Risk-Taking and Performance of Small and Medium-Sized Enterprises: Lessons from Tanzanian Bakeries
Bibliographic record
Abstract
SMEs are the major drivers of socioeconomic development of many economies. In order to influence economic growth, SMEs must be capable of enhancing their competiveness, growth, and sustainability. These capabilities are acquired by SMEs that understand and adopt entrepreneurial strategies that work. There is abundant literature confirming that one of these entrepreneurial strategies include risk-taking practices. SMEs are still facing challenges to understand and apply the right risk-taking strategies that influence their performance. We therefore characterise risk-taking as risk planning, risk controlling, and strategic risk initiatives, and seek to establish their contribution on SME performance. This study draws lessons from the risk management practices of small and medium-sized bakeries in Tanzania where agriculture, a sector that directly relates with bakery business, is one of the leading sectors in driving economic growth. We adopt a multi-stage sampling technique and receive responses from 161 questionnaires, and 20 in depth interviews from bakery owners/managers throughout Tanzania. The principal component analysis, qualitative content analysis (manifest analysis), and the moderator analysis are used in analyzing these data. We ascertain that both the firm age, and the gender, of the owner/manager moderate the relationship between risk-taking strategies and SME performance. We argue that SMEs have the responsibilities of improving their risk-taking practices and capabilities in order to drive their competitiveness. Additionally, SMEs need to employ their efforts and resources in supporting their risk management initiatives, and integrate them in their business operations, and policy development practices, and ultimately advance their sustainability.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".