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Record W2536372546

Factors affecting the sustainability of independent financial planners in KwaZulu-Natal Province, South Africa

2016· article· en· W2536372546 on OpenAlexaboutno aff
Keith Miles. Peter, Muhammad Hoque

Bibliographic record

VenueJournal of Contemporary Management · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsLegislationCommissionQuarter (Canadian coin)SustainabilityBusinessFinancial servicesFinanceMarketingGeographyPolitical science
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.131
Threshold uncertainty score0.522

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.032
GPT teacher head0.220
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2016
Admission routes1
Has abstractyes

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