Enterprise Risk Management: Re-Conceptualizing the Role of Risk and Trust on Information Sharing in Transnational Alliances
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Globalization places greater emphasis on the development of transnational alliances. The greatest benefits from alliances are derived from high-level information sharing, but vulnerability escalates with information sharing. This study examines risk in transnational alliances based on a theoretical model drawing from enterprise risk management (ERM) as a strategic management effort. This theoretical model posits that ERM strategies focus on business risk as the primary determinant of alliance partner selection and continuity, particularly within global relationships, whereas prior management control research focused on trust. The purpose of this study is to examine the influence of ERM on risk and trust associated with transnational alliances and the resulting impact on interorganizational information sharing. Survey data are gathered from 200 senior-level managers monitoring transnational alliances. Structural equation modeling is used to test the hypothesized relationships. Results provide strong support for the research model, showing that high ERM is associated with decreased risk, increased trust, and enhanced information sharing. Given the ongoing debate over the relationship directionality between trust and risk, we conducted additional sensitivity testing. Competing models focusing on trust as the key control mechanism are tested to assess the strength of our research model. Our risk-oriented research model demonstrates stronger explanatory power than competing models. Overall, our results show ERM substantially alters strategic management of transnational alliances, and has become a major influence on interorganizational risk, trust, and information sharing.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.007 |
| Open science | 0.000 | 0.000 |
| 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 it