Business models and sustainability in the newspaper industry: Perspectives from European and North American executives
Bibliographic record
Abstract
The digital age has posed considerable challenges to media business model sustainability while diversifying opportunities for editorial organizations and journalists. The chaotic management of media companies is threatening the very fabric of various media industries. This article aims at understanding the sustainability of the media business models, and how media managers tailor their practices to cope with digital transformation in a competitive market. Media executives from three US newspaper companies (from the United States of America and Canada) and three European newspaper companies (from Ireland, England and France) were interviewed for this article. The results of the interviews with executives from the six newspaper companies interviewed suggest that there is a better adaptation to the digital transformation on the part of North American companies compared to European companies. All interviewed newspaper companies continue to face significant challenges in the search for ways to enable the sustainability of their business models to motivate their partners, shareholders and employees and contribute to greater diversity in the information market. The six media companies agreed that sustaining a media business and financing model is not equivalent to achieving the long-term sustainability of a media business model and financing.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.006 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".