Examining business model innovation through a case study of a firm
Bibliographic record
Abstract
A business model can provide a clear framework for how a company delivers value to its customers. The better the business model is understood, the greater the ability for the company to understand how to improve its business to continually deliver greater value to it customers. Through the process of business model innovation, companies can transform their success by delivering customer value in an entirely different way. As such, understanding business model innovation is considered critical for transformational and sustained growth of a company. Business model innovation, however, is a fairly new concept and understanding how it actually occurs in practice is rare. This project offers a unique first-hand insight (i.e. autoethnography) into the process of how business model innovation occurs in a firm (Tesera Systems Inc.) over a long period of time (~7 years) and continues to evolve. This project shows that the actual experience of Tesera relates very well to the theories and concepts of business model innovation (i.e. framework, drivers/needs of business model innovation, typology, ontologies) and benefits (i.e. improvements in agility, resource velocity, cost structures). In addition, this project offers learning perspectives from this business model innovation experience (i.e. crisis as opportunity, value gained from understanding implicit business model innovation, the importance of a constructionist ontology, business model innovation to guide business processes externally and internally). As such, this project helps to provide value to both the theoretical and conceptual perspectives regarding business model innovation as well as those who in business are looking to better understand business model innovation from a practical experience to provide their firms the opportunity for transformational change and sustained success. --P. ii.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".