Leveraging human capital through an employee volunteer program
Bibliographic record
Abstract
Purpose The purpose of this paper is to explore how an employee volunteer program (EVP) as one aspect of responsible corporate citizenship (typically expressed in a mission statement) can influence the relationships among a firm, its employees and its community. Design/methodology/approach A pedagogical approach used in the educational sector known as “community service‐learning” or “service‐learning” was used as the basis for analyzing the experiences of 12 first‐time volunteering employees who described in a personal interview the motivations and outcomes associated with their participation in their EVP. Findings It was found that all three elements of service‐learning – that is, reciprocity, reflection, and development of responsible citizenship skills – were useful in understanding how an EVP can leverage human capital to benefit the firm, its employees and the community and make a firm's mission of responsible citizenship a reality. Research limitations/implications Despite the small sample size of 12 respondents, there were significant data in the comments from these respondents about the possible impact of an EVP experience in terms of various elements involved in service‐learning. Practical implications There are several corporate implications from the research which are related to various elements of service‐learning. For example, companies are encouraged to include in the creation and rollout of their EVP a reflection process which could also be connected to employee recognition programs, training programs and employee career development. Originality/value The paper presents a novel approach to assessing the motivations and possible outcomes associated with an EVP. It should be of interest to both academics and practitioners.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".