Enterprise Risk Management: Re-Conceptualizing the Role of Risk and Trust on Information Sharing in Transnational Alliances
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.
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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| 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".