RESEARCH: THE INFLUENCE OF UNCERTAINTY AVOIDANCE ON DYNAMIC BUSINESS DECISION MAKING ACROSS CULTURES: A GROWTH MIXTURE MODELING APPROACH
Bibliographic record
Abstract
Dynamic decision making (DDM) can follow various strategic patterns, one of them being stability versus flexibility. This paper explores the influence of uncertainty avoidance and expertise on stable versus flexible dynamic decision making. Participants were 40 German business students, 51 U.S. business students, and 66 U.S. psychology students. Every participant took the role of a manager in a computer-simulated company called CHOCO FINE and worked on the simulation individually over a period of 24 simulated months. Participants’ decisions were saved automatically in computer files and analyzed using growth mixture modeling in MPlus (GMM; Muthen & Muthen, 2006) which controls for interdependence of longitudinal data. Surprisingly, the German sample was more tolerant of ambiguity than the two U.S. samples, and uncertainty avoidance and intolerance of ambiguity did not predict DDM intensity and flexibility. The implications of this study are also discussed. In sum, results showed these unexpected cross-cultural differences, but no differences between novices and experts.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".