What Drives Consumers to Accept M-ads on Their Hand-Held Devices? A Literature Review, Insights and Propositions for Emerging Markets
Bibliographic record
Abstract
Mobile phone heavily penetrates into the consumers’ eventful routine and assists their shopping immensely. Marketing managers confront the query of conveyance of effectual information about products quiet often. Realizing the key dynamics of attitudes and acceptance of m-ads is crucial in designing the customized marketing message. The article attempts to give an overview on determinants of consumers’ attitude and acceptance of m-ads from the existing body of knowledge. Trust and credibility is recognized as sender’s characteristic that should be maintained by managers. Informativeness, entertainment, perceived ease of use and incentives are grouped into m-ads characteristics that managers should pay much attention in designing the message. And, Managers should ponder the features of target audience as well in creating the message. i.e., perceived intrusiveness, perceived usefulness, personalization and perceived control. Therefore, article is expedient in both academia and industry in emerging markets in terms of discovering elements that shape consumers’ attitudes and acceptance of mobile advertising.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".