Proposing an Extension of the Technology Acceptance Model to Explain Facebook User Acceptance of Facebook Event Page
Bibliographic record
Abstract
The emergence and growth of social media today has changed the way that people communicate and interact with each other. Thus, social media has considered as an effective tool in the marketing campaign. In regard to event marketing, event planners and organizers use social media (e.g. social network sites) as an important marketing medium to increase the number of potential attendees to visit the events. However, the major challenge to event marketers is to fully understand the process of how social media marketing gain special event customers’ acceptance. This study chose Facebook event page as study context and applied the technology acceptance model (TAM) as theoretical foundation. In addition, this paper synthesizes the theoretical basis of the event marketing, emotional factors, perceived relevance and its application to social media (e.g., Facebook event page) from previous studies. The study aims to come out with a conceptual model (extended TAM) which explains fully inner-mechanism of the relationships among variables: (1) the emotions that online fansexpress on Facebook affect their acceptance of the Facebook event page as a legitimate marketing tool; (2) perceived relevance from user perspective influence their acceptance of the Facebook event page; (3) this “acceptance” mechanism has an impact on fans’ intentions to attend the event.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".