Understanding the role of interpersonal identification in online review evaluation: An information processing perspective
International Journal of Information Management, ISSN: 0268-4012, Vol: 38, Issue: 1, Page: 140-149
2018
- 51Citations
- 2Usage
- 145Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations51
- Citation Indexes51
- 51
- CrossRef8
- Academic Citation Index (ACI) - airiti1
- Usage2
- Abstract Views2
- Captures145
- Readers145
- 145
Article Description
While the proliferation of online reviews has increased consumers’ access to resources for informing the purchase decision, it has also substantially increased the cognitive effort required for finding personally relevant information through this channel. In the face of this challenge, an increasingly valuable capability of online review platforms relates to delivering the right reviews to the right consumer at the right time. Many platforms have sought to develop this capability by leveraging generic review characteristics like recency and valence, or crowd-level performance metrics like helpfulness score. While useful, these approaches may be overlooking important individual-to-individual (dyadic) social mechanisms that underpin review evaluation and selection. In an effort to inform the development of more robust information management capabilities of online review platforms, we introduce and test a model that highlights the influence of dyadic social information processing in online review evaluation. Results from model testing support most of the hypotheses and reveal important social selection mechanisms consumers employ in this context, which could be leveraged to add additional value through online review platforms.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0268401216309057; http://dx.doi.org/10.1016/j.ijinfomgt.2017.08.001; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85030166207&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0268401216309057; https://api.elsevier.com/content/article/PII:S0268401216309057?httpAccept=text/xml; https://api.elsevier.com/content/article/PII:S0268401216309057?httpAccept=text/plain; https://bearworks.missouristate.edu/articles-cob/183; https://bearworks.missouristate.edu/cgi/viewcontent.cgi?article=1182&context=articles-cob; https://dx.doi.org/10.1016/j.ijinfomgt.2017.08.001
Elsevier BV
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