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Record W4413986292 · doi:10.1108/jec-03-2025-0073

From failure to success: rethinking business model design for community-based enterprises

2025· article· en· W4413986292 on OpenAlexaff
La Ode Sabaruddin, Wiwik Supratiwi, Philip Linsley

Bibliographic record

VenueJournal of Enterprising Communities People and Places in the Global Economy · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsYork University
FundersUniversitas Airlangga
KeywordsBusinessProcess managementMarketingKnowledge managementComputer science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.473
Threshold uncertainty score0.801

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.267
Teacher spread0.238 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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