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Record W4408393902 · doi:10.5296/ber.v15i2.22598

Are Agency Managers Driving Waqf-Takaful Death Compensation Product Success?

2025· article· en· W4408393902 on OpenAlexaff
Sukriah Ismail, Marina Abu Bakar, Hanizan Shaker Hussain, Mohamad Saufee Anuar, Md Nasri Ali, Afiffudin Mohammed Noor

Bibliographic record

VenueBusiness and Economic Research · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsQuest University Canada
Fundersnot available
KeywordsWaqfAgency (philosophy)Compensation (psychology)BusinessProduct (mathematics)AccountingActuarial scienceIslamMathematicsGeographyPsychologySociology

Abstract

fetched live from OpenAlex

This study examines the role of agency managers in driving the success of waqf-takaful death compensation products in Malaysia. The research identifies the challenges agency managers face and the strategies they use to promote the integration of Islamic financial products with waqf, a charitable endowment. A qualitative approach was employed, involving in-depth interviews with eight agency managers from various takaful companies. Thematic analysis was used to identify key themes related to product knowledge, customer education, marketing strategies, and incentives. The findings reveal that agency managers are pivotal in educating potential clients, overcoming skepticism, and bridging the gap between religious values and financial products. However, challenges such as limited market awareness, product misconceptions, and the need for improved promotional efforts remain significant obstacles. The study suggests that enhanced training for agency managers, better marketing strategies, and clearer communication of product benefits could contribute to the wider adoption of waqf-takaful products. This research offers valuable insights into the practical implications for takaful providers and policymakers in Malaysia, emphasizing the importance of agency managers in promoting financial inclusion and social responsibility through waqf-takaful products.

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)0.001
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 score0.190
Threshold uncertainty score0.617

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.057
GPT teacher head0.310
Teacher spread0.253 · 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
Published2025
Admission routes1
Has abstractyes

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