The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
The effects of convenience and speed in m-payment
Abstract
Purpose: – The purpose of this paper is to uncover the effects of perceived transaction convenience (PTC) and perceived transaction speed (PTS) on unified theory of acceptance and use of technology (UTAUT) in the context of m-payment. Design/methodology/approach: – A predictive analysis approach was used to examine the PTC and PTS using a two-stage partial least square (PLS) and neural network (NN) analyses. Findings: – The findings reveal that only effort expectancy (EE) and facilitating conditions (FC) were discovered to significantly influence BI. More importantly, PTC was found to have positive significant relationship with EE and performance expectancy (PE). Moreover, PTS also supported the positive relationship with BI and EE. Practical implications: – The findings of the study provided further insights to mobile payment service providers, online banking industry players, and all decision makers and stakeholders involved. Originality/value: – Despite of many attempts devoted to understand m-payment adoption, the effects of PTC and PTS on m-payment are not well understood.
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
Prospect Theory: An Analysis of Decision under Risk
Kahneman, D., & Tversky, A. (1979) · Econometrica · doi:10.2307/1914185 · Cited by 48,524
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
From Intentions to Actions: A Theory of Planned Behavior
Ajzen, I. (1985) · Action Control · doi:10.1007/978-3-642-69746-3_2 · Cited by 19,057
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012) · MIS Quarterly · doi:10.2307/41410412 · Cited by 15,756
Estimating Nonresponse Bias in Mail Surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.2307/3150783 · Cited by 12,222
Estimating nonresponse bias in mail surveys
Armstrong, J. S., & Overton, T. S. (1977) · Journal of Marketing Research · doi:10.1177/002224377701400320 · Cited by 10,390
The Relative Importance of Perceived Ease of Use in IS Adoption: A Study of E-Commerce Adoption
Drexel University, Gefen, D., Straub, D., & Georgia State University (2000) · Journal of the Association for Information Systems · doi:10.17705/1jais.00008 · Cited by 1,833
Witek-Krowiak, A., Chojnacka, K., Podstawczyk, D., Dawiec, A., & Bubała, K. (2014) · Bioresource Technology · doi:10.1016/j.biortech.2014.01.021 · Cited by 667
Exploring factors affecting the adoption of mobile commerce in Singapore
Yang, K. C. C. (2005) · Telematics and Informatics · doi:10.1016/j.tele.2004.11.003 · Cited by 656
Cited by
Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016) · Computers in Human Behavior · doi:10.1016/j.chb.2016.03.030 · Cited by 1,361
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-citationChopdar, P. K., Korfiatis, N., Sivakumar, V. J., & Lytras, M. D. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.04.017 · Cited by 468
Shao, Z., Zhang, L., Li, X., & Guo, Y. (2019) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2018.100823 · Cited by 423
What catalyses mobile apps usage intention: An empirical analysis
Hew, J.-J., Lee, V.-H., Ooi, K.-B., & Wei, J. (2015) · Industrial Management & Data Systems · doi:10.1108/imds-01-2015-0028 · Cited by 383
Self-citation
Metadata
- Authors
- Teo, A.-C., Tan, G. W.-H., Ooi, K.-B., Hew, T.-S., & Yew, K.-T.
- Venue
- Industrial Management & Data Systems
- Publisher
- Emerald
- Published
- 2015-03-09
- Volume
- 115
- Issue
- 2
- Pages
- 311-331
- ISSN
- 0263-5577 (ISSN-L)
- OpenAlex
- W2002177316
- Cited by
- 442
- References
- 10
- FWCI
- 41.17
- 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: The effects of convenience and speed in m-payment. https://app.cochat.ai/papers/10.1108/imds-08-2014-0231
- The paper
- Teo, A.-C., Tan, G. W.-H., Ooi, K.-B., Hew, T.-S., & Yew, K.-T. (2015). The effects of convenience and speed in m-payment. Industrial Management & Data Systems. https://doi.org/10.1108/imds-08-2014-0231
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.