1 Introduction
2 Basic concepts
2.1 Technology acceptance theory
2.2 TAM
2.3 Discriminant validity and heterotrait-monotrait ratio
3 Related works
3.1 Cloud computing
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Broad network access A user does not need to carry their own physical devices, nor worry about whether something (e.g., hardware/software crash, theft) can happen to them; instead of using local devices, a user only needs to upload data to the cloud over the internet, and then can use them on multiple devices (e.g., smartphone, laptop) at any time and place.
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On-demand self-service and rapid elasticity If necessary, a user can automatically provide, add, or expand storage resources. These capabilities reduce the user’s worry about storage capacity constraints and inflexibility to emergencies such as data loss.
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Resource pooling A user does not need to install, configure, and maintain his or her own online resources (e.g., storage space). For example, security measures against malicious acts like viruses are pooled to serve multiple users, and automatic backup eliminates the need for manual data backups and the fear of backup failures.
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Measured service Users are provided with computing resources (e.g., storage space, security patches) on a pay-per-use basis, similar to traditional utility services like power and water.
3.2 Cloud computing dependability and its attribute
3.3 Technology acceptance perspective
Study focus | Baseline theories | Authors | Key external variables (or integrated other theories) |
---|---|---|---|
Single group (30) | TOE (11) | Safari et al. (2015, SaaS, IT professionals) [49] | DOI (e.g., trialability, observability), security and privacy |
Alharbi et al. (2016, SaaS, employee) [50] | IS strategy triangle (e.g., strategic value), HOT-fit (e.g., CIO innovativeness, internal expertise) | ||
Al-Jabri and Alabdulhadi (2016, CC, IT staff) [33] | – | ||
Tomás et al. (2018, SaaS, SMEs) [13] | PVT (e.g., representation, reach, monitoring), INT (e.g., coercive pressures, normative pressures, mimetic pressures) | ||
Adiyasa et al. (2018, SaaS, employees) [51] | – | ||
Oliveira et al. (2019, SaaS, CIO/IS managers) [32] | INT (e.g., coercive pressures, normative pressures, mimetic pressures) | ||
Khayer et al. (2019, CC, SMEs CEO) [52] | Computer self-efficacy, social influence, perceived risks, cloud providers influence, server location | ||
Ali et al. (2020, CC, government IT staff) [53] | DOI (e.g., innovation), desires framework (e.g., anticipated benefits), security concerns | ||
Shahzad (2020, CC MOOC, Univ. employees) [54] | Cost reduction | ||
El-Haddadeh (2020, CC, SMEs) [55] | – | ||
Bhardwaj et al. (2021, CC, Univ. Faculty and staff) [56] | TAM (e.g., recognized usability, recognized usefulness), DOI (e.g., senior leadership support), security concern | ||
TAM (9) | Gangwar et al. (2015, CC, employees) [25] | TOE | |
Sharma et al. (2016, CC, IT professionals) [57] | Computer self-efficacy, trust, job opportunity | ||
Gangwar and Date (2016, CC, employees) [27] | Threat, risk, vulnerability, availability, compliance | ||
Chen (2017, CC, managers) [36] | Perceived risk, perceived trust | ||
Tripathi (2017, CC, employees) [20] | Perceived ubiquity, perceived risks, perceived costs | ||
Palos-Sanchez et al. (2017,SaaS,employees) [26] | Management support, communication, training, technological complexity, organization size | ||
Cengiz and Bakırtaş (2020, CC, employees) [38] | Perceived enjoyment, objective usability | ||
Tella et al. (2020, CC, librarians) [8] | Perceived security, perceived reliability, ease of maintenance, facilitating conditions, user friendliness, perceived flexibility, increased productivity | ||
Jahangiri et al. (2021, CC, univ. librarians) [9] | Individual factors, social factors, organizational factors, technology factors, economic factors, environment factors | ||
UTAUT (3) | Alotaibi (2016 SaaS, workers) [58] | – | |
Amin et al. (2017, CC, healthcare professionals) [59] | – | ||
Matar et al. (2020, CC, Univ. faculty and staff) [22] | – | ||
DOI (2) | Sabi et al. (2017, CC, IT experts) [31] | TAM (e.g., perceived usefulness, perceived ease of use) | |
Sallehudin et al. (2020, CC, public sectors) [60] | TOE, IS Success Model | ||
Dual-factor theory (1) | Hsieh and Lin (2018, SaaS, physicians) [34] | IS success model, SQB theory | |
TPB (1) | Asadi et al. (2020,, CC, Univ. faculty) [61] | – | |
INT (1) | Adjei et al. (2021, CC, employees) [6] | – | |
N/A (2) | Ali et al. (2021, CC, government IT managers) [7] | Complexity | |
Fretschner et al. (2021, SaaS, SMEs) [62] | Strategic orientations (e.g., innovation orientation, ambidextrous orientation, operations orientation), SaaS-related Beliefs (e.g., security beliefs, strategic flexibility, cost advantages beliefs) | ||
More than two groups (2) | N/A (1) | Cho and Chan (2015, SaaS, IT decision makers) [47] | Cost–benefit and risk evaluations factors (e.g., perceived cost advantage, gap in IT capabilities, perceived service quality, product differentiation) |
TAM (1) | Tripathi (2019, CC, CIO and senior managers) [48] | Perceived ubiquity, perceived benefits, perceived risks, perceived costs |
4 Research model and hypotheses
4.1 Research model
4.2 Hypothesis development
5 Methods
5.1 Measurement development
5.2 Sample
5.3 Data analysis
6 Results
6.1 Measurement model analysis
Construct and indicators | Factor loading | CR | AVE | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Full dataset | . Hi-ITi | Lo-ITi | SaaS | PaaS/IaaS | Full dataset | Hi-ITi | Lo-ITi | SaaS | PaaS/IaaS | Full dataset | Hi-ITi | Lo-ITi | SaaS | PaaS/IaaS | |
PAV | |||||||||||||||
PAV1 | 0.842 | 0.787 | 0.891 | 0.835 | 0.786 | 0.934 | 0.928 | 0.940 | 0.928 | 0.916 | 0.779 | 0.764 | 0.796 | 0.763 | 0.733 |
PAV2 | 0.893 | 0.880 | 0.909 | 0.869 | 0.892 | ||||||||||
PAV3 | 0.890 | 0.904 | 0.879 | 0.888 | 0.866 | ||||||||||
PAV4 | 0.905 | 0.919 | 0.890 | 0.902 | 0.877 | ||||||||||
PRE | |||||||||||||||
PRE1 | 0.865 | 0.858 | 0.865 | 0.874 | 0.842 | 0.903 | 0.889 | 0.909 | 0.900 | 0.892 | 0.700 | 0.667 | 0.714 | 0.695 | 0.674 |
PRE2 | 0.867 | 0.836 | 0.884 | 0.874 | 0.824 | ||||||||||
PRE3 | 0.716 | 0.703 | 0.721 | 0.682 | 0.761 | ||||||||||
PRE4 | 0.888 | 0.859 | 0.899 | 0.888 | 0.853 | ||||||||||
PSE | |||||||||||||||
PSE1 | 0.852 | 0.868 | 0.835 | 0.889 | 0.816 | 0.928 | 0.927 | 0.930 | 0.933 | 0.920 | 0.764 | 0.759 | 0.768 | 0.776 | 0.743 |
PSE2 | 0.879 | 0.867 | 0.891 | 0.884 | 0.858 | ||||||||||
PSE3 | 0.867 | 0.857 | 0.873 | 0.839 | 0.851 | ||||||||||
PSE4 | 0.898 | 0.893 | 0.905 | 0.910 | 0.920 | ||||||||||
PMA | |||||||||||||||
PMA1 | 0.817 | 0.814 | 0.818 | 0.816 | 0.820 | 0.902 | 0.886 | 0.912 | 0.903 | 0.908 | 0.697 | 0.661 | 0.722 | 0.699 | 0.711 |
PMA2 | 0.839 | 0.805 | 0.863 | 0.831 | 0.851 | ||||||||||
PMA3 | 0.843 | 0.822 | 0.854 | 0.834 | 0.840 | ||||||||||
PMA4 | 0.841 | 0.812 | 0.863 | 0.863 | 0.860 | ||||||||||
PUF | |||||||||||||||
PUF3 | 0.865 | 0.867 | 0.862 | 0.844 | 0.865 | 0.933 | 0.935 | 0.931 | 0.919 | 0.936 | 0.778 | 0.783 | 0.771 | 0.740 | 0.785 |
PUF4 | 0.859 | 0.836 | 0.887 | 0.878 | 0.849 | ||||||||||
PUF5 | 0.906 | 0.921 | 0.886 | 0.848 | 0.924 | ||||||||||
PUF6 | 0.898 | 0.913 | 0.877 | 0.872 | 0.905 | ||||||||||
PEU | |||||||||||||||
PEU1 | 0.847 | 0.828 | 0.860 | 0.826 | 0.853 | 0.946 | 0.943 | 0.947 | 0.937 | 0.951 | 0.779 | 0.769 | 0.781 | 0.747 | 0.795 |
PEU2 | 0.856 | 0.854 | 0.853 | 0.860 | 0.862 | ||||||||||
PEU3 | 0.908 | 0.907 | 0.905 | 0.897 | 0.920 | ||||||||||
PEU4 | 0.899 | 0.888 | 0.906 | 0.861 | 0.911 | ||||||||||
PEU5 | 0.900 | 0.905 | 0.893 | 0.877 | 0.909 | ||||||||||
BI | |||||||||||||||
BI1 | 0.906 | 0.892 | 0.916 | 0.894 | 0.922 | 0.937 | 0.927 | 0.944 | 0.936 | 0.940 | 0.788 | 0.761 | 0.810 | 0.785 | 0.796 |
BI2 | 0.890 | 0.848 | 0.925 | 0.889 | 0.825 | ||||||||||
BI3 | 0.861 | 0.863 | 0.858 | 0.903 | 0.909 | ||||||||||
BI4 | 0.893 | 0.887 | 0.899 | 0.858 | 0.908 |
Fornell-larcker criterion | Heterotrait-monotrait ratio (HTMT) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BI | PAV | PEU | PMA | PRE | PSE | PUF | BI | PAV | PEU | PMA | PRE | PSE | PUF | ||
BI | 0.888 | BI | |||||||||||||
PAV | 0.676 | 0.883 | PAV | 0.745 | |||||||||||
PEU | 0.613 | 0.632 | 0.882 | PEU | 0.664 | 0.687 | |||||||||
PMA | 0.676 | 0.612 | 0.546 | 0.835 | PMA | 0.766 | 0.698 | 0.612 | |||||||
PRE | 0.731 | 0.614 | 0.662 | 0.714 | 0.837 | PRE | 0.835 | 0.706 | 0.743 | 0.841 | |||||
PSE | 0.635 | 0.560 | 0.637 | 0.565 | 0.752 | 0.874 | PSE | 0.702 | 0.619 | 0.695 | 0.643 | 0.860 | |||
PUF | 0.636 | 0.567 | 0.734 | 0.460 | 0.558 | 0.547 | 0.882 | PUF | 0.700 | 0.624 | 0.795 | 0.523 | 0.632 | 0.605 |
Fornell-larcker criterion | Heterotrait-monotrait Ratio (HTMT) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BI | PAV | PEU | PMA | PRE | PSE | PUF | BI | PAV | PEU | PMA | PRE | PSE | PUF | ||
BI | 0.873 | BI | |||||||||||||
PAV | 0.636 | 0.874 | PAV | 0.713 | |||||||||||
PEU | 0.584 | 0.610 | 0.877 | PEU | 0.639 | 0.669 | |||||||||
PMA | 0.638 | 0.566 | 0.499 | 0.813 | PMA | 0.737 | 0.655 | 0.568 | |||||||
PRE | 0.728 | 0.655 | 0.671 | 0.685 | 0.817 | PRE | 0.858 | 0.765 | 0.755 | 0.836 | |||||
PSE | 0.608 | 0.570 | 0.687 | 0.513 | 0.770 | 0.871 | PSE | 0.675 | 0.630 | 0.751 | 0.592 | 0.886 | |||
PUF | 0.566 | 0.483 | 0.702 | 0.389 | 0.523 | 0.529 | 0.885 | PUF | 0.628 | 0.533 | 0.760 | 0.445 | 0.580 | 0.581 |
Fornell-larcker criterion | Heterotrait-monotrait ratio (HTMT) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BI | PAV | PEU | PMA | PRE | PSE | PUF | BI | PAV | PEU | PMA | PRE | PSE | PUF | ||
BI | 0.900 | BI | |||||||||||||
PAV | 0.718 | 0.892 | PAV | 0.778 | |||||||||||
PEU | 0.636 | 0.669 | 0.884 | PEU | 0.679 | 0.721 | |||||||||
PMA | 0.701 | 0.658 | 0.579 | 0.850 | PMA | 0.780 | 0.740 | 0.642 | |||||||
PRE | 0.727 | 0.608 | 0.654 | 0.729 | 0.845 | PRE | 0.815 | 0.683 | 0.727 | 0.841 | |||||
PSE | 0.657 | 0.555 | 0.586 | 0.606 | 0.749 | 0.876 | PSE | 0.723 | 0.610 | 0.635 | 0.683 | 0.849 | |||
PUF | 0.712 | 0.675 | 0.773 | 0.535 | 0.612 | 0.567 | 0.878 | PUF | 0.778 | 0.736 | 0.837 | 0.603 | 0.694 | 0.627 |
Fornell-larcker criterion | Heterotrait-monotrait ratio (HTMT) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BI | PAV | PEU | PMA | PRE | PSE | PUF | BI | PAV | PEU | PMA | PRE | PSE | PUF | ||
BI | 0.886 | BI | |||||||||||||
PAV | 0.703 | 0.874 | PAV | 0.781 | |||||||||||
PEU | 0.668 | 0.696 | 0.864 | PEU | 0.718 | 0.754 | |||||||||
PMA | 0.643 | 0.634 | 0.595 | 0.836 | PMA | 0.719 | 0.719 | 0.654 | |||||||
PRE | 0.803 | 0.664 | 0.701 | 0.687 | 0.834 | PRE | 0.912 | 0.765 | 0.788 | 0.800 | |||||
PSE | 0.682 | 0.605 | 0.665 | 0.542 | 0.773 | 0.881 | PSE | 0.751 | 0.670 | 0.720 | 0.604 | 0.881 | |||
PUF | 0.819 | 0.641 | 0.727 | 0.599 | 0.733 | 0.711 | 0.860 | PUF | 0.907 | 0.719 | 0.798 | 0.674 | 0.844 | 0.792 |
Fornell-larcker criterion | Heterotrait-monotrait ratio (HTMT) | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
BI | PAV | PEU | PMA | PRE | PSE | PUF | BI | PAV | PEU | PMA | PRE | PSE | PUF | ||
BI | 0.892 | BI | |||||||||||||
PAV | 0.643 | 0.856 | PAV | 0.712 | |||||||||||
PEU | 0.561 | 0.618 | 0.891 | PEU | 0.606 | 0.679 | |||||||||
PMA | 0.698 | 0.671 | 0.577 | 0.843 | PMA | 0.780 | 0.763 | 0.643 | |||||||
PRE | 0.712 | 0.643 | 0.654 | 0.735 | 0.821 | PRE | 0.813 | 0.745 | 0.726 | 0.864 | |||||
PSE | 0.610 | 0.614 | 0.697 | 0.637 | 0.743 | 0.862 | PSE | 0.677 | 0.687 | 0.761 | 0.722 | 0.861 | |||
PUF | 0.559 | 0.496 | 0.738 | 0.466 | 0.536 | 0.527 | 0.886 | PUF | 0.611 | 0.547 | 0.797 | 0.524 | 0.586 | 0.583 |
6.2 Structural model analysis
Construct | Full dataset | ||||
---|---|---|---|---|---|
R2 | Q2 | f2 | |||
BI | PEU | PUF | |||
BI | 0.590 | 0.436 | |||
PAV | 0.138 | 0.024 | |||
PEU | 0.539 | 0.391 | 0.012 | 0.340 | |
PMA | 0.358 | ||||
PRE | 0.064 | 0.001 | |||
PSE | 0.056 | 0.005 | |||
PUF | 0.554 | 0.405 | 0.134 |
Construct | Hi-ITi | Lo-ITi | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | Q2 | f2 | R2 | Q2 | f2 | |||||
BI | PEU | PUF | BI | PEU | PUF | |||||
BI | 0.527 | 0.377 | 0.641 | 0.479 | ||||||
PAV | 0.082 | 0.004 | 0.229 | 0.083 | ||||||
PEU | 0.546 | 0.392 | 0.028 | 0.324 | 0.539 | 0.395 | 0.001 | 0.351 | ||
PMA | 0.326 | 0.359 | ||||||||
PRE | 0.035 | 0.001 | 0.082 | 0.004 | ||||||
PSE | 0.128 | 0.002 | 0.015 | 0.007 | ||||||
PUF | 0.483 | 0.351 | 0.082 | 0.638 | 0.461 | 0.229 |
Construct | SaaS | PaaS/IaaS | ||||||||
---|---|---|---|---|---|---|---|---|---|---|
R2 | Q2 | f2 | R2 | Q2 | f2 | |||||
BI | PEU | PUF | BI | PEU | PUF | |||||
BI | 0.699 | 0.513 | 0.541 | 0.395 | ||||||
PAV | 0.192 | 0.015 | 0.073 | 0.002 | ||||||
PEU | 0.591 | 0.406 | 0.009 | 0.112 | 0.543 | 0.405 | 0.002 | 0.454 | ||
PMA | 0.097 | 0.446 | ||||||||
PRE | 0.068 | 0.061 | 0.036 | 0.008 | ||||||
PSE | 0.048 | 0.061 | 0.150 | 0.002 | ||||||
PUF | 0.638 | 0.440 | 0.586 | 0.528 | 0.381 | 0.075 |
Construct | Hi-ITi versus Lo-ITi | SaaS versus PaaS/IaaS | ||
---|---|---|---|---|
f2 diff | p-value | f2 diff | p-value | |
PAV → PUF | 0.079 | 0.726 | 0.014 | 0.351 |
PAV → PEU | 0.147 | 0.831 | 0.118 | 0.211 |
PRE → PUF | 0.003 | 0.435 | 0.053 | 0.305 |
PRE → PEU | 0.048 | 0.729 | 0.032 | 0.335 |
PSE → PUF | 0.006 | 0.680 | 0.059 | 0.115 |
PSE → PEU | 0.113 | 0.109 | 0.102 | 0.766 |
PMA → BI | 0.033 | 0.541 | 0.349 | 0.858 |
PUF → BI | 0.147 | 0.832 | 0.512 | 0.013 |
PEU → BI | 0.026 | 0.478 | 0.007 | 0.308 |
PEU → PUF | 0.026 | 0.561 | 0.343 | 0.809 |
Path | Full dataset | Hi-ITi | Lo-ITi | SaaS | PaaS/IaaS | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
β | p-values | Results | β | p-values | Results | β | p-values | Results | β | p-values | Results | β | p-values | Results | |
PAV → PUF | 0.141 | 0.159 | NS | 0.059 | 0.657 | NS | 0.242 | 0.095 | NS | 0.109 | 0.387 | NS | 0.040 | 0.793 | NS |
PAV → PEU | 0.323 | 0.000 | S | 0.255 | 0.024 | S | 0.411 | 0.000 | S | 0.375 | 0.000 | S | 0.244 | 0.103 | NS |
PRE → PUF | 0.035 | 0.761 | NS | 0.041 | 0.808 | NS | 0.064 | 0.655 | NS | 0.258 | 0.098 | NS | 0.099 | 0.497 | NS |
PRE → PEU | 0.277 | 0.002 | S | 0.210 | 0.100 | NS | 0.309 | 0.004 | S | 0.281 | 0.025 | S | 0.201 | 0.123 | NS |
PSE → PUF | 0.077 | 0.269 | NS | 0.048 | 0.700 | NS | 0.078 | 0.339 | NS | 0.238 | 0.024 | S | (0.046) | 0.718 | NS |
PSE → PEU | 0.248 | 0.015 | S | 0.377 | 0.013 | S | 0.126 | 0.297 | NS | 0.221 | 0.070 | NS | 0.398 | 0.021 | S |
PMA → BI | 0.457 | 0.000 | S | 0.448 | 0.001 | S | 0.440 | 0.000 | S | 0.219 | 0.023 | S | 0.545 | 0.000 | S |
PUF → BI | 0.345 | 0.000 | S | 0.273 | 0.008 | S | 0.452 | 0.000 | S | 0.630 | 0.000 | S | 0.270 | 0.030 | S |
PEU → BI | 0.111 | 0.171 | NS | 0.168 | 0.192 | NS | 0.032 | 0.751 | NS | 0.080 | 0.360 | NS | 0.048 | 0.751 | NS |
PEU → PUF | 0.572 | 0.000 | S | 0.605 | 0.000 | S | 0.523 | 0.000 | S | 0.313 | 0.002 | S | 0.681 | 0.000 | S |
6.3 Multi-group analysis
Path | Hi-ITi versus Lo-ITi | SaaS versus PaaS/IaaS | ||
---|---|---|---|---|
Path coefficients-diff | p-value | Path coefficients-diff | p-value | |
PAV → PUF | 0.183 | 0.821 | 0.069 | 0.355 |
PAV → PEU | 0.156 | 0.847 | 0.132 | 0.235 |
PRE → PUF | 0.023 | 0.546 | 0.158 | 0.224 |
PRE → PEU | 0.096 | 0.716 | 0.080 | 0.327 |
PSE → PUF | 0.030 | 0.580 | 0.284 | 0.042 |
PSE → PEU | 0.251 | 0.100 | 0.177 | 0.793 |
PMA → BI | 0.008 | 0.471 | 0.326 | 0.954 |
PUF → BI | 0.179 | 0.887 | 0.360 | 0.020 |
PEU → BI | 0.136 | 0.205 | 0.032 | 0.424 |
PEU → PUF | 0.083 | 0.324 | 0.368 | 0.951 |