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Record W2921482330 · doi:10.5430/ijba.v10n2p129

Packaging Features Effecting on Milk Buying Behavior in Karachi

2019· article· en· W2921482330 on OpenAlexvenueno aff
Osaf Ahmed Khan, Danish Ahmed Siddiqui

Bibliographic record

VenueInternational Journal of Business Administration · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Packaging Perceptions and Trends
Canadian institutionsnot available
Fundersnot available
KeywordsMarketingLikert scaleBusinessProduct (mathematics)Quality (philosophy)Consumption (sociology)Element (criminal law)Structural equation modelingOrder (exchange)AdvertisingConsumer behaviourNutritional informationFood scienceMathematicsStatistics

Abstract

fetched live from OpenAlex

This paper examined the packaging features effecting milk buying behavior. Four packaging features were selected that included nutritional information, price, country of origin, & quality standards, and their impact on milk consumption and purchase behavior is analyzed using Structural Equation Modeling. The study was carried out in urban areas of Karachi, among 318 respondents using Likert scale based questionnaire. The findings suggested that all four packaging features have a significant positive impact on consumers buying behavior. Consumers are attracted towards those products which provides enough and adequate amount of information on its products’ packaging. Nutritional information is among one of the important element that needs more focus and it will surely results in a positive way to the manufacturers of dairy products. Hence, producers and marketers of milk should focus on their product’s packaging features especially on the verbal element as a primary strategy in order to influences consumers buying behavior.

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 score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.276
Teacher spread0.261 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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