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Record W2134653381 · doi:10.5539/ijms.v5n1p142

Understanding the Consumption of Television Programming: Development and Validation of a Structural Model for Quality, Satisfaction and Audience Behaviour

2013· article· en· W2134653381 on OpenAlexvenueno aff
Carmen Berné Mañero, Esperanza García-Uceda, Víctor Orive Serrano

Bibliographic record

VenueInternational Journal of Marketing Studies · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCustomer Service Quality and Loyalty
Canadian institutionsnot available
FundersMinisterio de Ciencia e Innovación
KeywordsContext (archaeology)Quality (philosophy)Consumption (sociology)Computer scienceAdvertisingStructural equation modelingPerspective (graphical)Variable (mathematics)CognitionMarketingPsychologyBusinessArtificial intelligenceSociologyMathematics

Abstract

fetched live from OpenAlex

Within a nowadays context characterised by an increasing number of television channels and by a widely fragmentation of the audience, the study of television content from the consumer’s perspective acquires special interest for managers of television stations. This paper, given the aforementioned reality, analyses and identifies the relationship structure that underlies the constructs of satisfaction and quality in order to gain a more in-depth understanding of the behaviour of television consumers. The methodology applied is structural equation models and results clearly show a causal link between the variables and confirm the predictive validity of the proposed model. This work provides a framework of reference for developing a cognitive-affective model for the consumption of television programmes, thereby integrating perceived quality as a cognitive variable and satisfaction as an affective variable. The results of the work provide a more in-depth understanding of a television consumer’s behaviour, and they can therefore help to increase the effectiveness of actions by television advertisers and programmers, thereby allowing television stations to improve their results.

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)0.002
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 score0.050
Threshold uncertainty score0.197

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.172
GPT teacher head0.363
Teacher spread0.191 · 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

Citations21
Published2013
Admission routes1
Has abstractyes

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