The Journal for Information Professionals
Asia Pacific Journal of Information Systems (APJIS), a Scopus and ABDC indexed journal, is a
flagship journal of the information systems (IS) field in the Asia Pacific region.
ISSN 2288-5404 (Print) / ISSN 2288-6818 (Online)
Editor : Youngsok Bang
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Current Issue
| Date | December 2025 |
|---|---|
| Vol. No. | Vol. 35 No. 4 |
| DOI | https://doi.org/10.14329/apjis.2025.35.4.839 |
| Page | 839~865 |
| Title | From Voice to Value: How Linguistic Tone and Product Context Shape Online Review Helpfulness |
| Author | Yongsuk Kim, Xilan Jiang |
| Keyword | Online Reviews, Linguistic Tone, Mixed Emotional Tone, Review Helpfulness, Digital Persuasion, Tone– Product Fit, Source Credibility Theory, Cognitive Fit Theory |
| Abstract | Understanding what makes online reviews helpful remains a central concern in information systems research and platform design. While prior studies have examined sentiment and structure, less is known about how distinct linguistic tones―analytical, authentic, and mixed emotional―signal reviewer credibility in text-only environments. Drawing on Source Credibility Theory (SCT) and Cognitive Fit Theory (CFT), we propose that these tones act as stylistic heuristics for perceived expertise and trustworthiness, particularly when identity cues are absent and tone aligns with the reader’s evaluative goals. Using LIWC to analyze 54,759 Amazon reviews across search and experience goods, we find that all three tones enhance perceived helpfulness, but their effects vary by product type. Analytical tone is more persuasive for search goods, structured reasoning signals expertise and supports attribute-based comparisons. Authentic tone is more effective for experience goods, sincerity supports trust and affective simulation. We also introduce mixed emotional tone―the co-expression of positive and negative affect―as a novel signal of deliberation, which improves helpfulness ratings, especially for search goods. These findings clarify prior inconsistencies in sentiment research and advance a theory-driven framework for how tone–product fit influences review helpfulness in digital platforms. |
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