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Record W3128256405 · doi:10.1051/e3sconf/202123503009

Business Analysis on Sustainable Competitive Advantages

2021· article· en· W3128256405 on OpenAlexaff
Zeyu Wang

Bibliographic record

VenueE3S Web of Conferences · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal Trade and Competitiveness
Canadian institutionsCape Breton University
Fundersnot available
KeywordsCompetitive advantageSustainabilityBusinessContext (archaeology)Industrial organizationResource (disambiguation)Sustainable developmentResource-based viewStrategic managementCorporate sustainabilityEnvironmental economicsEconomicsMarketingComputer science

Abstract

fetched live from OpenAlex

With the rapid economic growth and globalization, sustainability begins to play a more and more important role in the strategic management of a company. This paper explains what the sustainability competitive advantages are, and what factors are related to sustainability. It also briefly introduces the sustainable competitive advantages from institutional and resource-based views. In addition, this paper presents that sustainable competitive advantages largely depend on the company’s capability to manage the institutional context of resource decision. Sustainable competitive advantages are not only related to internal context, but also related to external environment. This paper explains the link of sustainable competitive advantages in the internal and external environment. In conclusion, it presents the necessary understanding for entrepreneurs to concentrate on factors which influence corporate sustainability beyond numbers in the financial analysis.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.004
Science and technology studies0.0030.007
Scholarly communication0.0060.007
Open science0.0010.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0120.002

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 head0.014
GPT teacher head0.235
Teacher spread0.221 · 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 source (direct Gemma or distilled Codex), 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

Citations12
Published2021
Admission routes1
Has abstractyes

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