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Record W4284895192 · doi:10.31219/osf.io/n2yu3

Kajian Business Model pada Restoran Applebee’s Company

2022· preprint· id· W4284895192 on OpenAlexaff
Nathaneal Felix Poedjiono

Bibliographic record

Venuenot available
Typepreprint
Languageid
FieldSocial Sciences
TopicSMEs Development and Digital Marketing
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsValue propositionHumanitiesBusiness administrationBusinessPolitical scienceArtMarketing

Abstract

fetched live from OpenAlex

Bisnis kuliner Applebee’s di Amerika Serikat pernah mengalami masa kenaikan (rise). Namunsejak tahun 2015 industri kuliner ini mengalami tren penurunan (fall). Studi ini bertujuan untukmengetahui alasan penurunan (fall) dan kenaikan (rise) pada bisnis Applebee’s. Studi inimenggunakan studi kasus tunggal pada bisnis Applebee’s yang bergerak di bidang kulinerdengan analisis business model canvas, business pattern, business environment & valueproposition canvas. Bisnis Applebee’s pernah tren pertumbuhan dan sukses karena memilikivalue proposition restoran yang menyediakan berbagai menu minuman berbeda setiap bulannyadengan harga yang sangat terjangkau dan relatif murah yaitu sekitar $1 hingga $3. Namun, bisniskuliner ini mengalami tren penurunan karena value proposition menu yang ditawarkan untukvegetarian sangat terbatas. Studi merekomendasikan key activities membuka 1.700 cabang yangberlokasi di berbagai wilayah Amerika Serikat agar jangkauan customers menjadi lebih mudah.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.708
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0010.000
Open science0.0020.003
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.053
GPT teacher head0.292
Teacher spread0.240 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

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