Research Note—Designing Promotion Ladders to Mitigate Turnover of IT Professionals
Bibliographic record
Abstract
Chronic excessive turnover among information technology (IT) professionals has been costly to firms for decades with annual turnover rates as high as 24% even among Computerworld’s “100 Best Places to Work in IT.” Prior information systems literature has identified two key factors affecting turnover: boundary-spanning roles and low promotability in one’s current firm. We draw on tournament theory, which is primarily concerned with inducing effort in employees, to decompose promotability into two distinct constructs: the likelihood of promotion and benefit from promotion, and demonstrate that each has a distinct role in affecting turnover rates. Our key result is that a job ladder motivating IT professionals with large, infrequent promotions will lead to higher turnover than a job ladder with smaller, more frequent promotions. We describe the conditions under which rearranging the job ladder creates economic value for the firm. We also offer an explanation for the observation that jobs characterized by boundary-spanning activities have higher turnover, and show that such jobs are more sensitive to the effect of likelihood of promotion on turnover. We test our hypotheses on a detailed data set covering 5,704 IT professionals over a five-year period. We confirm that likelihood of promotion has the predicted effects on turnover of IT professionals. A one standard deviation increase in likelihood of promotion decreases turnover by over 99%, consistent with our prediction. The empirical analysis also confirms the predicted effects of boundary spanning activities.
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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 | 0.004 | 0.014 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".