Predicting Clients’ Intentions to Acquire Credit Facilities in Ghanaian Financial Market
Bibliographic record
Abstract
This paper assesses the key determinants of clients’ intentions to acquire future loans from financial service providers in a developing country. Drawing on the Theory of Planned Behaviour (TPB) and the Technology Acceptance Model (TAM), a conceptual model is developed and tested. The study involves a cross-sectional survey of 371 loan customers of leading financial service providers in Ghana. Due to the predictive focus of the study, data are analysed using Partial Least Squares structural equation modelling method available in SmartPLS 2.0. The results show that satisfaction, perceived usefulness and flexibility of loan terms and conditions are the significant factors, while trust, attitude towards loan and social influence do not contribute significantly to predicting client’s intentions to acquire future loans from financial service providers in Ghanaian financial market. This paper uniquely contributes to theory by testing a comprehensive framework of direct determinants of intentions to acquire loans in financial markets in developing countries, which is an under-researched area. Despite its limitations, the study provides important implications for managing clients’ loan acquisition intentions and behaviour in financial markets.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".