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Record W2751081749 · doi:10.5430/bmr.v6n3p17

The Research of the Financial Strategy of SY Education Consulting Group

2017· article· en· W2751081749 on OpenAlexvenueno aff
Yun Li

Bibliographic record

VenueBusiness and Management Research · 2017
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsSWOT analysisCompetition (biology)BottleneckMarketingStrategic managementBusinessBrand strategyEconomicsManagementPublic relationsOperations managementPolitical science

Abstract

fetched live from OpenAlex

The consulting industry in our country began in the late 1970s. After 30 years of development, it has been already a matured industry. Then the international education consulting group start to emerge. With the development of our society and increasing national income, the demand for it also increases. However, due to various reasons, most international consulting groups still have some difficult problems remaining to be solved. SY Consulting Group is one of them. Since its establishment in the year 1992, it has met its bottleneck period for development. Analyzing its current industry environment and working out a development strategy benefiting its development has become an important matter. This paper can be divided into five parts, which relies on theoretical knowledge and tools about strategic management and combines document research method and empirical study. Also it was written after the author summarized and analyzed international and domestic development strategy papers, and studied SY Group’s current development. The author uses Michael Porter's Five Forces Model and SWOT Analysis to analyze the company’s competition environment and its advantages and disadvantages to analyze its current business and finally raised a strategic target, comprehensive strategy and competition strategy for its development. The author also provided specific strategies including the structure, marketing and human resources for the company.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.855
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.002
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 head0.108
GPT teacher head0.383
Teacher spread0.275 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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