An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps
Abstract
:1. Introduction
2. Materials and Methods
2.1. Mobile Devices, Mobile Banking, and Mobile Banking Apps
2.2. Security and Privacy Issues of Mobile Banking Apps
2.3. Artificial Intelligence (AI) Based Techniques
2.4. Method
2.4.1. Research Models
2.4.2. Reliability Analysis of the Study Classic Model
2.4.3. Artificial Neural Network (ANN) Model of the Study
2.5. Hypotheses Formulation
2.5.1. Information Quality
2.5.2. Service Quality
2.5.3. System Quality
2.5.4. Trust
2.5.5. Legal Framework
2.5.6. Risk
2.5.7. Facilitating Conditions
2.5.8. Social Influence
2.5.9. Satisfaction and Intention to Use
2.6. Participants
2.7. Data Gathering Procedure
2.8. Data Analysis Methods
2.9. Sensitivity Analysis
3. Results
3.1. Pre-Processing and Performance Assessment of the Study Input Parameters
Sensitivity Examination Results
3.2. Results of Structural Equation Modeling (SEM)
3.3. Results of Artificial Neural Network (ANN) Modeling
4. Discussion
5. Conclusions, Recommendations, and Future Work
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Appendix A
Inputs Parameters | Items (Strongly Disagree–Strongly-Agree, 1–5 Scale) | Source |
---|---|---|
Information quality | “The information provided by mobile banking is up-to-date” | [25,29] |
“The information provided by m-banking is easy to understand” | ||
“The information provided by m-banking is complete” | ||
Service quality | “The call center representatives always help me when I need support with m-banking” | [25,29] |
“The call center representatives always pay personal attention when I experience problems with m-banking” | ||
“The call center representatives have adequate knowledge to answer my queries related to m-banking” | ||
System quality | “The mobile banking app is easy to navigate”. | [25,29] |
“The mobile banking app is well structured” | ||
“The mobile banking is easy to use” | ||
Trust | “I trust my bank to offer secure m-banking” | [16,31,34] |
“I find m-banking is secure in conducting transactions” | ||
“I find m-banking is safe for receiving bank statements” | ||
Legal framework | “The country’s laws covered Internet environment” | [33,44] |
“Laws that govern mobile banking provide adequate protection for my cash” | ||
“Penalties stipulated in mobile banking laws make me feel comfortable with the service channel” | ||
Risk | “I fear that while I am paying a bill by mobile phone, I might make mistakes since the correctness of the inputted information is difficult to check from the screen” | [16,31,34] |
“I fear that while I am using mobile banking services, the battery of the mobile phone will run out or the connection will otherwise be lost” | ||
“I fear that while I am using mobile banking services, third parties can access my account or see my account information” | ||
“I fear that the list of PIN codes may be lost and end up in the wrong hands” | ||
Facilitating condition | “I have the resources necessary to use Mobile banking apps” | [30,35] |
“I have the knowledge necessary to use Mobile banking apps” | ||
“Mobile banking apps is compatible with other technologies I use” | ||
Social influence | “People who are important to me think that I should use Mobile banking apps” | [30,35] |
“People who influence my behavior think that I should use Mobile banking apps” | ||
“People whose opinions that I value prefer that I use Mobile Banking” | ||
Satisfaction | “I am satisfied that m-banking apps meets my requirements” | [29] |
“I am satisfied with m-banking apps effectiveness” | ||
“I am satisfied with m-banking apps efficiency” | ||
Intention to use | “I plan to use m-banking apps regularly” | [29] |
“I intend to recommend m-banking apps to peers and relatives” | ||
“I intend to use m-banking apps when the opportunity arises” | ||
Actual usage | “I use m-banking apps multiple times in a week” | [29] |
“I use m-banking apps frequently” | ||
“I have many weekly requirements of m-banking apps” |
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Construct | Items | Load. (std.) | CA | CR |
---|---|---|---|---|
Information Quality (IQ) | IQ1 | 0.902 | 0.853 | 0.872 |
IQ2 | 0.892 | |||
IQ3 | 0.913 | |||
Service Quality (SQ) | SQ1 | 0.844 | 0.786 | 0.797 |
SQ2 | 0.894 | |||
SQ3 | 0.950 | |||
System Quality (SYSQ) | SYSQ1 | 0.861 | 0.847 | 0.873 |
SYSQ2 | 0.851 | |||
SYSQ3 | 0.958 | |||
Trust (TR) | TR1 | 0.713 | 0.850 | 0.887 |
TR2 | 0.630 | |||
TR3 | 0.640 | |||
Legal Framework (LF) | LF1 | 0.892 | 0.802 | 0.843 |
LF2 | 0.787 | |||
LF3 | 0.701 | |||
Risk (R) | R1 | 0.935 | 0.893 | 0.913 |
R2 | 0.878 | |||
R3 | 0.920 | |||
Facilitating Conditions (FC) | FC1 | 0.978 | 0.819 | 0.830 |
FC2 | 0.907 | |||
FC3 | 0.932 | |||
Social Influence (SI) | SI1 | 0.967 | 0.773 | 0.789 |
SI2 | 0.982 | 0.853 | 0.872 | |
SI3 | 0.868 |
Demographic Variable | Frequencies | Percentage |
---|---|---|
Gender | ||
Male | 454 | 59.3 |
Female | 311 | 40.7 |
Age group | ||
18–25 | 347 | 45.4 |
26–35 | 272 | 35.6 |
36 and above | 146 | 19 |
Educational Level | ||
SSCE | 70 | 9.2 |
ND | 190 | 24.8 |
Bachelor/HND | 398 | 52 |
Masters and above | 107 | 14 |
Employment status | ||
Academics | 72 | 9.4 |
Other civil servants | 284 | 37.1 |
Students | 265 | 34.7 |
Business peoples | 144 | 18.8 |
Input Parameters | Average DC Values | Rank |
---|---|---|
Risk | 0.9710 | 1 |
Trust | 0.9501 | 2 |
Facilitating conditions | 0.9492 | 3 |
Legal framework | 0.8920 | 4 |
System quality | 0.8831 | 5 |
Information quality | 0.8112 | 6 |
Social influence | 0.6820 | 7 |
Service quality | 0.6533 | 8 |
Hypotheses | Parameters | Std. | S.E. | p-Value | Results |
---|---|---|---|---|---|
H1a | Information quality → Intention to use | 1.124 | 0.041 | 0.000 | Supported |
H1b | Information quality → Satisfaction | 1.128 | 0.044 | 0.023 | Supported |
H2a | Service quality → Intention to use | 0.855 | 0.030 | 0.004 | Supported |
H2b | Service quality → Satisfaction | 0.895 | 0.038 | 0.011 | Supported |
H3a | System quality → Intention to use | 0.871 | 0.032 | 0.000 | Supported |
H3b | System quality → Satisfaction | 0.912 | 0.063 | 0.018 | Supported |
H4a | Trust → Intention to use | 0.644 | 0.023 | 0.000 | Supported |
H4b | Trust → Satisfaction | 0.651 | 0.037 | 0.026 | Supported |
H5a | Legal framework → Intention to use | 0.862 | 0.029 | 0.002 | Supported |
H5b | Legal framework → Satisfaction | 0.712 | 0.018 | 0.020 | Supported |
H6a | Risk → Intention to use | 0.921 | 0.033 | 0.000 | Supported |
H6b | Risk → Satisfaction | 0.873 | 0.038 | 0.006 | Supported |
H7a | Facilitating condition → Intention to Use | 0.936 | 0.034 | 0.012 | Supported |
H7b | Facilitating condition → Satisfaction | 0.883 | 0.047 | 0.022 | Supported |
H8a | Social influence → Intention to use | 0.926 | 0.033 | 0.003 | Supported |
H8b | Social influence → Satisfaction | 0.712 | 0.016 | 0.017 | Supported |
Training | Testing | |||||||
---|---|---|---|---|---|---|---|---|
MODEL | R | RMSE | NSE | PBIAS | R | RMSE | NSE | PBIAS |
ANN | 1.0000 | 0.0012 | 0.9998 | 0.0011 | 0.9998 | 0.0013 | 0.9998 | 0.0012 |
Inputs | Parameters | Model | Target | Training | Testing | Prediction | |
---|---|---|---|---|---|---|---|
H1a | Information quality → Intention to use | 1 | 1.0 | 0.9362 | 0.9280 | 0.9334 | Supported |
H1b | Information quality → Satisfaction | 2 | 1.0 | 0.9476 | 0.9132 | 0.9570 | Supported |
H2a | Service quality → Intention to use | 3 | 1.0 | 0.6238 | 0.6510 | 0.6881 | Not-Supported |
H2b | Service quality → Satisfaction | 4 | 1.0 | 0.7124 | 0.6338 | 0.7812 | Not-Supported |
H3a | System quality → Intention to use | 5 | 1.0 | 0.9646 | 0.9548 | 0.9614 | Supported |
H3b | System quality → Satisfaction | 6 | 1.0 | 0.9614 | 0.9136 | 0.9780 | Supported |
H4a | Trust → Intention to use | 7 | 1.0 | 0.9863 | 0.9794 | 0.9816 | Supported |
H4b | Trust → Satisfaction | 8 | 1.0 | 0.9894 | 0.9361 | 0.9872 | Supported |
H5a | Legal framework → Intention to use | 9 | 1.0 | 0.9836 | 0.9812 | 0.9653 | Supported |
H5b | Legal framework → Satisfaction | 10 | 1.0 | 0.9681 | 0.9682 | 0.9791 | Supported |
H6a | Risk → Intention to use | 11 | 1.0 | 0.9786 | 0.9710 | 0.9683 | Supported |
H6b | Risk → Satisfaction | 12 | 1.0 | 0.9613 | 0.9286 | 0.9741 | Supported |
H7a | Facilitating condition → Intention to Use | 13 | 1.0 | 0.9771 | 0.9673 | 0.9538 | Supported |
H7b | Facilitating condition → Satisfaction | 14 | 1.0 | 0.9816 | 0.9654 | 0.9730 | Supported |
H8a | Social influence → Intention to use | 15 | 1.0 | 0.6330 | 0.6884 | 0.6260 | Not-Supported |
H8b | Social influence → Satisfaction | 16 | 1.0 | 0.6110 | 0.6379 | 0.7310 | Not-Supported |
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Cavus, N.; Mohammed, Y.B.; Yakubu, M.N. An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps. Sustainability 2021, 13, 6206. https://doi.org/10.3390/su13116206
Cavus N, Mohammed YB, Yakubu MN. An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps. Sustainability. 2021; 13(11):6206. https://doi.org/10.3390/su13116206
Chicago/Turabian StyleCavus, Nadire, Yakubu Bala Mohammed, and Mohammed Nasiru Yakubu. 2021. "An Artificial Intelligence-Based Model for Prediction of Parameters Affecting Sustainable Growth of Mobile Banking Apps" Sustainability 13, no. 11: 6206. https://doi.org/10.3390/su13116206