Bibliographic record
Abstract
Focus on marketing strategy digital age, most commonly used in the virtual environment than Internet advertising, according to Digital Marketing Franchise Association statistics, in 2013 the first half of Taiwan's overall Internet advertising market reached 6.265 billion, according to Worldstream another survey, from the third quarter of 2010 to the second quarter of 2011 were within a year's time, Google (Google) revenues of $ 33.3 billion, of which 97% are from the Google AdWords keyword advertising as the main source of income has become the most popular network one ad. In this study, we used the price effect concept proposed by Monroe and Krishnan (1985) to explore whether consumers were influenced by relevant variables. We used keyword search intentions, information searching behavior, involvement degrees, and advertising effect variables to develop a framework for the study. Based on the Google search engine, we explored whether the effects of keyword advertising on the Internet were influenced by the consumers' degree of involvement, and whether keyword search intentions increased the degree of consumer involvement, which eventually influences the effects of advertising. The research results showed that based on the empirical data of 356 valid questionnaires, the intensity of keyword search intentions positively affected information searching behaviors. In addition, the degrees of advertisement involvement positively affected information searching behaviors and advertising effects. The intensity of information searching behaviors also positively affected advertising effects, which supported the research hypothesis. We suggest that enterprises or manufacturers use keyword advertising frequently and enhance the layouts of their advertisements to increase the amount of sales generated through online marketing.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".