Exploring Customer Dissatisfaction/Satisfaction and Complaining Responses among Bank Customers in Ghana
Bibliographic record
Abstract
The paper explores the relationship between customer dissatisfaction/satisfaction and complaining responses among bank customers in Ghana banking industry. The study was a cross-sectional survey that used a self-administered structured questionnaire to collect primary data from 448 customers from ten selected banks in Ghana. The findings are that, though dissatisfaction causes customer complaining, dissatisfaction was more prevalent among non-complainers than complainers. Again, frequency of complaining is more likely to increase overall satisfaction if managed effectively. The most likely to be used complaining responses are complaining in person and refraining from using the bank’s services, while the least likely to be used complaining responses are complaining to the mass media and consumer associations. Moreover, public bank customers are more likely to complain by refraining from using the bank’s services and warning family and friends than private bank customers do. There was significant correlation between complaining responses and frequency of complaining, and between complaining responses and overall satisfaction. Theoretical and managerial implications are discussed. In spite of its limitations, the paper contributes to the body of knowledge in the area of consumer complaining behaviour in banking industry in emerging economies.
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 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".