Dish Tv India Limited: the dilemma of celebrity endorsement
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Learning outcomes The learning outcomes of this paper are as follows: to understand the meaning of celebrity endorser, to understand factors that play a significant role in selecting a celebrity endorser for product endorsement, to decide when a brand needs a celebrity endorser and to generate option analysis factoring in the pros and limitations of celebrity endorsement. Case overview/synopsis Dish TV pioneered digital entertainment in India. It was July 2016, the first quarter board meeting of Dish TV India Limited at the company corporate office in Noida, India. One of the agenda items was whether the company needed to rely on celebrity endorsement 12 years after its inception. In three months, time, at its next meeting, the board was expected to come up with a product campaign that would most effectively impact its target customers. Complexity academic level The case is targeted at business management students pursuing a postgraduate management program. Supplementary materials Teaching notes are available for educators only. Subject code CSS 8: Marketing.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it