Competing for Talent: Employee Incentives for Nonprofits to Gain an Advantage in Human Capital
Bibliographic record
Abstract
Vital to our society, the success of nonprofit organizations (NPOs) often hinges on the effective management of human capital. Yet, while a significant body of research exists on human capital management in nonprofits, there lack studies adopting a comparative perspective and exploring management practices that can afford nonprofits an advantage in motivating and retaining human capital over for-profit organizations (FPOs), with which NPOs are increasingly competing for employees, particularly considering that FPOs are more frequently venturing into areas traditionally dominated by NPOs. In this study, we investigate the types of employee incentives that can differentially impact voluntary turnover in NPOs and FPOs, thereby potentially providing NPOs an advantage in motivating and retaining human capital. Specifically, adopting a firm-specific incentive perspective, we propose that employee incentives that demonstrate people orientation (i.e., employee benefits, employee participation, and employee training) can more effectively curb voluntary turnover in NPOs compared to their for-profit counterparts. Using a multi-level and longitudinal dataset from Statistics Canada (N = 11,555 organization-year observations from 2,867 organizations), we found support for our hypotheses. Our research sheds light on potential avenues for NPOs to achieve competitive advantage over NPOs with respect to retaining employees by aligning management practices with their organization contexts and employee values.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".