'How Do I Choose Thee? Let Me Count the Ways': A Textual Analysis of Similarities and Differences in Modes of Decision-Making in China and the United States
Bibliographic record
Abstract
This paper investigates the effect of decision-makers'culture on their implicit choice of how to make decisions. In a content analysis of major decisions described in American and Chinese twentieth-century novels, we test a series of hypotheses based on prior theoretical and empirical investigations of cross-cultural variation in human motivation and decision processes. The data show a striking degree of cultural similarity in the relationships between decision content, situational characteristics and the decision mode(s) employed, but also support several hypotheses about cultural differences. As predicted, Chinese decision-makers more frequently used role-based logic (a form of recognition-based decision-making) to arrive at decisions, by virtue of their greater awareness of and need for relational obligations. The hypothesis (based on conjectures about Chinese thinking style and personality differences) that Chinese decision-makers would show more rule- and case-based decision-making (two other variants of recognition-based decision-making) than decision-makers in American novels was also supported. After controlling for other predictor variables, there also was support for the hypothesis (based on comparative analyses of Chinese and Western philosophy) that analytic modes which base decisions on the calculation of best consequences would be used less frequently by Chinese decision-makers. There was no evidence of greater prevention focus in Chinese decisions. These and other observed cultural similarities and differences in the dynamics of decision mode selection have implications for the study and practice of decision-making in managerial settings.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".