One-Sided Advertising: How Does It Overcome Consumer Resistance?
Bibliographic record
Abstract
This study explores how to enhance the effectiveness of one-sided advertising based on time-connectedness theory, especially for products resisted by customers. If managers can find a way to persuade consumers who initially reject a product or a service, they can improve one-sided advertising effectiveness. This study uses cognitive association to help connect the current self of participants with their temporal self, which it was hypothesized could help change their initial negative attitude. This hypothesis was verified through two studies, involving 335 and 312 student participants respectively. We found that participants changed initial negative attitude after exposure to time-connected advertising. This change was influenced by their need for cognition (NFC)—individuals with high NFC were more likely to be persuaded (to develop a more positive attitude toward advertising) through role transportation. As for individuals with low NFC, initial negative attitude among them was less likely to change through role transportation. The findings indicate to best reduce audience resistance and enhance advertising effectiveness, that marketing personnel need to know whether the audience’s (initial) attitude toward a product or service is negative or positive before selecting or developing advertising appeals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".