An Empirical Evaluation of the Technology Acceptance Model for Peer-to-Peer Insurance Adoption: Does Income Really Matter?
Bibliographic record
Abstract
One essential component of insurance technology (Insurtech) is peer-to-peer (P2P) insurance, which represents a transformative shift from conventional insurance to digital platforms by fostering community-based risk sharing. This study contributes to the body of knowledge by engaging the Technology Acceptance Model (TAM2) to explore how perceived usefulness, perceived ease of use, subjective norms, and perceived trust influence the adoption of P2P insurance, and the moderating influence of income on these relationships. This study used a self-administered survey questionnaire to collect data from short-term insurance clients in South Africa. The survey was analysed using the confirmatory factor analysis and structural equation modelling (SEM) approach. The findings demonstrate that perceived usefulness, ease of use, and subjective norms present a significant positive influence on the adoption of P2P insurance, underscoring the relevance of value, ease of use, and social influence in predicting the adoption of insurance technologies, particularly P2P insurance. However, perceived risk and trust exhibit a positive but statistically insignificant relationship. Additionally, this study reveals that income exerts a significant positive moderating influence on perceived usefulness, ease of use, and subjective norms in affecting P2P adoption, implying that individuals with higher incomes are responsive to these factors when considering P2P insurance. This study highlights the need for policies that support the development of digital infrastructure, as its accessibility and ease of use, including social norms, are predicted as essential drivers of P2P insurance adoption. Also, policymakers should focus on creating a regulatory environment that encourages accountability and openness to P2P insurance.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".