Against all odds: a strategic analysis of the fall of Hong Kong, 1941
Bibliographic record
Abstract
Purpose The paper seeks to build on a model from extant literature which utilized a similar historical analysis approach in a study of strategic decision making. Using the (unsuccessful) defence of Hong Kong in World War II as the historical case, the paper seeks first to apply Chung and McLarney's model in the analysis, and then extend the model so as to better handle the unique sequence of events that took place in 1941. Design/methodology/approach The paper employs a historical case event in an analysis of competitive strategies. The first section provides a descriptive historical account of the battle of Hong Kong. The second section describes the decision‐making model, while the third section applies the model to explain three sets of decisions: the decision to defend the colony, decisions made during the battle and the decision to surrender. The fourth section draws implications for strategic decision making in organizations, while the last section presents conclusions. Findings Organization theorists seem to be fascinated with planning and strategy formulation, at the expense of strategy implementation. While designing organizational strategy is often more glamorous than execution, it is the execution of strategy that ultimately determines an organization's competitive advantage. Clearly, the strategy of the Allied Forces in Hong Kong was not hard to figure out (Mintzberg). However, there is growing research on how lower organizational levels have a tremendous contribution in fundamentally changing, formulating organizational strategy and sometimes even obstructing strategy formulated at the top. The decision to defend Hong Kong in the face of the Japanese invasion, decisions made during the battle and the decision to surrender were all major, critical decisions, especially susceptible to such biases as overconfidence, problem framing, availability heuristics and confirming‐evidence. Overconfidence is particularly dangerous. Originality/value The study not only modifies and extends the model, but also contributes to the literature by augmenting the validity of previous case research.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".