When standards become business models: reinterpreting “failure” in the standardization paradigm
Bibliographic record
Abstract
Purpose This paper aims to explore the question: “What is the relationship between standards and business models?” and illustrate the conceptual linkage with reference to developments in the mobile communications industry. Design/methodology/approach A succinct overview of literature on standardization, business models and platform markets in the paper leads to a hypothesis on the relationship between present‐day standardization processes and business model design. This is then explored by means of three short case studies. Findings The case studies of Mobile‐ICT illustrate that regardless of institutional orientation or process, the most important standardization strategy for equipment and service providers is to create platforms that are open to the development of complementary products and services while at the same time preserving the proprietary edge necessary to ensure lock‐in effects. All three cases yielded strong reasons to doubt whether many of the traditional advantages of standardization (interoperability, economies of scale, positive externalities, etc.) will be achieved equitably for all of the stakeholders. Originality/value This is an exploratory paper that aims to shed light on present‐day concerns about “failure” in the standardisation paradigm.
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 | 0.035 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.006 | 0.102 |
| Scholarly communication | 0.016 | 0.036 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.006 | 0.008 |
| 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".