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Record W1999518382 · doi:10.1108/17515630710686842

Pursuing three horizons of growth – three cases: Bombardier (Canada), Disney (US) and Hutchison Whampoa (China)

2007· article· en· W1999518382 on OpenAlexaboutno aff
Carol M. Connell

Bibliographic record

VenueBusiness Strategy Series · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Finance and Governance
Canadian institutionsnot available
Fundersnot available
KeywordsChinaInvestment (military)HorizonRecessionOutsourcingEconomicsOriginalityValue (mathematics)Time horizonBusinessFinanceMarketingPolitical scienceMacroeconomicsCreativity

Abstract

fetched live from OpenAlex

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?

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.493
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.196
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2007
Admission routes1
Has abstractyes

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