The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Predicting m-commerce adoption determinants: A neural network approach
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
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
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
Neural Networks: A Comprehensive Foundation
Haykin, S. (1998) · Cited by 30,070
A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies
Venkatesh, V., & Davis, F. D. (2000) · Management Science · doi:10.1287/mnsc.46.2.186.11926 · Cited by 23,066
Multivariate data analysis : a global perspective
Hair, J. F. (2010) · Cited by 12,255
Technology Acceptance Model 3 and a Research Agenda on Interventions
Venkatesh, V., & Bala, H. (2008) · Decision Sciences · doi:10.1111/j.1540-5915.2008.00192.x · Cited by 8,159
Are Individual Differences Germane to the Acceptance of New Information Technologies?
Agarwal, R., & Prasad, J. (1999) · Decision Sciences · doi:10.1111/j.1540-5915.1999.tb01614.x · Cited by 2,102
Value-based Adoption of Mobile Internet: An empirical investigation
Kim, H.-W., Chan, H. C., & Gupta, S. (2007) · Decision Support Systems · doi:10.1016/j.dss.2005.05.009 · Cited by 2,084
Integrating TTF and UTAUT to explain mobile banking user adoption
Zhou, T., Lu, Y., & Wang, B. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.01.013 · Cited by 1,696
Lu, J., Yao, J. E., & Yu, C.-S. (2005) · The Journal of Strategic Information Systems · doi:10.1016/j.jsis.2005.07.003 · Cited by 1,457
Cited by
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019) · Information Systems Frontiers · doi:10.1007/s10796-017-9774-y · Cited by 2,137
“Prior literature highlights several individual characteristics including attitude, computer self-efficacy, and personal innovativeness (e.g., Carter and Schaupp 2008; Chong 2013; Venkatesh et al. 2011a).”
BackgroundChatterjee, S., & Bhattacharjee, K. K. (2020) · Education and Information Technologies · doi:10.1007/s10639-020-10159-7 · Cited by 747
A SEM-neural network approach for predicting antecedents of m-commerce acceptance
Liébana-Cabanillas, F., Marinković, V., & Kalinić, Z. (2017) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2016.10.008 · Cited by 583
Developing a general extended UTAUT model for M-payment adoption
Al-Saedi, K., Al-Emran, M., Ramayah, T., & Abusham, E. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101293 · Cited by 523
Uzir, M. U. H., Al Halbusi, H., Thurasamy, R., Thiam Hock, R. L., Aljaberi, M. A., Hasan, N., & Hamid, M. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2021.102721 · Cited by 478
Metadata
- Authors
- Chong, A. Y.-L.
- Venue
- Expert Systems with Applications
- Publisher
- Elsevier BV
- Published
- 2013-02
- Volume
- 40
- Issue
- 2
- Pages
- 523-530
- ISSN
- 0957-4174 (ISSN-L)
- OpenAlex
- W1985870920
- Cited by
- 454
- References
- 39
- FWCI
- 30.50
- 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: Predicting m-commerce adoption determinants: A neural network approach. https://app.cochat.ai/papers/10.1016/j.eswa.2012.07.068
- The paper
- Chong, A. Y.-L. (2013). Predicting m-commerce adoption determinants: A neural network approach. Expert Systems with Applications. https://doi.org/10.1016/j.eswa.2012.07.068
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.
