big data analytics for business intelligence through the lens of marketing mix. Big Data Research, 2(1), 28-32.
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.
Grigori, D., Casati, F., Castellanos, M., Dayal, U., Sayal, M., & Shan, M.-C. (2004). Business process intelligence. Computers in industry, 53(3), 321-343.
Haimila, S. (2001). ‘KM in practice: the helping hand of BI. In: KMWorld.
Herschel, R. T., & Jones, N. E. (2005). Knowledge management and business intelligence: the importance of integration. Journal of knowledge management, 9(4), 45-55.
Herschel, R., & Yermish, I. (2009). Knowledge management in business intelligence. In Knowledge management and organizational learning (pp. 131-143): Springer.
IşıK, Ö., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13-23.
Kappelman, L., McLean, E., Luftman, J., & Johnson, V. (2013). Key Issues of IT Organizations and Their Leadership: The 2013 SIM IT Trends Study. MIS Quarterly Executive, 12(4).
Lee, C. K., Lau, H. C., Ho, G. T., & Ho, W. (2009). Design and development of agent-based procurement system to enhance business intelligence. Expert Systems with Applications, 36(1), 877-884.
Lee, J. H., & Park, S. C. (2005). Intelligent profitable customers segmentation system based on business intelligence tools. Expert Systems with Applications, 29(1), 145-152.
Liebowitz, J. (1999). Key ingredients to the success of an organization's knowledge management strategy. Knowledge and process management, 6(1), 37-40.
Lin, Y.-H., Tsai, K.-M., Shiang, W.-J., Kuo, T.-C., & Tsai, C.-H. (2009). Research on using ANP to establish a performance assessment model for business intelligence systems. Expert Systems with Applications, 36(2), 4135-4146.
Maghrabi, R. O., Oakley, R. L., Thambusamy, R., & Iyer, L. S. (2011). The Role of Business Intelligence (Bi) in Service Innovation: an Ambidexterity Perspective. Paper presented at the AMCIS.
Nilashi, M., Ahmadi, H., Ahani, A., Ravangard, R., & bin Ibrahim, O. (2016). Determining the importance of hospital information system adoption factors using fuzzy analytic network process (ANP). Technological Forecasting and Social Change, 111, 244-264.
Nilashi, M., Zakaria, R., Ibrahim, O., Majid, M. Z. A., Zin, R. M., & Farahmand, M. (2015). MCPCM: a DEMATEL-ANP-based multi-criteria decision-making approach to evaluate the critical success factors in construction projects. Arabian Journal for Science and Engineering, 40(2), 343-361.
Okkonen, J., Pirttimäki, V., Hannula, M., & Lönnqvist, A. (2002). Triangle of Business Intelligence, Performance Measurement and Knowledge Management. Paper presented at the IInd Annual Conference on Innovative Research in Management, May 9-11, Stockholm, Sweden.
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(1), 729-739.
Popovič, A., Turk, T., & Jaklič, J. (2010). Conceptual model of business value of business intelligence systems. Management: Journal of Contemporary Management Issues, 15(1), 5-30.
Ramakrishnan, T., Jones, M. C., & Sidorova, A. (2012). Factors influencing business intelligence (BI) data collection strategies: An empirical investigation. Decision Support Systems, 52(2), 486-496.
Rouhani, S., Ghazanfari, M., & Jafari, M. (2012). Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS. Expert Systems with Applications, 39(3), 3764-3771.
Rubin, E., & Rubin, A. (2013). The impact of business intelligence systems on stock return volatility. Information & Management, 50(2-3), 67-75.
Sanchez, R., & Mahoney, J. T. (1996). Modularity, flexibility, and knowledge management in product and organization design. Strategic management journal, 17(S2), 63-76.
Schultze, U., & Cox, E. L. (1998). Investigating the contradictions in knowledge management.
Torbati, A. R., & Sayadi, M. K. (2018). A New Approach to Investigate the Performance of Insurance Branches in Iran Using Best-Worst Method and Fuzzy Inference System. Journal of Soft Computing and Decision Support Systems, 5(4), 13-18.
Tutunea, M. F., & Rus, R. V. (2012). Business intelligence solutions for SME's. Procedia Economics and Finance, 3, 865-870.
Vukšić, V. B., Bach, M. P., & Popovič, A. (2013). Supporting performance management with business process management and business intelligence: A case analysis of integration and orchestration. International journal of information management, 33(4), 613-619.
Yadegaridehkordi, E., Hourmand, M., Nilashi, M., Shuib, L., Ahani, A., & Ibrahim, O. (2018b). Influence of big data adoption on manufacturing companies' performance: An integrated DEMATEL-ANFIS approach. Technological Forecasting and Social Change.
Yadegaridehkordi, E., Nilashi, M., Nasir, M. H. N. B. M., & Ibrahim, O. (2018a). Predicting determinants of hotel success and development using Structural Equation Modelling (SEM)-ANFIS method. Tourism Management, 66, 364-386.
Zare, M., Pahl, C., Rahnama, H., Nilashi, M., Mardani, A., Ibrahim, O., & Ahmadi, H. (2016). Multi-criteria decision making approach in E-learning: A systematic review and classification. Applied Soft Computing, 45, 108-128.
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