Bibliographic record
Abstract
This study investigates the impact of different advertising appeals on consumer behavior by comparing emotional, functional, and neutral advertisements. Utilizing an experimental design, 475 participants were randomly assigned to view one of three types of advertisements for a fictional smartphone, with their purchase intention and decision time measured through questionnaires. The results indicate that emotional advertisements significantly enhance purchase intention (β = 1.871, p < 0.01) compared to functional and neutral advertisements, supporting behavioral theories that position emotion as a critical driver of consumer choice. In contrast, functional advertisements reduce decision time (β = -0.210, p < 0.05), likely due to their clear, feature-focused information facilitating quicker evaluation. These findings align with the dual-process model of decision-making, where emotional appeals evoke affective responses, and functional appeals satisfy cognitive efficiency. The study provides actionable insights for marketers, suggesting that emotional strategies are more effective in boosting purchase intention, while functional approaches optimize decision processes. Limitations include sample imbalance and cultural specificity (data collected in China), which may affect the generalizability of the results. Future research should explore cross-cultural differences and diverse product categories to further validate these findings. Overall, this study underscores the importance of tailoring advertising strategies based on consumer psychology and product types.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".