A Two-Stage SEM–Artificial Neural Network Analysis of Integrating Ethical and Quality Requirements in Accounting Digital Technologies
Abstract
:1. Introduction
2. Theoretical Background
3. Methodology
3.1. Selected Variables, Hypotheses, and Methods
3.2. Selected Sample
4. Results
5. Discussion
6. Conclusions
6.1. Empirical and Practical Implications
6.2. Theoretical Implications
6.3. Limitations and Further Research
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Variables | Items | Scales |
---|---|---|
Demographic variables | Gender | Male (1), Female (2) |
Age | 18–30 years (1), 31–45 years (2), 46–65 years (3) | |
ER | Autonomy | 1 to 5 (1—not important, 5—most important) |
Trust | ||
Privacy | ||
QER | Security | |
Safety | ||
Correctness | ||
Transparency | ||
Confidentiality | ||
Responsibility | ||
QR | Reliability | |
Maintenance | ||
Interoperability | ||
Users’ satisfaction | Extent of use | 1 to 5 (1—minimal, 5—maximal extent) |
Stated satisfaction | 1 to 5 (1—very poor, 5—very good) |
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Ethical Requirement | Description |
---|---|
Transparency | Provides real-time information [70] to all stakeholders on accounting operations and decisions |
Confidentiality | Proper and correct management of information [70] |
Privacy | Ensuring non-intrusion into privacy through the use of AI solutions [69] |
Safety | Safety of users using DT results [68] |
Security | Security of information prior to DT implementation, as well as those arising from the adoption of the DT [72] |
Correctness | Adoption of a fair decision in the event of conflicting requirements [70] |
Responsibility | Explicit determination of shared responsibility between user and DT [70] |
Autonomy | The ability of computers to make real-time decisions based on data without human involvement [67] |
Trust | Ways to provide users with enhanced reliability by eliminating the risks of using DT [68] |
Variable | Min | Max | Mean | Standard Deviation | Skewness | Kurtosis |
---|---|---|---|---|---|---|
Sex | 1 | 2 | 1.46 | 0.499 | 0.155 | −1.990 |
Age | 1 | 3 | 2.04 | 0.753 | −0.069 | −1.227 |
Autonomy | 1 | 5 | 3.79 | 0.998 | −0.435 | −0.670 |
Trust | 1 | 5 | 3.63 | 1.057 | −0.425 | −0.433 |
Privacy | 1 | 5 | 3.93 | 0.882 | −0.519 | −0.271 |
Security | 1 | 5 | 3.70 | 1.036 | −0.591 | −0.163 |
Safety | 2 | 5 | 3.92 | 0.881 | −0.401 | −0.619 |
Correctness | 1 | 5 | 3.86 | 0.940 | −0.542 | −0.314 |
Transparency | 1 | 5 | 3.70 | 0.862 | −0.048 | −0.589 |
Confidentiality | 1 | 5 | 3.78 | 0.903 | −0.479 | −0.076 |
Responsibility | 2 | 5 | 3.99 | 0.951 | −0.514 | −0.780 |
Reliability | 1 | 5 | 3.80 | 0.964 | −0.473 | −0.394 |
Maintenance | 1 | 5 | 3.58 | 0.972 | −0.169 | −0.654 |
Interoperability | 1 | 5 | 3.81 | 0.878 | −0.561 | 0.037 |
Extent of use | 2 | 5 | 3.84 | 0.928 | −0.305 | −0.827 |
Stated satisfaction | 1 | 5 | 3.41 | 1.212 | −0.307 | −0.810 |
Cronbach’s Alpha | Composite Reliability | Average Variance Extracted | |
---|---|---|---|
ER | 0.782 | 0.873 | 0.698 |
QER | 0.881 | 0.910 | 0.627 |
QR | 0.875 | 0.923 | 0.800 |
Users’ satisfaction | 0.888 | 0.947 | 0.899 |
Category | Requirement | Outer Loadings |
---|---|---|
ER | Autonomy | 0.867 |
Trust | 0.912 | |
Privacy | 0.714 | |
QER | Security | 0.850 |
Safety | 0.789 | |
Correctness | 0.798 | |
Transparency | 0.731 | |
Confidentiality | 0.798 | |
Responsibility | 0.791 | |
QR | Reliability | 0.920 |
Maintenance | 0.898 | |
Interoperability | 0.865 |
Path | Original Sample | Standard Deviation | T Statistics | p Values |
---|---|---|---|---|
ER - > Users’ satisfaction (H2) | 0.215 | 0.056 | 3.856 | 0.000 |
QER - > Users’ satisfaction (H2) | 0.236 | 0.046 | 5.087 | 0.000 |
QR - > Users’ satisfaction (H2) | 0.525 | 0.058 | 9.042 | 0.000 |
Predictor | Predicted Values | |||||
---|---|---|---|---|---|---|
Hidden Layer 1 | Output Layer | Importance | Normalized Importance | |||
H(1:1) | Extent of Use | Stated Satisfaction | ||||
Input Layer | (Bias) | −0.733 | ||||
Autonomy | 0.014 | 0.009 | 2.3% | |||
Trust | 0.260 | 0.191 | 47.7% | |||
Privacy | 0.006 | 0.004 | 1.0% | |||
Security | 0.097 | 0.066 | 16.5% | |||
Safety | 0.008 | 0.005 | 1.3% | |||
Correctness | 0.134 | 0.093 | 23.3% | |||
Transparency | 0.047 | 0.031 | 7.8% | |||
Confidentiality | 0.049 | 0.033 | 8.3% | |||
Responsibility | 0.021 | 0.014 | 3.5% | |||
Reliability | 0.518 | 0.400 | 100.0% | |||
Maintenance | 0.185 | 0.131 | 32.9% | |||
Interoperability | 0.034 | 0.023 | 5.7% | |||
Hidden Layer 1 | (Bias) | −0.351 | −0.366 | |||
H(1:1) | 5.000 | 4.632 |
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Bocean, C.G.; Vărzaru, A.A. A Two-Stage SEM–Artificial Neural Network Analysis of Integrating Ethical and Quality Requirements in Accounting Digital Technologies. Systems 2022, 10, 121. https://doi.org/10.3390/systems10040121
Bocean CG, Vărzaru AA. A Two-Stage SEM–Artificial Neural Network Analysis of Integrating Ethical and Quality Requirements in Accounting Digital Technologies. Systems. 2022; 10(4):121. https://doi.org/10.3390/systems10040121
Chicago/Turabian StyleBocean, Claudiu George, and Anca Antoaneta Vărzaru. 2022. "A Two-Stage SEM–Artificial Neural Network Analysis of Integrating Ethical and Quality Requirements in Accounting Digital Technologies" Systems 10, no. 4: 121. https://doi.org/10.3390/systems10040121