Designing and Validating a Systematic Model of E-Advertising
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The paper’s aim is that how the electronic system is able to transmit the message and is considered as an advertising tool, influencing factors on consumer’s behavioral response should be identified in order to use this media desirably, effectively, and utilize the e-advertising advantages to satisfy consumers’ needs. This is a research an applied research and a descriptive one with field studies. There are some casual relationships among the research variables. A questionnaire is used to collect data. This study aims to designing, validating, and evaluating a model which explains the influence of e-advertising on consumer behavior as well as providing strengths and weaknesses of the model and suggesting solutions to enhance strengths and converting weaknesses to strengths. In this paper, capabilities of internet advertising are examined in a form of 14 content and communicate motives via a leading process (cognition, affection, and attitude) on consumer’s behavioral response (image and mentality, intention and desire, testing, purchasing and consuming) as “an e-advertising model” in Tehran Refah Chain Stores. Results show a suitability of the fitted structural model. The above mentioned company, however, should improve its website’s capability in content and communicate motives. In this way, internet advertisings of Refah Chain Store’s are able to have a desirable effectiveness in order to lead the consumer behavior.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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 it