Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 164

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Predicting continuance in online communities: model development and empirical test

Jin, Xiao-Ling, Lee, Matthew K.O., Cheung, Christy M.K.·2010·Behaviour & Information Technology

Abstract

Popular interest in online communities has grown rapidly in recent years as a result of the widespread diffusion of Web 2.0 applications. However, the full values and potential of online communities cannot be realised without users' ongoing participation. Thus, this study aims at developing and empirically testing a research model to examine users' continuance intention to participate in an online community based on an extended information systems (IS) continuance model. Specifically, entertainment value and affective commitment are included in the IS continuance model and empirically examined in the context of online communities. A total of 240 returns collected from an online survey, which was conducted among users of a website bulletin board-based community in China, were analysed using partial least squares. The results reveal that users' continuance intention to participate in an online community is determined by both satisfaction and affective commitment. Satisfaction and affective commitment are, in turn, influenced by positive disconfirmations of purposive and entertainment values. The findings of this study contribute not only to theory building in online community continuance but also inform online community moderators in their effort to develop strategies for retaining their users.

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.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1080/01449290903398190

Cites

6 of 6 references verified0 citation errors · 0 not checkable

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.

A few free checks a day · papers already verified open instantly · results are public

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

Show all 6 references

Cited by

See all 164

Metadata

Authors
Jin, X.-L., Lee, M. K. O., & Cheung, C. M. K.
Venue
Behaviour & Information Technology
Published
2010-07
Volume
29
Issue
4
Pages
383-394
ISSN
  • 0144-929X (ISSN-L)
  • 1362-3001
OpenAlex
W2013304147
Cited by
164
References
6
FWCI
12.26
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: Predicting continuance in online communities: model development and empirical test. https://app.cochat.ai/papers/10.1080/01449290903398190
The paper
Jin, X.-L., Lee, M. K. O., & Cheung, C. M. K. (2010). Predicting continuance in online communities: model development and empirical test. Behaviour & Information Technology. https://doi.org/10.1080/01449290903398190

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.