Line Managers’ Rationales for Professionals’ Reduced-Load Work in Embracing and Ambivalent Organizations
Bibliographic record
Abstract
This study examines line managers’ rationales regarding reduced-load work (RLW), an emerging talent management practice allowing professionals to reduce their workload and take a pay cut, while actively remaining on a career path. Unlike flextime and telework, RLW addresses professionals’ core problems of rising work hours and workloads. Interviews with 42 managers in 20 North American employers suggested that managers were more likely to support RLW for employees whom they saw as (1) high-performers, (2) flexible in their use of RLW, and (3) doing conducive jobs. Interviews with 20 HR experts and 24 senior executives revealed four dimensions of organizational support, two cultural (senior management support and discourse on career penalties) and two structural (adaptation of HR systems and organizational diffusion). In embracing organizations there was a higher frequency of more supportive managers than there was in ambivalent organizations. Managers’ rationales were connected to their organizational contexts, albeit loosely, suggesting managerial implementation agency. The same rationales were more likely to be used in supportive ways in embracing contexts and in less supportive ways in ambivalent contexts. This study suggests that managerial and organizational support for flexible talent management practices dovetail in nuanced and important ways. © 2015 Wiley Periodicals, Inc.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".