Exploring Ethical and Emotional Dimensions of Customer Value in Financial Services through Employee-Customer Dynamics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study focuses on exploring the ethical and emotional dimensions of customer perceived value (CPV) in financial services. This area is crucial to understanding customer dynamics within the finances sector, yet it remains underexplored in existing literature. Utilizing a quantitative analysis of 652 customer surveys from Canadian financial institutions, this research employs SmartPLS3 for structural equation modeling to ascertain the impacts of frontline employee job satisfaction on customer's ethical and emotional perceptions as well as their overall CPV. The data analysis reveals that customer-perceived employee job satisfaction positively affects CPV by significantly influencing ethical and emotional benefits. Although ethical benefits alone do not directly impact loyalty or word-of-mouth (WOM) recommendations, their influence is significant when mediated through CPV, with emotional benefits directly amplifying WOM and indirectly boosting loyalty. Correspondingly, the results highlight the importance of employee-centric policies and their direct correlation to customer loyalty and satisfaction. As such, this study contributes to the body of knowledge by linking employee satisfaction with ethical and emotional customer benefits, suggesting a reevaluation of business practices to integrate these dimensions for enhanced customer value creation. Future research is encouraged to explore these dynamics across varied contexts to expound upon these relationships' applicability and robustness. Our inquiry into the subject concludes by highlighting the strategic importance of fostering employee satisfaction when seeking to elevate CPV and achieve enduring customer loyalty. Keywords: Customer Perceived Value, Customer Loyalty, Employee-Customer Interactions, Employee Satisfaction, Financial Services
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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 | 0.009 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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 it