Pursuing three horizons of growth – three cases: Bombardier (Canada), Disney (US) and Hutchison Whampoa (China)
Bibliographic record
Abstract
Purpose The article looks at how companies pursuing a three‐horizon growth strategy weathered the last economic downturn and what became of their growth initiatives. Design/methodology/approach The paper examines the financial performance and continued investment of three growing companies from 1996‐2004: Bombardier (Canada), Hutchison Whampoa (Hong Kong/China) and Disney (US). Findings The Bombardier, Disney and Hutchison Whampoa cases teach a powerful lesson about the importance of using investment in growth to manage uncertainty and limit downside risk. Research limitations/implications While the focus of this article is on three companies only, the financial performances of a dozen other growing firms are examined over the same period for purposes of comparison. Practical implications Following the last downturn, companies sought to preserve the core and outsource non‐critical functions to reduce the cost of business. Some chose to sideline growth initiatives during this period. This article analyzes the outcomes for three companies that continued to invest in growth during and after this period. Originality/value This article addresses a series of questions. Is a three‐horizon growth strategy sustainable in a downturn? Have companies that pursued a three‐horizon strategy actually grown? Do they continue to finance the growth of horizon two and horizon three businesses? Have any viable options matured?
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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 | 0.001 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| 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".