Determinants of technology adoption by micro and small enterprises (MSEs) in Awi zone, Northwest Ethiopia
Bibliographic record
Abstract
Adoption of technology can enhance the development of micro and small enterprises (MSEs). But in Ethiopia, there is a very low adoption of broadband connections, mobile phones, computers, printers, scanners, copiers, and other technologies by MSEs. The main objective of this study was to identify the determinants of technology adoption by MSEs in Northwest Ethiopia. Cross-sectional data were collected from 327 MSEs selected using the stratified random sampling method and analyzed using the Heckman two-stage model. The first stage probit model estimation results showed that sex, educational level, source of start-up capital, size of the enterprise, and whether the owner of the enterprise has had technology related trainings were significant factors determining technology adoption decisions of MSEs. The second stage estimation results showed that sex, education level, experience, age, family size of the owner, and access to credit significantly influence the degree of technology adoption. The findings indicate that MSEs need more education (information) on technology, greater access to credit, and incentives provided by the government of Ethiopia and other agencies in order to increase their adoption of technology.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".