Message Sidedness Effects in Advertising: The Role of Yin-Yang Balancing Theory
Bibliographic record
Abstract
Past research has shown mixed results regarding the persuasiveness of two-sided messages. Various underlying constructs were suggested to explain the differences in results. This study draws on the Yin-Yang Balancing (YYB) theory and the construct of tolerance for contradiction (i.e., the tolerance for inconsistency and resolution among contrasts) to explain differences in the effectiveness of two-sided ads. The study consisted of a cross-cultural survey involving Easterners, who hold typically higher tolerance for contradiction, with Westerners characterized by a lower tolerance for contradiction. A series of analyses of variance (ANOVAs) were conducted to explore the difference between both groups on key variables. Structural equation modeling (SEM) tested the proposed conceptual model as a whole and for both groups, highlighting key cross-cultural differences. Additionally, the PROCESS macro was used to test the mediation effects posited in the model. The findings showed that although the tolerance for contradiction does not directly impact purchase intentions, it exerts both direct and indirect effects on purchase intentions through credibility and attitudes for Easterners but not for Westerners. The findings offer important theoretical and managerial implications: Two-sided ads are more effective to consumers with a higher tolerance for contradiction (e.g., Easterners) versus consumers with a lower tolerance for contradiction (e.g., Westerners).
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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 | 0.015 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".