Factors Associated with Implementation of Business Intelligence among Lebanese SMEs

Authors

  • Georges Kfouri Vilnius University

Keywords:

Business intelligence, correlation, descriptive observational design, Lebanese SMEs, quantitative method, self-administered structured questionnaire.

Abstract

Business Intelligence (BI) provides leverage to businesses by improving decision-making concerning the future in an industry or aspects of resources planning. Because of its valuable benefits, many organizations attempt to implement BI reap the gains. However, many factors determine the success of its implemetaion in business. Most of these factors relate to the organizational culture and the readiness of the employees to use it appropriately to serve its purpose. This study used decriptive observational design of a quantitative nature  to explore the level of adoption of BI among 56 Lebanese SMEs as well as the perspectives of both the employees and the senior management on the adoption of BI in the business. The data was collected using self-administered structured questionnaire and analyzed  using the SPSS software specifically measuring the nature and types of correlation among the business variables and the level of success of BI implementation in the SMEs. The tests were done using Spearman's rho correlation coefficients. The results  show that quality of  BI infrastructure, positive attitude among senior employees and junior workers alike toward adoption of BI, and the setting of suitable environment to implement BI are essential for success. Also, junior workers show more support for BI than the senior management. Therefore, a change of organizational culture among the senior employees is recommended to facilitate BI adoption success in the SMEs.

Business Intelligence (BI) provides leverage to businesses by improving decision-making concerning the future in an industry or aspects of resources planning. Because of its valuable benefits, many organizations attempt to implement BI reap the gains. However, many factors determine the success of its implementation in business. Most of these factors relate to the organizational culture and the readiness of the employees to use it appropriately to serve its purposes. This study used descriptive observational design of a quantitative nature to explore the level of adoption of BI among 56 Lebanese SMEs as well as the perspectives of both the employees and the senior management on the adoption of BI in the business. The data was collected using self-administered structured questionnaire and analyzed using the SPSS software specifically measuring the nature and types of correlation among the business variables and the level of success of BI implementation in the SMEs. The tests were done using Spearman’s rho correlation coefficients. The results show that quality BI infrastructure, positive attitude among senior employees and junior workers alike toward adoption of BI, and the setting of suitable environment to implement BI are essential for success. Also, junior workers show more support for BI than the senior management. Therefore, a change of organizational culture among the senior employees is recommended to facilitate BI adoption success in the SMEs.

Author Biography

Georges Kfouri, Vilnius University

Economics Informatics Department, Doctoral Student

References

• Adamala, S., & Cidrin, L. (2011). Key success factors in business intelligence. Journal of Intelligence Studies in Business, 1(1), 1-8.

• Baker, T.L. (1994). Doing Social Research (2nd edition). New York: McGraw-Hill Inc.

• Baxter, R., Bedard, J. C., Hoitash, R., & Yezege, A. (2013). Enterprise risk management program quality: Determinants, value relevance, and the financial crisis. Contemporary Accounting Research, 30(4), 1264-1295. DOI: 10.1111/j.1911-3846.2012.01194.x

• Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 4(8), 88-98. DOI: 10.1145/1978542.1978562

• Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: from big data to big impact. MIS Quarterly, 36(4), 1165-1189.

• Creswell, J. W. (2003). Research design: Qualitative, quantitative, and mixed method approaches. Thousand Oaks, Calif: Sage Publications.

• Dorantes, C., Li, C., Peters, G. F., & Richardson, V. J. (2013). The effect of enterprise systems implementation on the firm information environment. Contemporary Accounting Research, 30(4), 1427-1461. DOI: 10.1111/1911-3846.12001

• Duan, L. & Xu, L. D. (2012). Business intelligence for enterprise systems: A survey. IEEE Transactions on Industrial Informatics, 8(3), 679-687. DOI:10.1109/TII.2012.2188804

• Elbashir, M. Z., Collier, P. A., & Sutton, S. G. (2011). The role of organizational absorptive capacity in strategic use of business intelligence to support integrated management control systems. The Accounting Review, 86(1), 155-184. DOI: http://dx.doi.org/10.2308/accr.00000010

• 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 (IJCSES), 3(2), 1-10. DOI: 10.5121/ijcses.2012.3201

• Frankfort-Nachmias, C., and Leon-Guerrero, A. (2014). Social Statistics for a Diverse Society. (7th Ed.). New York, NY: SAGE Publications.

• Frey, L., Botan, C., & Kreps, G. (1999). Investigating communication: An introduction to research methods. (2nd Ed.). Boston: Allyn & Bacon.

• Garth, A. (2008). Analyzing Data Using SPSS. Yorkshire, UK: Sheffield Hallam University.

• Gurjar, Y. S., & Rathore, V. S. (2013). Cloud business intelligence-is what business need today.International Journal of Recent Technology and Engineering (IJRTE), 1(6), 81-86.

• Herzberg, N., Meyer, A., & Weske, M. (2015). Improving business process intelligence by observing object state transitions. Data and Knowledge Engineering, (In press). DOI:10.1016/j.datak.2015.07.008

• Herschel, R. T. (2012). Organizational Applications of Business Intelligence Management Emerging Trends. Hershey, PA: IGI Global.

• Isik, O., Jones, M. C., & Sidorova, A. (2013). Business intelligence success: The roles of BI capabilities and decision environments. Information & Management, 50(1), 13-23. DOI:10.1016/j.im.2012.12.001

• Jha, N. K. (2008). Research methodology. Chandigarh: Abhishek Publications.

• Li, X., Hsieh, J. J. P., & Rai, A. (2010). Motivational differences across post-acceptance information system usage behaviors: An investigation in the business intelligence systems context. Information Systems research, 23(4), 659-682.

• Olszak, C. M., & Ziemba, E. (2012). Critical success factors for implementing business intelligence systems in small and medium enterprises on the example of Upper Silesia, Poland. Interdisciplinary Journal of Information, Knowledge, and Management, 7(1), 129-150.

• Popovic, A., Coelho, P. S., & Jaklic, J. (2009). The impact of business intelligence system maturity on information quality. Information Research, 14(4), 729-739.

• Popovic, A., Hackney, R., Coelho, P. S., Jaklic, J. (2012). Towards business intelligence systems success: Effects of maturity and culture on analytical decision making. Decision Support Systems, 54(1), 729-739.

• Prescott, J. E., & Miree, C. E. (1998). Small business solutions: Building and leveraging a competitive intelligence capability without going broke. Journal of Small Business Strategy, 9(2), 57-76.

• 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. DOI:10.1016/J.Dss.2011.10.009

• Sharma, D. (2014). Dimension modelling techniques in business intelligence. International Journal of Emerging Trends & Technology in Computer Science (IJETTCS), 3(6), 61-64.

• Shen, C. (2015). Factors of data infrastructure and resource support influencing the integration of business intelligence into enterprise resource planning systems. International Journal of Intelligent Information and Database Systems, 9(1), 1-54. DOI: 10.1504/IJIIDS.2015.070822

• Welman, C., Kruger, F., Mitchell, B., & Huysamen, G. K. (2005). Research methodology. Cape Town: Oxford University Press.

• Yin, R. K. (1994). Case study research: Design and methods. Thousand Oaks: Sage Publications.

• Zeng, L., Li, L., & Duan, L. (2012). Business intelligence in business computing environment. Information Technology Management, 13(4), 297-310.

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Published

2016-06-17

How to Cite

Kfouri, G. (2016). Factors Associated with Implementation of Business Intelligence among Lebanese SMEs. Asian Journal of Computer and Information Systems, 4(3). Retrieved from https://www.ajouronline.com/index.php/AJCIS/article/view/3958

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