Managerial and organisational factors: Unravelling resource allocation choices in high-performing micro-firms
Bibliographic record
Abstract
Resource allocation decisions are pivotal in shaping the strategic direction of organisations, particularly in micro-firms that operate with limited resources and dispersed information. This research delves into the intricate interplay between managerial and organisational factors related to information collection, processing and resource allocation in the context of high-performing micro-firms. By advancing our understanding of how the internal coordination of information needed in decision-making and resource allocation evolves within micro-firms, we reveal the mechanisms that stabilise the relationship between participants and problems. Additionally, we explore how the capabilities of managers and owners, who often centralise final decisions in micro-firms, can catalyse the emergence of such coordination. This holistic view of strategic resource allocation in micro-firm settings addresses the fundamental question of how micro-enterprises overcome structural limitations to achieve high levels of performance. Our findings provide valuable insights for scholars, managers and policymakers, contributing to the broader discourse on resource management in micro-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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".