The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.
The age of mobile social commerce: An Artificial Neural Network analysis on its resistances
Abstract
Abstract Drivers of social commerce usage has been the focus of scholars in recent years, but mobile social media users' resistance behavior towards mobile social commerce has been in the darkness and therefore worth torched lights on. With the data collected from mobile social media users who have no experience in mobile social commerce, Artificial Neural Network analysis was engaged to capture both linear and nonlinear relationships in a research model that consists of innovation barriers and privacy concern. Surprisingly, all resistances positively correlated with usage intention, except for image barrier, which appeared to be the most influencing resistance. Several explanations were offered for such outcomes. The possible coexistence of resistance behavior and usage intention resembles the fitting justification. Mobile social media users intend to embrace mobile social commerce; however, their intentions have been held up by their perceptions on innovation barriers and privacy concern. Based upon these outcomes, this study has reaffirmed the coexistence of resistances and usage intention, as well as the “privacy paradox” phenomenon. These discoveries are believed to have contributed to the existing literature. Practitioners are then advised to act accordingly to these findings, and several methods on catalyzing mobile social media users' adoption decision were suggested.
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
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
Pls-Sem: indeed a silver bullet
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011) · Journal of Marketing Theory and Practice · doi:10.2753/mtp1069-6679190202 · Cited by 21,631
The use of partial least squares path modeling in international marketing
Henseler, J., Ringle, C. M., & Sinkovics, R. R. (2009) · Advances in International Marketing · doi:10.1108/s1474-7979(2009)0000020014 · Cited by 10,926
(1964) · Medical Entomology and Zoology · Cited by 10,439
An assessment of the use of partial least squares structural equation modeling in marketing research
Hair, J. F., Sarstedt, M., Ringle, C. M., & Mena, J. A. (2012) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-011-0261-6 · Cited by 8,088
Normality Tests for Statistical Analysis: A Guide for Non-Statisticians
Ghasemi, A., & Zahediasl, S. (2012) · International Journal of Endocrinology and Metabolism · doi:10.5812/ijem.3505 · Cited by 4,368
Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007) · MIS Quarterly · doi:10.2307/25148781 · Cited by 3,921
A Beginner’s Guide to Factor Analysis: Focusing on Exploratory Factor Analysis
Yong, A. G., & Pearce, S. (2013) · Tutorials in Quantitative Methods for Psychology · doi:10.20982/tqmp.09.2.p079 · Cited by 3,304
An Extended Privacy Calculus Model for E-Commerce Transactions
Dinev, T., & Hart, P. (2006) · Information Systems Research · doi:10.1287/isre.1060.0080 · Cited by 2,640
What Reviewers Should Expect from Authors Regarding Common Method Bias in Organizational Research
Conway, J. M., & Lance, C. E. (2010) · Journal of Business and Psychology · doi:10.1007/s10869-010-9181-6 · Cited by 2,392
Research works printed but not linked: 3
- Hair, 2014, AMOS covariance-based structural equation modeling (CB-SEM): guidelines on its application as a marketing research tool, Braz. J. Mark., 13, 44
- Martin, 2016, Service innovation, renewal, and adoption/rejection in dynamic global contexts, J. Bus. Res., 69, 2397, 10.1016/j.jbusres.2016.01.008 · doi:10.1016/j.jbusres.2016.01.008
- Samuel, 2014, Comparative study between sparse representation classification and classical classifiers on cervical cancer cell images, Int. J. Adv. Res. Comput. Commun. Eng., 3, 7788
Cited by
Leong, L.-Y., Hew, T.-S., Ooi, K.-B., & Wei, J. (2020) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2019.102047 · Cited by 528
Self-citationAn innovation resistance theory perspective on mobile payment solutions
Kaur, P., Dhir, A., Singh, N., Sahu, G., & Almotairi, M. (2020) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102059 · Cited by 487
The value proposition of food delivery apps from the perspective of theory of consumption value
Kaur, P., Dhir, A., Talwar, S., & Ghuman, K. (2021) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-05-2020-0477 · Cited by 377
Friedman, N., & Ormiston, J. (2022) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2021.121403 · Cited by 365
Consumers’ Resistance to Digital Innovations: A Systematic Review and Framework Development
Talwar, S., Talwar, M., Kaur, P., & Dhir, A. (2020) · Australasian Marketing Journal · doi:10.1016/j.ausmj.2020.06.014 · Cited by 348
Metadata
- Authors
- Hew, J.-J., Leong, L.-Y., Tan, G. W.-H., Ooi, K.-B., & Lee, V.-H.
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2019-07
- Volume
- 144
- Pages
- 311-324
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W2765910181
- Cited by
- 283
- References
- 95
- FWCI
- 17.95
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
- www.elsevier.com/legal/tdmrep-license (tdm)
- Funders
- Universiti Tunku Abdul Rahman (6251/L08)
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: The age of mobile social commerce: An Artificial Neural Network analysis on its resistances. https://app.cochat.ai/papers/10.1016/j.techfore.2017.10.007
- The paper
- Hew, J.-J., Leong, L.-Y., Tan, G. W.-H., Ooi, K.-B., & Lee, V.-H. (2019). The age of mobile social commerce: An Artificial Neural Network analysis on its resistances. Technological Forecasting and Social Change. https://doi.org/10.1016/j.techfore.2017.10.007
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.