Evaluating online review helpfulness based on elaboration likelihood model: The moderating role of readability

Boying Li, Zhengzhi GUAN, Fangfang HOU, Alain Yee Loong Chong, Xiaodie Pu

Research output: Contribution to conferencePaperpeer-review

9 Citations (Scopus)

Abstract

It is important to understand factors affecting the perceived online review helpfulness as it helps solve the problem of information overload in online shopping. Moreover, it is also crucial to explore the factors’ relative importance in predicting review helpfulness in order to effectively detect potential helpful reviews before they exert influences. Applying Elaboration Likelihood Model (ELM), this study first investigates the effects of central cues (review subjectivity and elaborateness) and peripheral cues (reviewer rank) on review helpfulness with readability as a moderator. Second, it also explores their relative predicting power using the machine learning technique. ELM is tested in online context and the results are compared between experience and search goods. Our results provide evidence that for both types of products review subjectivity can play a more significant role when the content readability is high. Furthermore, this study reveals that the dominant predictor is varied for different product types.

Original languageEnglish
Publication statusPublished - 2017
Event21st Pacific Asia Conference on Information Systems: Societal Transformation Through IS/IT, PACIS 2017 - Langkawi, Malaysia
Duration: 16 Jul 201720 Jul 2017

Conference

Conference21st Pacific Asia Conference on Information Systems: Societal Transformation Through IS/IT, PACIS 2017
Country/TerritoryMalaysia
CityLangkawi
Period16/07/1720/07/17

Keywords

  • Elaboration Likelihood Model (ELM)
  • Experience goods
  • Readability
  • Review helpfulness
  • Search goods

ASJC Scopus subject areas

  • Information Systems

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