Bibliographic record
Abstract
Purpose – The purpose of this paper is to explore the significance and dynamics of alternative operations strategy (OS) processes towards developing a more complete picture of the strategy process-context-performance nexus. The findings are based on the statistical analysis of empirical evidence drawn from the contract apparel manufacturing industry in a developing country. Design/methodology/approach – Using a structured questionnaire and the key-informant approach data were collected from 109 contract apparel manufacturing firms in Sri Lanka. Cluster analysis was used to identify alternative configurations of strategy process modes. Findings – The analyses confirmed that the existence of alternative forms of OS development is statistically significant and that the alternative configurations of strategy process modes tested can all lead to superior performance, under certain circumstances. Research limitations/implications – The generalizability of these findings to other industry sectors within developing countries should be treated with caution, mainly due to the fact that the vast majority organizations selected for this study were subsidiaries of large international companies or comparable local counterparts. In order to better understand the linkages between OS and performance, data should be collected from multiple countries preferably using mixed-methods approaches. Originality/value – The findings are expected to contribute to operations management theory as they corroborate, with statistical evidence, the findings of recent qualitative studies. The results also confirm the existence of OS processes in developing countries that are consistent with the conceptual understanding developed in the context of developed countries.
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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 | 0.008 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".