Exploring Emotional Triggers in Advertising: A Study of Consumer Buying Behavior in Kathmandu Valley
Bibliographic record
Abstract
This study aims to analyze the impact of emotional advertising on consumer buying behavior in Kathmandu Valley. An explanatory research approach is used for the study, employing convenience sampling under the non-probability sampling technique. To gather information from 412 respondents, structured questions and the KOBO toolbox are used. For data analysis, both descriptive and inferential analyses are employed. Findings reveal that emotions such as happiness, excitement, and humor are powerful tools in advertising. They help build trust, shape consumer perceptions, and influence buying behavior. However, challenges such as the gap between promises and delivery and the lack of creativity can hinder the effectiveness of emotional ads. To over-come these challenges, realistic advertisements that showcase the product accurately and creative approaches should be utilized to create a stronger impact on consumers' purchasing decisions. Keywords: Consumer buying behavior, emotional advertising, Kathmandu Valley, partial least squares structural equation modeling, trust
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.001 | 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".