Predicting intentions to return to the Web site: Extending the dual mediation hypothesis
Bibliographic record
Abstract
MacKenzie, Lutz, and Belch (1986) test four advertising attitude models and find that the Dual Mediation Hypothesis is the best. This research proposes an extended model within an online context, using intentions to return (I r ) to a Web site versus purchase intentions, with a direct path between attitudes toward the Web site (A site ) and I r . This path is hypothesized as Web sites contain informative or entertaining content that attracts subsequent visits, and I r depends on other non-brand-related factors such as security, ease of use, transactional capabilities, etc. Data from visitors to three actual Web sites––digital cameras, watches, and a charity––demonstrate significant relationships between A site and I r . In support of this perspective, when A site was decomposed into its claim and non-claim components, the non-claim component had a significant effect on I r for all three sites. Implications for online researchers and advertisers are discussed.
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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 | 0.009 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".