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Record W2617805235 · doi:10.1111/ijcs.12373

Jingle bells or ‘green’ bells? The impact of socially responsible consumption principles upon consumer behaviour at Christmas time

2017· article· en· W2617805235 on OpenAlexaffabout
Élisabeth Robinot, Myriam Ertz, Fabien Durif

Bibliographic record

VenueInternational Journal of Consumer Studies · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsUniversité du Québec à ChicoutimiUniversité du Québec à Montréal
Fundersnot available
KeywordsConsumption (sociology)Theory of planned behaviorAtmosphere (unit)PsychologySocial psychologyConsumer behaviourSituatedSample (material)AdvertisingControl (management)SociologyEconomicsBusinessGeography

Abstract

fetched live from OpenAlex

Abstract Socially responsible consumption (SRC) behaviours have progressed over the last few years and appear to show signs of a lasting trend. Situations of atypical consumption such as Christmas time, however, raise an important and as of yet unexplored question: What are the influences of unusual situations upon the relationship between people's socially responsible profile and their socially responsible purchase intentions (SRPI)? The objective of this article is thus to use the theory of planned behaviour (Ajzen, ) and environment‐based variables, called ‘atmospherics’, to answer to this question. A Web survey on a total sample of 301 Canadian consumers, shows that people's past SRC behaviours are positively related to their SRPI in unusual situations. Moreover, the atmosphere of the place consumers are situated in has a negative moderating influence upon this relationship. This result is explained by a change in people's attitude toward SRC. However, this negative moderating effect of atmosphere is contained and constrained by social desirability in the form of subjective norms on SRC and the level of behavioural control consumers perceive.

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)0.002
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 score0.005
Threshold uncertainty score0.670

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
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 head0.061
GPT teacher head0.349
Teacher spread0.288 · 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

Citations29
Published2017
Admission routes2
Has abstractyes

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