Congruence and Incongruence in Thematic Advertisement–Medium Combinations: Role of Awareness, Fluency, and Persuasion Knowledge
Bibliographic record
Abstract
We suggest that thematic ad–medium congruency versus incongruency evokes distinct effects on consumer evaluations through different underlying mechanisms. Specifically, we propose congruency largely has positive effects on consumer evaluations due to a relatively automatic fluency process, whereas incongruency evokes a more conscious and negative persuasion knowledge (PK) process that leads to negative evaluations. Study 1 showed that consumers were more attentive to incongruence than congruence, particularly when the ad–medium combination was presented with other ads or materials. Studies 2A and 2B confirmed that congruency led to positive evaluations through perceived fluency, whereas incongruency led to negative-PK thoughts involving manipulative intent and more negative evaluations. Studies 3A and 3B provided causal evidence for the role of PK by showing that positive PK attenuated the negative effects the incongruency tactic had otherwise. Overall, these findings suggest the common practice of directly comparing congruent and incongruent media tactics confounds two very different processes. Managerial implications for advertising and media marketing are discussed.
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 | 0.002 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".