The influence of top management green commitment and green intellectual capital on sustainable business performance of Thailand's thrift and credit cooperatives
Bibliographic record
Abstract
This article aims to conceptualize and examine the influence of top management green commitment and green intellectual capital on sustainable business performance of Thailand's thrift and credit cooperatives. Data were collected from a sample of 319 top and middle managers and full-time employees of Thailand's thrift and credit cooperatives and analyzed using Structural Equation Model (SEM). The study's findings demonstrated that top management green commitment and green intellectual capital have a positive influence on sustainable business performance. Top management green commitment and green intellectual capital also have a positive influence on green human resource management. Furthermore, green human resource management has a positive influence on sustainable business performance. Additionally, the novelty of this study is the contribution of green intellectual capital as an intangible resource for organizations integrated with top management green commitment through green human resource management practice to drive an operation in achieving sustainable business performance and a competitive advantage for future researchers. All business sectors can implement this strategy in operation, which may improve their cleaner production and service capacities.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".