The Human Resources Certification and its Effect on Learning Motivation and Proactive Behaviors
Bibliographic record
Abstract
Human Resources (HR) professionals can practice in their profession without being certified. However, every year, the number of HR professionals becoming certified is increasing. While the main purpose of the HR certification is to encourage HR professionals to remain up to date on the newest trends and regulations in the industry, there is a lack of research investigating whether the certification indeed has this effect. The purpose of this paper was therefore to examine the impact of the HR certification on HR professionals’ level of motivation to learn and be proactive, while including individual (goal orientation (GO) and age) and contextual (leader-member exchange (LMX)) moderating factors for further insights into this important relationship. Guided by the proactive motivation theory (Parker et al., 2010), this correlational study consists of 192 HR professionals in various industries across Canada who completed an online survey. The hypotheses were tested using hierarchical regression. Results revealed that the HR certification marginally impacts an HR professional’s motivation to learn and proactive behavior. Further, this relationship is stronger among more junior HR professionals and as the supportive environment from their leader decreases. Companies should consider encouraging HR certification for all of their professionals and leaders should understand the impact that the certification can have on their employee’s career development and desire to make changes.
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.
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 | 0.003 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".