Business Intelligence in Small and Medium Enterprises: Integrating Information Quality, Organization Readiness and Technology Infrastructure

Shuaib M. Abdulnabi (1)
(1) Department of Medical Equipment Technology Engineering Al-Hadba University College Mosul, Iraq, Iraq


The advent of new technologies in the era of digitization has made efficient use of business intelligence crucial for small and medium-sized enterprises (SMEs). Therefore, this study aims to measure the impact of the Technology Acceptance Model (TAM) and other factors, such as quality of information, organization readiness, and technology infrastructure, on business intelligence.

A quantitative research methodology was employed, with a sample size of 281 participants who were owners, managers, and information system staff at Iraqi SMEs who had experience using business intelligence.

The findings of this study indicated that information quality has a significant impact on perceived usefulness (PU) and perceived ease of use (PEOU). Similarly, PU, PEOU, organization readiness, and technology infrastructure positively and significantly impact business intelligence adoption.

This study offers a comprehensive analysis of the crucial aspects that contribute to the successful deployment of business intelligence, thereby influencing the outcomes of SMEs. The results of this study will help owners and managers of SMEs and academicians develop a business intelligence system that can enhance overall organizational efficiency in a dynamic economic setting. Putting in place a good business intelligence system will help managers make better decisions, boost economic growth for businesses, support new ideas in businesses, and improve their overall performance and output.

Full text article

Generated from XML file


Adeyelure, T. S., Kalema, B. M., & Bwalya, K. J. (2018). A framework for deployment of mobile business intelligence within small and medium enterprises in developing countries. Operational Research, 18(3), 825–839.

Ahmad, S., Miskon, S., Alabdan, R., & Tlili, I. (2021). Statistical Assessment of Business Intelligence System Adoption Model for Sustainable Textile and Apparel Industry. IEEE Access, 9, 106560–106574.

Ain, N., Vaia, G., DeLone, W. H., & Waheed, M. (2019). Two decades of research on business intelligence system adoption, utilization and success – A systematic literature review. Decision Support Systems, 125, 113113.

Al-Eisawi, D., Serrano, A., & Koulouri, T. (2020). The effect of organisational absorptive capacity on business intelligence systems efficiency and organisational efficiency. Industrial Management & Data Systems, 121(2), 519–544.

Al-Okaily, A., Al-Okaily, M., & Teoh, A. P. (2023). Evaluating ERP systems success: evidence from Jordanian firms in the age of the digital business. VINE Journal of Information and Knowledge Management Systems, 53(6), 1025–1040.

Al-Okaily, A., Teoh, A. P., Al-Okaily, M., Iranmanesh, M., & Al-Betar, M. A. (2023). The efficiency measurement of business intelligence systems in the big data-driven economy: a multidimensional model. Information Discovery and Delivery, 51(4), 404–416.

Alsibhawi, I. A. A., Yahaya, J. B., & Mohamed, H. B. (2023). Business Intelligence Adoption for Small and Medium Enterprises: Conceptual Framework. Applied Sciences, 13(7), 4121.

Alzoubi, K., Bataineh, K., Matalka, M. Al, Al-Rawashdeh, O., Malkawi, A., AlGhasawneh, Y., Alghadi, M., Alibraheem, M. M., & ALzoubi, M. (2023). Critical success factors for business intelligence and bank performance. Uncertain Supply Chain Management, 11(3), 1257–1264.

Arefin, M. S., Hoque, M. R., & Rasul, T. (2021). Organizational learning culture and business intelligence systems of health-care organizations in an emerging economy. Journal of Knowledge Management, 25(3), 573–594.

Ataseven, C., Prajogo, D. I., & Nair, A. (2013). ISO 9000 Internalization and Organizational Commitment—Implications for Process Improvement and Operational Performance. IEEE Transactions on Engineering Management, 61(1), 5–17.

Bach, M. P., Čeljo, A., & Zoroja, J. (2016). Technology Acceptance Model for Business Intelligence Systems: Preliminary Research. Procedia Computer Science, 100, 995–1001.

Bach, M. P., Zoroja, J., & Čeljo, A. (2022). An extension of the technology acceptance model for business intelligence systems: project management maturity perspective. International Journal of Information Systems and Project Management, 5(2), 5–21.

Bany Mohammad, A., Al-Okaily, M., Al-Majali, M., & Masa’deh, R. (2022). Business Intelligence and Analytics (BIA) Usage in the Banking Industry Sector: An Application of the TOE Framework. Journal of Open Innovation: Technology, Market, and Complexity, 8(4), 189.

Bhatiasevi, V., & Naglis, M. (2020). Elucidating the determinants of business intelligence adoption and organizational performance. Information Development, 36(1), 78–96.

Chen, Y., & Lin, Z. (2021). Business Intelligence Capabilities and Firm Performance: A Study in China. International Journal of Information Management, 57, 102232.

Choi, H. S., Hung, S.-Y., Peng, C.-Y., & Chen, C. (2022). Different Perspectives on BDA Usage by Management Levels. Journal of Computer Information Systems, 62(3), 503–515.

Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.

Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of Computer Technology: A Comparison of Two Theoretical Models. Management Science, 35(8), 982–1003.

Falk, R. F., & Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.

Fishbein, M., & Ajzen, I. (1975). Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Addison-Wesley, Reading.

Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models with Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39.

Gauzelin, S., & Bentz, H. (2017). An examination of the impact of business intelligence systems on organizational decision making and performance: The case of France. Journal of Intelligence Studies in Business, 7(2).

Gomwe, G., Potgieter, M., & Litheko, A. M. (2022). Proposed framework for innovative business intelligence for competitive advantage in small, medium and micro-organisations in the North West province of South Africa. The Southern African Journal of Entrepreneurship and Small Business Management, 14(1).

Gonzales, M. L., Mukhopadhyay, S., Bagchi, K., & Gemoets, L. (2019). Factors influencing business intelligence-enabled success in global companies: an empirical study. International Journal of Business Information Systems, 30(3), 324.

Guo, X., Wang, L., Gao, Y., & Guo, L. (2021). Analysis on Influence of Business Intelligence Information Quality over User Information Adoption Based on Multiple Mediating Effects. Discrete Dynamics in Nature and Society, 2021, 1–16.

Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24.

Hasan, R., Ashfaq, M., & Shao, L. (2021). Evaluating Drivers of Fintech Adoption in the Netherlands. Global Business Review, 097215092110274.

Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.

Hmoud, H., Al-Adwan, A. S., Horani, O., Yaseen, H., & Zoubi, J. Z. Al. (2023). Factors influencing business intelligence adoption by higher education institutions. Journal of Open Innovation: Technology, Market, and Complexity, 9(3), 100111.

Hou, C.-K. (2016). Understanding business intelligence system continuance intention. Information Development, 32(5), 1359–1371.

Jasim, Y. A., Saeed, M. G., & Raewf, M. B. (2022). Analyzing Social Media Sentiment: Twitter as a Case Study. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal, 11(4), 427-450.

Jameel, A. S., & Alheety, A. S. (2022). Blockchain technology adoption in SMEs: the extended model of UTAUT. 2022 International Conference on Intelligent Technology, System and Service for Internet of Everything (ITSS-IoE), 1–6.

Jameel, A. S., Harjan, S. A., & Ahmad, A. R. (2023). Behavioral intentions to use artificial intelligence among managers in small and medium enterprises. International Conference on Advances in Communication Technology and Computer Engineering, 020006.

Jameel, A. S., Hamdi, S. S., Karem, M. A., & Raewf, M. B. (2021, February). E-Satisfaction based on E-service Quality among university students. In Journal of Physics: Conference Series (Vol. 1804, No. 1, p. 012039). IOP Publishing.

Kline, R. B. (2013). Principales and practice of Strutural equation modeling. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9).

Kohnke, O., Wolf, T. R., & Mueller, K. (2011). Managing user acceptance: an empirical investigation in the context of business intelligence standard software. International Journal of Information Systems and Change Management, 5(4), 269.

Lateef, M., & Keikhosrokiani, P. (2023). Predicting Critical Success Factors of Business Intelligence Implementation for Improving SMEs’ Performances: a Case Study of Lagos State, Nigeria. Journal of the Knowledge Economy, 14(3), 2081–2106.

Maghsoudi, M., & Nezafati, N. (2023). Navigating the acceptance of implementing business intelligence in organizations: A system dynamics approach. Telematics and Informatics Reports, 11, 100070.

Masa’Deh, R., Obeidat, Z., Maqableh, M., & Shah, M. (2021). The Impact Of Business Intelligence Systems on an Organization’s Effectiveness: The Role of Metadata Quality From a Developing Country’S View. International Journal of Hospitality & Tourism Administration, 22(1), 64–84.

Nithya, N., & Kiruthika, R. (2021). Impact of Business Intelligence Adoption on performance of banks: a conceptual framework. Journal of Ambient Intelligence and Humanized Computing, 12(2), 3139–3150.

Niu, Y., Ying, L., Yang, J., Bao, M., & Sivaparthipan, C. B. (2021). Organizational business intelligence and decision making using big data analytics. Information Processing & Management, 58(6), 102725.

Peters, M. D., Wieder, B., Sutton, S. G., & Wakefield, J. (2016). Business intelligence systems use in performance measurement capabilities: Implications for enhanced competitive advantage. International Journal of Accounting Information Systems, 21, 1–17.

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.

Puklavec, B., Oliveira, T., & Popovič, A. (2018). Understanding the determinants of business intelligence system adoption stages. Industrial Management & Data Systems, 118(1), 236–261.

Ranjan, J., & Foropon, C. (2021). Big Data Analytics in Building the Competitive Intelligence of Organizations. International Journal of Information Management, 56, 102231.

Ritter, T., & Gemünden, H. G. (2004). The impact of a company’s business strategy on its technological competence, network competence and innovation success. Journal of Business Research, 57(5), 548–556.

Saeed, K., Sidorova, A., & Vasanthan, A. (2023). The Bundling of Business Intelligence and Analytics. Journal of Computer Information Systems, 63(4), 781–792.

Seo, K. H., & Lee, J. H. (2021). The Emergence of Service Robots at Restaurants: Integrating Trust, Perceived Risk, and Satisfaction. Sustainability, 13(8), 4431.

Stjepić, A.-M., Pejić Bach, M., & Bosilj Vukšić, V. (2021). Exploring Risks in the Adoption of Business Intelligence in SMEs Using the TOE Framework. Journal of Risk and Financial Management, 14(2), 58.

Thabit, T. H., & Abdullah, S. H. (2023). Perceived Trust of Stakeholders: Predicting the Use of COBIT 2019 to Reduce Information Asymmetry. 2023 3rd International Conference on Emerging Smart Technologies and Applications (ESmarTA), 1–5.

Trieu, V.-H. (2023). Towards an understanding of actual business intelligence technology use: an individual user perspective. Information Technology & People, 36(1), 409–432.

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204.

Wanda, P., & Stian, S. (2015). The Secret of my Success: An exploratory study of Business Intelligence management in the Norwegian Industry. Procedia Computer Science, 64, 240–247.

Wong, L.-W., Leong, L.-Y., Hew, J.-J., Tan, G. W.-H., & Ooi, K.-B. (2020). Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. International Journal of Information Management, 52, 101997.


Shuaib M. Abdulnabi (Primary Contact)
M. Abdulnabi, S. Business Intelligence in Small and Medium Enterprises: Integrating Information Quality, Organization Readiness and Technology Infrastructure. Journal of Intercultural Communication, 24(3).

Article Details

Smart Citations via scite_