Evaluating online review helpfulness based on Elaboration Likelihood Model: the moderating role of readabilityTools 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.
Official URL: http://aisel.aisnet.org/pacis2017/257/
AbstractIt 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.
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