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All Journal International Journal of Evaluation and Research in Education (IJERE) Equity
Maisarah Mohamed Saat
Universiti Teknologi Malaysia
Economics, Econometrics & Finance Education

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Skills of future workforce: skills gap based on perspectives from academicians and industry players Noor Nazihah Mohd Noor; Shazaitul Azreen Rodzalan; Nor Hazana Abdullah; Maisarah Mohamed Saat; Aniza Othman; Harcharanjit Singh
International Journal of Evaluation and Research in Education (IJERE) Vol 13, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v13i2.25163

Abstract

Apart from having specific knowledge, graduates are expected to possess a set of soft and hard skills to be employed. This study aims to identify soft and hard skills relevant to the future workforce in the electrical and electronic (E&E) industry based on two perspectives; academicians from public higher education institution (HEI) and E&E industry players. Further, the study aims to investigate skills gaps between two stakeholders. A total of 50 academicians and 31 industry players in Malaysia were surveyed using a structured questionnaire. Statistical analysis was performed using an independent t-test. In terms of soft skills, analytical thinking skills, communication skills, and discipline were more perceived by academicians, whereas decision-making skills, teamwork skills, and discipline were more favored by industry players. For hard skills, both players favored technology use, except for organizational capabilities which were perceived more by academicians while troubleshooting was favored more by industry players. This study contributes to the collaboration between public HEI and the E&E industry to address the skills gaps, which will benefit all stakeholders. This study focuses on the skills that are perceived more by both stakeholders.
Artificial Intelligence and Sustainability Reporting: Performance Outcomes in ESG Investing Lin Oktris; Siti Fathimah Azzahra; Nengzih Nengzih; Nurhafifah Amalina; Maisarah Mohamed Saat
EQUITY Vol 28 No 2 (2025): EQUITY
Publisher : Department of Accounting, Faculty of Economics and Business, Universitas Pembangunan Nasional Veteran Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34209/equ.v28i2.13063

Abstract

The convergence of artificial intelligence and sustainable finance represents a fundamental transformation in investment decision-making, yet empirical evidence concerning effectiveness remains fragmented across diverse research domains. This study synthesises evidence from 43 peer-reviewed investigations spanning 2020 to 2024, examining artificial intelligence applications in environmental, social, and governance investing through systematic meta-analysis following PRISMA 2020 guidelines. Random-effects models demonstrate that artificial intelligence technologies significantly enhance risk-adjusted financial returns (standardised mean difference = 0.58; 95% confidence interval: 0.44-0.72; p<0.001), translating to approximately 5.2 per cent annual performance improvement, and environmental, social, and governance prediction accuracy (standardised mean difference = 0.53; 95% confidence interval: 0.38-0.68; p<0.001), representing 15 per cent error reduction compared with traditional methodologies. Ensemble machine learning demonstrates robust performance (standardised mean difference = 0.64; I²=45 per cent), whilst deep learning exhibits highest effects with substantial variability (standardised mean difference = 0.71; I²=68 per cent). Implementation success depends critically on data quality infrastructure (identified in 88 per cent of studies) and phased deployment strategies (effective in 64 per cent of cases). Moderate evidence certainty supports that artificial intelligence represents genuine capability advancement, though unexplained heterogeneity (I²=58-62 per cent) limits precise outcome prediction in specific contexts. Findings provide evidence-based guidance for investment managers adopting artificial intelligence technologies, policymakers developing regulatory frameworks, and researchers identifying future research priorities. Keywords: artificial intelligence; sustainable finance; ESG investing; meta-analysis; machine learning; investment decision-making; financial technology
Co-Authors Amalina, Nurhafifah Aniza Othman Harcharanjit Singh Lin Oktris Nengzih Nengzih Noor Nazihah Mohd Noor Nor Hazana Abdullah Shazaitul Azreen Rodzalan Siti Fathimah Azzahra