TY - GEN
T1 - Measuring Business Model Disclosure Quality in Integrated Reports Using NLP Techniques
AU - Sukhari, Aneetha
AU - Ade-Ibijola, Abejide
AU - Coetsee, Daniël
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Corporate disclosure has significantly evolved in recent years, providing stakeholders with a wealth of information in diverse formats. This information is unstructured data, which cannot be easily retrieved and analysed. Consequently, there is a need to transform information in corporate reports into structured data to facilitate in-depth analysis by users of corporate reports. As a starting point to analysing unstructured data in corporate disclosure, we selected the business model aspect of integrated reports. This paper investigates the quality of business model disclosure in 370 integrated reports of JSE-listed companies and compares the evolution of business model disclosure quality over a period of five years. In doing so, we designed a multi-dimensional disclosure index to measure business model disclosure quality based on dimensions such as quantity, dispersion, coverage, and depth. The content analysis was automated by incorporating the multi-dimensional disclosure index into a newly designed text analysis software tool, called Business Model Analysis Tool (BMAT). BMAT uses algorithms based on natural language processing (NLP) techniques. The results from BMAT depict an increase in dispersion, coverage, and depth of disclosure of all components of business models, indicating an enhancement in the quality of business model disclosure. An overall adherence to the International Integrated Reporting Framework was noted.
AB - Corporate disclosure has significantly evolved in recent years, providing stakeholders with a wealth of information in diverse formats. This information is unstructured data, which cannot be easily retrieved and analysed. Consequently, there is a need to transform information in corporate reports into structured data to facilitate in-depth analysis by users of corporate reports. As a starting point to analysing unstructured data in corporate disclosure, we selected the business model aspect of integrated reports. This paper investigates the quality of business model disclosure in 370 integrated reports of JSE-listed companies and compares the evolution of business model disclosure quality over a period of five years. In doing so, we designed a multi-dimensional disclosure index to measure business model disclosure quality based on dimensions such as quantity, dispersion, coverage, and depth. The content analysis was automated by incorporating the multi-dimensional disclosure index into a newly designed text analysis software tool, called Business Model Analysis Tool (BMAT). BMAT uses algorithms based on natural language processing (NLP) techniques. The results from BMAT depict an increase in dispersion, coverage, and depth of disclosure of all components of business models, indicating an enhancement in the quality of business model disclosure. An overall adherence to the International Integrated Reporting Framework was noted.
KW - algorithms
KW - business model disclosure
KW - content analysis
KW - Integrated reporting
KW - NLP techniques
UR - http://www.scopus.com/inward/record.url?scp=85202299901&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-68617-7_24
DO - 10.1007/978-3-031-68617-7_24
M3 - Conference contribution
AN - SCOPUS:85202299901
SN - 9783031686160
T3 - Communications in Computer and Information Science
SP - 324
EP - 343
BT - Artificial Intelligence and Knowledge Processing - 3rd International Conference, AIKP 2023, Revised Selected Papers
A2 - K, Hemachandran
A2 - Rodriguez, Raul Villamarin
A2 - Rege, Manjeet
A2 - Piuri, Vincenzo
A2 - Xu, Guandong
A2 - Ong, Kok-Leong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd International Conference on Artificial Intelligence and Knowledge Processing, AIKP 2023
Y2 - 6 October 2023 through 8 October 2023
ER -