Bibliographic record
Abstract
Purpose Explains SITEL Direct's approach to staff retention and how successful strategies to empower, encourage and promote employees provide business benefits to its clients and their customers. Design/methodology/approach Highlights the main benefits available to agents working in SITEL's bureau and fulfillment programs: varied work, flexible hours, good training and personal development opportunities, and the chance to work in one of England's prettiest towns. Emphasizes the importance of having a settled team. Findings Shows that SITEL has established a monthly retention target of 95 percent for its bureau agents, but in 2004, there was an average monthly retention rate of 97.2 percent in quarter one, 95.9 percent in quarter two and 94.3 percent in quarter three. Fulfillment has achieved even higher retention rates. With a similar target of 95 percent monthly retention, the program in 2004 achieved an average monthly retention rate of 100 percent in quarter one, 97.2 percent in quarter two and 97.8 percent in quarter three. Practical implications Demonstrates that high staff turnover need not, in all cases, characterize the call‐centre industry. Originality/value Emphasizes that the agents working in SITEL's bureau and fulfillment programs are critical to the success of a client's campaign, as they are the first people that consumers interact with either directly or indirectly.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.057 | 0.010 |
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".