The Influence of Offline, Online Media and Counterfeit Awareness among Non-Perishable and Non-Food Products in Ghana
Bibliographic record
Abstract
Previous research has shown that counterfeit activity causes major losses to individuals and country’s economy in the global market. This is as a result of less awareness being created across various markets. The study introduced an integrated cognitive behavioural theory in order to advance knowledge in such dimension. The aim of this research study is to investigate the influence of offline, online media and counterfeit awareness among non-perishable and non-food products in Ghana. The research mainly assessed offline media such as, television, radio, newspaper, and billboard, as well as online media such as, facebook, linkedIn, whatsapp, and twitter. A sample of 196 respondents was employed using convenient selection method. A self-completed 5 point Likert structured questionnaire survey was the data collection instrument used. The data collected were computed and analysed by multiple regression to determine the significant association between the variables involved in this study. The overall results of the offline media variables revealed that, only radio is found significant association with counterfeit awareness among non-perishable and non-food products in Ghana. Among the variables that are rejected with no significant association with counterfeit awareness involved television, newspaper and billboard. With regard to the results of online media, all the variables were rejected with no significant association with counterfeit awareness among non-perishable and non-food products in Ghana, involved facebook, linkedin, whatsapp and twitter. The study recommends that consequences about using counterfeit product should be explored to ascertain its effect.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".