The DOI matches its Crossref record; 89 of the 92 references it cites were found in a registry or an index.
The Integration of the Technology Acceptance Model and Value-Based Adoption Model to Study the Adoption of E-Learning: The Moderating Role of e-WOM
Open accessAbstract
The Technology Acceptance Model (TAM) has lately been utilized in a number of studies to investigate why people reject or adopt new technologies like mobile commerce or e-learning. However, several studies have found weaknesses in TAM’s ability to predict consumers’ purchase intention behavior. To compensate for TAM’s weaknesses, this study presents a model that integrates all of TAM’s components with the Value-Based Adoption Model (VAM). The perceived benefits and sacrifices were considered to provide a list of the implications for both researchers and e-learning service providers. Furthermore, the moderating role of e-word of mouth was utilized to examine the relationship between attitude, intention, perceived value, and intention toward e-learning, in order to match the current circumstances with the growing popularity of social networks. This study was conducted with a quantitative analysis by using data collected from survey 417 e-learning consumers. Except for perceived fee, which has a negative effect on perceived value, the results demonstrate that all hypotheses of latent correlations in TAM and VAM were strongly significant. Furthermore, attitude and perceived value have a significant role in determining consumer adoption of e-learning. Consumers’ perceived value will be driven by the high and low levels of the moderating influence of e-word of mouth, influencing their intention toward e-learning. Since e-learning is an effective sustainable education system, the result of this study can provide a good solution to facilitate e-learning in current and future conditions.
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.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Chen et al. [11] and Liao et al. [28] used TAM to assess educators' acceptance of AI-driven educational tools, emphasizing the role of perceived usefulness in shaping positive attitudes and perceptions.”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [28]
In the citing paper
“A growing body of research supports the notion that an inverse correlation exists between perceived risks and perceived value when predicting technology use intention [28,29,30,36,43]”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [29]
In the citing paper
“Research indicates that the degree of perceived fees has a notable influence on customers’ perceived value [29,43,45,46,47].”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [29]
In the citing paper
“Likewise, Liao et al. [29] confirmed that, in adopting an e-learning system, perceived value is strongly predicted by perceived usefulness.”
From ChatGPT in Learning: Assessing Students’ Use Intentions through the Lens of Perceived Value and the Influence of AI Literacy (2024) · cites this paper as [29]
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
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
User Acceptance of Computer Technology: A Comparison of Two Theoretical Models
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989) · Management Science · doi:10.1287/mnsc.35.8.982 · Cited by 25,709
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
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921
Consumer Perceptions of Price, Quality, and Value: A Means-End Model and Synthesis of Evidence
Zeithaml, V. A. (1988) · Journal of Marketing · doi:10.1177/002224298805200302 · Cited by 18,292
(2014) · DigitalCommons - Kennesaw State University (Kennesaw State University) · Cited by 12,785
(1998) · Cited by 12,725
Hennig-Thurau, T., Gwinner, K. P., Walsh, G., & Gremler, D. D. (2004) · Journal of Interactive Marketing · doi:10.1002/dir.10073 · Cited by 5,916
Research works printed but not linked: 3
- Newel, F., and Newell-Lemon, K. (2001). Wireless Rules, McGraw-Hill.
- Kwon, 2013, Application of value-based adoption model to analyze saas adoption behavior in korean b2b cloud market, Int. J. Adv. Comput. Technol., 5, 368
- Fang, 2016, Consumer heterogeneity, perceived value, and repurchase decision-making in online shopping: The role of gender, age, and shopping motives, J. Electron. Commer. Res., 17, 116
Cited by
Sorted by citations
Use of ChatGPT in academia: Academic integrity hangs in the balance
Bin-Nashwan, S. A., Sadallah, M., & Bouteraa, M. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102370 · Cited by 394
Promoting tourism business through digital marketing in the new normal era: a sustainable approach
Deb, S. K., Nafi, S. M., & Valeri, M. (2022) · European Journal of Innovation Management · doi:10.1108/ejim-04-2022-0218 · Cited by 252
Al-Abdullatif, A. M. (2024) · Education Sciences · doi:10.3390/educsci14111209 · Cited by 169
Al-Abdullatif, A. M., & Alsubaie, M. A. (2024) · Behavioral Sciences · doi:10.3390/bs14090845 · Cited by 126
“Studies have shown that the level of perceived fees significantly influences users’ perceived value of a service or product [29,43,45–47].”
InfluentialPark, D. Y., & Kim, H. (2023) · Sustainability · doi:10.3390/su15010872 · Cited by 132
Metadata
- Authors
- Liao, Y.-K., Wu, W.-Y., Le, T. Q., & Phung, T. T. T.
- Affiliations
- Nanhua University (TW)
- Ton Duc Thang University (VN)
- Corresponding author
- Le, Trang Quang
- Venue
- Sustainability
- Publisher
- MDPI AG
- Published
- 2022-01-12
- Volume
- 14
- Issue
- 2
- Pages
- 815
- ISSN
- 2071-1050 (ISSN-L)
- OpenAlex
- W4205109486
- Cited by
- 123
- References
- 92
- Citations by year
- 2026: 24 · 2025: 33 · 2024: 37 · 2023: 23 · 2022: 6
- FWCI
- 20.71
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Sustainability (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Technology Adoption and User Behaviour
- Social Sciences › Education › Technology-Enhanced Education Studies
- Health Sciences › Health Professions › Health Information Management › Health, Technology, Consumer Behavior
- Keywords
- e-learning adoption, technology acceptance model, value-based adoption model, e-word-of-mouth, perceived value, consumer attitudes, purchase intention, perceived cost, moderating effect, social networks
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
