Impact of perceived experiential advertising on customers' responses: a multi-method approach
Bibliographic record
Abstract
Purpose This article proposes two studies to demonstrate the impact of three dimensions of perceived experiential advertising – cognitive/affective/sensory advertising, relate advertising and behavioural advertising – on consumer behaviour (brand credibility, affective commitment and emotions) in the banking sector. Design/methodology/approach For study 1, a total of 506 online panellists of a recognized Canadian research firm were asked to evaluate a local bank advertisement using an online self-reported questionnaire. For study 2, a total of 65 Canadian respondents recruited through Facebook and Google adverts were asked to watch two video advertisements (one more experiential and the other less experiential). After viewing the advertisements on a computer equipped with FaceReader software by Noldus, participants completed a short online questionnaire. Findings Using structural equations modelling, the first study shows that brand credibility explains the positive impact of perceived cognitive/affective/sensory advertising (complementary mediation) and perceived behavioural advertising (indirect mediation only) on affective commitment. The second study illustrates that the cognitive/affective/sensory dimension is more important for experiential advertising than experiential advertising. Employing FaceReader facial expression recognition software results indicate that the bank advertisement with a higher score of perceived cognitive/affective/sensory advertising produces a higher level of happiness among respondents. Originality/value Both studies provide new insights into perceived experiential advertising and the impact of the latter on consumers. Benefits to scholars and practitioners include an enhanced understanding of advertising effectiveness in the banking sector.
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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 | 0.021 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".