References
Adepoju, A., & Adeniji, A. (2020). Technology Acceptance of E-Banking Services in an Unnatural Environment. SEISENSE Journal of Management, 3(3), 34–50. https://doi.org/10.33215/sjom.v3i3.336
Akinwande, M. O., Dikko, H. G., & Samson, A. (2015). Variance Inflation Factor: As a Condition for the Inclusion of Suppressor Variable(s) in Regression Analysis. Open Journal of Statistics, 05(07), 754–767. https://doi.org/10.4236/ojs.2015.57075
Arshad Khan, M., & Alhumoudi, H. A. (2022). Performance of E-Banking and the Mediating Effect of Customer Satisfaction: A Structural Equation Model Approach. Sustainability, 14(12), 7224. https://doi.org/10.3390/SU14127224
Arshad Khan, M., Tiwari, A., Sharma, T., et al. (2021). Artificial Intelligence in Commerce and Business to Deal with COVID-19 Pandemic. Turkish Journal of Computer and Mathematics Education, 12(13), 1748–1760.
Cao, L. (2021). AI in Finance: Challenges, Techniques and Opportunities. SSRN Electronic Journal, 1(1), 1–40. https://doi.org/10.2139/ssrn.3869625
Cioffi, R., Travaglioni, M., Piscitelli, G., et al. (2020). Artificial intelligence and machine learning applications in smart production: Progress, trends, and directions. Sustainability, 12(2). https://doi.org/10.3390/su12020492
Data, S. B., & Analytics, D. (2000). Big Data, Data Analytics and Artificial Intelligence in Accounting: An Overview Citation: Big Data, Data Analytics, and Artificial Intelligence in Accounting: An Overview. ResearchGate, 61(2), 1–34.
Fornell, & Lacker. (1981). Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion. Journal of Physics: Conference Series PAPER, 890(1), 1–6.
FSB. (2017). Artificial intelligence and machine learning in financial services Market developments and financial stability implications. Available online: https://www.fsb.org/uploads/P011117.pdf (accessed on 2 June 2023).
Gandomi, A. H., Chen, F., & Abualigah, L. (2023). Big Data Analytics Using Artificial Intelligence. Electronics, 12(4), 3–7. https://doi.org/10.3390/electronics12040957
Goodell, J. W., Kumar, S., Lim, W. M., et al. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577
Haider, A., Khan, M. A., Khoja, M., et al. (2024). The role of e-banking, mobile-banking, and e-wallet with response to e-payment and customer trust as a mediating factor using a structural equation modelling approach. Journal of Infrastructure, Policy and Development, 8(9), 6644. https://doi.org/10.24294/jipd.v8i9.6644
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
Inci, A. C. (2023). Artificial Intelligence in Finance. Contemporary Issues in Quantitative Finance, 266–274. https://doi.org/10.4324/9781003213697-19
Khan, M. A. (2021). Netizens’ Perspective towards Electronic Money and Its Essence in the Virtual Economy: An Empirical Analysis with Special Reference to Delhi‐NCR, India. Complexity, 2021(1). https://doi.org/10.1155/2021/7772929
Khan, M. A., Hussain, M. M., Pervez, A., et al. (2022). Intraday Price Discovery between Spot and Futures Markets of NIFTY 50: An Empirical Study during the Times of COVID-19. Journal of Mathematics, 2022(1), 1–9. https://doi.org/10.1155/2022/2164974
Khan, M. A., Roy, P., Siddiqui, S., et al. (2021). Systemic Risk Assessment: Aggregated and Disaggregated Analysis on Selected Indian Banks. Complexity, 2021(1). https://doi.org/10.1155/2021/8360778
Khan, M. A., Vivek, Minhaj, S. M., et al. (2023). Impact of Store Design and Atmosphere on Shoppers’ Purchase Decisions: An Empirical Study with Special Reference to Delhi-NCR. Sustainability, 15(1). https://doi.org/10.3390/su15010095
Khanifar, H., Molavi, Z., Jandaghi, G. R., et al. (2012). Factors influencing the intendancy of e-banking: An integration of TAM & TPB with eservice quality. Journal of Applied Sciences Research, 8(3), 1842–1852.
Kibria, M. G., Nguyen, K., Villardi, G. P., et al. (2017). Big Data Analytics, Machine Learning and Artificial Intelligence in Next-Generation Wireless Networks. IEEE Access, 6, 32328–32338. https://doi.org/10.1109/access.2018.2837692
Kühl, N., Goutier, M., Hirt, R., et al. (2019). Machine learning in artificial intelligence: Towards a common understanding. In: Proceedings of the Annual Hawaii International Conference on System Sciences. https://doi.org/10.24251/hicss.2019.630
Lu, L. (2018). Decoding Alipay: mobile payments, a cashless society and regulatory challenges. Butterworths Journal of International Banking and Financial Law, 33(1), 40–43.
Luan, H., Geczy, P., Lai, H., et al. (2020). Challenges and Future Directions of Big Data and Artificial Intelligence in Education. Frontiers in Psychology, 11, 1–11. https://doi.org/10.3389/fpsyg.2020.580820
Minhaj, S. M., & Khan, M. A. (n.d.). The revolutionary impact of micro-finance and role of financial institutions on agriculture income of farmers: An empirical analysis’s. International Journal of Business Innovation and Research. doi: 10.1504/IJBIR.2022.10052373
Minhaj, S. M., Rehman, A., Das, A. K., et al. (2024). Investor Sentiment and The Function of Blockchain Technology in Relation to Digital Currencies: The Here and Now and The Future. Educational Administration: Theory and Practice. https://doi.org/10.53555/kuey.v30i5.3942
Mukhamediev, R. I., Popova, Y., Kuchin, Y., et al. (2022). Review of Artificial Intelligence and Machine Learning Technologies: Classification, Restrictions, Opportunities and Challenges. Mathematics, 10(15), 1–25. https://doi.org/10.3390/math10152552
Najafabadi, M. M., Villanustre, F., Khoshgoftaar, T. M., et al. (2015). Deep learning applications and challenges in big data analytics. 1–21. https://doi.org/10.1186/s40537-014-0007-7
OECD. (2021). Artificial Intelligence, Machine Learning and Big Data in Finance: Opportunities, Challenges, and Implications for Policy Makers. Available online: https://www.oecd.org/finance/financial-markets/Artificial-intelligence-machine-learning-big-data-in-finance.pdf (accessed on 2 June 2023).
Ojokoh, B. A., Samuel, O. W., Omisore, O. M., et al. (2020). Big data, analytics and artificial intelligence for sustainability. Scientific African, 9, e00551. https://doi.org/10.1016/j.sciaf.2020.e00551
Rahmani, A. M., Azhir, E., Ali, S., et al. (2021). Artificial intelligence approaches and mechanisms for big data analytics: a systematic study. PeerJ Computer Science, 7, e488. https://doi.org/10.7717/peerj-cs.488
Rathore, M. M., Shah, S. A., & Shukla, D. (2021). The Role of AI, Machine Learning, and Big Data in Digital Twinning: A Systematic Literature Review, Challenges, and Opportunities. IEEE Access, 9, 32030–32052. https://doi.org/10.1109/access.2021.3060863
Soni, P. (2021). A Study on Artificial Intelligence in Finance Sector. International Journal of creative research thoughts, 9(5), 2320–2882.
Srivastava, U., & Gopalkrishnan, S. (2015). Impact of Big Data Analytics on Banking Sector: Learning for Indian Banks. Procedia Computer Science, 50, 643–652. https://doi.org/10.1016/J.PROCS.2015.04.098
Ventre, I., & Kolbe, D. (2020). The Impact of Perceived Usefulness of Online Reviews, Trust and Perceived Risk on Online Purchase Intention in Emerging Markets: A Mexican Perspective. Journal of International Consumer Marketing, 32(4), 287–299. https://doi.org/10.1080/08961530.2020.1712293
Zamani, E. D., Smyth, C., Gupta, S., et al. (2023). Artificial intelligence and big data analytics for supply chain resilience: a systematic literature review. Annals of Operations Research, 327(2), 605–632. https://doi.org/10.1007/s10479-022-04983-y
Copyright (c) 2024 Mohammed Arshad Khan, Hamad Alhumoudi, Abdullah Alakkas, Syed Mohd Minhaj, Mohammed Alhashem