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Record W1955001552 · doi:10.5539/ijef.v7n9p287

Achieving Competitive Advantage in Economic Crisis

2015· article· en· W1955001552 on OpenAlexvenueno aff
Salem Ahmad Alrhaimi

Bibliographic record

VenueInternational Journal of Economics and Finance · 2015
Typearticle
Languageen
FieldEnergy
TopicEnergy, Economy, and Technology Trends
Canadian institutionsnot available
Fundersnot available
KeywordsRecessionCompetitive advantageEntrepreneurshipCompetition (biology)Quality (philosophy)BusinessGlobal recessionSpace (punctuation)Business cycleValue (mathematics)EconomicsIndustrial organizationMarket economyEconomic systemMarketingFinanceMacroeconomicsComputer science

Abstract

fetched live from OpenAlex

This paper seeks to encourage potential and existing entrepreneurs to do business, even in economic crisis and global recession. The global economic crisis strongly affected less developed areas, especially small entrepreneurs in these areas. In such conditions there is not much space left for successful entrepreneurship, entrepreneurs are forced to search new opportunities, and to do businesses with higher risks. They must have a modern approach to entrepreneurship, which tries to discover new opportunities in an innovative way. The paper shows that crisis and recession can provide new opportunities that need to be detected at the right time Competitive advantage may be gained if the entrepreneur is able to offer something valuable and important to the market, and if it differs from the competition in a way that offers better quality. In addition, there must be many other sources of competitive advantages which should support the main source of competitive value. The aim of the paper is to help entrepreneurs to find sources of competitive advantage in time of economic crises.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0030.004
Scholarly communication0.0070.006
Open science0.0010.006
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.244
Teacher spread0.230 · 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 designNot applicable
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

Citations1
Published2015
Admission routes1
Has abstractyes

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