Collective Intelligence and Entrepreneurial Resilience in the Context of Covid-19
Bibliographic record
Abstract
Research on the covid-19 pandemic, conducted to date, has clearly shown its negative impact on entrepreneurs. However, there are few relevant studies on the resilience of these entrepreneurs. Even economic stimulus packages developed by governments ignore collective intelligence, which is seen as an appropriate posture and path that can lead to the resilience of entrepreneurs in unpredictable situations. Thanks to the theoretical anchoring of collaborative management, we have developed and tested a conceptual model through the approach of deconstructing collective intelligence into (i) the sharing of capacities (ii) mutual aid (iii) collective competence and (iv) dynamic capacity. The data production was carried out through 15 semi-structured interviews and 282 surveys of Cameroonian and Chadian entrepreneurs. The results showed that mutual support (β = 0.32) and ability to share (β = 0.29) are indirectly the best predictors of economic and strategic entrepreneurial resilience. Because they participate effectively in building the collective competence of entrepreneurs in a context of crisis. This collective competence positively generates the level of variation in economic resilience (β = 0.38) and that of strategic resilience (β = 0.36). These results are the manifestation that covid-19 is boosting social dialogue between entrepreneurs. On the other hand, dynamic capacity appears less effective for the entrepreneurial economic resilience (β = 0.04) and strategic entrepreneurial resilience (β = 0.02) of the entrepreneurs studied due to the measures to combat covid-19. These findings contrast with previous research focused on entrepreneurial resilience through collective intelligence. They lead us to stress the importance of continuing research on the subject and to draw comparisons between entrepreneurs in crisis situations and those working in a stable ecosystem. The article is useful for researchers who find proven evidence that is more relevant. Then entrepreneurs will find new factors to make their entrepreneurial project viable. Finally, governments and their partners are urged to further promote entrepreneurship education based on dynamic capacity at the expense of confrontation and selfishness. Our article is part of the theory of collaborative management and organizational theory and reveals the existence of a relational contingency in the different stages of the entrepreneurial resilience process.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.000 | 0.001 |
| 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".