Does language concreteness influence consumers’ perceived deception in online reviews?
Bibliographic record
Abstract
Purpose Although online reviews have become a key source of information for consumer purchasing decisions, little is known about how the concreteness of language used in these reviews influences perceptions of deception. This study aims to address this important gap by drawing on psycholinguistic research and Language Expectancy Theory to examine how and when the concreteness of online reviews (abstract vs concrete) impacts consumers’ perceived deception. Design/methodology/approach Two scenario-based experiments were conducted to examine how the concreteness of online reviews (abstract vs concrete) influences consumers’ perceptions of deception, considering the mediating role of psychological distance to online reviews and the moderating effects of Machiavellianism (Mach) and reviewer identity disclosure. Findings Online reviews that include concrete language lead to lower perceived deception by reducing consumers’ psychological distance from the review. For consumers with higher levels of Mach, online reviews written in abstract (vs concrete) language result in higher perceived deception via psychological distance, while for consumers with lower Mach, online reviews written in concrete (vs abstract) language result in higher perceived deception via psychological distance. Research limitations/implications To the best of the authors’ knowledge, this study is one of the first to highlight the relevance of linguistic style (i.e. concrete review vs abstract review) on consumers’ perceived deception toward online reviews in the context of e-commerce. Practical implications The framework enables managers of online retailing platforms to identify the most effective strategies to decrease consumers’ perceived deception via the appropriate utilize of linguistic styles of online reviews. Originality/value This study contributes to both theory and practice by deepening knowledge of how and when the concreteness of online reviews (abstract vs concrete) affects consumers’ perceived deception and by helping managers of online retailing platforms make the most effective\ strategies for reducing consumers’ perceived deception toward online reviews during online shopping.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.105 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".