The effect of service employees' competence on financial institutions' image: benevolence as a moderator variable
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the contribution of benevolence as a moderator variable that enhances the effect of service employees' competence on the customer's perception of a service firm's image. Design/methodology/approach A hierarchical multiple regression analysis was performed on data collected from 445 customers in a financial service setting to assess the influence of competence and benevolence, as well as their interactive effects on corporate image. Findings The results show a significant interaction between competence and benevolence in their influence on corporate image. The results reinforce the idea that benevolence intervenes as a moderator variable that enhances the impact of competence on corporate image. Research limitations/implications The study has limited generalisation given the convenient sample and the great variety of service industries. The efficacy of the direct measures and the hierarchical multiple regression must be considered. It would be helpful to realise similar studies in other service settings by using multidimensional scales of competence, benevolence and corporate image. Practical implications Service firms should not only highlight the role of the service employees' expertise but also their attitude and behaviour during the service encounter in a manner so as to increase the customer's trust in the firm's capability, to satisfy his/her needs and to enhance the firm's image. Originality/value The present study contributes specifically to understanding how major characteristics of service employees can influence the assessment of corporate image by consumers.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".