Manufacturer's Contexts, Supply Chain Risk Management, and Agility Performance
Bibliographic record
Abstract
The dynamism of the current business environment emanates significant challenges and disruption risks for supply chains. These vulnerabilities in contemporary supply chains have motivated a substantial academic focus on supply chain risk management (SCRM). In the empirical literature on SCRM, a firm's external environment is conceptualized as a source of risk, and various organizational and technological factors are discussed as influencers of SCRM. However, the factors studied in the literature are generally narrow and analyzed in isolation, which has resulted in a fragmented and inconsistent understanding of the role of organizational and technological setups in SCRM. This study offers a systematic understanding of the antecedents and consequences of effective SCRM by investigating the associations between a manufacturer's environmental, organizational, and technological contexts, SCRM, and agility. The study employs the information processing view as the primary theoretical lens and relies on large-scale multi-industry and multicountry survey data for empirical analysis. In contrast to the threat-rigidity thesis, the findings of this study suggest that manufacturers seek collaborative and flexible work settings to respond to environmental challenges. Besides increasing efficiency, such organizational settings and enhanced technological setups can increase information processing capability to enable SCRM and agility. These findings challenge the suggestions that initiatives taken for efficiency can increase the risk factor and deteriorate performance. The study provides novel insights into the underlying information processing mechanisms for effective SCRM and highlights the importance of organizational and technological setups in enhancing these core mechanisms.
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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 | 0.002 | 0.011 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".