An Empirical Study on Antecedents of Perceived Service Recovery Qualityin E-banking Context
Bibliographic record
Abstract
The wide usage of IT enabled options for service delivery has increased the occurrence of irrepressible service failures in the contemporary banking landscape. The enhanced service quality levels exhibited by the dedicated employees suffer badly due to ever-increasing number of tech-driven service failures. The purpose of this study was to examine the linkages among constructs such as perceived service quality, perceived organizational service orientation, perceived automation quality, perceived employee proficiency and perceived service recovery quality to recovery satisfaction in the E-banking context. This investigation examined the moderating role of perceived initial negative feelings of the customer due to service failures, on recovery satisfaction. Data collected from 248 banking customers were analyzed by structural equation modeling approach using, Smart PLS 2.0 M 3, software to identify significant linkages among variables under study. Apart from perceived employee proficiency, all other variables significantly developed perceived service recovery quality leading to recovery satisfaction. Initial negative feelings from a service failure failed to cause significant moderating effect on post recovery satisfaction. The most disturbing service failures in the automated service delivery environment was identified as technical failures such as delay in online transactions, issues related to ATMs and interrupted connectivity. The study could establish that excellent service recovery quality develops service recovery satisfaction and customers gain more confidence in the bank and perceive higher value in their association with the bank.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".