The impact of call centre stressors on inbound and outbound call‐centre agent burnout
Bibliographic record
Abstract
Purpose The aim of this study is to draw on various models of burnout and test hypotheses relating to anticipated differences in the burnout process between inbound versus outbound call centre agents. This is achieved by comparing the magnitude of the relationships in the sequence of customer stressors → emotional exhaustion → depersonalization → reduced personal accomplishment across a sample of inbound and outbound call centre agents working in a large retail bank call centre in New Zealand. Design/methodology/approach Data were collected from inbound and outbound call centre agents of a large retail bank call centre in New Zealand via a self‐administered survey questionnaire electronically distributed to all 195 call centre agents working in the bank's two call centre locations. Data obtained from the call centre agents were analysed using the SEM‐based partial least squares (PLS) methodology. Findings The findings of the study reveal significant differences between inbound and outbound call centre agents in terms of the extent to which emotional exhaustion impacts depersonalisation as well as the extent to which depersonalisation influences feelings of reduced personal accomplishment. Practical implications The research advances understanding of differences in the burnout process as perceived by inbound versus outbound call centre agents. Call centre management might consider improving the work environment to bring about greater job discretion/autonomy, greater job variety and performance monitoring in order to attenuate the stronger impact of these relationships in an inbound context. Originality/value These findings extend our understanding of these phenomena in the largely unexplored yet important context of call centre agent‐customer interaction in specifically highlighting differences between inbound and outbound call centre agent burnout.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".