The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Mobile commerce usage activities: The roles of demographic and motivation variables
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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
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
AGE DIFFERENCES IN TECHNOLOGY ADOPTION DECISIONS: IMPLICATIONS FOR A CHANGING WORK FORCE
MORRIS, M. G., & VENKATESH, V. (2000) · Personnel Psychology · doi:10.1111/j.1744-6570.2000.tb00206.x · Cited by 1,723
A Review of the Predictors, Linkages, and Biases in IT Innovation Adoption Research
Jeyaraj, A., Rottman, J. W., & Lacity, M. C. (2006) · Journal of Information Technology · doi:10.1057/palgrave.jit.2000056 · Cited by 1,136
Gender, Internet and computer attitudes and experiences
Schumacher, P., & Morahan-Martin, J. (2001) · Computers in Human Behavior · doi:10.1016/s0747-5632(00)00032-7 · Cited by 592
What drives Malaysian m-commerce adoption? An empirical analysis
Tsu Wei, T., Marthandan, G., Yee‐Loong Chong, A., Ooi, K., & Arumugam, S. (2009) · Industrial Management & Data Systems · doi:10.1108/02635570910939399 · Cited by 550
Cited by
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
Rese, A., Baier, D., Geyer-Schulz, A., & Schreiber, S. (2017) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2016.10.010 · Cited by 470
Natarajan, T., Balasubramanian, S. A., & Kasilingam, D. L. (2017) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2017.02.010 · Cited by 396
The age of mobile social commerce: An Artificial Neural Network analysis on its resistances
Hew, J.-J., Leong, L.-Y., Tan, G. W.-H., Ooi, K.-B., & Lee, V.-H. (2019) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2017.10.007 · Cited by 283
de Blanes Sebastián, M. G., Antonovica, A., & Sarmiento Guede, J. R. (2023) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2022.122235 · Cited by 86
Metadata
- Authors
- Chong, A. Y.-L.
- Venue
- Technological Forecasting and Social Change
- Publisher
- Elsevier BV
- Published
- 2013-09
- Volume
- 80
- Issue
- 7
- Pages
- 1350-1359
- ISSN
- 0040-1625 (ISSN-L)
- OpenAlex
- W2074502275
- Cited by
- 171
- References
- 6
- FWCI
- 28.86
- Type
- journal article
- Language
- en
- Open access
- closed
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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.
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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.
