Using a moderator-type research model to identify the determining factors in satisfaction of using high speed internet in household
Bibliographic record
Abstract
Telecommunications industry is continually in a shift of change, alimented by technological innovation and consumers’ demand for always better and faster communication tools. High speed Internet is now an integral part of everyday life of more than a billion people. And, as the tendency is showing up, its use will be still increasing in the future. Thus, this technology has and will continue to have major social and economic impacts. Individual adoption of technology has been studied extensively in the workplace, but far less attention has been paid to adoption of technology in household (Brown & Venkatesh, 2005). So, few studies have been conducted until now to verify satisfaction of household people using high speed Internet. The aim of this study is then to investigate the determining factors in satisfaction of using high speed Internet by people in household. On the basis of the moderator-type research model developed by Brown and Venkatesh (2005) to verify the determining factors in intention to adopt a personal computer in household by American people, this study examines the determining factors in satisfaction of using high speed Internet in household by Canadian people. The methodology followed to conduct the study was the telephone survey research. Data were collected from 322 randomly selected Atlantic Canadian people using high speed Internet at home. Data analysis was performed using the structural equation modeling software Partial Least Squares (PLS). The results got revealed that near from half of the variables examined in the study showed to be determining factors in satisfaction of using high speed Internet by people in household.
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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 | 0.016 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.015 | 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".