The irrelevance of R&D intensity in the ESG disclosure? Insights from top 10 listed companies on global Islamic indices

This study investigates the potential influence of several pertinent factors including R&D intensity, directors' education, and firm size towards ESG disclosure. This study utilised samples from top 10 companies listed in 6 (six) different Global Islamic Indices with a three-year observation period (2017–2019) resulting in 99 observations. Global Islamic listed companies have rarely been studied in ESG-related issues. The pre-COVID-19 pandemic period was chosen to avoid the potential effects of pandemic on the subject of this study. Multiple linear regression analysis was employed to test the hypotheses. It was found that all the independent variables simultaneously influence the ESG disclosure, while partially directors' education are influencing the variable, and both R&D intensity and firm size do not influence the ESG disclosure. Confirming the agency theory, it is argued that the board characteristics are important in predicting overall board performance in carrying out their monitoring responsibilities, in this case, monitoring and encouraging companies to disclose more ESG information in their sustainability reports. This study signifies the role of directors even within the Islamic listed companies that the more highly educated the members of the board, they will tend to disclose more ESG information on their sustainability reports. This study contributes to the existing ESG disclosure and sharia-based investing literature by utilizing global-based indices instead of local indices in Muslim-majority countries, mirroring the current uptrend in world-wide sharia investing and the call for companies to be more sustainable in doing their business. © 2023 The Author(s). This open access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license.

Machine learning prediction of video-based learning with technology acceptance model

COVID-19 outbreak has significant impacts on education system as almost all countries shift to new way of teaching and learning;online learning. In this new environment, various innovative teaching methods have been created to deliver educational material in ensuring the learning outcomes such as video content. Thus, this research aims to implement machine learning prediction models for video-based learning in higher education institutions. Using survey data from 103 final year accounting students at Malaysian public university, this paper presents the fundamental frameworks of evaluating three machine learning models namely generalized linear model, random forest and decision tree. Besides demography attributes, the performance of each machine learning algorithm on the video-based learning usage has been observed based on the attributes of technology acceptance model namely perceived ease of use, perceived usefulness and attitude. The findings revealed that the perceived ease of use has given the highest weight of contributions to the generalized linear model and random forest while the major effects in decision tree has been given by the attitude variable. However, generalized linear model outperformed the two algorithms in term of the prediction accuracy. © 2023 Institute of Advanced Engineering and Science. All rights reserved.