Big Data Analytics of Corporate Internet Disclosures
Research output: Contribution to journal › Article › peer-review
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In: Accounting Research Journal, Vol. 35, No. 1, 08.02.2022, p. 4-20.
Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Big Data Analytics of Corporate Internet Disclosures
AU - Basuony, Mohamed
AU - Mohamed, Ehab
AU - Elragal, Ahmed
AU - Hussainey, Khaled
PY - 2022/2/8
Y1 - 2022/2/8
N2 - PurposeThis study aims to investigate the extent and characteristics of corporate internet disclosure via companies’ websites as well via social media and networks sites in the four leading English-speaking stock markets, namely, Australia, Canada, the UK and the USA.Design/methodology/approachA disclosure index comprising a set of items that encompasses two facets of online disclosure, namely, company websites and social media sites, is used. This paper adopts a data science approach to investigate corporate internet disclosure practices among top listed firms in Australia, Canada, the UK and the USA.FindingsThe results reveal the underlying relations between the determining factors of corporate disclosure, i.e. profitability, leverage, liquidity and firm size. Profitability in its own has no great effect on the degree of corporate internet disclosure whether via company websites or social media sites. Liquidity has an impact on the degree of disclosure. Firm size and leverage appear to be the most important factors driving better disclosure via social media. American companies tend to be on the cutting edge of technology when it comes to corporate disclosure.Practical implicationsThis paper provides new insights into corporate internet disclosure that will benefit all stakeholders with an interest in corporate reporting. Social media is an influential means of communication that can enable corporate office to get instant feedback enhancing their decision-making process.Originality/valueTo the best of the authors’ knowledge, this study is amongst few studies of corporate disclosure via social media platforms. This study has adopted disclosure index incorporating social media as well as applying data science approach in disclosure in an attempt to unfold how accounting could benefit from data science techniques.
AB - PurposeThis study aims to investigate the extent and characteristics of corporate internet disclosure via companies’ websites as well via social media and networks sites in the four leading English-speaking stock markets, namely, Australia, Canada, the UK and the USA.Design/methodology/approachA disclosure index comprising a set of items that encompasses two facets of online disclosure, namely, company websites and social media sites, is used. This paper adopts a data science approach to investigate corporate internet disclosure practices among top listed firms in Australia, Canada, the UK and the USA.FindingsThe results reveal the underlying relations between the determining factors of corporate disclosure, i.e. profitability, leverage, liquidity and firm size. Profitability in its own has no great effect on the degree of corporate internet disclosure whether via company websites or social media sites. Liquidity has an impact on the degree of disclosure. Firm size and leverage appear to be the most important factors driving better disclosure via social media. American companies tend to be on the cutting edge of technology when it comes to corporate disclosure.Practical implicationsThis paper provides new insights into corporate internet disclosure that will benefit all stakeholders with an interest in corporate reporting. Social media is an influential means of communication that can enable corporate office to get instant feedback enhancing their decision-making process.Originality/valueTo the best of the authors’ knowledge, this study is amongst few studies of corporate disclosure via social media platforms. This study has adopted disclosure index incorporating social media as well as applying data science approach in disclosure in an attempt to unfold how accounting could benefit from data science techniques.
U2 - 10.1108/ARJ-09-2019-0165
DO - 10.1108/ARJ-09-2019-0165
M3 - Article
VL - 35
SP - 4
EP - 20
JO - Accounting Research Journal
JF - Accounting Research Journal
SN - 1030-9616
IS - 1
ER -