Understanding the Consumption of Television Programming: Development and Validation of a Structural Model for Quality, Satisfaction and Audience Behaviour
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".