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Record W2028517725 · doi:10.1504/ijbe.2008.016841

Application of physical science analysis to strategic management monitoring and control systems

2008· article· en· W2028517725 on OpenAlexaff
Dave Valliere, Steven A. Gedeon

Bibliographic record

VenueInternational Journal of Business Environment · 2008
Typearticle
Languageen
FieldDecision Sciences
TopicInnovation Diffusion and Forecasting
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAnalogyComputer scienceOrder (exchange)Shock (circulatory)Mathematical modelSystem dynamicsControl (management)Risk analysis (engineering)Operations researchEconomicsBusinessEngineeringMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

This article illustrates the potential for using mathematical reasoning and analogy from physical sciences to conceptualise and explore the effect of dynamic external forces on the response of a company. An example of a generalised second-order linear differential model is applied to study the dynamic response of a company or industry to an exogenous shock. In most systems, significantly over-damped or under-damped response characteristics are expected. However, strategic management monitoring and control systems can scan for external shocks in order to apply dynamic adaptive controls to optimise the company's response. Mathematical tools are used to predict conditions under which an efficient response to external shocks is expected. It is shown that active strategic management can provide superior response characteristics. Additional complementary mathematical modelling approaches are outlined to show how the model may be expanded to address inter-firm linkages, supply chain dynamics, and the propagation of shocks through an industry.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score0.228

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.062
GPT teacher head0.331
Teacher spread0.269 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2008
Admission routes1
Has abstractyes

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