Abstract

The present research aims to assess the feature importance of Enterprise Resource Planning (ERP) impediments for implementation and to classify usage intention. To achieve the objective, responses were collected from 750 users. Classification model was designed using an RF algorithm which was developed for classifying intention to use the ERP system. The model has achieved 83% precision in predicting intention to use. Recall value and F1-score were noted as 0.67 and 0.74, respectively.  The feature importance of Implementation Impediments of ERP systems was elicited through a correlation analysis between various features among the Implementation Impediments of ERP systems. Further, ROC curve establishes an AUC value of 0.92, representing highest discriminative power. The CV accuracy is noted as 0.798. The study outcome Firstly, it helps to explore feature importance impediments in the implementation process of ERP to comprehend and tailor the ERP features system itself drives the intention to use to enhance business performance, in turn, user satisfaction. Secondly, comprehending critical impediments and usage intentions of the system helps the organisations to effectiveness of system usage, which impacts user satisfaction. Thirdly, by integrating AI/ML, this research contributes by deploying an interpretable framework that offers data-driven decisions for planning, implementation, and evaluation of ERP performance. Fourthly, the outcome of this study is valuable to design and develop implementation strategies suitable for ERP users, which enhance stakeholders’ engagement and foster sustainable growth. By integrating AI/ML, this research contributes by deploying an interpretable framework that offers data-driven decisions for design, application, and evaluation of system performance. Helps to comprehend critical impediments and to enhance usage intentions of the ERP system. Also, offers data-driven decisions for ERP successful implementation.

Keywords

Enterprise Resource Planning, Implementation Impediments, Feature Importance Analysis, Random Forest Model, Artificial Intelligence,

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References

  1. C.A. Rajan, R. Baral, Adoption of ERP system: An Empirical Study of Factors Influencing the Usage of ERP and its Impact on End User. IIMB Management Review, 27(2), (2015) 105–117. https://doi.org/10.1016/j.iimb.2015.04.008
  2. A. Al Maruf, A Systematic Review of ERP-Integrated Decision Support Systems for Financial and Operational Optimization in Global Retail Business. American Journal of Interdisciplinary Studies, 6(1), (2025) 236–262. https://doi.org/10.63125/qgbrmf24
  3. S. Alsughayer, Role of ERP Systems in Management Accounting in SMEs in Saudi Arabia. Journal of Accounting & Organizational Change, 21(2), (2025) 382–403. https://doi.org/10.1108/JAOC-11-2022-0176
  4. Z. Jaradat, A. AL-Hawamleh, A. Hamdan, Examining the Integration of ERP and BI in the Industrial Sector and its Impact on Decision-Making Processes in KSA. Digital Policy, Regulation and Governance, 27(2), (2025) 117–144. https://doi.org/10.1108/DPRG-04-2024-0077
  5. M. Abu Ghazaleh, S. Abdallah, A. Zabadi, Promoting Successful ERP Post-Implementation: A Case Study. Journal of Systems and Information Technology, 21(3), (2019) 325–346. https://doi.org/10.1108/JSIT-05-2018-0073
  6. M. Bhattacharya, T. Ramakrishnan, S. Fosso Wamba, Leveraging ERP Systems for Improving ERP Effectiveness in Emergency Service Organizations: An Empirical Study. Business Process Management Journal, 29(3), (2023) 710–736. https://doi.org/10.1108/BPMJ-06-2022-0303
  7. C. Leyh, A. Lorenz, M. J. Faruga, L. Koller, (2024). Critical Success Factors for ERP Projects Revisited: An Update of Literature Reviews. 2024 19th Conference on Computer Science and Intelligence Systems (FedCSIS), IEEE, Belgrade, Serbia. https://doi.org/10.15439/2024F6271
  8. V. Christiansen, M. Haddara, M. Langseth, Factors Affecting Cloud ERP Adoption Decisions in Organizations, Procedia Computer Science, 196, (2022) 255–262. https://doi.org/10.1016/j.procs.2021.12.012
  9. U. Farooq, K. Shahzad, Z. Guan, A. Rauf, Unlocking the Potential of Blockchain Technology in China’s Supply Chain: A Survey of Industry Professionals. Journal of Entrepreneurship in Public Policy, 13(2), (2024) 333–356. https://doi.org/10.1108/JEPP-03-2023-0028
  10. M. Al-Amin, T. Hossain, J. Islam, S.K. Biwas, History, Features, Challenges, and Critical Success Factors of Enterprise Resource Planning (ERP) in the Era of Industry 4.0. European Scientific Journal, 19(6), (2023) 31. https://doi.org/10.19044/esj.2023.v19n6p31
  11. K. Amoako-Gyampah, ERP Implementation Factors: A Comparison of Managerial and End-User Perspectives. Business Process Management Journal, 10(2), (2004) 171–183. https://doi.org/10.1108/14637150410530244
  12. S. Sternad, S. Bobek, Impacts of TAM-Based External Factors on ERP Acceptance. Procedia Technology, 9, (2013) 33–42. https://doi.org/10.1016/j.protcy.2013.12.004
  13. M. Ali, L. Miller, ERP System Implementation in Large Enterprises – A Systematic Literature Review, Journal of Enterprise Information Management, 30(4), (2017) 666–692. https://doi.org/10.1108/JEIM-07-2014-0071
  14. C.J. Mueller, N. Fritsch, M.J. Hofmann, L. Kuchinke, Differences in the Dynamics of Affective and Cognitive Processing – An ERP Study. Brain Research, 1655, (2017) 41–47. https://doi.org/10.1016/j.brainres.2016.11.018
  15. A. Zare Ravasan, T. Mansouri, A Dynamic ERP Critical Failure Factors Modelling with FCM Throughout Project Lifecycle Phases. Production Planning & Control, 27(2), (2015) 65–82. https://doi.org/10.1080/09537287.2015.1064551
  16. V. Hasheela-Mufeti, K. Smolander, What are the Requirements of a Successful ERP Implementation in SMEs? Special Focus on Southern Africa. International Journal of Information Systems and Project Management, 5(3), (2017) 5–20. https://doi.org/10.12821/ijispm050301
  17. C. Lambeck, R. Muller, C. Fohrholz, C. Leyh, (Re-) Evaluating User Interface Aspects in ERP Systems – An Empirical User Study. In Proceeding. 47th Hawaii International Conference on System Sciences, (2014) 396–405. https://doi.org/10.1109/HICSS.2014.57
  18. O. Turetken, J. Ondracek, W. IJsselsteijn, Influential Characteristics of Enterprise Information System User Interfaces. Journal of Computer Information Systems, 59(3), (2019) 243–255. https://doi.org/10.1080/08874417.2017.1339367
  19. M. Ali, F. Ahmed, Toward Sustainable ERP Systems and their Impact on Individual Performance in Manufacturing SMEs: Evidence from a North African Developing Country. International Journal of Emerging Markets, early access, 21(1), (2024) 1–24. https://doi.org/10.1108/IJOEM-06-2024-1102
  20. M.A. Abobakr, M. Abdel-Kader, A.F. Elbayoumi, An Experimental Investigation of the Impact of Sustainable ERP Systems Implementation on Sustainability Performance. Journal of Financial Reporting and Accounting, 24(2), (2024) 970–990. https://doi.org/10.1108/JFRA-04-2023-0207
  21. Y. Xue, H. Liang, W.R. Boulton, C.A. Snyder, ERP Implementation Failures in China: Case Studies with Implications for ERP Vendors. International Journal of Production Economics, 97(3), (2005) 279–295. https://doi.org/10.1016/j.ijpe.2004.07.008
  22. U. Jambaldorj, S. Batzangia, B. Baasanjav, E. Ginjbaatar, Critical Success Factors for ERP System Implementation in Mongolia. International Journal of Social Science & Humanity Research, 4(1), (2024) 20–35. https://doi.org/10.53468/mifyr.2024.04.01.20
  23. P. Sunarya, U. Rahardja, S. chih Chen, Y. ming Lic, M. Hardini, Deciphering Digital Social Dynamics: A Comparative Study of Logistic Regression and Random Forest in Predicting E-Commerce Customer Behavior. Journal of Applied Data Sciences, 5(1), (2024) 100–113. https://doi.org/10.47738/jads.v5i1.155
  24. E. Priyanto, A. Saekhu, P.A. Prasetyo, Analysis of Demographic and Consumer Behavior Factors on Satisfaction with AI Technology Usage in Digital Retail using the Random Forest Algorithm. International Journal for Applied Information Management, 4(4), (2024) 202–216. https://doi.org/10.47738/ijaim.v4i4.91
  25. Z.N. Jawad, V. Balázs, Machine Learning-Driven Optimization of Enterprise Resource Planning (ERP) Systems: A Comprehensive Review. Beni-Suef University Journal of Basic and Applied Sciences, 13, (2024) 4. https://doi.org/10.1186/s43088-023-00460-y
  26. M. Benjelloun, H. Hmamed, B. Rzine, A. Dadda, Navigating Challenges when Integrating Artificial Intelligence with Enterprise Resource Planning: A Literature Review. in AI2SD 2024, Lecture Notes in Networks and Systems, Springer, Cham, 1403, (2025) 562–574. https://doi.org/10.1007/978-3-031-91337-2_53
  27. K. Grobler-Dębska, H. Kucharska, A. Domagała, E. Kucharska, J. Wąs, Using AI Tools to Increase the Efficiency of ERP Implementation Projects. In: Hernes, M., Walaszczyk, E., Rot, A. (eds) Emerging Challenges in Intelligent Management Information Systems. ECAI 2025. Lecture Notes in Networks and Systems, 1643, (2025). https://doi.org/10.1007/978-3-032-06611-4_12
  28. R. Luo, Improved Random Forest Based on Grid Search for Customer Satisfaction Prediction. Advances in Economics, Management and Political Sciences, 38(1), (2023) 198–207. https://doi.org/10.54254/2754-1169/38/20231913
  29. R. Belwal, S. Belwal, Z. Morgan, L.H. Al Badi, Profiling Consumers for their Shopping Motivations in Modern Retail Formats in Oman. International Journal of Retail & Distribution Management, 53(1), (2025) 74–93. https://doi.org/10.1108/IJRDM-09-2023-0581
  30. P.S.R.P. Muntala, S.K. Jangam, Automated Risk Scoring in Oracle Fusion ERP using Machine Learning. International Journal of Artificial Intelligence, Data Science, and Machine Learning, 5(4), (2024) 105–116. https://doi.org/10.63282/3050-9262.IJAIDSML-V5I4P111
  31. M. Bekiaris, A. Markogiannopoulou, Enterprise Resource Planning System Reforms of European Union Member States in Association with Central Government Accrual Accounting and IPSAS Adoption. Journal of Public Budgeting, Accounting & Financial Management, 35(1), (2023) 115–140. https://doi.org/10.1108/JPBAFM-06-2021-0104
  32. I. Naidenova, A. Smirnov, ERP Systems and Operational Efficiency: Comparison of the Effectiveness of Implementing Foreign and Domestic Systems. Industrial Management & Data Systems, 125(4), (2025) 1554–1572. https://doi.org/10.1108/IMDS-09-2024-0880
  33. Q. Ren, S. Pinmanee, S. Chaveesuk, Use of Enterprise Resource Planning (ERP) System in Chinese Small and Medium Enterprises (SMEs). Operational Research in Engineering Sciences: Theory and Applications, 7(2), (2024). https://oresta.org/menu script/index.php/oresta/article/view/763
  34. X.J. Mamakou, S. Cohen, D. Manolopoulos, Post-Implementation Evaluation of Enterprise Resource Planning (ERP) Systems: An Internal Auditors’ Perspective. Journal of Systems and Information Technology, 26(3), (2024) 363–394. https://doi.org/10.1108/JSIT-11-2023-0264
  35. P. Agarwal, ERP-Integrated Supply Chain Analysis and Risk Management: A Machine Learning Approach. In Proceeding 2nd International Conference on Emerging Technologies and Sustainable Business Practices (ICETSBP 2024), (2024) 550–561, https://doi.org/10.2991/978-94-6463-544-7_36
  36. A. Parupalli, Business-Oriented Employee Performance Assessment via Machine Learning in ERP Systems. Challenge, 15, (2024) 16.
  37. S. Katragadda, ERP-based Quality Prediction using Long Short-Term Memory Networks Approach for Bio Industry. International Journal of Multidisciplinary Transactions, 7(11), (2025) 38–55. https://doi.org/10.5281/zenodo.17960667
  38. P. Nagesh, S. Kulenur, K. Jagadeesh, Employee Competency Mapping. SDMIMD Journal of Management, 8(2), (2017) 1–5. https://doi.org/10.18311/sdmimd/2017/18058
  39. F. Mahmood, A.Z. Khan, R.H. Bokhari, ERP Issues and Challenges: A Research Synthesis, Kybernetes. 49(3), (2020) 629–659. https://doi.org/10.1108/K-12-2018-0699