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Record W2769392823 · doi:10.5539/ijms.v9n6p24

Impact of TV Advertising on Children’s Food Choices

2017· article· en· W2769392823 on OpenAlexvenueno aff
Ayda Sabaghzadeh Tousi, Zelha Altınkaya

Bibliographic record

VenueInternational Journal of Marketing Studies · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicCulinary Culture and Tourism
Canadian institutionsnot available
Fundersnot available
KeywordsAdvertisingConsumption (sociology)PsychologyMusicalTelevision advertisingStatistical analysisRegression analysisFood consumptionMarketingBusinessMathematicsStatisticsSociologyEconomicsAgricultural economics

Abstract

fetched live from OpenAlex

This research emphasizes on the impact of TV advertising on children’s food choices. For this reason, a questionnaire was prepared and focused on Food Advertisement, TV Advertisement, School Advertisement, Musical Advertisement, Children’s Consumption Attitudes and analysis children’s opinions who are aging from 8 to 11 years old from primary school. In a college in Avcilar—Istanbul was chosen for the survey area. This study is proposed to discover the effect of TV food advertising on children as a target group. This survey will make use of statistical techniques Hypothesis was conveyed to show the significance ANOVA and factor analysis used. Also, SPSS statistical tool used for analyzing hypothesis.Results show that all of the four factors, food advertisement, TV advertisement, musical advertisement and musical advertisement affects the children’s food consumption behavior, the results emphasis on the reality of this hypothesis and its true, and importance of it and also show that in this research obtained the result which wanted. However, regression equation can be used to estimate. In this case, according to regression analysis, TV Advertisement and Musical Advertising can be used to estimate children’s food consumption behavior scores.

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)0.001
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 score0.213
Threshold uncertainty score0.164

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.031
GPT teacher head0.326
Teacher spread0.294 · 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

Citations5
Published2017
Admission routes1
Has abstractyes

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