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The purpose of this study is to examine within a unified framework the timeliness and conservatism of accounting disclosure accommodating the transmission of news among the Scandinavian stock markets.

To this end the authors have used an augmented ordinary least squares (OLS) approach and univariate generalized autoregressive conditional heteroskedastic and vector autoregressive (VAR) modeling. The sample covers the period from 1987 to 2020, totaling 1452 observations. The sample was collected from the datastream database.

The empirical results of this study are consistent with previous findings and provide evidence that accounting reporting is timely and conservative while news is transmitted amongst the Scandinavian stock markets.

The findings could be important for investors, firms and regulators since failure of considering information that is derived from more advanced approaches could result in lower quality of annual reports of companies.

The authors examined the relationship between earnings yield and conditional risk using an augmented OLS model and the transmission of news among Scandinavian stock markets using a VAR model.

This paper aims to examine the impact of the recent financial crisis on audit quality by analysing discretionary accruals.

This study considers a sample of German, French, Italian and Spanish non-financial firms from 2005 to 2013 to investigate the auditor’s independence. It uses a cross-sectional and time-series ordinary least squares regression model to control for other predictors of the auditor’s independence when the financial crisis produces a decrease in audit quality.

The proportion of the non-financial firms having lower audit quality was higher during the financial crisis. In addition, during the crisis auditors were less likely to provide a higher audit quality for these non-financial firms. The level of audit quality returned to normal levels during the post-crisis years when the crisis had ceased.

These findings contribute to the literature on the impact of economic and financial changes on audit quality. In addition, this research finds that the Big Four accounting firms provide a higher audit quality in different circumstances from non-Big Four accounting firms, and that audit quality decreased during the crisis and returned to normal in the post-crisis period.

The purpose of this paper is to examine the hypothesis of feedback trading along with the short-term return dynamics of three size-based stock portfolios of Athens Stock Exchange during the Greek debt crisis period.

To this end, the authors employ for the first time in the literature two well-known models while the variance equation is modeled by means of a multivariate EGARCH specification. As a robustness test an innovative nested-EGARCH model is also employed.

The assumption that positive feedback trading is an important component of the short-term return movements across the three stock portfolios receives significant support. Moreover, the volatility interdependence, both in magnitude and sign, is almost similar across the three models. Finally, bad news originating from the portfolio of small stock appears to have a higher impact on the volatility of large and medium size stock returns than good news during the Greek debt crisis period.

The methodology is innovative and the authors test for the first time the feedback trading hypothesis across different size stocks. The authors believe that the results might entail significant policy implications for investors and market regulators.

Gender competence as part of engineering education can better prepare men and women to work on sustainable solutions that benefit entire societies. This chapter describes the framework and lessons learned of a community of practice (CoP) for gender equality facilitated by the Mediterranean Engineering Schools Network. Faculty and students from Mediterranean European, North African and Middle Eastern countries came together in this CoP, which was supported by the TARGET project, to develop a practical plan using a reflexive approach. The transfer of knowledge between generations is achieved by using participatory learning processes, facilitating mindful awareness, widening experiences, deepening understandings and building a gender-sensitive mindset. Students embarked on the journey to become change agents. The process led to the consolidation of gender equality knowledge, competence building and the development of change agents for gender equality. This CoP can inspire other institutions to undertake a participatory path towards gender equality – at local, regional, or global level.

The number of candidates applying to public contests (PC) is increasing compared to the number of human resources employees required for selecting them for the Police Force (PF). This work intends to perceive how those public institutions can evaluate and select their candidates efficiently during the different phases of the recruitment process. To achieve this purpose, artificial intelligence (AI) was studied. This paper aims to focus on analysing the AI technologies most used and appropriate to the PF as a complementary recruitment strategy of the National Criminal Investigation police agency of Portugal – Polícia Judiciária.

Using design science research as a methodological approach, the authors suggest a theoretical framework in pair with the segmentation of the candidates and comprehend the most important facts facing public institutions regarding the usage of AI technologies to make decisions about evaluating and selecting candidates. Following the preferred reporting items for systematic reviews and meta-analyses methodology guidelines, a systematic literature review and meta-analyses method was adopted to identify how the usage and exploitation of transparent AI positively impact the recruitment process of a public institution, resulting in an analysis of 34 papers between 2017 and 2021.

Results suggest that the conceptual pairing of evaluation and selection problems of candidates who apply to PC with applicable AI technology such as K-means, hierarchical clustering, artificial neural network and convolutional neural network algorithms can support the recruitment process and could help reduce the workload in the entire process while maintaining the standard of responsibility. The combination of AI and human decision-making is a fair, objective and unbiased process emphasising a decision-making process free of nepotism and favouritism when carefully developed. Innovative and modern as a category, group the statements that emphasise the innovative and contemporary nature of the process.

There are two main limitations in this study that should be considered. Firstly, the difficulty regarding the timetable, privacy and legal issues associated with public institutions. Secondly, a small group of experts served as the validation group for the new framework. Individual semi-structured interviews were conducted to alleviate this constraint. They provide additional insights into an interviewee’s opinions and beliefs.

Ensure that the system is fair, transparent and facilitates their application process.

The main contribution is the AI-based theoretical framework, applicable within the analysis of literature papers, focusing on the problem of how the institutions can gain insights about their candidates while profiling them, how to obtain more accurate information from the interview phase and how to reach a more rigorous assessment of their emotional intelligence providing a better alignment of moral values. This work aims to improve the decision-making process of a PF institution recruiter by turning it into a more automated and evidence-based decision when recruiting an adequate candidate for the job vacancy.

This study aims to examine the role of institutional investors in improving board diversity for the companies in which they invest (investee companies) using evidence from corporate board characteristics across the globe. Additionally, this study also investigates the association between institutional investors and board diversity under various institutional settings, including varying economic conditions (pre-crisis, crisis and post-crisis), legal systems and ownership structures.

Using a sample collected from 15 countries for the period 2006 to 2012, the paper uses panel data analysis to examine the association between institutional investors and board diversity.

The study provides evidence that institutional investors do not promote board diversity and show that in general there is no association between institutional ownership and various board diversity attributes such as gender, age, nationality and education. However, the study finds that institutional investors are positively associated with the educational diversity of boards during times of crisis and are negatively associated with board age diversity during pre-crisis and post-crisis periods. Furthermore, while in common law countries institutional investors are found to be negatively associated with board age diversity, they do not influence board diversity outcomes (i.e. gender, age, nationality and education) in civil law countries. The results also show that the associations between institutional investors and board diversity are mixed and insignificant according to different ownership structures (family and non-family owned firms). The main findings of the study are robust and apply to various estimation methods.

This study provides a unique perspective on the impact of institutional investors on board diversity using a sample collected from 15 countries. Furthermore, the study provides an insight that the institutional settings should be considered when investigating the activism of institutional investors in improving governance practices.

Increased fraudulent practices have heightened the need for innovation in anti-fraud programs, necessitating the development of analytics techniques for detecting and preventing fraud. The subject of fraud analytics will continue to expand in the future for public-sector organizations; therefore, this research examined the progress of fraud analytics in public-sector transactions and offers suggestions for its future development.

This study systematically reviewed research on fraud analytics development in public-sector transactions. The review was conducted from June 2021 to June 2023 by identifying research objectives and questions, performing literature quality assessment and extraction, data synthesis and research reporting. The research mainly identified 43 relevant articles that were used as references.

This research examined fraud analytics development related to public-sector financial transactions. The results revealed that fraud analytics expansion has not spread equally, as most programs have been implemented by governments and healthcare organizations in developed countries. This research also exposed that the analytics optimization in fraud prevention is higher than for fraud detection. Such analytics help organizations detect fraud, improve business effectiveness and efficiency, and refine administrative systems and work standards.

This research offers comprehensive insights for researchers and public-sector professionals regarding current fraud analytics development in public-sector financial transactions and future trends.

This study presents the first systematic literature review to investigate the development of fraud analytics in public-sector transactions. The findings can aid scholars' and practitioners' future fraud analytics development.