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Record W2212191175 · doi:10.5539/hes.v6n1p40

Strategic Architecture for School of Business, Bogor Agricultural University

2015· article· en· W2212191175 on OpenAlexvenueno aff
Agustina Widi Palupiningrum

Bibliographic record

VenueHigher Education Studies · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsnot available
Fundersnot available
KeywordsReputationSWOT analysisCompetitor analysisCompetitive advantageExcellenceBenchmarkingCurriculumBusinessMindsetStrategic planningInternationalizationMarketingHigher educationPublic relationsSociologyPedagogyPolitical scienceEconomicsEconomic growthComputer science

Abstract

fetched live from OpenAlex

<p class="Default">This study aims to analyze the internal and external factors that influence the development of SB-IPB, analyze SB-IPB strategic foresight and designing SB-IPB strategic architecture. This research is a descriptive research in the form of a case study. Respondents in this study are experts from inside and outside of IPB who are policy makers, alumni users, competitors, and alumni. Based on the internal environment analysis, it is found that SB-IPB internal resource advantages will still have the quality of competitive parity, the curriculum will have a temporary competitive advantage, and the reputation will have a sustained competitive advantage. In the external environment analysis, it is indicated that changes in the external environment provides great opportunities to the development of SB-IPB. Important issues that affect the development of SB-IPB in the future will be institutional change, change of mindset in the curriculum, internationalization, and changes in the business world. Five focus strategies will be compiled in 2015 until 2019, they are: development and strengthening of the institution and the curriculum in the first year, strengthening in networking and benchmarking in the second year, internationalization in the third year, and strengthening SB-IPB excellence in the fourth year to achieve its goal to be a first class business school in the fifth year. </p>

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.174

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.103
GPT teacher head0.337
Teacher spread0.234 · 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.

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

Citations2
Published2015
Admission routes1
Has abstractyes

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