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Record W3199063268 · doi:10.46806/ja.v9i2.762

PENGARUH LEVEL DIVERSIFIKASI, JUMLAH SEGMEN, DAN JENIS SEKTOR INDUSTRI TERHADAP KINERJA PERUSAHAAN PADA PERUSAHAAN MANUFAKTUR YANG TERDAFTAR DI BURSA EFEK INDONESIA TAHUN 2016 – 2018

2020· article· en· W3199063268 on OpenAlex
Felicia Felicia, Rizka Indri Arfianti

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJurnal Akuntansi · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinancial Analysis and Corporate Governance
Canadian institutionsnot available
Fundersnot available
KeywordsDiversification (marketing strategy)BusinessBusiness administrationStock exchangeCompetitor analysisVariablesSecondary sector of the economyEconomicsEconomyMarketingFinanceMathematicsStatistics

Abstract

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The high competition in the business world with many competitors has forced the company to develop its business, one of them by diversification. This study aims to investigate the influence of diversification level, segment numbers, and industrial sector types on firm performance measured by the excess value. The theory underlying this research is agency theory, which describes the relationship between the company owner with the company management, The number of samples of this study are 333 companies from miscellaneous industry sector, and consumer goods sector, and basic industry and chemicals sector listed on the Indonesia Stock Exchange throughout 2017 – 2018. The results showed that data can be pooled for 3 years, all classic assumption tests are fulfilled, and partial regression coefficient test found that variable diversification level and variable number of segments > 0.05, then Ho1 and Ho2 rejected, while type of miscellaneous industry sector and Type of consumer goods sector <0.05, then Ho3 and Ho4 received. The conclusion showed that variable diversification level and variable number of segments has not sufficient evidence of negative effect on excess value, while type of miscellaneous industry sector and Type of consumer goods sector sufficient evidence of negative effect on excess value.
 Keywords: Firm Performance, Diversification Level, Number of Segments, and Industrial Sector.
 
 References:
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 Berger, P. G., & Eli, O. (1995), “Diversification’s Effect on Firm Value”, Journal of Financial Economics, Vol.37, pp.39–65.
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 Chandra, D., & Triyani, Y. (2015), “Pengaruh Level Diversifikasi, Leverage, Return On Asset, Umur Perusahaan, Dan Sektor Industri Terhadap Nilai Perusahaan Yang Terdaftar Di BEI Periode 2009-2011”, Jurnal Akuntansi Manajemen, Vol.4, no.2, pp.66–84.
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 Lucyanda, J., & Wardhani, R. H. K. (2017), “Pengaruh Diversifikasi Dan Karakteristik Perusahaan Terhadap Kinerja Perusahaan”, Jurnal Riset Akuntansi dan Keuangan.
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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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.214
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.040
GPT teacher head0.198
Teacher spread0.159 · 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