The Impact of Board Characteristics on Co-operative Reputation From the Lense of Resource-Based View Theory (RBVT)
Bibliographic record
Abstract
Numerous studies on corporate governance proposed the connection of boards’ characteristics on firm performance and reputation. However, the results are mixed and limited research in the co-operatives context has creates a great interest to fill the conspicuous gap. This study seeks to examine the potential relationship between board characteristics and co-operative reputation in Malaysia from the perspective of resource-based view theory (RBVT). Hence, multiple regressions were conducted to analyze the relationship between co-operatives reputation with the respect of Top 100 Co-operatives Index and board characteristics in terms of board size, ethnic diversity, gender diversity, age diversity and education diversity in this study. The sample is composed of 61 listed co-operatives in the Top 100 Co-operatives Index for the three-consecutive year during the period 2015 to 2017. Reputation data were obtained from Top 100 Co-operative Index that published at Malaysia Co-operative Societies Commission (MCSC) website. While, the data of board characteristics and co-operative size on log of total assets which lagged by a year were extracted from MCSC’s INFOKOP system that provides both financial and non-financial data. The results found that Malaysian co-operatives that appear high up in terms of ranking in the reputation MCSC index tend to have a greater proportion of high educational directors on their board. Other board characteristics including board size, ethnic diversity, gender diversity and age diversity were not associated with the reputation of co-operatives. This generally can be explained that different types of law, geography, historical background, cultural environment and other factors may affect composition and diversity; and particularly the board in co-operative societies. Findings of this study provide insights into potential strategies in relation to corporate governance towards improving co-operative’s values and indirectly help the government in achieving the national economic goals.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".