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Record W1997019362 · doi:10.1108/17515630710686914

Utilizing simple rules to enhance performance measurement competitiveness and accountability growth

2007· article· en· W1997019362 on OpenAlexaff
Rocky J. Dwyer

Bibliographic record

VenueBusiness Strategy Series · 2007
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicQuality and Supply Management
Canadian institutionsSaint Paul UniversityAthabasca University
Fundersnot available
KeywordsFlexibility (engineering)AccountabilityOriginalityValue (mathematics)Process managementKnowledge managementBusinessStrategic managementSimple (philosophy)MarketingComputer scienceEconomicsManagementCreativityPsychology

Abstract

fetched live from OpenAlex

Purpose This article aims to outline and discuss how to incorporate simple rules to guide strategic processes to enhance competitiveness and growth, while improving performance measurement and accountability of organizations. Design/methodology/approach An examination of the theorists' perspectives was undertaken to determine the relevancy of the theory to guide business flexibility and decision‐making to enhance competitiveness and growth, in a changing business environment. Findings Understanding the importance of flexibility, strategic processes would enable individuals and organizations to better respond to factors associated with changing business opportunities and customer demands. Practical implications The article advocates that an understanding of the simple rules concept can enable business leaders to create practical business strategies to build organizational flexibility, which in turn will lead to enhanced competitiveness and growth opportunities. Originality/value This article presents an overview of the literature which both enhances personal knowledge and understanding at the theoretical and practical levels enabling business leaders to gain insight on the inherent factors that may be influenced to advance organizational goals and objectives.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.577
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.003
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 head0.040
GPT teacher head0.269
Teacher spread0.229 · 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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