Bibliographic record
Abstract
Nowadays, people are willing to purchase their own smartphone and they heavily rely on their smartphone. In this case, smartphones have become the daily necessity among Hong Kong people. Also, nowadays Hong Kong people always look for the new model of smartphones, the trend of changing smartphones is still very strong. The purpose of this research is to study the factors affecting the purchase intention of smartphones of post 90s in Hong Kong. After reviewing the literature, this study chose three variables to study the relationship between brand name, price and social influence and purchase intention. An online questionnaire was adopted to carry out a quantitative study of post 90s in Hong Kong. The content of the survey included demographic factors and questions based on each variable. The result of the survey shows that there are two hypotheses are support in the study. One is the relationship between brand name and purchase intention and the other is relationship between social influence and purchase intention whilst price is not a significant factor influencing purchase intention. Therefore, it is strongly believe that management of smartphone producers and traders need to pay more attention to brand name and social influence in enhancing the purchase intention among post 90s in Hong Kong.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".