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Record W4413677664 · doi:10.1002/mar.70032

Cultural Drivers of Vice–Virtue Preferences: The Role of Justification and Trait Self‐Control

2025· article· en· W4413677664 on OpenAlexaffabout
Abbas Heydari, Michel Laroche, Michèle Paulin, Marie‐Odile Richard

Bibliographic record

VenuePsychology and Marketing · 2025
Typearticle
Languageen
FieldPsychology
TopicCultural Differences and Values
Canadian institutionsConcordia UniversityCape Breton University
Fundersnot available
KeywordsVirtueTraitPsychologySocial psychologyControl (management)Self-controlEconomicsEpistemologyComputer sciencePhilosophyManagement

Abstract

fetched live from OpenAlex

ABSTRACT Despite extensive research on contextual factors affecting consumer behavior in the vice‐virtue dilemma, the role of cultural dimensions in these choices remains underexplored. By reviewing two streams of research (i.e., justification and trait self‐control), this article examines the impact of Hofstede's indulgence versus restraint cultural dimension on the preferences between vice and virtue products in the contexts of snack consumption and environmentally friendly versus unfriendly behaviors. Two surveys examining vice–virtue choice were conducted via Prolific in Canada, the U.S., and Germany. Study 1 ( n = 213) focused on snack consumption, while Study 2 ( n = 242) explored environmentally friendly versus unfriendly behaviors. The findings confirmed that individuals with higher indulgence (i.e., lower restraint) are more likely to choose vice over virtue options. Moreover, the effect of indulgence versus restraint on vice–virtue choice is sequentially mediated: indulgence influences trait self‐control, which in turn affects justification, ultimately guiding the choice. Justification mediates the relationship between indulgence and vice–virtue choice, while self‐control mediates the effect of indulgence on justification. These findings highlight the importance of considering cultural values in developing marketing strategies for vice and virtue products and in promoting healthier and more sustainable behaviors.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score0.238

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 integrity0.0000.000
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 head0.026
GPT teacher head0.338
Teacher spread0.313 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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