The Impact of Young Celebrity Endorsements in Social Media Advertisements and Brand Image Towards the Purchase Intention of Young Consumers
Bibliographic record
Abstract
Introduction: The power of young celebrities and brand image in influencing young consumers are becoming more relevant in the marketing and advertising of products and services of the plethora of brands we have in the twenty-first century. Businesses and brands use young celebrities to endorse their products to appeal more towards younger consumers. This research investigates the impact of young celebrity endorsements in social media advertisements and brand image towards the purchase intention of young consumers.Methodology: The theoretical framework from Shimp’s TEARS Model of celebrity endorsement is derived from 4 research studies and the variables were tested using 5-point Likert scale on a sample of 282 respondents who are young consumers, aged between 13 to 18 years old. All respondents were recruited using stratified sampling technique and data were analyzed using SmartPLS. The results derived from the data analyses conducted highlights eight main findings.Results: From the TEARS Model, similarity and respect has an influence on celebrity endorsement in social media whereas expertise, physical attractiveness and trustworthiness do not influence celebrity endorsement in social media. Brand image and celebrity endorsement in social media also found to be significant antecedents for the purchase intention. These findings will provide insights to marketer of businesses who requires to understand the attributes of young celebrity endorsements on social media advertisements that will appeal to young consumer. In this technological era, businesses build their brand image via investing in advertising; especially in social media advertising and celebrity endorsements.Recommendations: Following the current findings of the insignificance of expertise, physical attractiveness, and trustworthiness of the celebrity endorsers towards young consumers; marketers should look into similarity and respect qualities of their young celebrity endorsers if they would like to appeal their brands and products to pique the interests of young consumer which now become one of the major group of consumers in the world.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".