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Record W2905977419 · doi:10.21767/2471-7975.100028

Consumer Attitude and Behavior of Religious Consumers towards Offensive Advertisements

2017· article· en· W2905977419 on OpenAlexaff
Alina Rashid, Nasar Khan

Bibliographic record

VenueAnnals of Behavioural Science · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicHalal products and consumer behavior
Canadian institutionsQueen's University
Fundersnot available
KeywordsReligiosityPsychologyAdvertisingOffensiveTest (biology)Affect (linguistics)Social psychologyScale (ratio)Consumer behaviourBusinessMathematics

Abstract

fetched live from OpenAlex

The study attempts to investigate the effect of controversial advertisements on consumer attitude, behavior and purchase intention of religious consumers. The study also aims to investigate what difference of attitude is prevailing between men and women. 200 participants ranging in age from 20 to 25 at first were randomly selected. Religious Commitment Inventory – 10 was distributed to the 200 participants. 40 participants (20 males; 20 females) having the highest and nearly equal scores on the RCI-10 were selected. The selected participants were shown censored advertisements first and the responses were collected on Consumer Attitude Questionnaire and Juster’s 11 Point Probability Scale. After collecting the questionnaires, participants were shown uncensored versions of the same advertisements, and responses were then collected again on same scales. The analysis of results using t-test and Pearson correlation co-efficient suggested that controversial advertisements negatively affect the consumer attitudes of religious consumers; moreover females with high religiosity are more offended by controversial advertisements as compared to males with high religiosity. However, the analysis of results showed that consumer attitude is not a strong predictor of consumer behavior, as there might be other mediating and moderating variables that shape the ultimate purchase intention and consumer behavior.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.995

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 studies0.0010.008
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.170
GPT teacher head0.414
Teacher spread0.244 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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