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Record W2946235673 · doi:10.5539/ijms.v11n2p29

Sustainability as a Matrix of Experiential Marketing

2019· article· en· W2946235673 on OpenAlexvenueno aff
Angela Dettori

Bibliographic record

VenueInternational Journal of Marketing Studies · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Retail Behavior Studies
Canadian institutionsnot available
Fundersnot available
KeywordsSustainabilityMarketingBusinessExperiential learningLoyaltyRelevance (law)Affect (linguistics)PsychologyPolitical science

Abstract

fetched live from OpenAlex

Recent years have highlighted a significant increase in the relevance of environmental protection and sustainability to consumers, firms, and society as a whole. Taking this new situation into account, most organizations are aware of this change and wish to be seen as taking steps to improve behaviors in this regard. This challenge requires managers to make wise strategic choices and for organizations to be more flexible in adapting to changes in the economy, in the environment and in the “whims” of consumers. The aim of the present study is to examine the potential role of sustainability as a matrix of experiential marketing to understand how a memorable, emotional and responsible connection between consumer and sustainable brand can generate customer loyalty and affect the purchase decision. Taking a case study approach, this study analyzes an ecological campaign called “The Fun Theory”, contributing to strengthening this new theory, according to which “fun” is the best way to change the bad habits of people in responsible and sustainable manner.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.008
Scholarly communication0.0050.003
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.326
Teacher spread0.311 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations5
Published2019
Admission routes1
Has abstractyes

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