Search results
1 – 10 of 410Delane Deborah Naidu, Kerry McCullough and Faeezah Peerbhai
The purpose of this study is to construct a robust index and subindices to measure the quality of corporate governance for 266 firms listed in South Africa from 2004 to 2021.
Abstract
Purpose
The purpose of this study is to construct a robust index and subindices to measure the quality of corporate governance for 266 firms listed in South Africa from 2004 to 2021.
Design/methodology/approach
Public information on the compliance of King Code of Good Corporate Governance is used to construct a main index predicated on provisions relating to board characteristics, accounting and auditing and risk management. These categories are transformed into three subindices. All constructs are scored with binary coding and equally weighted.
Findings
Cronbach’s alpha test reveals that the index and subindices are highly reliable measures of corporate governance. The principal component analysis supports the construct validity of all measures.
Research limitations/implications
The index is limited to only three corporate governance subcategories and only focuses on South Africa.
Practical implications
These corporate governance indices provide governing authorities, policymakers, investors and other market participants direct information on the quality of corporate governance in South African firms.
Originality/value
As South Africa lacks a formal corporate governance indicator, the development of an appropriate corporate governance index and subindices contributes towards understanding the quality of corporate governance in South African firms. To the best of the authors’ knowledge, this is the first paper to conduct robustness tests on corporate governance indices designed for South African companies.
Details
Keywords
Ahlem Lamine, Ahmed Jeribi and Tarek Fakhfakh
This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021…
Abstract
Purpose
This study analyzes the static and dynamic risk spillover between US/Chinese stock markets, cryptocurrencies and gold using daily data from August 24, 2018, to January 29, 2021. This study provides practical policy implications for investors and portfolio managers.
Design/methodology/approach
The authors use the Diebold and Yilmaz (2012) spillover indices based on the forecast error variance decomposition from vector autoregression framework. This approach allows the authors to examine both return and volatility spillover before and after the COVID-19 pandemic crisis. First, the authors used a static analysis to calculate the return and volatility spillover indices. Second, the authors make a dynamic analysis based on the 30-day moving window spillover index estimation.
Findings
Generally, results show evidence of significant spillovers between markets, particularly during the COVID-19 pandemic. In addition, cryptocurrencies and gold markets are net receivers of risk. This study provides also practical policy implications for investors and portfolio managers. The reached findings suggest that the mix of Bitcoin (or Ethereum), gold and equities could offer diversification opportunities for US and Chinese investors. Gold, Bitcoin and Ethereum can be considered as safe havens or as hedging instruments during the COVID-19 crisis. In contrast, Stablecoins (Tether and TrueUSD) do not offer hedging opportunities for US and Chinese investors.
Originality/value
The paper's empirical contribution lies in examining both return and volatility spillover between the US and Chinese stock market indices, gold and cryptocurrencies before and after the COVID-19 pandemic crisis. This contribution goes a long way in helping investors to identify optimal diversification and hedging strategies during a crisis.
Details
Keywords
Mario Testa, Maddalena Della Volpe, Antonio D’Amato and Adriana Apuzzo
In the era of artificial intelligence, natural language processing (NLP) models are revolutionizing numerous sectors. This research aims to explore the perceived value of them…
Abstract
Purpose
In the era of artificial intelligence, natural language processing (NLP) models are revolutionizing numerous sectors. This research aims to explore the perceived value of them among university students. In particular, it aims to investigate how gender may influence students’ intention to use these models in educational contexts, highlighting potentially significant differences that could inform the implementation and adoption of educational technologies.
Design/methodology/approach
This study investigates the relationship between perceived value and students' intention to adopt NLP models, considering gender as a moderator. The research involves 562 students from the University of Salerno, in Italy, and uses confirmatory factor analysis to evaluate the reliability and validity of the measurement scales. A regression model with robust errors is used to explore the moderating role of gender on the relationship between perceived value and intentions of use of NLP models.
Findings
The results reveal a significant positive association between perceived value and intention to use NLP models, confirming that students with higher perceived value are more likely to adopt these technologies. Furthermore, gender moderates this relationship, indicating that females are less prone to use NLP models than male counterparts.
Originality/value
Research takes on a significant role in the academic field, underlining the importance of adapting teaching practices to the increasingly widespread digitalization. The inclusion of NLP models in university programs emerges as a possible improvement of the learning experience, ensuring cutting-edge education in tune with the needs of the digital society.
Details
Keywords
Andrew Cram, Stephanie Wilson, Matthew Taylor and Craig Mellare
This paper aims to identify and evaluate resolutions to key learning and teaching challenges in very large courses that involve practical mathematics, such as foundational finance.
Abstract
Purpose
This paper aims to identify and evaluate resolutions to key learning and teaching challenges in very large courses that involve practical mathematics, such as foundational finance.
Design/methodology/approach
A design-based research approach is used across three semesters to iteratively identify practical problems within the course and then develop and evaluate resolutions to these problems. Data are collected from both students and teachers and analysed using a mixed-method approach.
Findings
The results indicate that key learning and teaching challenges in large foundational finance courses can be mitigated through appropriate consistency of learning materials; check-your-understanding interactive online content targeting foundational concepts in the early weeks; connection points between students and the coordinator to increase teacher presence; a sustained focus on supporting student achievement within assessments; and signposting relevance of content for the broader program and professional settings. Multiple design iterations using a co-design approach were beneficial to incrementally improve the course and consider multiple perspectives within the design process.
Practical implications
This paper develops a set of design principles to provide guidance to other practitioners who seek to improve their own courses.
Originality/value
The use of design-based research and mixed-method approaches that consider both student and teacher perspectives to examine the design of very large, foundational finance courses is novel.
Details