A systems thinking approach to international business education
Bibliographic record
Abstract
Purpose International business (IB) education typically focuses on the multinational enterprise (MNE) and how it navigates varying institutional setups for its own benefit. This reductionist and MNE-centric approach underplays the influence these firms have on the societal and environmental fabric of the geographies they are operating in. This paper aims to propose integrating systems thinking into IB education to address this shortcoming with the intention to setup IB education to engage with wicked grand challenges. Design/methodology/approach This conceptual paper offers an approach for integrating complexity, criticality and diversity into IB education through teaching systems thinking capabilities. Findings Integrating systems thinking into IB education allows for a more realistic appreciation of IB’s contribution to addressing grand challenges. The authors propose a systems thinking perspective to IB education and offer how systems thinking capabilities could be taught in IB. Originality/value Grand challenges are characterised by wicked problems. Addressing them requires a multilevel, cross-disciplinary approach that takes into consideration the inter- and intradependencies of all actors within a system.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".