The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Interactivity in Brand Web Sites: Cognitive, Affective, and Behavioral Responses Explained by Consumers’ Online Flow Experience
Abstract
Web site interactivity creates numerous opportunities for marketers to persuade online consumers and receives extensive attention in the marketing literature. However, research on cognitive and behavioral responses to web site interactivity is scarce, and more importantly, it does not provide empirical evidence for how interactivity effects can be explained. Therefore, the aim of this study was to investigate the underlying principle that explains the influence of web site interactivity on consumers’ cognitive, affective and behavioral responses: online flow, the web site users’ complete immersion in an online activity (Hoffman and Novak 2009). In two studies, the hypothesis was tested that a visitor's flow experience in a specific brand web site mediates the effects of interactivity on the number and type (web site vs. product related) of thoughts, on attitudes toward the brand and web site, and on several behavioral intentions. The results provide evidence for the importance of flow in a marketing context, and the notion that the flow experienced on a specific web site is the underlying mechanism by which cognitive, attitudinal, and behavioral responses to an interactive brand web site can be explained.
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
SPSS and SAS procedures for estimating indirect effects in simple mediation models
Preacher, K. J., & Hayes, A. F. (2004) · Behavior Research Methods, Instruments, & Computers · doi:10.3758/bf03206553 · Cited by 17,383
Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium
Hayes, A. F. (2009) · Communication Monographs · doi:10.1080/03637750903310360 · Cited by 9,757
A comparison of methods to test mediation and other intervening variable effects
MacKinnon, D. P., Lockwood, C. M., Hoffman, J. M., West, S. G., & Sheets, V. (2002) · Psychological Methods · doi:10.1037/1082-989x.7.1.83 · Cited by 9,406
Defining virtual reality: Dimensions determining telepresence
Steuer, J. (1992) · Journal of Communication · doi:10.1111/j.1460-2466.1992.tb00812.x · Cited by 5,459
Equivalence of the Mediation, Confounding and Suppression Effect
MacKinnon, D. P., Krull, J. L., & Lockwood, C. M. (2000) · Prevention Science · doi:10.1023/a:1026595011371 · Cited by 4,188
Exploratory and confirmatory factor analysis: Understanding concepts and applications.
Thompson, B. (2004) · doi:10.1037/10694-000 · Cited by 3,469
Flow online: lessons learned and future prospects.
Hoffman, D. L., & Novak, T. P. (2009) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2008.10.003 · Cited by 954
Flow on the net–detecting Web users' positive affects and their flow states
Chen, H. (2006) · Computers in Human Behavior · doi:10.1016/j.chb.2004.07.001 · Cited by 216
Cited by
The Role of Live Streaming in Building Consumer Trust and Engagement with Social Commerce Sellers
Wongkitrungrueng, A., & Assarut, N. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2018.08.032 · Cited by 1,477
Yim, M. Y.-C., Chu, S.-C., & Sauer, P. L. (2017) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2017.04.001 · Cited by 1,142
Chen, C.-C., & Lin, Y.-C. (2018) · Telematics and Informatics · doi:10.1016/j.tele.2017.12.003 · Cited by 810
Why smartphone advertising attracts customers: A model of Web advertising, flow, and personalization
Kim, Y. J., & Han, J. (2014) · Computers in Human Behavior · doi:10.1016/j.chb.2014.01.015 · Cited by 454
Optimal experience of flow enhanced by telepresence: evidence from social media use
Pelet, J.-É., Ettis, S., & Cowart, K. (2017) · Information & Management · doi:10.1016/j.im.2016.05.001 · Cited by 354
Metadata
- Authors
- van Noort, G., Voorveld, H. A. M., & van Reijmersdal, E. A.
- Venue
- Journal of Interactive Marketing
- Publisher
- SAGE Publications
- Published
- 2012-11
- Volume
- 26
- Issue
- 4
- Pages
- 223-234
- ISSN
- 1094-9968 (ISSN-L)
- 1520-6653
- OpenAlex
- W2065972503
- Cited by
- 447
- References
- 8
- FWCI
- 53.93
- 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
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.
