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Record W3133248066 · doi:10.5267/j.ac.2021.2.004

The effect of COVID-19 in European union on the performance of Indonesian publicly listed palm oil companies

2021· article· en· W3133248066 on OpenAlexvenueno aff
Hansen Tandra, Arif Imam Suroso, Mukhamad Najib, Yusman Syaukat

Bibliographic record

VenueAccounting · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicOil Palm Production and Sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsEuropean unionCertificationBusinessPalm oilIndonesianStock exchangePanel dataStock (firearms)SustainabilityAgricultural economicsInternational tradeFinanceEconomicsAgricultural scienceGeography

Abstract

fetched live from OpenAlex

One of the leading industries that affect economic growth in Indonesia is the palm oil industry. The role of this industry depends on the level of exports from European Union countries. Based on the COVID-19 pandemic situation, international trade activities are hampered and could affect industry performance from a stock perspective. Therefore, this study aims to explore the impact of the COVID-19 cases that occurred in the European Union and related macroeconomic variables on the stock performance of the oil palm industry in Indonesia. This research also examines the impact of COVID-19 on certified sustainable companies and companies that are not certified. Panel regression was applied in this study with Eviews 11 Software.This research's observations are 13 palm oil companies in Indonesia which are listed on the Indonesia Stock Exchange (IDX) from March 2, 2020, to August 31, 2020. This study's results reveal that the world CPO prices and market capitalization affect the activities shares of palm oil companies in Indonesia.Meanwhile, from the grouping of certifications within companies, the impact of COVID-19 in the European Union was more substantial on companies that were certified as sustainable. Based on these results, The COVID-19 case in the European Union must be a concern for palm oil companies in Indonesia.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score0.173

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
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 head0.009
GPT teacher head0.233
Teacher spread0.224 · 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 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

Citations11
Published2021
Admission routes1
Has abstractyes

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