Bibliographic record
Abstract
Purpose – This paper aims to illustrate the resource dependency theory by making ties between the different resources needed by a firm and the members of the board through their allegiances to different organizations. Many researchers have explained the formation of the board through a controlling function. Alternative explanation is proposed by the resource dependency theory. Design/methodology/approach – To investigate the case of the largest company in Canada, the authors took their data in the Boardex database. Then drawing an affiliation matrix, they used the Pajek software to analyze these connections. They obtained a non-directional social network prone to illustrate the resource dependency theory. Findings – The authors found different categories of resources being placed at firm’s disposal: political, social and economic, under different forms. Because a case study approach was used, the findings will be used to complete the theory rather than confirm or contradict it. The case firm is well-connected at every level, although having a quite conservative board: only one woman, no representative of the social or environmental worlds. Through a program for designing networks, the authors show that board member’s networks are encompassing a spectrum of resources. Comparing with a previous study, it was found that the proportions of these resources remain the same in 2013 than in 2007. Research limitations/implications – This case is a very large group. Therefore, it can be expected that it will need every kind of resources. It might be interesting to replicate the study on smaller firms. The results imply that boards may not be the best structure to control the firm’s inside activities. Originality/value – Although many theoretical papers exist on this question, the board is mainly studied through the insiders/outsiders dichotomy, but there are few practical studies taking the resource dependency theory perspective.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.018 | 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".