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Record W3005484588 · doi:10.1002/mar.21336

All at once or one at a time? The effect of simultaneous versus sequential discount presentation on store patronage intentions

2020· article· en· W3005484588 on OpenAlexaff
Devon DelVecchio, Jessie J. Wang, Neil Brigden

Bibliographic record

VenuePsychology and Marketing · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Market Behavior and Pricing
Canadian institutionsBow Valley CollegeUniversity of Alberta
Fundersnot available
KeywordsExtant taxonPresentation (obstetrics)AdvertisingAffect (linguistics)PsychologyProcess (computing)MarketingBusinessComputer scienceCommunication

Abstract

fetched live from OpenAlex

Abstract Retailers often employ store flyers, be they in print or digital form, to drive store traffic. A fundamental difference in the presentation of multicomponent information, such as the multiple discounts presented in flyers, is whether the components are displayed simultaneously (all at once) of sequentially (one at a time). Yet a little extant research examines how these different presentations affect individuals' responses to retailer price promotions. Three experiments demonstrate that a sequential display of price discounts is associated with more positive store patronage intentions. Evidence, gleaned by both measuring and manipulating the process by which the discounts are evaluated, implicates a greater sense of accumulating benefit with each successive discount when presented sequentially as the driver of the cross‐format difference in patronage intentions.

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)0.003
metaresearch head score (Gemma)0.029
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.029
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.001

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 head0.040
GPT teacher head0.313
Teacher spread0.273 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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