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Record W4403103549 · doi:10.59613/global.v2i9.304

Sustainable Marketing Strategies: Aligning Brand Values with Consumer Demand for Environmental Responsibility

2024· article· en· W4403103549 on OpenAlexaff
RR. Yulianti Prihatiningrum, Joelianti Dwi Supraptiningsih, Lutfi Lutfi, Ali Imron, Ahmad Fithoni

Bibliographic record

VenueGlobal International Journal of Innovative Research · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsInstitut national de psychiatrie légale Philippe-Pinel
Fundersnot available
KeywordsBusinessMarketingGreen marketingSocial responsibilityEnvironmental economicsEconomicsPublic relations

Abstract

fetched live from OpenAlex

Sustainable marketing has become an essential component for businesses aiming to meet growing consumer demand for environmental responsibility. This paper explores how companies can align their brand values with sustainability efforts to foster long-term customer loyalty and market competitiveness. It examines key sustainable marketing strategies, including eco-friendly product design, transparent communication, and corporate social responsibility (CSR) initiatives. The study emphasizes the importance of authenticity, as consumers are increasingly wary of "greenwashing" tactics and seek brands that genuinely prioritize environmental stewardship. Case studies from various industries demonstrate successful integration of sustainability into brand identity, resulting in enhanced brand reputation, consumer trust, and business growth. The findings highlight the need for a comprehensive approach that includes stakeholder engagement, sustainable supply chains, and marketing campaigns that educate consumers on eco-friendly choices. By aligning brand values with consumer expectations for environmental responsibility, companies can create a competitive advantage while contributing to global sustainability efforts.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.003
Open science0.0010.000
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 head0.353
Teacher spread0.327 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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