Moderating Effect of Ethnicity on the Purchase Decisions of Females in the Beauty Care Product Market of Sri Lanka
Bibliographic record
Abstract
The main focal point in the eye of any marketer should be to satisfy the customer to the best possible level, in order to sustain in the ever-growing, dynamic and highly competitive marketplace. This leads them to the point where understanding the process of consumer buying behavior is of utmost importance. As the entire process of consumer buying behavior ultimately leads to their purchasing decision, deep understanding of the factors that affect this purchase decision should be the centralized core based on which all other marketing efforts should be built upon by the marketers. Based on this vitality, although these factors have already been found by past researchers in international contexts, these cannot be considered as directly applicable to the Sri Lankan context as Sri Lanka is a multiethnic country in which consumers are heavily driven by traditions and values of the ethnic group they belong to. Therefore, this study has been conducted in order to analyze the moderating effect of “Ethnicity” on the relationship between these factors and the purchasing decision. The sample has been selected through Random Sampling and the Quantitative Research Approach has been applied. Chi-Square analysis method has been used to analyze the relationships between the independent variables (price, brand name and composition) and dependent variable (purchase decision). Two-way Anova analysis method has been used to analyze the moderating effect of the moderating variable (ethnicity) on the relationships between independent variables and the dependent variable. Based on interpretation guidelines of Chi-square test, all three independent variables, namely price, brand name and composition, affect the purchase decision of females in the beauty care product market of Sri Lanka. Thereafter, based on the interpretation guidelines of Two-way Anova, research findings of the second analysis conclude that ethnicity of the Sri Lankan females of the beauty care product market (Sinhalese, Tamils and Muslims) affect the relationships between “brand name and purchase decision” and “composition and purchase decision”, but does not affect the relationship between “price and purchase decision”.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".