The DOI matches its Crossref record; 195 of the 195 references it cites were found in a registry or an index.
Gender and age: Do they really moderate mobile tourism shopping behavior?
Abstract
Abstract With the growing popularity of mobile devices (m-devices) and technological advancement in wireless networks, a new form of shopping channel has emerged in the tourism industry. Mobile tourism shopping refers to the use of m-devices to shop for tourism products and services. Given the benefits and potential of this channel, the acceptance however is not widespread and currently an under-addressed topic among academicians. Specifically practitioners are also sceptical if consumers are ready to accept this alternative channel. Drawing upon the Unified Theory of Acceptance and Usage of Technology (UTAUT) as a baseline model, the proposed model was integrated with flow theory, psychological characteristic, social environment and security concern factors to study on the mobile consumers' behavioral intention. Through the use of Partial Least Squares-Structural Equation Modelling of 503 mobile respondents, it was discovered that some of the structural paths are not consistent with past literatures. Additionally, a non-parametric permutation approach has also been conducted to discover the gender and age differences in the decision making. The findings however are mixed and therefore require further testing. This study has valuable contributions to academicians, practitioners, and society. Academically, the integrated framework contributes to the tourism and marketing literatures by uniting UTAUT, flow theory, psychological characteristic, social environment and security concern factors. Practically, by focusing on the important factors affecting consumers’ decision to adopt mobile tourism shopping, organisations can create effective mobile marketing campaigns. Socially, the study contributes to job creations and revenue generation for the host country.
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
Structural equation modeling in practice: A review and recommended two-step approach
Anderson, J. C., & Gerbing, D. W. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.3.411 · Cited by 41,941
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
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
(1998) · Cited by 12,725
Hu, L.-T., & Bentler, P. M. (1998) · Psychological Methods · doi:10.1037/1082-989x.3.4.424 · Cited by 12,298
Using pls path modeling in new technology research: updated guidelines
Henseler, J., Hubona, G., & Ray, P. A. (2016) · Industrial Management & Data Systems · doi:10.1108/imds-09-2015-0382 · Cited by 7,173
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-citationThe 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
Why do people purchase from online travel agencies (OTAs)? A consumption values perspective
Talwar, S., Dhir, A., Kaur, P., & Mäntymäki, M. (2020) · International Journal of Hospitality Management · doi:10.1016/j.ijhm.2020.102534 · Cited by 322
QR code and mobile payment: The disruptive forces in retail
Yan, L.-Y., Tan, G. W.-H., Loh, X.-M., Hew, J.-J., & Ooi, K.-B. (2021) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2020.102300 · Cited by 252
Self-citationUsing TikTok in tourism destination choice: A young Chinese tourists' perspective
Zhou, Q., Sotiriadis, M., & Shen, S. (2023) · Tourism Management Perspectives · doi:10.1016/j.tmp.2023.101101 · Cited by 187
Metadata
- Authors
- Tan, G. W.-H., & Ooi, K.-B.
- Venue
- Telematics and Informatics
- Publisher
- Elsevier BV
- Published
- 2018-09
- Volume
- 35
- Issue
- 6
- Pages
- 1617-1642
- ISSN
- 0736-5853 (ISSN-L)
- OpenAlex
- W2801239813
- Cited by
- 282
- References
- 195
- FWCI
- 33.44
- 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: Gender and age: Do they really moderate mobile tourism shopping behavior? https://app.cochat.ai/papers/10.1016/j.tele.2018.04.009
- The paper
- Tan, G. W.-H., & Ooi, K.-B. (2018). Gender and age: Do they really moderate mobile tourism shopping behavior? Telematics and Informatics. https://doi.org/10.1016/j.tele.2018.04.009
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.