The DOI matches its Crossref record.
TAM or Just Plain Habit: A Look at Experienced Online Shoppers
Abstract
According to the Technology Acceptance Model (TAM), behavioral intentions to use a new IT are primarily the product of a rational analysis of its desirable perceived outcomes, namely perceived usefulness (PU) and perceived ease of use (PEOU). But what happens with the continued use of an IT among experienced users? Does habit also kick in as a major factor or is continued use only the product of its desirable outcomes? This study examines this question in the context of experienced online shoppers. The data show that, as hypothesized, online shoppers’ intentions to continue using a website that they last bought at depend not only on PU and PEOU, but also on habit. In fact, habit alone can explain a large proportion of the variance of continued use of a website. Moreover, the explained variance indicates that habit may also be a major predictor of PU and PEOU among experienced shoppers. Implications are discussed.
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
Cited by
Online customer experience in e-retailing: An empirical model of antecedents and outcomes
Rose, S., Clark, M., Samouel, P., & Hair, N. (2012) · Journal of Retailing · doi:10.1016/j.jretai.2012.03.001 · Cited by 1,131
Modelling technology acceptance in education: A study of pre-service teachers
Teo, T. (2009) · Computers & Education · doi:10.1016/j.compedu.2008.08.006 · Cited by 1,009
The effect of web interface features on consumer online purchase intentions
Hausman, A. V., & Siekpe, J. S. (2009) · Journal of Business Research · doi:10.1016/j.jbusres.2008.01.018 · Cited by 981
Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012) · Computers in Human Behavior · doi:10.1016/j.chb.2011.08.019 · Cited by 679
Polites, G. L., & Karahanna, E. (2012) · MIS Quarterly · doi:10.25300/misq/2012/36.1.02 · Cited by 215
Hsu, C.-L., & Lin, J. C.-C. (2015) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2014.11.003 · Cited by 662
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
Technology acceptance: a meta‐analysis of the TAM: Part 1
Yousafzai, S. Y., Foxall, G. R., & Pallister, J. G. (2007) · Journal of Modelling in Management · doi:10.1108/17465660710834453 · Cited by 624
Pavlou, P. A., & Gefen, D. (2005) · Information Systems Research · doi:10.1287/isre.1050.0065 · Cited by 580
Self-citationA unified model of IT continuance: Three complementary perspectives and crossover effects
Bhattacherjee, A., & Lin, C.-P. (2015) · European Journal of Information Systems · doi:10.1057/ejis.2013.36 · Cited by 336
Celik, H. (2016) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-05-2015-0077 · Cited by 209
Polites, G. L., & Karahanna, E. (2013) · MIS Quarterly · doi:10.25300/misq/2013/37.1.10 · Cited by 174
A trust-risk perspective on social commerce use: an examination of the biasing role of habit
Farivar, S., Turel, O., & Yuan, Y. (2017) · Internet Research · doi:10.1108/intr-06-2016-0175 · Cited by 169
Kim, B. (2012) · Telecommunications Policy · doi:10.1016/j.telpol.2011.11.011 · Cited by 133
Online Purchase Intentions: An Empirical Testing of a Multiple-Theory Model
Järveläinen, J. (2007) · Journal of Organizational Computing and Electronic Commerce · doi:10.1080/10919390701291000 · Cited by 86
Metadata
- Authors
- Gefen, D.
- Venue
- Journal of Organizational and End User Computing
- Publisher
- IGI Global
- Published
- 2003-07-01
- Volume
- 15
- Issue
- 3
- Pages
- 1-13
- ISSN
- 1546-2234 (ISSN-L)
- 1546-5012
- OpenAlex
- W2036949601
- Cited by
- 438
- References
- 0
- FWCI
- 22.13
- Type
- journal article
- Language
- ng
- 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
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.