Embracing the E-era: Constructing Internet Marketing Strategies and Practices in Teacher Education Departments
Bibliographic record
Abstract
This research uses confirmation factor analysis both to construct a measurement model of Internet marketing strategies and to conduct an importance-performance analysis to examine how teacher education departments provide such strategies. The unit of analysis in this research is students and focuses on their attitudes toward the quality of Internet marketing at teacher education departments in Taiwan. The author used a questionnaire to collect data. Six hundred and sixty-four usable questionnaires and 12 invalid questionnaires were collected. The effective response rate was 69 %. The model of the existence of four-factor structure (need, convenience, cost and communication) was tested using AMOS and goodness of fit. Thus, it was concluded that the four-factor model both fits well and represents a reasonably close approximation of the population. Next, the results of the study highlight the usefulness of importance-performance analysis for helping management improve its Internet marketing strategies. This study finds that the managers of teacher education departments should not only focus on concentrating their benefits but also allocate resources to improve Internet marketing strategies.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".