Exploring determinants of attraction and helpfulness of online product review: a consumer behaviour perspective

Xu Chen, Jie Sheng, Xiaojun Wang, Jiangshan Deng

Research output: Contribution to journalArticle (Academic Journal)peer-review

9 Citations (Scopus)
1167 Downloads (Pure)

Abstract

To assist filtering and sorting massive review messages, this paper attempts to examine the determinants of review attraction and helpfulness. Our analysis divides consumers’ reading process into “notice stage” and “comprehend stage”, and considers the impact of “explicit information” and “implicit information” of review attraction and review helpfulness. 633 online product reviews were collected from Amazon.cn. A mixed-method approach is employed to test the conceptual model proposed for examining the influencing factors of review attraction and helpfulness. The empirical results show that reviews with negative extremity, more words and higher reviewer rank easily gain more attraction and reviews with negative extremity, higher reviewer rank, mixed subjective property and mixed sentiment seem to be more helpful. The research findings provide some important insights, which will help online businesses to encourage consumers to write good quality reviews and take more active actions to maximize the value of online reviews.
Original languageEnglish
Article number9354519
Number of pages19
JournalDiscrete Dynamics in Nature and Society
Volume2016
DOIs
Publication statusPublished - 28 Nov 2016

Keywords

  • online product review
  • review attraction
  • review helpfulness
  • text analytics

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