Enterprise Risk Management as a Strategic Governance Mechanism in B2B-Enabled Transnational Supply Chains
Bibliographic record
Abstract
ABSTRACT As organizations increasingly face the need to compete for market share by building highly integrated global supply chains, governance of these complex relationships becomes a major strategic challenge. Research reporting high failure rates for collaborative alliances with supply chain partners makes formation of global supply chains a high-risk venture. This study examines the influence of strategic enterprise risk management (ERM) processes on improving supply chain capability while mitigating risks. ERM has become a major strategic management focus, and researchers suggest this momentum arises from the need for governance mechanisms that counter the ineffectiveness of government intervention and cooperation in cross-border relationships. We survey 207 organizations on their perceptions of their own ERM processes and a specific supply chain partner's absorptive capacity, B2B e-commerce business risk, and the global business risk associated with that partner relationship. The results support theorized relationships positing that stronger ERM promotes higher levels of partner absorptive capacity, lower B2B risk, and lower associated global business risk. Results further show that associated global business risk is reduced through managing and controlling partner absorptive capacity and B2B risk. Additional analyses show that stronger ERM is associated with partners being from countries with cultural traits conducive to strong supply chain performance.
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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 | 0.004 | 0.010 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".