Bibliometric analysis of the literature on business Intelligence systems adoption and acceptance, trends and perspectives

Abdelhamid El Malki; Samira Touate

doi:10.31893/multiscience.2024ss0517

Keywords:

Bibliometric Analysis

Business Intelligence

Adoption

Acceptance

Business Intelligence Factors

Abstract

In today's economic landscape, marked by a rapid surge in data, information has grown in volume, speed, and volatility. This has led to heightened competitive pressures within the business environment. As a result, organizations must adopt improved techniques and tools for information management. Business Intelligence is one of these tools, seen as a crucial technological advancement for improving the decision-making process, then Business Intelligence Systems (BIS) assist companies and organizations in making well-informed strategic decisions. Despite of the importance of these tools, literature shows that most BI projects do not deliver the expected results and BIS’s full benefits have largely been missed by organizations. In this respect, this work aims to analyze papers published between 2000 and 2022 that have focused on the adoption of BI. It presents bibliometric data, including high-frequency words, disciplinary distribution, lexical fields, approaches, trends, and evolution of research in this area. The study also presents some factors influencing the Business Intelligence Adoption (BIA).
References
1. Ahmad, A. (2015). Business intelligence for sustainable competitive advantage. Advances in Business Marketing and Purchasing, 22A, 3–220. https://doi.org/10.1108/S1069-096420150000022014
2. 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.
3. Ahmad, S., Miskon, S., Alkanhal, T. A., & Tlili, I. (2020). Modeling of business intelligence systems using the potential determinants and theories with the lens of individual, technological, organizational, and environmental contexts-a systematic literature review. Applied Sciences (Switzerland), 10(9). MDPI AG. https://doi.org/10.3390/app10093208
4. Ain, N. U., 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. https://doi.org/10.1016/j.dss.2019.113113
5. Antoniadis, I., Tsiakiris, T., & Tsopogloy, S. (2015). Business intelligence during times of crisis: Adoption and usage of ERP systems by SMEs. Procedia - Social and Behavioral Sciences, 175, 299–307. https://doi.org/10.1016/j.sbspro.2015.01.1204
6. 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
7. Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. The Winners, 13(2), 10–114.
8. Chaveesuk, S., & Horkondee, S. (2015). An integrated model of business intelligence adoption in Thailand logistics service firms. In 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 604–608). https://doi.org/10.1109/ICITEED.2015.7409018
9. Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 1165–1188.
10. Chen, Y., & Lin, Z. (2021). Business intelligence capabilities and firm performance: A study in China. International Journal of Information Management, 57. https://doi.org/10.1016/j.ijinfomgt.2020.102232
11. Deci, E. L., & Ryan, R. M. (2004). Handbook of self-determination research. University Rochester Press.
12. Hatta, N. N. M., Miskon, S., Ali, N. M., Abdullah, N. S., Ahmad, N., Hashim, H., Alias, R. A., & Maarof, M. A. (2015). Business intelligence system adoption theories in SMEs: A literature review. ARPN Journal of Engineering and Applied Sciences, 10(23), 18165–18174.
13. Hill, J., & Scott, T. (2004). A consideration of the roles of business intelligence and e‐business in management and marketing decision making in knowledge‐based and high‐tech start‐ups. Qualitative Market Research: An International Journal.
14. Indriasari, E., Wayan, S., Gaol, F. L., Trisetyarso, A., Saleh Abbas, B., & Ho Kang, C. (2019). Adoption of cloud business intelligence in Indonesia’s financial services sector. Asian Conference on Intelligent Information and Database Systems, 520–529.
15. Jalil, N. A., & Hwang, H. J. (2019). Technological-centric business intelligence: Critical success factors. International Journal of Innovation, Creativity and Change, 5(2), 1499.
16. Jalil, N. A., Prapinit, P., Melan, M., & Mustaffa, A. Bin. (2019). Adoption of business intelligence - Technological, individual and supply chain efficiency. In 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) (pp. 67–73). https://doi.org/10.1109/MLBDBI48998.2019.00021
17. Jayakrishnan, M., Mohamad, A., Azmi, F., & Abdullah, A. (2018). Adoption of business intelligence insights towards inaugurate business performance of Malaysian halal food manufacturing. Management Science Letters, 8(7), 725–736.
18. Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14, 21–38.
19. Liang, T. P., & Liu, Y. H. (2018). Research landscape of business intelligence and big data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2–10. https://doi.org/10.1016/j.eswa.2018.05.018
20. Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192
21. Olexova, C. (2014). Business intelligence adoption: A case study in the retail chain. WSEAS Transactions on Business and Economics, 11(1), 95–106.
22. Owusu, A. (2020). Determinants of cloud business intelligence adoption among Ghanaian SMEs. International Journal of Cloud Applications and Computing (IJCAC), 10(4), 48–69.
23. Ramesh, K. P., Amudha, R., Prasob, K. C., & Francis, J. (2023). Robo-advisory an intrinsic convergence of AI in enhancing investment returns–An empirical analysis. Multidisciplinary Science Journal, 5.
24. Rouhani, S., Ashrafi, A., Ravasan, A. Z., & Afshari, S. (2018). Business intelligence systems adoption model: An empirical investigation. Journal of Organizational and End User Computing, 30(2), 43–70. https://doi.org/10.4018/JOEUC.2018040103
25. Torkzadeh, G., & Lee, J. (2003). Measures of perceived end-user computing skills. Information & Management, 40(7), 607–615.
26. Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation. Lexington Books.
27. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Malki, A. E., & Touate, S. (2024). Bibliometric analysis of the literature on business Intelligence systems adoption and acceptance, trends and perspectives. Multidisciplinary Science Journal, 6, 2024ss0517. https://doi.org/10.31893/multiscience.2024ss0517

[1] Ahmad, A. (2015). Business intelligence for sustainable competitive advantage. Advances in Business Marketing and Purchasing, 22A, 3–220. https://doi.org/10.1108/S1069-096420150000022014

[2] 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.

[3] Ahmad, S., Miskon, S., Alkanhal, T. A., & Tlili, I. (2020). Modeling of business intelligence systems using the potential determinants and theories with the lens of individual, technological, organizational, and environmental contexts-a systematic literature review. Applied Sciences (Switzerland), 10(9). MDPI AG. https://doi.org/10.3390/app10093208

[4] Ain, N. U., 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. https://doi.org/10.1016/j.dss.2019.113113

[5] Antoniadis, I., Tsiakiris, T., & Tsopogloy, S. (2015). Business intelligence during times of crisis: Adoption and usage of ERP systems by SMEs. Procedia - Social and Behavioral Sciences, 175, 299–307. https://doi.org/10.1016/j.sbspro.2015.01.1204

[6] 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

[7] Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. The Winners, 13(2), 10–114.

[8] Chaveesuk, S., & Horkondee, S. (2015). An integrated model of business intelligence adoption in Thailand logistics service firms. In 2015 7th International Conference on Information Technology and Electrical Engineering (ICITEE) (pp. 604–608). https://doi.org/10.1109/ICITEED.2015.7409018

[9] Chen, H., Chiang, R. H. L., & Storey, V. C. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, 1165–1188.

[10] Chen, Y., & Lin, Z. (2021). Business intelligence capabilities and firm performance: A study in China. International Journal of Information Management, 57. https://doi.org/10.1016/j.ijinfomgt.2020.102232

[11] Deci, E. L., & Ryan, R. M. (2004). Handbook of self-determination research. University Rochester Press.

[12] Hatta, N. N. M., Miskon, S., Ali, N. M., Abdullah, N. S., Ahmad, N., Hashim, H., Alias, R. A., & Maarof, M. A. (2015). Business intelligence system adoption theories in SMEs: A literature review. ARPN Journal of Engineering and Applied Sciences, 10(23), 18165–18174.

[13] Hill, J., & Scott, T. (2004). A consideration of the roles of business intelligence and e‐business in management and marketing decision making in knowledge‐based and high‐tech start‐ups. Qualitative Market Research: An International Journal.

[14] Indriasari, E., Wayan, S., Gaol, F. L., Trisetyarso, A., Saleh Abbas, B., & Ho Kang, C. (2019). Adoption of cloud business intelligence in Indonesia’s financial services sector. Asian Conference on Intelligent Information and Database Systems, 520–529.

[15] Jalil, N. A., & Hwang, H. J. (2019). Technological-centric business intelligence: Critical success factors. International Journal of Innovation, Creativity and Change, 5(2), 1499.

[16] Jalil, N. A., Prapinit, P., Melan, M., & Mustaffa, A. Bin. (2019). Adoption of business intelligence - Technological, individual and supply chain efficiency. In 2019 International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI) (pp. 67–73). https://doi.org/10.1109/MLBDBI48998.2019.00021

[17] Jayakrishnan, M., Mohamad, A., Azmi, F., & Abdullah, A. (2018). Adoption of business intelligence insights towards inaugurate business performance of Malaysian halal food manufacturing. Management Science Letters, 8(7), 725–736.

[18] Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14, 21–38.

[19] Liang, T. P., & Liu, Y. H. (2018). Research landscape of business intelligence and big data analytics: A bibliometrics study. Expert Systems with Applications, 111, 2–10. https://doi.org/10.1016/j.eswa.2018.05.018

[20] Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192

[21] Olexova, C. (2014). Business intelligence adoption: A case study in the retail chain. WSEAS Transactions on Business and Economics, 11(1), 95–106.

[22] Owusu, A. (2020). Determinants of cloud business intelligence adoption among Ghanaian SMEs. International Journal of Cloud Applications and Computing (IJCAC), 10(4), 48–69.

[23] Ramesh, K. P., Amudha, R., Prasob, K. C., & Francis, J. (2023). Robo-advisory an intrinsic convergence of AI in enhancing investment returns–An empirical analysis. Multidisciplinary Science Journal, 5.

[24] Rouhani, S., Ashrafi, A., Ravasan, A. Z., & Afshari, S. (2018). Business intelligence systems adoption model: An empirical investigation. Journal of Organizational and End User Computing, 30(2), 43–70. https://doi.org/10.4018/JOEUC.2018040103

[25] Torkzadeh, G., & Lee, J. (2003). Measures of perceived end-user computing skills. Information & Management, 40(7), 607–615.

[26] Tornatzky, L. G., Fleischer, M., & Chakrabarti, A. K. (1990). Processes of technological innovation. Lexington Books.

[27] Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Abstract

References

How to cite