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

Abstract

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.

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References

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. https://doi.org/10.1007/s12351-017-0343-4

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. https://doi.org/10.1109/ACCESS.2021.3100410

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. https://doi.org/10.1016/j.dss.2019.113113

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. https://doi.org/10.1108/IMDS-02-2020-0120

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. https://doi.org/10.1108/VJIKMS-04-2021-0061

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. https://doi.org/10.1108/IDD-01-2022-0008

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. https://doi.org/10.3390/app13074121

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. https://doi.org/10.5267/j.uscm.2023.3.022

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. https://doi.org/10.1108/JKM-09-2019-0517

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. https://doi.org/10.1109/TEM.2013.2285344

Bach, M. P., Čeljo, A., & Zoroja, J. (2016). Technology Acceptance Model for Business Intelligence Systems: Preliminary Research. Procedia Computer Science, 100, 995–1001. https://doi.org/10.1016/j.procs.2016.09.270

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. https://doi.org/10.12821/ijispm050201

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. https://doi.org/10.3390/joitmc8040189

Bhatiasevi, V., & Naglis, M. (2020). Elucidating the determinants of business intelligence adoption and organizational performance. Information Development, 36(1), 78–96. https://doi.org/10.1177/0266666918811394

Chen, Y., & Lin, Z. (2021). Business Intelligence Capabilities and Firm Performance: A Study in China. International Journal of Information Management, 57, 102232. https://doi.org/10.1016/j.ijinfomgt.2020.102232

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. https://doi.org/10.1080/08874417.2020.1858729

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. https://doi.org/10.1287/mnsc.35.8.982

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. https://doi.org/10.2307/3151312

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). https://doi.org/10.4102/sajesbm.v14i1.501

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. https://doi.org/10.1504/IJBIS.2019.098246

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. https://doi.org/10.1155/2021/7032037

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. https://doi.org/10.1108/EBR-11-2018-0203

Hasan, R., Ashfaq, M., & Shao, L. (2021). Evaluating Drivers of Fintech Adoption in the Netherlands. Global Business Review, 097215092110274. https://doi.org/10.1177/09721509211027402

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. https://doi.org/10.1007/s11747-014-0403-8

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. https://doi.org/10.1016/j.joitmc.2023.100111

Hou, C.-K. (2016). Understanding business intelligence system continuance intention. Information Development, 32(5), 1359–1371. https://doi.org/10.1177/0266666915599588

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. https://doi.org/10.14201/adcaij.28394

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. https://doi.org/10.1109/ITSS-IoE56359.2022.9990950

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. https://doi.org/10.1063/5.0148676

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. https://doi.org/10.1088/1742-6596/1804/1/012039

Kline, R. B. (2013). Principales and practice of Strutural equation modeling. In Journal of Chemical Information and Modeling (Vol. 53, Issue 9). https://doi.org/10.1017/CBO9781107415324.004

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. https://doi.org/10.1504/IJISCM.2011.045833

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. https://doi.org/10.1007/s13132-022-00961-8

Maghsoudi, M., & Nezafati, N. (2023). Navigating the acceptance of implementing business intelligence in organizations: A system dynamics approach. Telematics and Informatics Reports, 11, 100070. https://doi.org/10.1016/j.teler.2023.100070

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. https://doi.org/10.1080/15256480.2018.1547239

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. https://doi.org/10.1007/s12652-020-02473-2

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. https://doi.org/10.1016/j.ipm.2021.102725

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. https://doi.org/10.1016/j.accinf.2016.03.001

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. https://doi.org/10.1016/j.dss.2012.08.017

Puklavec, B., Oliveira, T., & Popovič, A. (2018). Understanding the determinants of business intelligence system adoption stages. Industrial Management & Data Systems, 118(1), 236–261. https://doi.org/10.1108/IMDS-05-2017-0170

Ranjan, J., & Foropon, C. (2021). Big Data Analytics in Building the Competitive Intelligence of Organizations. International Journal of Information Management, 56, 102231. https://doi.org/10.1016/j.ijinfomgt.2020.102231

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. https://doi.org/10.1016/S0148-2963(02)00320-X

Saeed, K., Sidorova, A., & Vasanthan, A. (2023). The Bundling of Business Intelligence and Analytics. Journal of Computer Information Systems, 63(4), 781–792. https://doi.org/10.1080/08874417.2022.2103856

Seo, K. H., & Lee, J. H. (2021). The Emergence of Service Robots at Restaurants: Integrating Trust, Perceived Risk, and Satisfaction. Sustainability, 13(8), 4431. https://doi.org/10.3390/su13084431

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. https://doi.org/10.3390/jrfm14020058

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. https://doi.org/10.1109/eSmarTA59349.2023.10293688

Trieu, V.-H. (2023). Towards an understanding of actual business intelligence technology use: an individual user perspective. Information Technology & People, 36(1), 409–432. https://doi.org/10.1108/ITP-11-2020-0786

Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46(2), 186–204. https://doi.org/10.1287/mnsc.46.2.186.11926

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. https://doi.org/10.1016/j.procs.2015.08.486

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. https://doi.org/10.1016/j.ijinfomgt.2019.08.005

Authors

Shuaib M. Abdulnabi
shuaib.muqdad@hcu.edu.iq (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). https://doi.org/10.36923/jicc.v24i3.833

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