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Record W4391445781 · doi:10.1002/jcpy.1412

The different roads not taken: Considering diverse foregone alternatives motivates future goal persistence

2024· article· en· W4391445781 on OpenAlexfundno aff
Hye‐Young Kim, Oleg Urminsky

Bibliographic record

VenueJournal of Consumer Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsnot available
FundersMcGill University
KeywordsDeliberationContext (archaeology)Diversity (politics)EconomicsGoal pursuitPsychologyGoal settingPersistence (discontinuity)Action (physics)MarketingMicroeconomicsPublic economicsSocial psychologyBusiness

Abstract

fetched live from OpenAlex

Abstract Decisions are rarely made in isolation. Instead, deliberation often occurs in the context of prior related choices. This article finds that goal‐inconsistent foregone alternatives, options that were previously considered but not chosen, shape how consumers subsequently pursue their goals. Going beyond previous research on foregone alternatives and consumer satisfaction, the current research suggests that how consumers mentally construe foregone goal‐inconsistent alternatives impacts how they evaluate their prior goal‐consistent choices, which will, in turn, impact their motivation to continue making goal‐consistent choices. Specifically, we find the foregone alternative diversity effect: consumers who consider having previously foregone diverse (vs. similar) goal‐inconsistent alternatives in favor of a goal‐consistent action then believe that they have made a greater sacrifice, which had more of an impact on their focal goal. As a result, they are then more likely to subsequently make goal‐consistent choices. Our findings hold across different types of goals (exercise: Study 1, healthy eating: Studies 2, 3, and 5, weight loss: Study 4), and both real and hypothetical choices. We also identify theoretically motivated boundary conditions for the observed effect of considering foregone alternatives.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.707
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.001
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 head0.096
GPT teacher head0.413
Teacher spread0.317 · 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 designNot applicable
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

Citations2
Published2024
Admission routes1
Has abstractyes

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