Modelling the Antecedent and Consequence of Consumer Perceived Deception in Loan Services
Bibliographic record
Abstract
This paper assesses the antecedent and consequence of consumer perceived deception (CPD) on consumer trust, satisfaction, attitude recommendation and intentions to acquire future loans from financial service providers. The proposed research model was tested using data from a survey of 371 loan customers of leading financial service providers in Ghana. Data were analysed using SmartPLS 2.0 for Partial Least Squares Structural Equation Modelling. The results show high information quality could significantly reduce CPD. Moreover, results indicate that CPD has negative effects on trust, satisfaction and likelihood to recommend loan service providers. However, CPD did not influence respondents’ general attitude towards loans and future intentions for loan acquisition. This paper uniquely contributes to theory by testing a framework of antecedent and consequence of CPD in order to extend scholar’s understanding of CPD in loan financial service context. The findings provide important implications for managing CPD in loan service delivery, and sustaining customer future intentions in spite of CPD in loan service. While this study is limited in terms of generalizability of the findings in developing countries, it provides avenues for further research to test the applicability of the proposed research model in financial markets in other research settings.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".