From failure to success: rethinking business model design for community-based enterprises
Bibliographic record
Abstract
Purpose Community-based enterprises (CBEs) offer a promising strategy for facilitating sustainable local development, yet many experience failure or underperformance. This study aims to explore the concept of community-based business model (CBBM) as a tailored approach to CBE success, analyzing the configuration, development processes, challenges and key factors that contribute its success. Design/methodology/approach Using a multiple case study approach, the authors investigate five leading CBEs in Indonesia, each located in different provinces and include various businesses such as agriculture, farming, tourism and crafts, ensuring a broad representation of CBE business models. Findings The study identifies key characteristics and design elements of CBBM, providing a structured approach on how “successful” CBEs create, deliver and capture value. It also examines the development processes, challenges and key success factors that enhance CBBM effectiveness, offering insights into what drives the development of a “successful” CBBM. Practical implications The concept of CBBM offers an important implication for organizations engaging in CBE that they should shift from generic BM to a more customized BM to enhance the likelihood of creating sustainable and impactful CBEs. Originality/value This study advances the literature on CBE and BM by conceptualizing CBBM as a distinct BM tailored for CBEs. It provides theoretical and practical insights into how BM design can enhance the effectiveness of CBEs, laying the groundwork for future research on BM design in CBEs.
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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 | 0.031 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".