Combined framing effects on attitudes and behavioral intentions toward mortgage advertisements
Bibliographic record
Abstract
Purpose Mortgage lenders often combine a variety of framing strategies when developing mortgage advertisements. To date, these frames have mostly been studied separately. This paper, however, studies the combined framing effects of message valence, specificity, and temporality on consumers' mortgage decision-making. Design/methodology/approach A mixed methods design was used. First, 13 unique print ads collected from a Canadian newspaper were analyzed for content. Second, a 2 × 2 × 2 scenario-based experiment with 400 undergraduate participants examined the framing effects of valence, specificity and temporality on attitudes toward the mortgage advertising message, the product advertised, and the brand, as well as on consumers' behavioral intentions toward the advertised mortgage product. Findings The content analysis suggests that combined framing does exist in print ads. A positive message with a fixed term and a specific interest rate were the most commonly used frames. The experiment revealed that, for behavioral intentions, the main effect of the message temporality was significant. The effects of advertising a long-term mortgage on behavioral intentions were more favorable than those of advertising a short-term mortgage. Practical implications This research provides a combined framing model for designing advertising strategies for the financial services industry to market complex financial products, such as mortgage loans to consumers. This is relevant to lenders when designing a persuasive package or ads for potential customers. Originality/value This study is the first of its kind to investigate the effects of combinations of message frames on consumers' mortgage decision-making, while also advancing the understanding of message framing theory for the financial services industry.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".