Brand Personification: A Study on Humanizing Personal Care Products in Sri Lanka
Bibliographic record
Abstract
In the context of marketing communications where the concern is to create effective brand promotions, associating a brand with the personality of the consumers commonly known brand personification has become one of the most prominent aspects over recent years. In the process of designing the message, incorporating an appeal where the brand is associated to a human-like character is called the humanization of brands in advertising. There, the brand focuses on attracting the consumers with a reflection of the consumer’s personality in the brand being promoted. This strategy becomes thousand times more attractive to those products which are closely associated with the consumers. Thus, humanizing a brand alone isn’t sufficient, while humanizing them in the appropriate appealing manner is much more vital. Hence, this research will mainly be focusing on identifying the most suitable personality dimension to be associated for some of the top of mind personal care brands in Sri Lanka. The research approach, which takes the form of both quantitative and qualitative follows the use of multiple sources of data collection methods. The qualitative aspect clarifies the initial stage with the identification of the top of mind personal care brands and the personality dimensions which are closely associated with the targeted group. The quantitative aspect of the study is satisfied via the data gathered through a self-administered questionnaire developed by the researcher. The study then focuses upon an in-depth statistical analysis with the application of the Kruskal Wallis H Test followed by the Nemeyni Post Hoc test to identify the most suitable personality dimension to be associated with each brand in the humanizing them. The findings reveal some interesting facts over the consumer’s preferred dimensions to be reflected on those brands.
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 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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".