Culture and Business: How Can Cultural Psychologists Contribute to Research on Behaviors in the Marketplace and Workplace?
Bibliographic record
Abstract
Cultural psychology has great potential to expand its research frameworks to more applied research fields in business such as marketing and organizational studies, while going beyond basic psychological processes to more complex social practices. In fact, the number of cross-cultural business studies have grown constantly over the past 20 years. Nonetheless, the theoretical and methodological closeness between cultural psychology and these business-oriented studies has not been fully recognized by scholars in cultural psychology. In this paper, we briefly introduce six representative cultural constructs commonly applied in business research, which include (1) individualism vs. collectivism, (2) independence vs. interdependence, (3) analytic vs. holistic cognition, (4) vertical vs. horizontal orientation, (5) tightness vs. looseness, and (6) strong vs. weak uncertainty avoidance. We plot the constructs on a chart to conceptually represent a common ground between cultural psychology and business research. We then review some representative empirical studies from the research fields of marketing and organizational studies which utilize at least one of these six constructs in their research frameworks. At the end of the paper, we recommend some future directions for further advancing collaboration with scholars in the field of marketing and organizational studies, while referring to theoretical and methodological issues.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.007 | 0.012 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".