Entrepreneurial Finance and HRM Practices in Small Firms
Bibliographic record
Abstract
Abstract As new ventures grow, they face significant challenges to their internal operations and organizational structure. These challenges are particularly evident in small, entrepreneurial firms, who have limited resources, under‐developed capabilities and often seek funding from external investors to enable growth. We draw on institutional theory, particularly institutional logics, to explore the role of different investors on human resource management (HRM) in small, entrepreneurial firms. Using qualitative multiple‐case study analysis of seven firms within the Irish agrifood industry, our study shows how external investors can prompt changes to HRM, illustrating three approaches to HRM practice: operational – aimed at improving efficiency and internal functioning through human resources; strategic – aimed at improving firm performance and competitive advantage; and transformational – leading to a fundamental redirection of the firm. These findings facilitate the development of a framework for how investor logic prompts changes to HRM practices of small, entrepreneurial firms. By examining the interaction of institutional logics, this paper contributes a more nuanced understanding of entrepreneurial finance and its implications on HRM practices in small, entrepreneurial firms.
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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 | 0.003 | 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 | 0.003 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".