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The motivational dynamics of arousal and values in promoting sustainable behavior: A cognitive energetics perspective

2022· article· en· W4313334126 on OpenAlexaff
Yan Li, Kyle B. Murray

Bibliographic record

VenueInternational Journal of Research in Marketing · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicEnvironmental Education and Sustainability
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsOpenness to experienceArousalPsychologyValue (mathematics)Perspective (graphical)CognitionSocial psychology

Abstract

fetched live from OpenAlex

This research applies Cognitive Energetics Theory (CET) to explain when and why consumers engage in sustainable behavior. Across six studies, we find a positive interaction effect of arousal and openness-to-change on sustainable behaviors. In particular, openness-to-change (vs conservation) increases the likelihood of engaging in effortful sustainable behaviors in a high-arousal state rather than in a low-arousal state. Interestingly, our results reveal that this interactive effect is explained by the tendency of consumers to believe that the target sustainable behavior requires less effort, when they are in a high-arousal state and endorsing openness-to-change. Moreover, perceived effort is positively related to sustainable behavior for experienced consumers but negatively related to the behavior for less experienced consumers. In addition, the effect of value and arousal on perceived effort is stronger among less experienced consumers but attenuated among more experienced consumers. Thus, arousal can serve as a catalyst to enhance value-consistent sustainable behaviors and help the less experienced consumers form habits. These findings contribute to CET by highlighting the important roles that values and arousal play in the motivational forces that drive and restrain sustainable behaviors. The results improve our understanding of how to motivate value-consistent sustainable behaviors, with implications for both marketers and policy-makers.

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)0.011
metaresearch head score (Gemma)0.004
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 score0.206
Threshold uncertainty score0.811

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.004
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 science0.0000.001
Research integrity0.0000.001
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 head0.015
GPT teacher head0.358
Teacher spread0.343 · 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

Citations16
Published2022
Admission routes1
Has abstractyes

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