Entrepreneurial Literacy and Financial Behavior Among Indonesian Mompreneurs: Insights from a Knowledge-Based Innovation Perspective
Bibliographic record
Abstract
This study aims to develop an entrepreneurial literacy model for mompreneurs that contributes to Sustainable Development Goal (SDG) 1 (poverty reduction) and Sustainable Development Goal (SDG) 8 (decent work and economic growth), focusing on how entrepreneurial literacy transforms into financial behavior and fosters innovation in micro-business management. A qualitative case study was conducted in several districts of Makassar City, Indonesia. Participants were selected based on their status as mompreneurs and their type of business. Data were collected through in-depth interviews, observations, and documentation, then analyzed using a thematic spiral model. Entrepreneurial literacy, obtained through formal and informal education, translates into financial behaviors such as basic planning, financial management, and reporting. Innovation emerges through improved creativity, marketing, customer relationships, product development, and service enhancement. This study proposes a conceptual model linking entrepreneurial literacy, financial behavior, and innovation, offering insights for developing training programs that empower women in entrepreneurship. The scope of this study is limited to mompreneurs operating micro-scale businesses in Makassar City; therefore, the findings cannot be generalized to different socio-economic contexts. Nevertheless, the results provide theoretical implications for enriching entrepreneurial literacy models from an accounting perspective and practical implications for policymakers to design gender-responsive entrepreneurship and financial literacy programs.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".