Factors affecting the sustainability of independent financial planners in KwaZulu-Natal Province, South Africa
Bibliographic record
Abstract
A large number of independent financial planners are leaving the industry permanently or joining alternative models. The aim of this article was to determine the factors affecting independent financial planners on the sustainability of independent financial planners. This was a cross-sectional study conducted among 525 independent financial planners practising in the long-term insurance industry in KwaZulu-Natal province, South Africa. A self-administered, anonymous questionnaire was distributed via QuestionPro software program. A total of 115 participants completed the questionnaire. Results had shown that legislation was a key factor affecting independent financial planners. A quarter of respondents indicated that they had considered exiting the industry within the last five years and among them 73% reported compliance legislation as the major reason. One third of respondents experienced a decrease in income over the last five years with 60% confirming that commission regulations negatively impacted on their income. Over half of the respondents agreed that clients are using alternative channels for the purchase of insurance products. Independent financial planners need to incorporate social media and other forms of technology in their practices for the marketing, distribution and sale of products and services to retain and attract new clients.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".