Business models in technology-based firms: a cognitive approach to regional differences
Bibliographic record
Abstract
The business model concept has only recently been discussed in the research literature. Some authors have pointed out that it is a second-order construct and have examined its theoretical underpinnings as a cognitive mechanism for opportunity perception and identification, using it as a tool to systematically approach the analysis of the beliefs and decisions that entrepreneurs use in building their businesses. We present a theoretical model that contributes to this prior work in three respects: a) it is explicitly applied to the analysis of technology-based firms, b) it identifies key regional factors that differentiate the entrepreneurial context in different parts of the world, and c) it portrays the relationship these regional factors have to different elements of business models in technology-based firms. We combine the cognitive role of business models with a regional context view in order to analyze the structure and process by which entrepreneurs focus on or ignore different aspects of a business model at different times. To illustrate the our model we provide case data to illustrate how entrepreneurs from two different regions - Western Mexico (Jalisco) and Western Canada (British Columbia) - use and rely on different elements of business models, and to exemplify how differences in the cultural, technological and industry context of our case study firms influence different elements of the business model. © 2010 IEEE.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".