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Record W2092395892 · doi:10.1108/10610421211246711

Perceived variance and preference for sequences of outcomes

2012· article· en· W2092395892 on OpenAlexaff
Eric Dolansky, Mark Vandenbosch

Bibliographic record

VenueJournal of Product & Brand Management · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Environmental Valuation
Canadian institutionsWestern UniversityBrock University
Fundersnot available
KeywordsPreferenceRevealed preferenceVariance (accounting)Sequence (biology)ReceiptEconometricsValue (mathematics)EconomicsPerceptionContrast (vision)Variable (mathematics)Affect (linguistics)MicroeconomicsPsychologyComputer scienceStatisticsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to propose a new explanation for the well‐documented preference among individuals for sequences of increasing utility. It is put forward here that while there may be a preference for ascending‐utility sequences, this relationship is mediated by perceptions of variance. Specifically, there is reason to believe that sequences of ascending utility (e.g. receipt of payments) are perceived to be less variable than sequences of descending utility (e.g. prices). Design/methodology/approach Past and present price research supports the idea that perceived variance plays a key role in preference, which fits with established theory. This theory is examined and applied to a hypothetical scenario involving sequences of uncertain outcomes. The predicted effect is tested in two experimental studies. Findings The two studies lend support to the proposed explanation of sequence preference. Study one demonstrates that the effect of sequence direction on preference is mediated by perceptions of variability, and that individuals perceive a sequence of ascending utility to be less variable than an equivalent descending sequence. Study two shows that individuals prefer a sequence when it represents wages (ascending utility) than when it does prices (descending utility). Originality/value The paper provides a sound theoretical framework, as well as supporting evidence, for how sequences of outcomes, such as prices, are perceived by consumers. Given that such sequences are increasingly available thanks to information technology, and the increased use of yield management systems (leading to more price fluctuation), how they affect decisions is of obvious importance.

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)0.000
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 score0.082
Threshold uncertainty score0.256

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.144
GPT teacher head0.242
Teacher spread0.098 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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