Leave, Stay or Progress? The Intention Profiles of Call Centre Agents
Bibliographic record
Abstract
The theory of reasoned action and voluntary turnover models have always regarded the intention to terminate employment in order to go to another employer as the best predictor of turnover. However, in practice, employees have two other options: to move to another job within the same company (internal turnover) or stay in their current job for an indefinite period. From the perspective of turnover prevention, it would be advantageous if research would identify intention profiles according to these three options. This study aims to explore the different intention profiles of employees and whether job satisfaction, job-search behaviour and rates of voluntary and internal turnover differ according to these profiles.The analysis of results collected from 434 agents from three call centres suggests the existence of four intention profiles, which are about equal in number: (1) Stay in present job (strong intention to remain in current job, low intention to progress internally and low intention to leave for a job externally); (2) Stay whilst waiting to progress (strong intention to remain in current job, but strong intention to progress to a job internally and low intention to leave for a job externally); (3) Priority is to progress (low intention to stay in current job, strong intention to progress internally and low intention to leave for an external job); (4) Priority is to move on (low intention to stay in current job, strong intention to progress internally and leave for a job externally).The results of the study show that these four intention profiles reveal different levels of job satisfaction and job-search behaviour. Similarly, the rates of voluntary staff turnover and internal turnover vary according to the intention profile.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".