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Record W3155258096 · doi:10.1002/mar.21492

Consumers as creative agents: How required effort influences willingness to engage

2021· article· en· W3155258096 on OpenAlexaff
Xianfang Zeng, Mehdi Mourali

Bibliographic record

VenuePsychology and Marketing · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsExpectancy theoryPsychologyValue (mathematics)Context (archaeology)Situational ethicsProduct (mathematics)CreativityPromotion (chess)Affect (linguistics)MarketingSocial psychologyBusiness

Abstract

fetched live from OpenAlex

Abstract Consumers often engage with brands by participating in activities such as co‐creating products and contributing ideas about product promotion. Such engagement enables consumers to be creative agents rather than mere end‐users. However, it also places a burden on them, as it inevitably requires effort on the consumer part. This study investigates the impact of expected effort level (low vs. high) on consumers' inclination toward engagement, and its underlying mechanisms. Three experiments find that higher expected effort leads to lower intention to engage. This effect is mediated by the perceived probability of success and perceived value of engagement, and the two mediators operate in tandem. Effort levels negatively affect the perceived probability of success, which exerts a positive impact on the perceived value of engagement and then on willingness to engage. We also examine the moderating effect of consumer mindsets and find that chronic and situational consumer mindsets work differently. Specifically, primed mindsets have a significant effect, but enduring mindsets do not. This study contributes to the literature on engagement and expectancy–value theory by exploring consumer effort in a context where effort aids in implementing creativity.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.348
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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.323
Teacher spread0.278 · 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.

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

Citations14
Published2021
Admission routes1
Has abstractyes

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