Challenges of rural women entrepreneurs in Bangladesh to survive their family entrepreneurship: a narrative inquiry through storytelling
Bibliographic record
Abstract
Purpose Family entrepreneurship benefits women because of their economic, family, and social needs. But, as rural women, it is not much easy for them to maintain their family entrepreneurship successfully. Thus, this paper aims to explore the main challenges faced by rural women entrepreneurs in Bangladesh to survive their family entrepreneurship. Design/methodology/approach This study is qualitative in nature, based on narrative inquiry. The purposive sampling technique was used as a part of a non-probability sampling method to collect the data from rural women entrepreneurs from three districts (Khulna, Shatkhira, and Sylhet) in Bangladesh engaged in family entrepreneurship. No new information was found after collecting the data from seven (07) respondents; thus, they were chosen as the final sample size. Findings The findings show that rural women entrepreneurs faced primarily social and cultural, financial, and skill-related challenges, though they face other challenges to survive their family entrepreneurship. The attitude and perception of society toward women and their roles are at the root of social and cultural barriers. Researchers also found that financial challenges have a colossal impact on rural women and the other problem. Practical implications Although entrepreneurial activities are essential for socio-economic development in these developing countries, this research adds to the existing information by highlighting the main challenges that rural women face when they want to be business owners and entrepreneurs. Originality/value Research on rural women entrepreneurship in Bangladesh is limited and new. This study can provide an overview of the challenges faced by the rural women entrepreneurs and provide them with a blueprint for the development of women entrepreneurs in developing countries.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".