Factors that affect the trust of business clients in their banks
Bibliographic record
Abstract
Purpose The paper seeks to extend Coulter and Coulter (2002) findings regarding the impact of “person‐related” service characteristics (empathy, politeness, and similarity) and “offer‐related” service characteristics (customization, competence, and promptness) by examining business client trust in their current bank service representatives based on the length of the relationships with their banks. Design/methodology/approach The paper tested the effects of the above variables by collecting data from small business owners in the transportation industry in British Columbia, Canada. Clients were surveyed as to their beliefs about and feelings toward their bank service representatives. Findings The findings in this paper demonstrate that all six factors are related to trust building in general, but the factors are more salient at different periods of the relationship with their banks. Customization was found to be of particular importance at “crucial” periods of time in the business life cycle. Practical implications The results in this paper demonstrate how relationship‐managers at banks can work toward the establishment of their clients' trust by emphasizing the attributes that meet their clients' respective and timely needs. Originality/value In this paper Coulter and Coulter (2002) documented that both “person‐related” and “offer‐related” service characteristics have an impact on trust. This study focused on and presents the relative importance of these characteristics in general and across various time periods in particular. The results uniquely demonstrate that the relative importance of the factors in building trust varies according to stages in the life cycle of the businesses.
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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 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".