Enhancing organizational performance through fintech innovation: a multi-dimensional analysis of healthcare projects in Africa
Bibliographic record
Abstract
This communication explores the factors driving the continuous adoption of digital technologies and fintech innovations in healthcare services in Africa, with a particular emphasis on task-technology fit. By analyzing task-technology alignment within healthcare projects, the study seeks to provide insights into how fintech can enhance organizational performance, supported by effective training programs and government involvement. To assess the scope of fintech in healthcare services, particularly in World Bank-financed projects in Africa, the paper employs a multi-dimensional approach that examines key indicators across four critical dimensions: technology, economy, and environment. Using Principal Component Analysis, the research evaluates fintech development at two key stages of digital transformation investment and development phases allowing for a nuanced examination of the interactions and synergies that shape fintech evolution. Data analysis is conducted using R software to ensure robust and accurate insights. The findings reveal that fintech enhances organizational performance through cost savings, improved transparency, innovative business model creation, and optimized service supply chains. Moreover, fintech facilitates operational adaptation, boosts connectivity, and increases agility in a competitive and complex environment, enabling organizations to operate more efficiently and maintain a competitive edge. This study contributes to the existing literature by providing a comprehensive assessment of fintech's impact on healthcare services within the context of World Bank-financed projects in Africa, highlighting the significance of task-technology fit in enhancing organizational performance and offering valuable insights for practitioners and policymakers looking to leverage fintech for improved healthcare outcomes.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".