Evaluating online review helpfulness based on Elaboration Likelihood Model: the moderating role of readability

Li, Boying, Hou, Fangfang, Guan, Zhengzhi, Chong, Alain Yee-Loong and Pu, Xiaodie (2017) Evaluating online review helpfulness based on Elaboration Likelihood Model: the moderating role of readability. In: 21st Pacific Asia Conference on Information Systems (PACIS 2017), 16-20 July, 2017, Langkawi, Malaysia.

Full text not available from this repository.


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.

Item Type: Conference or Workshop Item (Paper)
RIS ID: https://nottingham-repository.worktribe.com/output/873040
Keywords: Review helpfulness; Elaboration Likelihood Model (ELM); readability; search goods; experience goods
Schools/Departments: University of Nottingham Ningbo China > Faculty of Business > Nottingham University Business School China
Depositing User: Yu, Tiffany
Date Deposited: 26 Jun 2018 11:21
Last Modified: 04 May 2020 18:55
URI: https://eprints.nottingham.ac.uk/id/eprint/52602

Actions (Archive Staff Only)

Edit View Edit View