Assessment of managers’ proclivity for social dialogue in Bangladeshi Textile Industries
Bibliographic record
Abstract
This study examines the relationship of two important constructs (the belief and attitude constructs) with managers’ proclivity for using Social Dialogue – a tool for managing people at work (patronised by ILO) for maintaining peace and harmony in Bangladeshi textile industries. Managers’ personal belief and attitude within an organisation might affect the managerial responses towards social dialogue. The obtained data from the questionnaires are analysed through the 1st generation statistical packaged software (SPSS) and hypotheses are tested using Smart PLS software package well known as 2nd generation data analysis software. Analyses results strongly supported the relationship of the belief and attitude construct with managers’ proclivity for using social dialogue. The research surveyed three hundred fifty-one managers at 49 textile industries. The findings of this study suggest that the belief and attitude construct have a significant positive relationship with managers’ penchant for supporting social dialogue. The result of the paper provides managers with a credible argument to continually question their employee perceptions and objectively analyse whether their beliefs and attitudes impact their intention to use social dialogue. The selected sample should have been from strategic, tactical, and operational level of management that remains as a research limitation.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".