How E-Government Can Help Societies during a Crisis: Implications of UTAUT Model in Lebanon
Abstract
:1. Introduction
2. Theoretical Framework
3. Hypothesis Development
3.1. Task–Technology Fit
3.2. Sense of Virtual Community
3.3. Mediating Role of UTAUT
4. Methodology
4.1. Sampling and Data Procedures
4.2. Measurements
5. Results
5.1. Measurement Model Assessment
5.2. Structural Model Assessment
6. Discussions
7. Conclusions
7.1. Theoretical Implications
7.2. Practical Implications
8. Limitations
9. Recommendations for Future Studies
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A. Survey (Factor Items)
TTF | Task–Technology Fit |
Quality | 1. The data is current and fits the needs of current situation. 2. The detail of information is appropriate for the tasks. |
Authorization | 1. Authorization is not difficult. 2. There are different authorization levels depending on data usage. |
Compatibility | 1. The data is hard to consolidate/compare or use in different sources 2. Inconsistency can be seen in data from different sources. |
Easiness | 1. It is easy to learn how to work with the systems. 2. It does not need much training to be familiar with all options. |
Production timeliness | 1. Updates and reports are routine and follow schedules. 2. Regular activities (e.g., daily updates) are scheduled on the system. |
SVC | Sense of Virtual Community |
Immersion | 1. I spend much time online with my e-participation community. 2. I spend more time than expected with my e-participation community. 3. I’ve spent more time with my e-participation community during the pandemic. |
Influence | 1. I am known in my e-participation community. 2. I feel I have control in the e-participation community. 3. Other members review my activities on e-participation. |
Membership | 1. I feel belongingness to my e-participation community. 2. I feel other members in e-participation community are my friends 3. I like other members in e-participation community. |
UTAUT | Unified Theory of Acceptance and Use of Technology |
Performance expectancy | 1. E-government services are useful for my life. 2. E-government services provide results that meet my needs. 3. E-government services will benefit me in the future. |
Social influence | 1. People who influence me think it is important to use e-government. 2. People whose opinions I value think it is necessary to use e-government. 3. People who are important to me prefer me to use e-government. |
Facilitating conditions | 1. There is infrastructure for digital governmental services. 2. Using e-government is facilitated by instructions. 3. There is technical assistant available to use e-government. |
Effort expectancy | 1. I think it is easy to use e-government services. 2. I can learn all features and settings of e-government by myself. 3. I think I will be able to use e-government fluently. |
CIU | Continuous Intention to Use (ICTs) |
1. I plan to use e-government services in my day. 2. I think I will use e-government services in the future. 3. I intend to use e-government services routinely. |
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Construct | Dimensions | Indicator | Outer Loadings | α | Rho A | CR | AVE |
---|---|---|---|---|---|---|---|
TTF | Quality | QL1 | 0.741 | 0.751 | 0.758 | 0.761 | 0.615 |
QL2 | 0.825 | ||||||
Authorization | AUT1 | 0.833 | 0.792 | 0.787 | 0.808 | 0.678 | |
AUT2 | 0.814 | ||||||
Compatibility | CPT1 | 0.825 | 0.743 | 0.754 | 0.757 | 0.610 | |
CPT2 | 0.735 | ||||||
Easiness | EAS1 | 0.810 | 0.736 | 0.742 | 0.748 | 0.598 | |
EAS2 | 0.806 | ||||||
Production timeliness | PTL1 | 0.823 | 0.808 | 0.821 | 0.823 | 0.699 | |
PTL2 | 0.849 | ||||||
SVC | Immersion | IMR1 | 0.851 | 0.877 | 0.881 | 0.883 | 0.715 |
IMR2 | 0.837 | ||||||
IMR3 | 0.849 | ||||||
Influence | INF1 | 0.807 | 0.809 | 0.814 | 0.816 | 0.596 | |
INF2 | 0.752 | ||||||
INF3 | 0.756 | ||||||
Membership | MMP1 | 0.766 | 0.835 | 0.837 | 0.837 | 0.631 | |
MMP2 | 0.798 | ||||||
MMP3 | 0.818 | ||||||
UTAUT | Performance expectancy | PE1 | 0.803 | 0.861 | 0.872 | 0.874 | 0.698 |
PE2 | 0.841 | ||||||
PE3 | 0.862 | ||||||
Social influence | SI1 | 0.870 | 0.834 | 0.846 | 0.848 | 0.652 | |
SI2 | 0.822 | ||||||
SI3 | 0.724 | ||||||
Facilitating conditions | FC1 | 0.808 | 0.837 | 0.855 | 0.857 | 0.667 | |
FC2 | 0.843 | ||||||
FC3 | 0.799 | ||||||
Effort expectancy | EE1 | 0.747 | 0.849 | 0.857 | 0.857 | 0.668 | |
EE2 | 0.861 | ||||||
EE3 | 0.839 | ||||||
CIU | - | CIU1 | 0.767 | 0.792 | 0.805 | 0.810 | 0.586 |
CIU2 | 0.798 | ||||||
CIU3 | 0.731 |
Construct | Items | Convergent Validity | Weights | VIF | t-Statistics |
---|---|---|---|---|---|
TTF | Quality | 0.727 | 0.386 | 1.889 | 4.488 |
Authorization | 0.379 | 1.801 | 4.251 | ||
Compatibility | 0.524 | 2.305 | 5.863 | ||
Easiness | 0.408 | 2.377 | 5.282 | ||
Production timeliness | 0.459 | 1.846 | 5.607 | ||
SVC | Immersion | 0.719 | 0.427 | 1.946 | 5.359 |
Influence | 0.416 | 2.034 | 5.461 | ||
Membership | 0.459 | 1.958 | 5.329 | ||
UTAUT | Performance Expectancy | 0.758 | 0.383 | 1.807 | 3.404 |
Social Influence | 0.422 | 2.229 | 4.617 | ||
Facilitating Conditions | 0.469 | 2.118 | 5.176 | ||
Effort Expectancy | 0.427 | 2.099 | 4.949 |
QL | AUT | CPT | EAS | PTL | IMR | INF | MMP | PE | SI | FC | EE | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
QL | ||||||||||||
AUT | 0.722 | |||||||||||
CPT | 0.481 | 0.509 | ||||||||||
EAS | 0.722 | 0.613 | 0.776 | |||||||||
PTL | 0.586 | 0.725 | 0.639 | 0.757 | ||||||||
IMR | 0.725 | 0.478 | 0.681 | 0.705 | 0.822 | |||||||
INF | 0.710 | 0.718 | 0.721 | 0.730 | 0.744 | 0.801 | ||||||
MMP | 0.623 | 0.601 | 0.727 | 0.664 | 0.707 | 0.711 | 0.722 | |||||
PE | 0.676 | 0.639 | 0.702 | 0.724 | 0.701 | 0.667 | 0.706 | 0.762 | ||||
SI | 0.670 | 0.491 | 0.787 | 0.599 | 0.507 | 0.629 | 0.606 | 0.705 | 0.712 | |||
FC | 0.579 | 0.641 | 0.667 | 0.713 | 0.649 | 0.726 | 0.697 | 0.593 | 0.708 | 0.711 | ||
EE | 0.632 | 0.728 | 0.764 | 0.565 | 0.603 | 0.618 | 0.594 | 0.644 | 0.589 | 0.720 | 0.746 | |
CIU | 0.597 | 0.609 | 0.646 | 0.599 | 0.697 | 0.678 | 0.711 | 0.649 | 0.723 | 0.737 | 0.743 | 0.765 |
Effects | Relations | β | t-Statistics | Ƒ2 | Decision |
---|---|---|---|---|---|
Direct | |||||
H1 | TTF → CIU | 0.307 | 4.387 *** | 0.133 | Supported |
H2 | SVC → CIU | 0.321 | 4.556 ** | 0.126 | Supported |
Mediation | |||||
H3 | TTF → UTAUT → CIU | 0.279 | 3.704 ** | 0.049 | Supported |
H4 | SVC → UTAUT → CIU | 0.241 | 3.648 * | 0.044 | Supported |
Control Variables | |||||
Gender → CIU | 0.122 | 2.224 * | |||
Age → CIU | 0.131 | 2.130 * | |||
Education → CIU | 0.120 | 2.264 * | |||
R2CIU = 0.43/Q2CIU = 0.23 R2UTAUT = 0.65/Q2UTAUT = 0.45 SRMR: 0.021; NFI: 0.911 |
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El Hajj, B.; Karadas, G.; Zargar, P. How E-Government Can Help Societies during a Crisis: Implications of UTAUT Model in Lebanon. Sustainability 2023, 15, 5368. https://doi.org/10.3390/su15065368
El Hajj B, Karadas G, Zargar P. How E-Government Can Help Societies during a Crisis: Implications of UTAUT Model in Lebanon. Sustainability. 2023; 15(6):5368. https://doi.org/10.3390/su15065368
Chicago/Turabian StyleEl Hajj, Bassel, Georgiana Karadas, and Pouya Zargar. 2023. "How E-Government Can Help Societies during a Crisis: Implications of UTAUT Model in Lebanon" Sustainability 15, no. 6: 5368. https://doi.org/10.3390/su15065368
APA StyleEl Hajj, B., Karadas, G., & Zargar, P. (2023). How E-Government Can Help Societies during a Crisis: Implications of UTAUT Model in Lebanon. Sustainability, 15(6), 5368. https://doi.org/10.3390/su15065368