The DOI matches its Crossref record; 92 of the 92 references it cites were found in a registry or an index.
Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach
Abstract
Abstract This paper investigates the key predictors of cloud computing adoption, and further, assesses how cloud computing adoption affects small and medium enterprises' (SMEs') performance. To test the proposed model, we have applied a dual-stage analytical approach by combining structural equation modeling (SEM) and artificial neural network (ANN). SEM results reveal that relative advantage, service quality, perceived risks, top management supports, facilitating conditions, cloud providers influence, server location, computer self-efficacy, and resistance to change have a significant effect on the adoption of cloud computing. Also, this study confirms the positive impact of cloud computing adoption on firm performance. The results of importance-performance map analysis (IPMA) suggest that managerial actions should focus more on improving perceived risk, relative advantage, and top management support. Besides, the results of neural network analysis indicate that the most significant predictor of cloud adoption is server location followed by facilitating conditions, relative advantage, service quality, top management support, computer self-efficacy, perceived risks, cloud provider's influence, and resistance to change. We also discuss the implications of the research that can assist researchers, owners/managers, policymakers, and cloud providers by offering valuable insights regarding cloud computing adoption in SMEs.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
User Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
Rogers, E. M., Singhal, A., & Quinlan, M. M. (2019) · An Integrated Approach to Communication Theory and Research · doi:10.4324/9780203710753-35 · Cited by 37,322
Herne, H., Cooley, W. W., & Lohnes, P. R. (1973) · Journal of the Royal Statistical Society. Series A (General) · doi:10.2307/2344428 · Cited by 35,697
(1980) · Medical Entomology and Zoology · Cited by 25,810
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921
Self-efficacy mechanism in human agency
Bandura, A. (1982) · American Psychologist · doi:10.1037/0003-066x.37.2.122 · Cited by 15,975
Cited by
Islam, A. K. M. N., Laato, S., Talukder, S., & Sutinen, E. (2020) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2020.120201 · Cited by 538
Hybrid artificial neural network and structural equation modelling techniques: A survey
Albahri, A. S., Alnoor, A., Zaidan, A. A., Albahri, O. S., Hameed, H., Zaidan, B. B., Peh, S. S., Zain, A. B., Siraj, S. B., Masnan, A. H. B., et al. (2022) · Complex & Intelligent Systems · doi:10.1007/s40747-021-00503-w · Cited by 173
Tawil, A.-R. H., Mohamed, M., Schmoor, X., Vlachos, K., & Haidar, D. (2024) · Big Data and Cognitive Computing · doi:10.3390/bdcc8070079 · Cited by 44
The Roles of Cloud-Based Systems on the Cancer-Related Studies: A Systematic Literature Review
Xu, B., & Zhou, F. (2022) · IEEE Access · doi:10.1109/access.2022.3181147 · Cited by 13
Metadata
- Authors
- Khayer, A., Talukder, M. S., Bao, Y., & Hossain, M. N.
- Venue
- Technology in Society
- Publisher
- Elsevier BV
- Published
- 2020-02
- Volume
- 60
- Pages
- 101225
- ISSN
- 0160-791X (ISSN-L)
- OpenAlex
- W2993336504
- Cited by
- 308
- References
- 92
- FWCI
- 30.37
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach. https://app.cochat.ai/papers/10.1016/j.techsoc.2019.101225
- The paper
- Khayer, A., Talukder, M. S., Bao, Y., & Hossain, M. N. (2020). Cloud computing adoption and its impact on SMEs’ performance for cloud supported operations: A dual-stage analytical approach. Technology in Society. https://doi.org/10.1016/j.techsoc.2019.101225
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.