Understanding the Electronic Word-of-Mouth Communication Process: Communication Effectiveness and Analytic Tools

Abstract

Electronic word-of-mouth (eWOM) has increasingly become an important topic in marketing and consumer research. However, theory construction and methodology development in this area are still in their infancy. This leaves some basic and important questions unanswered including whether eWOM communication is effective, what roles are played by different communication cues, and how valuable information from text reviews can be generated. This study intends to answer these fundamental questions.Based on the Brunswik's Len Model, this study developed the Process Model of eWOM Communication. It extends the Brunswik's Lens Model in several important ways and provides a systematic tool to examine the effectiveness of eWOM communication processes. Furthermore, a simplified model of eWOM communication was developed to test the validity of automatic text analysis as a promising tool in studying eWOM communication.Two focus group interviews and a throughout literature review were conducted first to identify the communication cues employed by eWOM partners. Then, two web-based self-administered surveys were carried out to collect data from both eWOM senders and readers. Last, the data from both eWOM senders and readers were matched, forming a final dataset with 90 reviews. Correlations, regressions, and path analyses were employed to evaluate the models and test the hypotheses.Results showed that eWOM communication is effective, and the relative strength of information flow varies in different eWOM communication links when communicating different types of information.This study identified a list of eWOM communication cues and found that consumers employ different cues in communicating different types of information. EWOM readers' inference structure in decoding may not exactly mirror eWOM senders' encoding structure. Moreover, communication cues especially verbal cues play an important role in eWOM communication and explain additional variance in eWOM partners' intentions and perceptions beyond and above the star ratings. In general, negative emotion words are the most important cues across various situations.In addition, this study provides initial evidence for the validity of automatic text analysis in studying eWOM. Linguistic indicators such as Negations, Negative Emotions, and Money can explain additional variance in eWOM partners' attitudes and emotions beyond and above the star ratings.