Achieving project performance goals in extreme resource-constrained environments: the role of bricolage in social enterprise projects
Bibliographic record
Abstract
Purpose Achieving project performance goals in extremely resource-constrained environments, such as those of social enterprises, is challenging. These organizations often employ bricolage – making the most of available resources – to navigate challenging landscapes. This study aims to understand how bricolage capabilities enhance or attenuate organizational project outcomes in resource-constrained social enterprises. Design/methodology/approach An exploratory survey was conducted to understand project management practices in Canadian social enterprises. Established scales were used to measure constructs with confirmatory factor analysis, and linear regression was employed to analyze relationships. Findings The study provides empirical evidence of the positive influence of bricolage on organizational project performance, with a crossover interaction observed for moderators – entrepreneurial leadership and project management capabilities. While project capabilities strengthen the positive impact of bricolage capabilities on project performance, entrepreneurial leadership has the opposite effect. Practical implications The insights from this study offer an initial roadmap for project managers for effective resource acquisition and utilization through bricolage, ultimately enhancing project management effectiveness in resource-constrained environments. Originality/value Despite the crucial role of bricolage capabilities in resource-constrained environments, the project management literature has largely neglected this concept. It is unclear how organizations use bricolage to manage projects. This lack of understanding challenges organizations, hindering their ability to apply bricolage consistently and thoughtfully in managing projects. Our study provides a deeper understanding of how bricolage facilitates project performance and enriches our understanding of it as an effective resource mobilization strategy within social enterprises.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.008 |
| 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".