Faktor-Faktor Yang Mempengaruhi Cost Of Equity (Studi Pada Perusahaan Manufaktur Sub Sektor Industri Yang Terdaftar Di Bursa Efek Indonesia) Tahun 2013-2017
Bibliographic record
Abstract
Tesis ini meneliti pengaruh manajemen laba, asimetri informasi, pengungkapan corporate social responsibility dan pengungkapan intellectual capital berpengaruh terhadap cost of equity. Tujuan penelitian: untuk menguji dan menganalisis pengaruh manajemen laba, asimetri informasi, pengungkapan corporate social responsibility dan pengungkapan intellectual capital terhadap cost of equity. Jenis penelitian adalah penjelasan (explanatory), penelitian ini berupaya menjelaskan hubungan antara variabel-variabel dan pengaruhnya dengan pengujian hipotesis. Populasi dalam penelitian ini adalah perusahaan–perusahaan manufaktur sub sektor barang industri konsumsi yang terdaftar di Bursa Efek Indonesia (BEI) tahun 2013-2017. Data kemudian diuji dengan uji asumsi klasik, dan hipotesis diuji dengan analisis regresi berganda; dengan bantuan aplikasi SPSS. Temuan membuktikan bahwa manajemen laba, pengungkapan corporate social responsibility dan pengungkapan intellectual capital berpengaruh terhadap cost of equity. Asimetri informasi tidak berpengaruh terhadap cost of equit, tetapi secara simultan manajemen laba, asimetri informasi, pengungkapan corporate social responsibility dan pengungkapan intellectual capital berpengaruh terhadap cost of equity. Kata Kunci: Manajemen laba, asimetri informasi, pengungkapan corporate social responsibility, pengungkapan intellectual capital, cost of equity
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".