Relational Activities and Decision- Making Support: The Role of Directors' Associations
Bibliographic record
Abstract
DR PAMELA BAILLETTE IS ASSISTANT Professor at the University of Perpignan (Jerem) in France. She is also Research Associate at the CREGO 'Research Centre for Organisational Management' at the University of Montpellier in France. Decision-making is a risky activity for all companies. Owner-directors of small and medium-sized businesses are in a particularly delicate situation, however, as the risks taken can rapidly affect the survival of their company and often their personal patrimony. Moreover, their relatively limited means reduce the possibilities of using information sources that are available to the managers of large companies. The relations established with other directors within networks can constitute an appropriate response to the difficulties encountered by the managers of small and medium-sized companies in the decision-making process. Directors' clubs with a vocation of analysis into company management techniques display particularly interesting characteristics. Their missio-.:oriented approach towards the exchange of professional information, the homogeneity of their public composed of company directors, and the formalisation of the network as well as the way meetings are run, are all ways of providing assistance in the decision-making process. This paper is based on the study of three such associations located in two countries: the 'Centre for Young Directors' ('Centre des Jeunes Dirigeants') and 'Rhodanim club' ('Rhodanim') in France, and the 'Groups of Company Directors' in Canada- Quebec ('Groupement des chefs d'entreprise de Quebec'). It answers the following two questions: do the manager-s of SME perceive the association as a pertinent instrument for decision-making support? What factors favour the success of the decision-making support provided to the managers by the association? Following a theoretical analysis of the concepts involved, this paper explores the results of a deeper study conducted with 39 members to highlight a series of factors favourable to the success of decision support.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".