What are the best HRM practices for retaining experts? A longitudinal study in the Canadian information technology sector
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to answer the following two questions: What are the HRM practices that have a significant impact on employees’ functional retention?, and Does the impact of these HRM practices on functional retention differ based on the employee’s status as an expert or a non-expert? Our theoretical foundation rests on human capital theory and social exchange theory. Design/methodology/approach – This study uses longitudinal data that come from multiple surveys conducted on new employees within a Canadian subsidiary of an international information technology (IT) firm. Findings – Results show that four out of five HRM practices under study have a significant and positive impact on functional retention of employees regardless of their expert status: satisfaction with a respectful and stimulating work environment, satisfaction with training and development, satisfaction with innovative benefits and satisfaction with incentive compensation significantly increase functional retention of employees. Functional retention was found to be higher for experts than for their non-expert counterparts. Last, results show that expert/non-expert status play a moderating role between HRM practices and functional retention. Originality/value – In short, this study offers five main contributions to the literature: first, it focuses on retention rather than turnover; second, it goes further by examining functional retention as the dependant variable; third, it distinguishes between two categories of employees: experts and non-experts; fourth, it extends the limited literature on IT workers, HRM practices and retention; and fifth, it is based on longitudinal data whereas the overwhelming majority of published studies have been based on cross-sectional data.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it