The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
Consumer Processing of Product Trial and the Influence of Prior Advertising: A Structural Modeling Approach
Abstract
A major goal of this study is to develop and test a structural model of trial processing to help marketers and researchers better understand the dynamics of this important stage in the purchase process. No such model exists, even though prior research has demonstrated that product trial can be an important determinant of brand beliefs and attitudes. Accordingly, the authors develop a general model of how consumers process and respond to trial experiences. This trial model then is integrated with a well-known advertising model to trace how (1) consumers react when both types of information are available and (2) advertising achieves its influence on trial perceptions. The authors estimate these models for two products that vary significantly in the diagnosticity of the trial experience and discuss implications for marketing research and practice.
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Cites
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Cited by
Pavlou, P. A., & Fygenson, M. (2006) · MIS Quarterly · doi:10.2307/25148720 · Cited by 2,685
Pavlou, P. A., Liang, H., & Xue, Y. (2007) · MIS Quarterly · doi:10.2307/25148783 · Cited by 2,646
On Product Uncertainty in Online Markets: Theory and Evidence1
Dimoka, A., Hong, Y., & Pavlou, P. A. (2012) · MIS Quarterly · doi:10.2307/41703461 · Cited by 682
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Searching for Experiences: The Web-Based Virtual Tour in Tourism Marketing
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Metadata
- Authors
- Kempf, D. S., & Smith, R. E.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 1998-08
- Volume
- 35
- Issue
- 3
- Pages
- 325-338
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W2324925384
- Cited by
- 501
- References
- 7
- FWCI
- 20.40
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
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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.
