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Record W4385647547 · doi:10.5267/j.ac.2023.6.001

Directors’ compensation and firm performance in pharmaceuticals, chemicals and paper industry of Bangladesh

2023· article· en· W4385647547 on OpenAlexvenueno aff
Sadia Sultana Hoque, M. Sadiqul Islam

Bibliographic record

VenueAccounting · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicIslamic Finance and Banking Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAsset (computer security)Hausman testEnterprise valueBusinessAsset turnoverCompensation (psychology)Fixed effects modelEconometricsValue (mathematics)Panel dataEconomicsIndustrial organizationAccountingReturn on assetsStatisticsFinanceMathematicsComputer science

Abstract

fetched live from OpenAlex

Theories working in the developed world sometimes fail to prove their accuracy in the developing world. So, researchers study these theories based on various countries, on different timelines as the real world is not so straight forward as theories & assumptions. In Bangladesh, very little work has been done regarding the effect of directors’ compensation on firm performance. So, this study has been undertaken to examine the relationship between these two. To test the theory a model comprising the age of the firm, log value of its assets, total asset turnover, firm size, and firm performance was developed. Using a sample of 38 listed firms of DSE from the Pharmaceutical & Chemical and Paper & Printing Industry as per the DSE website fixed-effect model & random effect model was run on the data from 2015 to 2021. And as the Hausman test suggested, the fixed-effect model is chosen to be more fit for BEP (Basic Earning Power). The focus of the study was the impact of directors’ compensation on firm performance. According to the analysis, it showed a negative correlation between directors’ compensation and firm performance. Firm size and asset turnover ratio have moderately positive correlation to the firm’s performance. On the other hand, board independence holds an opposite relation. It was also found that the firm’s age and board size have little to no correlation to the firm’s performance.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.391

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 communication0.0000.001
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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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