Factors Affecting Mobile Phone Purchase in the Greater Accra Region of Ghana: A Binary Logit Model Approach
Bibliographic record
Abstract
This paper investigated the factors affecting mobile phone purchase decision in the Greater Accra Region of Ghana, using a binary logit regression model approach. Through a multiple-stage random sampling technique, structured questionnaire was used to collect primary data from 200 mobile phone users in four districts in the study area. Results of the descriptive statistic show that Nokia and Samsung phones were the two main brands of phones used by majority of the respondents interviewed. In addition, of the 54 percent of respondents who expressed their intention to acquire new phones majority were male, between the ages of 21-30 years and has tertiary level of education. The result of the binary logit regression model revealed that advanced technology features such as internet browsing and durability or quality of mobile phone handsets are the two main factors that are likely to positively and significantly affect mobile phone purchase decision. It is therefore recommended that manufacturers and marketers of mobile phone handsets should produce and market more durable and high quality mobile phone handsets with modern technology features that are targeted at the educated youth.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".