Decoding eWOM effectiveness: How textual and non-textual recommendation cues shape purchase intentions in online bookstores
Year: 2026
Type: article
Abstract: With the rapid growth of online stores, electronic word-of-mouth (eWOM) has become a key influence on consumer purchase decisions. This study examines how textual cues (information quality) and non-textual cues (popularity heuristics, performance visual heuristics, user-generated pictures, and emojis) affect purchase intentions in digital bookstores. Using a between-subjects experiment across five conditions, the results showed that perceived information quality plays a fundamental and significant positive role in predicting purchase intention within the control group. Moreover, combining information quality with non-textual cues, especially user-generated pictures, popularity heuristics, and performance visual heuristics, has a notable effect in enhancing purchase intention compared to information quality alone. However, emojis did not produce a significant effect, suggesting that purely decorative cues lack sufficient diagnostic value in high-involvement purchase contexts. Regarding moderation, consumers’ prior online shopping experience plays a significant but cue-specific moderating role. Rather than uniformly amplifying cue effects, experience significantly reduces reliance on popularity heuristics and user-generated pictures, leaves the effect of performance visual heuristics unchanged, and exerts only a marginal influence on emojis. This pattern reflects an “expertise compensation” mechanism within the proposed Diagnostic Multimodal Processing (DMP) framework. The findings confirm the importance of a multidimensional approach in designing eWOM messages and emphasize the a strategically selected combination of textual and non-textual cues based on their diagnostic value. The DMP framework explains cue effects via social proof, authenticity, and processing efficiency, offering practical guidelines for bookstore recommendation advertising.
Institution: Allameh Tabataba'i University
Language: English
Subfield: Marketing
Domain: Social Sciences
Open Access status: gold
Estimated APC paid: $1,250
OpenAlex ID: W7166345592