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Record W2896698939 · doi:10.1108/ijbm-01-2017-0021

Does experiential advertising impact credibility?

2018· article· en· W2896698939 on OpenAlexaff
Lova Rajaobelina, Caroline Lacroix, Anik St-Onge

Bibliographic record

VenueInternational Journal of Bank Marketing · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsCredibilityRespondentExperiential learningAdvertisingOriginalitySource credibilityPsychologyValue (mathematics)MarketingStructural equation modelingSocial psychologyBusinessPolitical science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to investigate the impact of five dimensions of experiential advertising (cognitive, emotional, sensory, relational and behavioural) on advertising credibility in the banking sector. Design/methodology/approach A total of 277 undergraduate students were asked to complete a questionnaire after viewing two versions of a bank advertisement. Results were analysed using structural modelling equations (EQS 6.2). Findings Findings show that all dimensions of experiential advertising positively impact advertisement credibility. Cognitive/emotional/sensory advertisements exert the greatest impact, followed by relational and then behavioural advertisements which have only a marginal impact. Post hoc results show that the impact of experiential advertising on advertising credibility varies according to both actor and respondent gender. Originality/value This study enhances the literature on experiential marketing and credibility, especially banking sector advertising, and provides more in-depth insight into the role of respondent and actor gender in influencing responses. Financial services practitioners would be well advised to devote particular attention to the formulation of experiential strategies when developing advertising campaigns.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.630
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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.

Opus teacher head0.015
GPT teacher head0.298
Teacher spread0.283 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations29
Published2018
Admission routes1
Has abstractyes

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