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Record W2102513340 · doi:10.5539/ass.v10n23p123

Marketing Mechanism of Consumer Demand for Ecological Products Identification

2014· article· en· W2102513340 on OpenAlexvenueno aff
Uliana A. Pozdniakova, Larisa V. Ponomareva, Viacheslav U. Lapshin, Alexey V. Bolotin, Галина Хмелева

Bibliographic record

VenueAsian Social Science · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicEnvironmental Sustainability in Business
Canadian institutionsnot available
FundersMinistry of Education and Science of the Russian Federation
KeywordsBusinessAccessionHarmPopulationLegislationMarketingProduction (economics)Green marketingQuality (philosophy)CommerceEconomicsInternational tradeEuropean union

Abstract

fetched live from OpenAlex

In the global economy, causing the inefficient use of natural resources, the development of industry productionof genetically modified foods and the reduction of quality agricultural land, info propaganda against thedeteriorating environmental situation becomes larger and modern society increasingly focuses on improving thequality of life of the population. The current socio-economic situation encourages Russian companies to developnew environmentally-oriented approaches to the organization of marketing activities.But in Russia, the institutional environment of the Russian market does not allow environmentally-orientedenterprises to introduce new marketing tools, i.e. it is characterized by inadequate perfect the legal frameworkgoverning the interaction of all stakeholders in this market. Including Russia not adopted legislation onenvironmental labeling, and therefore extend unfair environmentally-oriented advertising and eco-brandingenterprises, industrial and commercial activity which often does not respond positional enterprises environmentalperformance of products. Despite the lack of development of the institutional environment of the market,production and sale of organic products as a relatively advanced segment of the markets of developed foreigncountries and is the premium segment of the market of developing countries, including in Russia. Due to theincreasing environmental food crises of the last decade, increasing worries about the harm of geneticallymodified products, the expansion of state initiatives environmentally-oriented production and trade, Russia'sWTO accession is obvious very promising development of the market of organic products in Russia. However,tools of marketing activities of Russian eco-oriented businesses are also not worked either theoretically orpractically. Recognized only need environmental responsibility before society, and formal areas and forms of itsrealization are virtually absent.The relevance of the study is due to the need of development of methods and tools for environmental marketingas a condition for the development of domestic enterprises competitiveness. The article deals with environmentalmarketing as a tool to control consumer preferences through a marketing mechanism to identify consumerdemand for environmental products and a set of tools to promote products based on the greening of business(office space, product, business principles). The authors propose methods of marketing and statistical research ofconsumer preferences in the market, the use of which allows you to effectively perform market objectives topromote the national environmental products in the regional market, and it is filled with a variety of qualityproducts and competitive child, primarily Russian-made, and create export potential to exit to electronic foreignmarket.

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)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.132

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.002
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0390.005

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 head0.011
GPT teacher head0.238
Teacher spread0.227 · 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 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
Published2014
Admission routes1
Has abstractyes

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