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Record W2618665460

Impact of Purchasing Decisions on Eco Friendly Products in Fast Moving Consumer Goods Sector with Special Reference to Calicut District, Kerala

2017· article· en· W2618665460 on OpenAlexvenueno aff
Sruthiya Vn

Bibliographic record

VenueThe Journal of Internet Banking and Commerce · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsEnvironmentally friendlyPurchasingBusinessProfit (economics)Product (mathematics)MarketingDilemmaFast-moving consumer goodsConsumer awarenessProfit marginPerceptionEconomics
DOInot available

Abstract

fetched live from OpenAlex

Today’s world is witnessed by a drastic increase in the products that are either substitutes or complementary. The consumers are in a dilemma with regard to their purchase decision. The availability of the products is a boon to the economy, but are these products being utilized in a proper manner where in the environmental issues are taken into consideration. The resources that are available in the economy are less as compared to what is needed. Therefore a proper balance is to be kept between the utilization and its disposal. The producers may be concerned about their profit margin, still keeping the objectives the production could be effectively done without harming the environment. If the products which are environment friendly, are produced the attitude and awareness of the consumer is important. The paper draws a light into the awareness and attitude of consumers with regard to FMCG eco-friendly products which is confined to Calicut district. A questionnaire is designed to find out the market awareness of eco-friendly products, to analyse the perception of consumers and also to identify the consumer’s willingness to pay for eco-friendly products. The data is alsobeen collected from various secondary sources. The results illustrates that majority of the consumers are not aware of products available in the market. Consumers are willing to pay more if there is green feature for the product but they are averse because of green washing by the companies.

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)0.000
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 score0.186
Threshold uncertainty score0.287

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.037
GPT teacher head0.279
Teacher spread0.242 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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