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Record W2583012190 · doi:10.1016/j.jcps.2017.01.002

Just do it! Why committed consumers react negatively to assertive ads

2017· article· en· W2583012190 on OpenAlexaff
Yael Zemack‐Rugar, Sarah G. Moore, Gavan J. Fitzsimons

Bibliographic record

VenueJournal of Consumer Psychology · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsAssertivenessReactanceFeelingAdvertisingCompliance (psychology)PsychologySocial psychologyBusiness

Abstract

fetched live from OpenAlex

Abstract Research shows that assertive ads, which direct consumers to take specific actions (e.g., Visit us; Just do it!), are ineffective due to reactance. However, such ads remain prevalent. We reexamine assertive ads, showing that their effectiveness depends on consumers' relationship with the advertising brand. Across studies, we compare committed and uncommitted consumers' reactions to assertive ads. We find that because committed (vs. uncommitted) brand relationships involve stronger compliance norms, assertive ads create greater pressure to comply for committed consumers. Specifically, we propose and show that committed consumers anticipate feeling guilty if they ignore an assertive message, creating pressure to comply. Pressure to comply increases reactance, which paradoxically reduces compliance, ultimately leading to decreased ad and brand liking as well as decreased monetary allocations to the brand. Our results show the perils that assertive ads pose for marketers and their most valuable customers.

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 imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.014
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.103
GPT teacher head0.385
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations89
Published2017
Admission routes1
Has abstractyes

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