Designing and Validating a Systematic Model of E-Advertising
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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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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".