Business Model Innovation Factors of Small and Medium-Sized Enterprises in Bolivia
Bibliographic record
Abstract
This paper aims to explore how four Bolivian small and medium-sized enterprises’ business has overcome the gaps in reliance on traditional small and medium-sized enterprises’ business models, i.e., to extract and sell raw unrefined natural resources in a local area, and instead make productive use of innovation inputs (technology, higher-educated people) by innovating their business models. We were particularly interested in how the small and medium-sized enterprises could manage to develop their business models in relation to the socio-cultural, economic, and technological contexts in a lower middle-income country such as Bolivia. We employ an exploratory multiple case study. The study’s results show that the four selected small and medium-sized enterprises’ business model innovation processes followed two different business model innovation patterns, a technology-driven pattern and market-driven pattern shaped by the macro-level factors of availability of natural resources, the informally organized economy, regulations, and access to higher education resources. The paper ends with presenting the managerial, policy, and theoretical implications of the study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".