Bibliographic record
Abstract
Purpose The purpose of this study is to find the role of online informediaries on the perspective of price comparison and information aggregator. Specifically, the author wants to explain how the level of product involvement moderates the effect of price dispersion and product information quality on attitude toward product in online informediaries. Design/methodology/approach The data for this study are obtained from a three‐way factorial experimental research design. Data were collected from 258 college students who have an experience with an online informediary. Combining ANCOVA and regression analysis enables the study of attitude formation and yields encouraging results. Findings The study finds that high‐involvement consumers focus on systematic cues (e.g. product attributes) in evaluating product quality. However, when they feel that their initial search yields insufficient results, causing them to perceive more product performance risk, they search for additional cues (e.g. price dispersion). Low‐involvement consumers are mainly affected by price dispersion, which is a heuristic cue, and they evaluate the product more favorably under a high (vs low) level of price dispersion. Originality/value This paper is one of the first to consider and empirically test a heuristic‐systematic model for attitude toward product in online informediaries. It also uniquely tests the level of price dispersion to discern the important motivating factors.
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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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".