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An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps

Author

Listed:
  • Nadire Cavus

    (Computer Information Systems Research and Technology Centre, Near East University, 99138 Nicosia, Cyprus
    Department of Computer Information Systems, Near East University, 61300 Mersin, Turkey)

  • Yakubu Bala Mohammed

    (Department of Computer Information Systems, Near East University, 61300 Mersin, Turkey
    Department of Computer Science, Abubakar Tatari Ali Polytechnic, 740272 Bauchi, Nigeria)

  • Mohammed Nasiru Yakubu

    (American University of Nigeria, 98 Lamido Zubairu Way, 640231 Yola, Nigeria)

Abstract

Nowadays, mobile banking apps are becoming an integral part of people lives due to its suppleness and convenience. Despite these benefits, yet its growth in evolving states is beyond expectations. However, using mobiles devices to conduct financial transactions involved a lot of risk. This paper aims to investigate customers’ reasons for non-usage of the new conduits in developing countries with distinct interest in Nigeria. The study adopts two methods of analysis, artificial intelligence-based methods (AI), and structural equations modeling (SEM). A feed-forward neural network (FFNN) sensitivity examination technique was used to choose the most dominant parameters of mobile banking data collected from 823 respondents. Four algebraic directories were used to corroborate the study AI-based model. The study AI results found risk, trust, facilitating conditions, and inadequate digital laws to be the most dominant parameters that affect mobile banking growth in Nigeria, and discovered social influence and service quality to have no influence on Nigerians’ resolve to use moveable banking apps. Moreover, the results proved the superiority of AI-based models above the classical models. Government and pecuniary institutes can use the study outcomes to ensure secured services offering, and improve growth. Finally, the study suggests some areas for future studies.

Suggested Citation

  • Nadire Cavus & Yakubu Bala Mohammed & Mohammed Nasiru Yakubu, 2021. "An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps," Sustainability, MDPI, vol. 13(11), pages 1-21, May.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:11:p:6206-:d:566457
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    References listed on IDEAS

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    Cited by:

    1. Nadire Cavus & Yakubu Bala Mohammed & Mohammed Bulama & Muhammad Lamir Isah, 2023. "Examining User Verification Schemes, Safety and Secrecy Issues Affecting M-Banking: Systematic Literature Review," SAGE Open, , vol. 13(1), pages 21582440231, January.
    2. Nadire Cavus & Nuriye Sancar, 2023. "The Importance of Digital Signature in Sustainable Businesses: A Scale Development Study," Sustainability, MDPI, vol. 15(6), pages 1-15, March.
    3. Filiz Karpuz & Erdal Güryay & Dervis Kirikkaleli, 2021. "Sustainable-Performance Instrument Development and Validation in the Northern Cyprus Banking Sector," Sustainability, MDPI, vol. 13(14), pages 1-14, July.
    4. Kayenaat Bahl & Ravi Kiran & Anupam Sharma, 2023. "Scaling Up Banking Performance for the Realisation of Specific Sustainable Development Goals: The Interplay of Digitalisation and Training in the Transformation Journey," Sustainability, MDPI, vol. 15(18), pages 1-22, September.
    5. Nadire Cavus & Yakubu Bala Mohammed & Abdulsalam Ya’u Gital & Mohammed Bulama & Adamu Muhammad Tukur & Danlami Mohammed & Muhammad Lamir Isah & Abba Hassan, 2022. "Emotional Artificial Neural Networks and Gaussian Process-Regression-Based Hybrid Machine-Learning Model for Prediction of Security and Privacy Effects on M-Banking Attractiveness," Sustainability, MDPI, vol. 14(10), pages 1-21, May.

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