Smart for whom? Cost ambiguity as corporate strategy in the 21st century telco sector
Bibliographic record
Abstract
Purpose – This paper aims to expose techniques that telco vendors use for maximising revenue from their clients. Although the five-point strategy unearthed was based on the Canadian telco industry, it is interpreted as generic to the digital-age. Design/methodology/approach – Findings are based on focus groups with telco vendors and client perception data. Inductive reasoning is used to generalise findings to other distinctively digital-age industries. Findings – This paper finds five generic techniques that are used within the Canadian telecommunications (telco) industry to ensure that customers cannot control the cost of a smartphone. These techniques are described as an array of telco hybrid offerings, each with its own cost-structure and pricing strategy; the underestimation problem; devices are not geostationary; third-party agreements; and death-by-a-thousand-qualifications. Research limitations/implications – The research develops theory about modularity and platform technologies. Practical implications – Findings and insights have implications for strengthening consumer protection arrangements in the teleco industry, as well as other distinctively digital-age industries. Originality/value – This paper elaborates theory (particularly with respect to platform technologies and modularity). It interprets the flexibility that comes with modern technology as having a specific downside for consumers, namely, the removal of their capacity to control cost. As far as the authors have been able to ascertain, such an interpretation has not hitherto been presented. It is hoped that the classification of findings will become something of a public policy template for ensuring consumer protection.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.019 |
| Scholarly communication | 0.019 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".