The effect of consumer review on the perceived trustworthiness of online retailers: Item response theory perspective
Bibliographic record
Abstract
Word of Mouth also works as a primary determinant of people's positive attitude towards online shopping and retailers. Notably, people are more likely to spend online shopping if they trust online retailers. This study also focuses on the online retail industry in the United Arab Emirates. The Item Response Theory primarily supports the conceptual model of the current research. We employed a cross-sectional design and selected a sample of n= 304 online consumers. Results revealed that the relationships between Positive Word of Mouth, Brand Image, and Causal Attributions are strongly validated (p> 0.000). Besides, the relationships between. Product Quality, Brand Image, and Causal Attributions are also affirmed with the path value at 1.014 and significance value at p> 0.000. Moreover, we also found a potentially significant relationship between Positive Word of Mouth and Product Quality p> 0.000). We also affirmed that the relationship between Brand Image and Causal Attribution is also validated with the significance values at p> 0.000. Lastly, the proposed relationships between we proposed a significant relationship are also validated (p> 0.000). Thus, we conclude that, today, when competition is increasing day by day, it is crucial to examine the consumer psychology that may highlight our several factors as done by the Item Response Theory. Further, we have discussed the study limitations and contributions accordingly.
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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 | 0.025 | 0.128 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".