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Record W2809703623 · doi:10.3390/joitmc4030024

Factors Affecting the Buying Intention of Organic Tea Consumers of Bangladesh

2018· article· en· W2809703623 on OpenAlexaff
Razia Sultana Sumi, Golam Kabir

Bibliographic record

VenueJournal of Open Innovation Technology Market and Complexity · 2018
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicOrganic Food and Agriculture
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsBusinessSafeguardingMarketingProduct (mathematics)Organic productGlobalizationScale (ratio)AdvertisingAgricultureGeographyEconomics

Abstract

fetched live from OpenAlex

In the modern era of globalization, consumers become aware and concerned about their health as well as natural resources and the environment.Technological improvement and economic growth are continuously exploiting the earth's resources, resulting in an overwhelming burden on earth's ecology.Confirming a state of equilibrium between economic growth and safeguarding the environment becomes a challenge for business people and marketers.Though people worldwide are becoming interested in buying organic food, the concept of organic farming is relatively new in Bangladesh.The tea industry has started off with producing organic tea on a very limited scale.In this study, the researchers tried to examine the buying intention of organic tea among the consumers of Bangladesh.The study demonstrated that trust and perceived price significantly affect the buying intention of organic tea consumers along with product attributes, health consciousness, and environmental concern.Marketers may consider the stated factors to create an influence on the selection process of organic tea by consumers.

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)0.000
metaresearch head score (Gemma)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.061
GPT teacher head0.273
Teacher spread0.211 · 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

Citations51
Published2018
Admission routes1
Has abstractyes

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