The Effect of Herding Behavior on Online Review Voting Participation
2017
- 589Usage
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.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
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
- Usage589
- Abstract Views468
- Downloads121
Artifact Description
Online review is an important form of electronic word of mouth (eWOM) that helps customers make purchasing decisions. In a set of reviews, the review with the most helpfulness votes are seen as most helpful. While researchers have demonstrated how review and reviewer characteristics influence helpfulness votes, a largely uninvestigated issue is how herding behaviors can influence customers’ voting participation and direction. Drawing on herd behavior literature, we propose that review voters will discount their own information when faced with clear and strong signals from previous voters. Thus, they will herd previous voters’ voting direction. On the other hand, review voters will value their own judgments when faced with weak signals from previous voters. Herding can influence both a voter’s perception of a review’s helpfulness and his/her vote. This research extends review helpfulness literature that herd behaviors could moderates customers’ perception of review helpfulness and voting direction.
Bibliographic Details
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