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Record W4379928390 · doi:10.1111/basr.12314

Sustainable marketing: an exploratory study of a sustain‐centric, versus profit‐centric, approach

2023· article· en· W4379928390 on OpenAlexafffund
Bruno Dyck, Rajesh V. Manchanda, Savanna Vagianos, Michèle Bernardin

Bibliographic record

VenueBusiness and Society Review · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsResearch ManitobaUniversity of Manitoba
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Manitoba
KeywordsMarketingGreenwashingBusinessTriple bottom lineExploratory researchProfit (economics)SustainabilitySocial marketingEconomicsSociologyEcologyMicroeconomics

Abstract

fetched live from OpenAlex

Abstract As the need for business to address pressing social and ecological issues intensifies, so does the importance of enhancing the development of sustainable marketing. The current dominant approach to sustainable marketing is based on a Triple Bottom Line (TBL) profit‐centric worldview, which suggests that firms can simultaneously improve their financial well‐being as they reduce negative social and ecological externalities. However, whereas the scope of TBL marketing is limited to sustainability initiatives that enhance profits, there is a growing need for—and interest in—developing a sustain‐centric approach to marketing that relaxes the need to maximize financial well‐being in order to optimize social and ecological well‐being. Even so, because of the dominance of the profit‐centric worldview, hallmarks of sustain‐centric marketing practices remain under‐developed and may even lend themselves to becoming inauthentically mimicked on a piecemeal basis by greenwashing profit‐centric firms. We provide an exploratory empirical study of marketing practices evident in two sustain‐centric firms and draw implications to advance theory for both sustain‐centric and profit‐centric marketing.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.443
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.004
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.001
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 head0.028
GPT teacher head0.254
Teacher spread0.226 · 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 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

Citations15
Published2023
Admission routes2
Has abstractyes

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