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The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.

Understanding consumer intention in online shopping: a respecification and validation of the DeLone and McLean model

Chen, Chien-Wen David, Cheng, Chiang-Yu John·2009·Behaviour & Information Technology

Abstract

The increasing popularity of online shopping has led to the emergence of new economic activities. To succeed in the highly competitive e-commerce environment, it is vital to understand consumer intention. Understanding what motivates consumer intention is critical because such intention is key to survival in this fast-paced and hypercompetitive environment. Where prior research has attempted at most a limited adaptation of the information system success model, we propose a comprehensive, empirical model that separates the ‘use’ construct into ‘intention to use’ and ‘actual use’. This makes it possible to test the importance of user intentions in determining their online shopping behaviour. Our results suggest that the consumer's intention to use is quite important, and accurately predicts the usage behaviour of consumers. In contrast, consumer satisfaction has a significant impact on intention to use but no direct causal relation with actual use.

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Metadata

Authors
Chen, C.-W. D., & Cheng, C.-Y. J.
Venue
Behaviour & Information Technology
Published
2009-07
Volume
28
Issue
4
Pages
335-345
ISSN
  • 0144-929X (ISSN-L)
  • 1362-3001
OpenAlex
W1979538031
Cited by
193
References
9
FWCI
11.54
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Understanding consumer intention in online shopping: a respecification and validation of the DeLone and McLean model. https://app.cochat.ai/papers/10.1080/01449290701850111
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Chen, C.-W. D., & Cheng, C.-Y. J. (2009). Understanding consumer intention in online shopping: a respecification and validation of the DeLone and McLean model. Behaviour & Information Technology. https://doi.org/10.1080/01449290701850111

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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.