Brand placements in Films and Television: An effective marketing communication strategy to influence customers
Bibliographic record
Abstract
There is a growing trend of brand or product placements in films and television shows globally, as well as in Pakistan (Aijaz, 2016; Balakrishnan, Shuaib, Dousin, & Permarupan, 2012). The aim of thisstudy is to explore the impact of brand placement strategies in film and television as a communication strategy from a viewpoint of Pakistani market.Surveys are conducted and questionnaires were distributed online among a sample of Pakistanis belonging to different income levels. Brand Placement Acceptance (BPA) is taken as independent variable, and its influence is tested on three dependent variables: Brand Loyalty (BL), Intention to Purchase (IP) and Attitude towards Brand (ATB). The results indicate that BPA has significant influence on BL, IP and ATB separately, and it can be used as an effective marketing tool to influence customers in a hyper competitive marketing landscape.This research concludes that brand placement has significantly favorable acceptance in Pakistani market.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".