Leading to intrinsically reward professionals for sustained engagement
Bibliographic record
Abstract
Purpose – The purpose of this paper is to identify leader behaviors that foster intrinsic rewards (IRs) in technical professionals, sustain their felt and behavioral engagement, and relate the career outcomes of performance, satisfaction with the organization, and retention. Design/methodology/approach – Employing an action research approach, four studies were undertaken to: first, identify what intrinsically motivates professionals in a large R & D organization; second, create a survey of the leader behaviors that foster a sense of IR and engagement; and third, use the survey with two samples (Canada, Europe) to examine the relationships of engagement with three desired career outcomes. Findings – Leader behaviors can foster a sense of IRs which are related to performance, satisfaction with the organization, and retention. These relationships were partially mediated by felt and behavioral engagement, with felt engagement more strongly associated with satisfaction and retention, and behavioral engagement with performance. Research limitations/implications – Leaders play a significant role in fostering a sense of IR in technical professionals, which helps to sustain their engagement. Important distinctions among IRs, felt engagement, and behavioral engagement are made that contribute to a better understanding of how these constructs affect the careers of professionals. Originality/value – Professionals and other knowledge workers are often thought to be self-motivated, or motivated by the tasks they perform. Leaders can greatly enhance this motivation and important career outcomes of satisfaction, performance, and intent to stay.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".