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Corporate governance practices, barriers and drivers: A survey dataset

The dataset presented in this paper is used to examine the corporate governance (CG) practices and compute the CG index among Pakistan Stock Exchange (PSX) listed firms. Dataset is useful in exploring the different barriers to and drivers of good CG practices. The survey questionnaire was distribute...

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Detalles Bibliográficos
Autores principales: Arslan, Muhammad, Alqatan, Ahmad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721603/
https://www.ncbi.nlm.nih.gov/pubmed/33313368
http://dx.doi.org/10.1016/j.dib.2020.106603
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author Arslan, Muhammad
Alqatan, Ahmad
author_facet Arslan, Muhammad
Alqatan, Ahmad
author_sort Arslan, Muhammad
collection PubMed
description The dataset presented in this paper is used to examine the corporate governance (CG) practices and compute the CG index among Pakistan Stock Exchange (PSX) listed firms. Dataset is useful in exploring the different barriers to and drivers of good CG practices. The survey questionnaire was distributed to 350 respondents from 350 PSX listed firms, nevertheless, 120 respondents returned the questionnaire. After reviewing the filled questionnaires, fifteen questionnaires were found to be incomplete. Therefore, the final sample consisted of 105 respondents from 105 PSX listed firms. The survey dataset was analysed by descriptive statistics, correlation analysis, exploratory factor analysis (EFA), and hierarchical multiple linear regression. SPSS 24 was used to analyse the data. For further findings and interpretation, please refer to the research article entitled “Corporate Governance in Extreme Institutional Environment: Evidence From Emerging Economy” [1]. We also suggest referring to another article that was used to develop the survey questionnaire [2].
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spelling pubmed-77216032020-12-11 Corporate governance practices, barriers and drivers: A survey dataset Arslan, Muhammad Alqatan, Ahmad Data Brief Data Article The dataset presented in this paper is used to examine the corporate governance (CG) practices and compute the CG index among Pakistan Stock Exchange (PSX) listed firms. Dataset is useful in exploring the different barriers to and drivers of good CG practices. The survey questionnaire was distributed to 350 respondents from 350 PSX listed firms, nevertheless, 120 respondents returned the questionnaire. After reviewing the filled questionnaires, fifteen questionnaires were found to be incomplete. Therefore, the final sample consisted of 105 respondents from 105 PSX listed firms. The survey dataset was analysed by descriptive statistics, correlation analysis, exploratory factor analysis (EFA), and hierarchical multiple linear regression. SPSS 24 was used to analyse the data. For further findings and interpretation, please refer to the research article entitled “Corporate Governance in Extreme Institutional Environment: Evidence From Emerging Economy” [1]. We also suggest referring to another article that was used to develop the survey questionnaire [2]. Elsevier 2020-11-29 /pmc/articles/PMC7721603/ /pubmed/33313368 http://dx.doi.org/10.1016/j.dib.2020.106603 Text en © 2020 The Authors http://creativecommons.org/licenses/by/4.0/ This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Arslan, Muhammad
Alqatan, Ahmad
Corporate governance practices, barriers and drivers: A survey dataset
title Corporate governance practices, barriers and drivers: A survey dataset
title_full Corporate governance practices, barriers and drivers: A survey dataset
title_fullStr Corporate governance practices, barriers and drivers: A survey dataset
title_full_unstemmed Corporate governance practices, barriers and drivers: A survey dataset
title_short Corporate governance practices, barriers and drivers: A survey dataset
title_sort corporate governance practices, barriers and drivers: a survey dataset
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7721603/
https://www.ncbi.nlm.nih.gov/pubmed/33313368
http://dx.doi.org/10.1016/j.dib.2020.106603
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