Insurance Inclusion in Uganda: Impact of Perceived Value, Insurance Literacy and Perceived Trust
Bibliographic record
Abstract
The study examined the impact of perceived value, insurance literacy and perceived trust on insurance inclusion in Uganda. The study employed a cross-sectional design to solicit responses from 400 individuals that voluntarily enrolled on an insurance programme. The study hypotheses were tested using Covariance-Based Structural Equation Modelling. The results showed that perceived value, insurance literacy and perceived trust have a significant and positive prediction of insurance inclusion in Uganda. However, perceived trust explained more of the variations in insurance inclusion than perceived value and insurance literacy. Overall, the predictor variables explained 63.2% of the variance in insurance inclusion. This study contributes to the limited nascent literature on insurance inclusion. The implication of this study is that insurance providers need to focus on trust and delivering value to customers in order to promote insurance inclusion. Further, the study proffers advice to policymakers to include insurance literacy in the national financial inclusion strategies to foster insurance inclusion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".