Assessing the effect of Waiting Time Management Strategies on Waiting Time Satisfaction among Bank Customers in Ghana
Bibliographic record
Abstract
This paper empirically assesses the effect of waiting time management strategies on consumer waiting time satisfaction (WTS) in bank institutions in an emerging economy, using Ghana banking industry as the research context. Drawing from relevant banking and financial marketing literature, a conceptual framework was developed and tested using empirical data from a cross-sectional survey of 480 sampled customers of commercial banks in Ghana. Data were analysed using partial least squares structural equation modelling (PLS-SEM). The findings indicate that, with the exception of apology for delays, the key factors that influence consumer WTS are perceived compensation, waiting environment, quality of delay information and customer mind-engagement strategies. The findings offer important theoretical and managerial implications to scholars and practitioners in the banking service context. This paper provides an initial study into waiting time management in financial services context in Sub-Saharan Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".