Bibliographic record
Abstract
Purpose This study aims to develop a set of dimensions for measuring the service of moneychangers. Design/methodology/approach Attributes for measuring the moneychanger service were created from exit interviews and a review of the literature, and then administered a survey to consumers who had just completed a currency exchange. Respondent feedback was factor analysed, creating six service dimensions: “appearances of the tangibles”, “cost of service”, “interpersonal skills”, “service delivery”, “administrative matters” and “display board”. Findings The “service delivery” and the “cost of service” dimensions exceeded customer expectations; the “appearances of tangibles” and the “interpersonal skills” dimensions fell short of expectations; “administrative matters” and the “display board” dimensions merely met expectations. A general finding of this study is that moneychangers, even though they are small businesses, are able to effectively compete with corporate giants (i.e. banks) in exchanging currencies; they achieve this through more than meeting customer expectations in respect of their service delivery and costs. Research limitations/implications Based on this research, it is recommended that researchers who conduct studies in service areas might first take steps to identify measures which are specific to the type of business being investigated. Practical implications From a practical viewpoint, if the proprietors of moneychanger businesses want to improve their service, they should take steps to upgrade the interpersonal skills of the staff who serve customers and the appearances of those tangibles which are visible during service encounters. Originality/value Develops a set of dimensions for a specific type of business.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".