A Conceptual Framework for Sustainable AI-ERP Integration in Dark Factories: Synthesising TOE, TAM, and IS Success Models for Autonomous Industrial Environments
Abstract
1. Introduction
- What are the critical factors influencing the adoption of AI-ERP systems in dark factory settings?
- How can existing theoretical frameworks, such as TOE, TAM, and IS Success Models, be integrated to create a unified framework for AI-ERP adoption?
- What strategies can organisations implement to mitigate risks and enhance the stability and effectiveness of AI-ERP systems post-adoption?
- To analyse the impact of AI on ERP functionalities in dark industry settings
- To synthesise TOE, TAM, and IS Success Models into an integrated framework for AI-ERP adoption
- To provide actionable strategies for organisations navigating AI-ERP implementation
2. Literature Review
2.1. Overview of Previous Studies
2.2. Challenges in AI-ERP Implementations in Industrial Sectors
2.3. Sustainability Contributions of AI-ERP in Dark-Factory Supply Chains
2.4. Theoretical Foundations
2.4.1. TOE Framework
2.4.2. TAM Framework
2.4.3. IS Success Model
2.4.4. Integration of TOE, TAM, and IS Success Models
2.5. Research Hypotheses
2.6. Gaps in Existing Research
3. Methodology
4. Findings
4.1. Proposed Conceptual Framework for AI-ERP in the Dark Industry Era
4.2. Application of Integrated Framework
4.3. Application of AI-ERP Across Industries Using an Integrated Framework
4.4. Integrating TOE, TAM, and IS Success Model in Business Settings
4.5. Regression Analysis: Predicting User Satisfaction
5. Discussion
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Mohammed, I.A. AI and machine learning in predictive analytics for supply chain optimisation in the global economy. In Multidisciplinary Research Nexus: Ideas for the Modern World; San International Scientific Publications: Tamil Nadu, India, 2025; Volume 18, pp. 146–153. [Google Scholar] [CrossRef]
- Chatterjee, S.; Rana, N.P.; Dwivedi, Y.K.; Baabdullah, A.M. Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model. Technol. Forecast. Soc. Change 2021, 173, 121007. [Google Scholar] [CrossRef]
- Adenekan, T.K. Challenges in integrating AI with ERP systems: A comparative study of industry practices. Int. J. Manag. Entrep. Res. 2025, 6, 1607–1624. [Google Scholar]
- Zhong, R.Y.; Newman, S.T.; Huang, G.Q.; Lan, S. Big data for supply chain management in the service and manufacturing sectors: Challenges, opportunities, and future perspectives. Comput. Ind. Eng. 2025, 101, 572–591. [Google Scholar] [CrossRef]
- Hrischev, R.; Shakev, N. Artificial intelligence in ERP systems. Eng. Sci. 2023, 1, 3–15. [Google Scholar] [CrossRef]
- Dumitru, V.F.; Ionescu, B.; Rîndașu, S.-M.; Barna, L.-E.; Crîjman, A.-M. Crîjman. Implications for sustainability accounting and reporting in the context of the automation-driven evolution of ERP systems. Electronics 2023, 12, 1819. [Google Scholar] [CrossRef]
- Kumar, A.N.P.; Bogner, J.; Funke, M.; Lago, P. Balancing progress and responsibility: A synthesis of sustainability trade-offs of AI-based systems. In Proceedings of the 2024 IEEE 21st International Conference on Software Architecture Companion (ICSA-C), Hyderabad, India, 4–8 June 2024; pp. 207–214. [Google Scholar] [CrossRef]
- Issa, J.; Abdulrahman, L.M.; Abdullah, R.M.; Sami, T.M.G.; Wasfi, B. AI-powered sustainability management in enterprise systems based on cloud and web technology: Integrating IoT data for environmental impact reduction. J. Inf. Technol. Inform. 2024, 3, 154. [Google Scholar]
- Samuels, A. Examining the integration of artificial intelligence in supply chain management from Industry 4.0 to 6.0: A systematic literature review. Front. Artif. Intell. 2025, 7, 1477044. [Google Scholar] [CrossRef]
- Mhaskey, S.V. Integration of artificial intelligence (AI) in enterprise resource planning (ERP) systems: Opportunities, challenges, and implications. Int. J. Comput. Eng. Res. Trends 2024, 11, 1–9. [Google Scholar] [CrossRef]
- Barna, L.E.L. The impact of using artificial intelligence and ERP systems in the work of accounting professionals and auditors. Ann. Univ. Oradea Econ. Sci. Ser. 2024, 33, 1. [Google Scholar] [CrossRef]
- Singh, V. AI-driven ERP evolution: Enhancing supply chain resilience with neural networks and predictive LSTM models. Eur. J. Adv. Eng. Technol. 2025, 12, 47–52. [Google Scholar] [CrossRef]
- Dai, J.; Wang, L.; Zhang, H. Data integration challenges in IoT-enabled manufacturing systems. J. Ind. Inform. 2018, 14, 123–135. [Google Scholar]
- Madaki, A.S.A.; Ahmad, K.; Singh, D.; Alshurideh, M.T.; Alzoubi, M.R.; Falaki, N.; Alzyoud, M. Understanding theories and models of information technology integration implementation in non-western organisations: Elaborating the TOE model in Nigeria’s public sector. In Intelligence-Driven Circular Economy: Regeneration Towards Sustainability and Social Responsibility; Springer: Cham, Switzerland, 2025; pp. 17–36. [Google Scholar] [CrossRef]
- Yathiraju, N. Investigating the use of an artificial intelligence model in an ERP cloud-based system. Int. J. Electr. Electron. Comput. 2022, 7, 1–26. [Google Scholar] [CrossRef]
- Jo, H.; Bang, Y. Understanding continuance intention of enterprise resource planning (ERP): TOE, TAM, and IS success model. Heliyon 2023, 9, e21019. [Google Scholar] [CrossRef]
- Al-Mashaqbeh, I.A. Digital Transformation in Management Accounting: Shaping Corporate Strategies—A Case Study Based on Swedish Service-Related Firms. Master’s Thesis, University of Gothenburg, Gothenburg, Sweden, 2024. Available online: https://gupea.ub.gu.se/handle/2077/82968 (accessed on 15 August 2024).
- Bertram, Y. Intelligent ERP: The General Concept and a System Assessment. Master’s Thesis, Universidade NOVA de Lisboa, Lisbon, Portugal, 2022. Available online: http://hdl.handle.net/10362/142295 (accessed on 15 August 2025).
- Na, S.; Heo, S.; Han, S.; Shin, Y.; Roh, Y. Acceptance model of artificial intelligence (AI)-based technologies in construction firms: Applying the technology acceptance model (TAM) in combination with the technology–organisation–environment (TOE) framework. Buildings 2022, 12, 90. [Google Scholar] [CrossRef]
- Niropam Das, H.R.; Siddiqa, K.B.; Barikdar, C.R.; Hassan, J.; Bhuiyan, M.M.R.; Mahmud, F. The strategic impact of business intelligence tools: A review of decision-making and ambidexterity. Membrane Technol. 2025, 1, 542–553. [Google Scholar] [CrossRef]
- Barua, A.; Karim, F.; Islam, M.M.; Das, N.; Sumon, F.I.; Rahman, A.; Debnath, P.; Karmakar, M.; Khan, A. Optimising energy consumption patterns in southern California: An AI-driven approach to sustainable resource management. J. Ecohumanism 2025, 4, 2920–2935. [Google Scholar] [CrossRef]
- International Federation of Robotics (IFR). World Robotics Report 2023; IFR: Frankfurt, Germany, 2023; Available online: https://ifr.org/img/worldrobotics/2023_WR_extended_version.pdf (accessed on 15 August 2025).
- Annanth, V.K.; Abinash, M.; Rao, L.B. Intelligent manufacturing in the context of industry 4.0: A case study of Siemens industry. J. Phys. Conf. Ser. 2021, 1969, 012019. [Google Scholar] [CrossRef]
- Yaqub, M.Z.; Alsabban, A. Industry-4.0-enabled digital transformation: Prospects, instruments, challenges, and implications for business strategies. Sustainability 2023, 15, 8553. [Google Scholar] [CrossRef]
- Bhavikatta, N.B. AI-Driven Inventory Optimization in Supply Chains: A Comprehensive Review on Reducing Stockouts and Mitigating Overstock Risks. J. Comput. Sci. Technol. Stud. 2025, 7, 1–13. [Google Scholar] [CrossRef]
- Ibrahim, F.; Münscher, J.-C.; Daseking, M.; Telle, N.-T. The technology acceptance model and adopter type analysis in the context of artificial intelligence. Front. Artif. Intell. 2025, 7, 1496518. [Google Scholar] [CrossRef]
- Tornatzky, L.G.; Fleischer, M.; Chakrabarti, A.K. Processes of Technological Innovation; Lexington Books: Lexington, MA, USA, 1990. [Google Scholar]
- Awa, H.O.; Ojiabo, O.U. A model of adoption determinants of ERP within T-O-E framework. Inf. Technol. People 2016, 29, 901–930. [Google Scholar] [CrossRef]
- DeLone, W.H.; McLean, E.R. Information systems success: The quest for the dependent variable. Inf. Syst. Res. 1992, 3, 60–95. [Google Scholar] [CrossRef]
- William, F.; Tjhin, V.U. The evaluation of enterprise resource planning application using information systems success model. J. Manag. Inf. Decis. Sci. 2021, 24, 1–13. [Google Scholar]
- Pokala, P. The integration and impact of artificial intelligence in modern enterprise resource planning systems: A comprehensive review. Int. J. Comput. Eng. Technol. 2024, 15, 79–88. [Google Scholar] [CrossRef]
- Huong, D.T.; Ngoc, L.T.M.; Mai, N.P. Applying the TAM and TOE integrated framework in researching the social media adoption in retail businesses in Vietnam. VNU J. Sci. Econ. Bus. 2020, 36, 1–10. [Google Scholar] [CrossRef]
- Al-Okaily, A.; Al-Okaily, M.; Teoh, A.P. ERP success in Jordanian firms: A conceptual model based on the DeLone and McLean IS Success Model. VINE J. Inf. Knowl. Manag. Syst. 2021, 53, 1025–1040. [Google Scholar] [CrossRef]
- Alam Mozumder, S.; Sakil, M.B.H.; Hasan, R.; Hasan, A.; Fuad, K.M.N.R.; Mridha, M.F.; Islam, R.; Watanobe, Y. Hybrid contrastive learning with attention-based neural networks for robust fraud detection in digital payment systems. IEEE Open J. Comput. Soc. 2025, 6, 1053–1064. [Google Scholar] [CrossRef]
- Alam Mozumder, S.; Hasan, R.; Sakil, M.B.H.; Hasan, A.; Eva, A.A.; Maua, J. AI-driven financial knowledge graphs: Bridging traditional finance and blockchain ecosystems with graph neural networks. In Proceedings of the 2025 International Conference on Electrical, Computer and Communication Engineering (ECCE), Chittagong, Bangladesh, 13–15 February 2025; pp. 1–6. [Google Scholar] [CrossRef]
- Pugna, I.B.; Boldeanu, D.M. The role of ERP systems in driving corporate sustainability. Proc. Int. Conf. Bus. Excel. 2025, 19, 402–412. [Google Scholar] [CrossRef]
- Shabur, A.; Shahriar, A.; Ara, M.A. From automation to collaboration: Exploring the impact of Industry 5.0 on sustainable manufacturing. Discov. Sustain. 2025, 6, 341. [Google Scholar] [CrossRef]
- Davis, F.D. Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 1989, 13, 319–340. [Google Scholar] [CrossRef]
- Dwivedi, Y.K.; Hughes, L.; Ismagilova, E.; Aarts, G.; Coombs, C.; Crick, T.; Duan, Y.; Dwivedi, R.; Edwards, J.; Eirug, A.; et al. Artificial intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. Int. J. Inf. Manag. 2019, 57, 101994. [Google Scholar] [CrossRef]
- Frankfurt. Robot Density in the Manufacturing Industry 2023 Data. IFR International Federation of Robotics. 2024. Available online: https://ifr.org/ifr-press-releases/news/global-robot-density-in-factories-doubled-in-seven-years (accessed on 20 November 2024).
- Fabra-Boluda, R.; Ferri, C.; Ramírez-Quintana, M.J.; Martínez-Plumed, F. Unveiling the robustness of machine learning families. Mach. Learn. Sci. Technol. 2024, 5, 035040. [Google Scholar] [CrossRef]
- Mondal, R.S.; Bhuiyan, M.N.A. Predictive analytics for chronic disease management: A machine learning approach to early intervention and personalised treatment. J. Comput. Anal. Appl. 2024, 33, 4096–4107. Available online: https://eudoxuspress.com/index.php/pub/article/view/2743 (accessed on 30 May 2025).
- Ali, C.S.M.; Zeebaree, S.R.M. Cloud-based web applications for enterprise systems: A review of AI and marketing innovations. Asian J. Res. Comput. Sci. 2025, 18, 427–451. [Google Scholar] [CrossRef]
- Jawad, Z.N.; Balázs, V. Machine learning-driven optimisation of enterprise resource planning (ERP) systems: A comprehensive review. Beni-Suef Univ. J. Basic. Appl. Sci. 2024, 13, 4. [Google Scholar] [CrossRef]
- Kunduru, A.R. Effective usage of artificial intelligence in enterprise resource planning applications. Int. J. Comput. Trends Technol. 2023, 71, 73–80. [Google Scholar] [CrossRef]
- Gujarati, D.N.; Porter, D.C. Basic Econometrics, 5th ed.; McGraw-Hill: New York, NY, USA, 2009. [Google Scholar]
- Chan, J.Y.-L.; Leow, S.M.H.; Bea, K.T.; Cheng, W.K.; Phoong, S.W.; Hong, Z.-W.; Chen, Y.-L. Mitigating the multicollinearity problem and its machine learning approach: A review. Mathematics 2022, 10, 1283. [Google Scholar] [CrossRef]
- Heinze, G.; Wallisch, C.; Dunkler, D. Variable selection—A review and recommendations for the practicing statistician. Biom. J. 2018, 60, 431–449. [Google Scholar] [CrossRef]
- Xi, W.-F.; Jiang, Q.-W.; Yang, A.-M.M. Using stepwise regression to address multicollinearity is not appropriate. Int. J. Surg. 2024, 110, 3122–3123. [Google Scholar] [CrossRef]
- Martin, N. Robust and efficient Breusch-Pagan test-statistic: An application of the beta-score Lagrange multipliers test for non-identically distributed individuals. arXiv 2023, arXiv:2301.07245v1. [Google Scholar]
- Hasan, A.; Alam Mozumder, S.; Hasan, R.; Sakil, M.B.H.; Eva, A.A.; Hasan, N. CAMICS: A context-aware multi-intent conversational system for enhanced AI-driven customer interaction models. In Proceedings of the 2025 International Conference on Emerging Smart Computing and Informatics (ESCI), Pune, India, 5–7 March 2025; pp. 1–6. [Google Scholar] [CrossRef]
- Sakil, M.B.H.; Hasan, A.; Alam Mozumder, S.; Hasan, R.; Opee, S.A.; Mridha, M.F.; Aung, Z. Enhancing Medicare Fraud Detection With a CNN-Transformer-XGBoost Framework and Explainable AI. IEEE Access 2025, 13, 79609–79622. [Google Scholar] [CrossRef]
- Evjemo, L.D.; Gjerstad, T.; Grøtli, E.I.; Sziebig, G. Trends in smart manufacturing: Role of humans and industrial robots in smart factories. Curr. Robot. Rep. 2020, 1, 35–41. [Google Scholar] [CrossRef]
Model | Core Focus | Strength in AI-ERP | Key Variables | Limitations When Used Alone | Justification for Integration |
---|---|---|---|---|---|
TOE | Internal organisation and external factors influencing adoption, including technology characteristics, firm size, and environmental pressures | Captures broader organisational and environmental potentials and barriers, ensuring alignment with market competitiveness and regulatory requirements | Technological readiness, AI infrastructure, organisational structure, and external pressures (e.g., industry competition) | Does not consider user perspectives or system performance metrics | Integrating TOE with TAM and IS Success bridges this gap by incorporating individual-level insights and post-adoption performance evaluations, offering a complete view from infrastructure readiness to user satisfaction and system impact. |
TAM | Individual user behaviour, focusing on perceived usefulness and ease of use | Predicts employees’ acceptance of AI-enabled ERP features and their behavioural intention to adopt the system | Perceived usefulness, perceived ease of use, attitudes toward AI | Ignores technological, organisational, and environmental factors, as well as post-adoption success measures | Combining TAM with TOE and IS success ensures that organisational readiness and environmental influences are considered alongside user acceptance, addressing the ‘human factor’ in AI-ERP adoption. |
IS Success Model | Post-adoption evaluation of system quality, information quality, and user satisfaction | Evaluates AI-ERP outcomes, assessing decision-making capabilities, operational efficiency, and overall benefits | Information quality, system quality, service quality, user satisfaction, net benefits | Does not explore pre-adoption factors such as reasons for adoption or the influence of technological and environmental contexts | Integrating IS Success with TOE and TAM enables a complete lifecycle evaluation, ensuring that AI-ERP systems are not only accepted but also deliver measurable and impactful results. |
Phase | AI Integration Module | Purpose | Actions for IT Organisations | Reference |
---|---|---|---|---|
Pre-Adoption (TOE Framework) | AI Infrastructure Readiness | Ensure AI components like cloud platforms, machine learning engines, and cybersecurity protocols are in place. | Audit current IT assets, upgrade ERP to AI-compatible systems, and invest in cloud and cybersecurity. | [1,19] |
Data Management and Quality | Create clean, structured, integrated datasets for AI processing. | Launch data cleansing projects, establish centralised data warehouses, and enforce data governance. | [15,16] | |
AI-ERP Compatibility Assessment | Ensure the ERP can integrate AI features, such as automation and predictive analytics. | Check ERP vendors for AI compatibility, develop APIs and middleware solutions. | [12,17] | |
Organisational Training and Leadership Buy-In | Build organisational support and skills for AI adoption. | Conduct leadership workshops, appoint AI-ERP project leaders, and schedule AI training for staff. | [3,26] | |
Market and Regulatory Analysis | Align AI strategies with market trends and legal compliance. | Analyse competition, monitor regulatory requirements, and survey customers about AI service needs. | [14,45] | |
Adoption (TAM) | AI-Enhanced User Interfaces (UX/UI) | Make AI features intuitive and user-friendly. | Redesign ERP dashboards with AI elements; invest in UX testing and feedback loops. | [32,44] |
AI Training Modules for Employees | Empower users with AI knowledge and skills. | Create e-learning platforms, simulate AI usage scenarios, and organise certification programmes. | [15,29] | |
Internal Communication and Change Management | Increase employee motivation and reduce resistance to AI. | Share success stories, reward early adopters, hold town halls, and manage expectations proactively. | [3,17] | |
User Support Systems | Ensure users feel supported during the transition to AI-ERP. | Deploy AI-driven chatbots, set up AI-focused helpdesks, provide FAQ portals and live assistance. | [15,31] | |
Post-Adoption (IS Success Model) | System Reliability and Performance Monitoring | Ensure stable and efficient operation of AI-ERP systems. | Install real-time system monitoring dashboards, use AI for automatic anomaly detection. | [16,33] |
Information Quality and Predictive Analytics | Generate actionable and accurate business insights. | Track forecasting accuracy, continuously retrain AI models with updated business data. | [12,31] | |
Service and Support Enhancement (AI-driven) | Enhance ongoing system support and user experience. | Use AI bots for user support, and implement proactive maintenance alerts. | [16,30] | |
User Satisfaction Tracking | Measure how users perceive the AI-ERP system post-implementation. | Conduct user surveys, track NPS, and analyse feedback through AI sentiment tools. | [16,33] | |
Business Impact Measurement (KPIs) | Demonstrate tangible benefits of AI-ERP adoption. | Monitor cost savings, efficiency gains, and revenue growth; present quarterly performance reports. | [1,14] |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Islam, M.S.; Islam, M.I.; Mozumder, A.Q.; Khan, M.T.H.; Das, N.; Mohammad, N. A Conceptual Framework for Sustainable AI-ERP Integration in Dark Factories: Synthesising TOE, TAM, and IS Success Models for Autonomous Industrial Environments. Sustainability 2025, 17, 9234. https://doi.org/10.3390/su17209234
Islam MS, Islam MI, Mozumder AQ, Khan MTH, Das N, Mohammad N. A Conceptual Framework for Sustainable AI-ERP Integration in Dark Factories: Synthesising TOE, TAM, and IS Success Models for Autonomous Industrial Environments. Sustainability. 2025; 17(20):9234. https://doi.org/10.3390/su17209234
Chicago/Turabian StyleIslam, Md Samirul, Md Iftakhayrul Islam, Abdul Quddus Mozumder, Md Tamjidul Haq Khan, Niropam Das, and Nur Mohammad. 2025. "A Conceptual Framework for Sustainable AI-ERP Integration in Dark Factories: Synthesising TOE, TAM, and IS Success Models for Autonomous Industrial Environments" Sustainability 17, no. 20: 9234. https://doi.org/10.3390/su17209234
APA StyleIslam, M. S., Islam, M. I., Mozumder, A. Q., Khan, M. T. H., Das, N., & Mohammad, N. (2025). A Conceptual Framework for Sustainable AI-ERP Integration in Dark Factories: Synthesising TOE, TAM, and IS Success Models for Autonomous Industrial Environments. Sustainability, 17(20), 9234. https://doi.org/10.3390/su17209234