The Influence of 4G Mobile Value Added Services on Usage Intention: The Mediating Effect of Perceived Value of Smartphone and Phablet
Bibliographic record
Abstract
With the development of Phablet, as well as the coming of new age of 4G network, people pay more attention to the network quality and the market demand for network increases day by day. Telecommunications network service offers diversified products and service to satisfy different consumers’ demands. Telecommunications service has gradually become the trend, but the issues about consumers’ degree of cognitive involvement in using Smartphone or Phablet are the problems concerned by operators. In addition, the items supplied by the operators of telecommunications service will directly influence consumers’ usage intention. Therefore, this study discussed whether the telecommunications mobile value added services will influence consumers’ usage intention through the perceived value of Smartphone or Phablet. The research subjects were the users of general Smartphone. In total, 300 questionnaires were distributed, 240 questionnaires were retrieved and 224 questionnaires were valid. The research results show that (1) 4G mobile value added services have the significant positive influence on the perceived value of Smartphone; (2) 4G mobile value added services have the significant positive influence on usage intention; (3) Smartphone and Phablet have the mediating effect on 4G mobile value added services and usage intention. All the functions and value added services provided by Smartphone or Phablet play the key mediating effect. Thus, users will generate the sense of worth where acquisition utility is greater than expenditure cost, and they will generate the purchase intention and behavior towards this mobile phone products. Keywords:4G mobile value added services,perceived value of smartphone,perceived value of phablet,usage intention
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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 | 0.001 | 0.007 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".