Marketing strategies to successfully sell voice over internet protocol to mainstream Canadian markets
Bibliographic record
Abstract
Voice over Internet Protocol (VoiP) is an emerging innovation which allows for low cost voice communications similar to conventional circuit-based telephone, but over the Internet either in part or in whole.Because VoiP uses the Internet as its transmission medium, it has the capacity to disrupt the more than $15 billion dollar per year Canadian local access and long distance telephone industry.While V oiP offers much potential for unique functionality, at its current level of advancement it underperforms conventional telephone in the areas of security, quality, and reliability.Everett M. Rogers' seminal work on the diffusion of innovations serves as useful theory to examine VoiP adoption within a population.Additionally, disruptive technology theory presented by Clayton M. Christensen and Geoffrey Moore' s theory on marketing technology to mainstream customers is reviewed.Additional data and information was collected by completing semi-structured interviews of telecommunications industry stakeholders, plus through the completion of a focus group with early adopters of Internet communication technologies.This paper synthesizes fundamentals of diffusion, disruptive technology and marketing theory, plus data collected, to draw conclusions of how marketers ofVoiP should proceed to sell their services to mainstream Canadian markets.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".