International Journal of Finance & Managerial Accounting

International Journal of Finance & Managerial Accounting

Identification and Prioritization of Effective Business Intelligence Factors for Enhancing the Quality of Management Accounting Reports Using the Fuzzy Delphi Analytic Hierarchy Process (FDAHP) Technique

Articles in Press

Document Type : Original Article

Authors
maryam saremi 1
Ali Rahmani 2
mohammad mahmoodi 3
1 PhD Student, Department of Accounting, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran.
2 Professor, Department of Accounting, Faculty of Social Sciences & Economics, Alzahra University, Tehran, Iran.
3 Associate Professor, Department of Accounting, Firoozkooh Branch, Islamic Azad university, Firoozkooh, Iran.
10.22034/ijfma.2026.79487.2403
Abstract
Business Intelligence (BI), as one of the emerging information management technologies, integrates, processes, and analyzes organizational data to provide accurate, timely, and reliable information for managerial decision-making. Despite the widespread adoption of BI, identifying and prioritizing the critical factors influencing its successful implementation to enhance the quality of management accounting reports remains a significant research challenge. Accordingly, this study aimed to identify and prioritize the factors affecting the implementation of Business Intelligence for improving the quality of management accounting reporting. The research is applied in terms of purpose and adopts a mixed-methods (qualitative–quantitative) approach. In the qualitative phase, the initial factors were identified through a comprehensive literature review and semi-structured interviews with ten experts and analyzed using the Grounded Theory approach. In the quantitative phase, the identified factors were refined through the Fuzzy Delphi Method and subsequently prioritized using the Fuzzy Delphi Analytic Hierarchy Process (FDAHP). The findings revealed that the lack of information systems integration, data fragmentation, information technology maturity, top management support, data governance, Business Intelligence implementation, and the Extract, Transform, and Load (ETL) process are the most influential factors for successful BI implementation. Furthermore, improved managerial decision-making, enhanced reporting accuracy and timeliness, greater financial information transparency, the development of data-driven decision-making, and improved organizational performance were identified as the most significant outcomes of BI implementation. The findings provide a practical framework for managers and policymakers seeking to implement Business Intelligence systems and improve the quality of management accounting reporting within organizations.
Keywords
Business Intelligence
Management Accounting
Quality of Management Accounting Reporting
Fuzzy Delphi
Fuzzy Delphi Analytic Hierarchy Process (FDAHP)
  1. Achahbia, A., & Ammari, A. (2022). Business intelligence systems and management accounting: A systematic literature review. Journal of Intelligence Studies in Business, 12(2), 43–61. https://doi.org/10.37380/jisib.v12i2.934
  2. Albashrawi, M. (2016). Business intelligence and analytics in management accounting research: Status and future focus. International Journal of Business Intelligence Research, 7(2), 1–17. https://doi.org/10.4018/IJBIR.2016070101
  3. Ansarian, Ramin; Rahnamay-Roodposhti, Freydoon; Zomordian, Gholamreza; Nikomaram, Hashem. (2023). Designing a business intelligence model with respect to ethical decision-making in accounting businesses. Investment Knowledge, 14(53), 575-596. (In Persian). doi: 10.30495/jik.0621.23478
  4. Anthony, R. N. (1965). Planning and control systems: A framework for analysis. Harvard University Press.
  5. Appelbaum, D., Kogan, A., Vasarhelyi, M. A., & Yan, Z. (2017). Impact of business analytics and enterprise systems on managerial accounting. International Journal of Accounting Information Systems, 25, 29–44. https://doi.org/10.1016/j.accinf.2017.03.003
  6. Bhimani, A. (2020). Digital data and management accounting: Why we need to rethink research methods. Journal of Management Control, 31(1–2), 9–23. https://doi.org/10.1007/s00187-020-00295-z
  7. Burns, J., Quinn, M., Warren, L., & Oliveira, J. (2019). Management accounting. Oxford University Press.
  8. Caserio, C., & Trucco, S. (2018). Business intelligence and accounting information systems: A framework for management accounting. International Journal of Digital Accounting Research, 18, 1–29. https://doi.org/10.4192/1577-8517-v18_1
  9. Caserio, C., & Trucco, S. (2018). Enterprise resource planning and business intelligence systems for information quality: Evidence from Italy. Journal of Modern Accounting and Auditing, 14(3), 125–137.
  10. Chaudhuri, S., Dayal, U., & Narasayya, V. (2011). An overview of business intelligence technology. Communications of the ACM, 54(8), 88–98. https://doi.org/10.1145/1978542.1978562
  11. Drury, C. (2021). Management and cost accounting (11th ed.). Cengage Learning.
  12. Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., ... Williams, M. D. (2021). Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994. https://doi.org/10.1016/j.ijinfomgt.2019.08.002
  13. Fani, Majid. (2022). The effect of business intelligence on the quality of entrepreneurs' decision-making with the mediating variables of information quality, data quality and innovation. Journal of Executive Management 28. 229-253. (In Persian). DOI: 10.22080/jem.2022.21670.3572
  14. Financial Accounting Standards Board (FASB). (2010). Statement of Financial Accounting Concepts No. 8: Conceptual Framework for Financial Reporting. Norwalk, CT.
  15. Gonçalves, M., Ferreira, J., & Fernandes, C. (2025). Smart management accounting practices and business performance in SMEs. Journal of Small Business Management.
  16. Hansen, D. R., Mowen, M. M., & Heitger, D. L. (2022). Cornerstones of cost management (5th ed.). Cengage Learning.
  17. Horngren, C. T., Datar, S. M., & Rajan, M. V. (2021). Cost Accounting: A Managerial Emphasis (17th ed.). Pearson.
  18. IFAC. (2023). International good practice guidance: Professional accountants in business. International Federation of Accountants.
  19. International Accounting Standards Board (IASB). (2018). Conceptual Framework for Financial Reporting. IFRS Foundation.
  20. International Federation of Accountants (IFAC). (2023). International Management Accounting Practice Guidance. New York: IFAC.
  21. 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. https://doi.org/10.1016/j.im.2012.12.001
  22. Järvenpää, M., Ranta, M., & Ylinen, M. (2023). Making sense of business intelligence in management accounting: A qualitative case study. Journal of Accounting & Organizational Change, 19(4), 596–616.
  23. Johnson, H. T., & Kaplan, R. S. (1987). Relevance lost: The rise and fall of management accounting. Harvard Business School Press.
  24. Kaplan, R. S., & Atkinson, A. A. (2015). Advanced management accounting (3rd ed.). Pearson.
  25. Kirsten, E. (2025). How Management Accountants use Business Intelligence & Analytics: The road to improved decision-making. University of Pretoria. (AICPA & CIMA)
  26. Maleki, M., Ahmadi, A., & Rahimi, S. (2025). Business intelligence capability and financial reporting quality: Evidence from listed firms. Journal of Information Systems and Accounting.
  27. Oesterreich, T. D., & Teuteberg, F. (2019). The role of business analytics in the digital transformation of controlling and management accounting. Journal of Accounting & Organizational Change, 15(2), 330–356. https://doi.org/10.1108/JAOC-10-2018-0097
  28. Pardaza, M., & Daramola, O. (2021). Business intelligence adoption and organizational performance: A systematic review. Applied Sciences, 11(16), 7332. https://doi.org/10.3390/app11167332
  29. Popovič, A., Turk, T., Jaklič, J., & Coelho, P. S. (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
  30. Quinn, M., Strauss, E., & Kristandl, G. (2018). The effects of digitalisation on management accounting. Meditari Accountancy Research, 26(3), 425–442. https://doi.org/10.1108/MEDAR-03-2018-0300
  31. Rieg, R. (2025). Exploring the determinants and performance effects of digital dashboard use by management accountants. Journal of Management Control. Advance online publication. https://doi.org/10.1007/s00187-025-00400-0
  32. Rikhardsson, P., & Yigitbasioglu, O. (2018). Business intelligence and analytics in management accounting research: Status and future focus. International Journal of Accounting Information Systems, 29, 37–58. https://doi.org/10.1016/j.accinf.2018.03.001
  33. Santos, C., Rocha, Á., & Silva, A. (2025). Management accounting as a business intelligence system: Examination in Portuguese small and medium enterprises. Neural Computing and Applications, 37(31), 25789–25803. https://doi.org/10.1007/s00521-025-11118-4
  34. Shahvaroughi Farahani, Alireza; Pourbahrami, Babak (2024). The mediating role of auditors' intellectual capital in the impact of business intelligence on the commercialization of audit firms. Journal of Professional Auditing Research 17. 144-161. (In Persian). https://doi.org/10.22034/jpar.2024.2043833.1359
  35. Srivastava, P. R., Kumar, U., & Gupta, S. (2022). Business intelligence and analytics for organizational decision support: A review and future research agenda. Information Systems Frontiers, 24(5), 1625–1647. https://doi.org/10.1007/s10796-021-10190-5
International Journal of Finance & Managerial Accounting

Articles in Press, Accepted Manuscript
Available Online from 13 September 2026