Malaysian Consumers Attitude towards Mobile Advertising, the Role of Permission and Its Impact on Purchase Intention: A Structural Equation Modeling Approach
Bibliographic record
Abstract
The recent development of a global telecommunication technology provides a new mechanism for companies to promote their products and services. This modern electronic advertising concept is commercially known as mobile advertising. Despite its remarkable opportunities claimed by many quarters, the attitude of Malaysian consumers towards mobile advertising remains unclear. A wise understanding is essential to ensure the effective use of this medium in the future. This study analyzed the consumers’ attitude towards mobile advertising and their intention to purchase the advertised products and services. The conceptual framework for this study was derived from the abstract ideas of the Theory of Planned Behavior and the Theory of Permission Marketing. Accordingly, factors such as subjective norms and perceived behavioral control also included to understand its impact on the purchase intention. More importantly, the dimension of permission also included to understand its role between the attitude towards mobile advertising and purchase intention. The convenience sampling technique was applied to obtain responses from the mobile phone users throughout Malaysia. It resulted in 856 usable responses. The analysis of Structural Equation Modeling (SEM) confirmed that the proposed conceptual model fits well within the context of Malaysian consumers. The results of SEM demonstrated significant relationships between the consumers’ attitude towards mobile advertising, subjective norms, perceived behavioral control and their intention to purchase products and services. Further analysis also found that the dimension of permission partially mediates the relationship between the attitude and purchase intention. The study reveals several implications for theory and practice relating to the future development of the mobile advertising industry in this country.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".