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MENINGKATKAN KEUNGGULAN KOMPETITIF PERUSAHAAN PERENCANA DI PASAR GLOBAL: Sebuah Kajian Literature

2024· article· en· W4399930011 on OpenAlexaff
Fielda Roza, Martalius Peli, Khadavi Khadavi, Vivi Ariani

Bibliographic record

VenueSIGMA TEKNIKA · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Fiscal Policies
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Rapid change in global marketplace offers formidable challenges to firms in the construction industry. Firms need to understand the nature of internal and external environment and apply competitive strategy that suits the firms‟ objectives to keep an edge over competition. Offering professional service in the industry, Construction firms are different than product-based industry in terms services provided, relationship between firm-client and competing style with the existing and new firms. How do Construction firms compete and what strategies are suitable for them? There are four competitive strategies such as cost leadership, focus, differentiation and growth to enhance the ways of competing style. How does the competitive strategy chosen impact the business performance? A success of any strategy adopted is determined by the performance of the firm. Thus, this paper stressed that for a Construction firm to maintain their competitive advantage, they need to relate the company performance with the competitive strategy adopted.

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.811
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.017
GPT teacher head0.216
Teacher spread0.199 · 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 designTheoretical or conceptual
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
Published2024
Admission routes1
Has abstractyes

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