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This study aims to examine the effects of religiosity, ethical leadership and local wisdom on the relationship between fraud risk management and asset misappropriation in Indonesia.

Data were collected using a set of questionnaire surveys administered to the head office, local government internal auditors (inspectorate) and local government employees in Indonesia. Sample selection used purposive techniques and obtained 151 respondents who became research data. The dependent variable was asset misappropriation. The independent variable was fraud risk management. The moderating variables for this study were religiosity, leader ethics and local wisdom. The analysis technique applied the structural equation model-partial least square (SEM-PLS).

Fraud risk management has a significant negative effect on asset misappropriation. In addition, this study finds evidence that religiosity, ethical leadership and local wisdom increase fraud risk management against asset misappropriation.

This study proposes an integrative model that enables local governments to understand fraud risk management. By integrating religiosity, ethical leadership and local wisdom, managers can design strategies to prevent asset misappropriation.

This research has the advantage of proposing an integrative model for mitigating asset misappropriation. Research on asset misappropriation is limited. Therefore, this study provides insights into fraud risk management, particularly in Indonesia’s local governments. In addition, this study adds ethical aspects such as religiosity, leadership and local wisdom to complement the weaknesses of fraud risk management and reduce the potential for asset misappropriation.

The purpose of this paper is to provide a conceptual discussion and analysis of the Covid-19 impact on financial crime and regulatory compliance. The analysis is conducted to make a comparison of the financial crime and regulatory compliance patterns before and after the Covid-19 pandemic occurred.

This paper contextualises the impact of Covid-19 on financial crime and regulatory compliance. Moreover, this paper explores different ways of conceptualising the Covid-19 impacts in terms of financial crimes and regulatory compliance patterns based on the surveys by PricewaterhouseCoopers and Deloitte.

The Covid-19 pandemic has brought both challenges and opportunities to financial crime and regulatory compliance. In the aspects of financial crime patterns, this study found a reduction in physical crime whilst on the other hand increment in cybercrime. Nevertheless, this study discovered regulatory compliance not at a satisfactory stage even before the Covid-19 pandemic, let alone during the pandemic.

This study implies that the financial institutions must work together to combat the risks of financial crimes, not only amongst the institutions but also with the regulators. Digitalisation and robust risk management need to be improved at a massive level to beat the criminals’ high fintech skills and systems. The initiatives of fund packages from the governments to assist the companies especially the small firms need to be fully used by the companies to improve regulatory compliance.

Whilst some studies discussed the impact of Covid-19 on the economy, there are still scarce resources on the comparative analysis on the financial crime and regulatory compliance, not to mention the before and after effect of the Covid-19 pandemic. This is the first paper to integrate the issues surrounding the Covid-19 impact, financial crimes and regulatory compliance in Malaysia.

The purpose of this paper is to examine the effects of self-efficacy, goal orientation and task complexity on audit judgement performance in correctly linking audit procedures to audit objectives and types of misstatements.

The authors conducted an experiment audit with 154 auditors from small and medium audit firms in Malaysia as participants. The experimental task required them to link audit procedures to audit objectives and types of misstatements.

For sample of auditors from small and medium audit firms in Malaysia, the authors found that learning goal orientation has a stronger effect on audit judgement performance than performance-approach and performance-avoidance goal orientations. Self-efficacy mediates the effect of goal orientation when an audit task is less complex compared to when the task is more complex.

These results highlight the importance of social cognitive factors in explaining variations in audit judgement performance for audit judgement tasks with different levels of complexity.

The incorporation of individual psychological differences as explanatory variables in audit judgement studies may lead to a better understanding of auditors’ judgement and decision-making processes in small and medium audit firms located in developing economies.

The purpose of this paper is to examine the relationship between political connections and corporate tax aggressiveness in Malaysia. In addition, this paper investigates the relationship between corporate governance variables and corporate tax aggressiveness. Next, the study investigates the mitigating role of corporate governance in the relationship between political connections and corporate tax aggressiveness.

The sample of this study is based on 2,538 firm-year observations during the 2000-2009 periods. This study employs a panel least square regression with both period and industry fixed effects. The study retrieved the corporate governance variables from the downloaded annual reports, whilst the remaining data were collected from Compustat Global.

This study finds that politically connected firms are more tax aggressive than non-connected firms. Furthermore, the study finds that large board size decreases the likelihood of tax aggressiveness and a non-linear relationship exists between institutional ownership and tax aggressiveness suggesting increase in monitoring as the ownership increases. However, the study finds no evidence to suggest that corporate governance mitigates the influence of political connections in promoting tax aggressiveness behavior. The findings suggest that the impact of political connections could outweigh the benefits of changes in corporate governance in Malaysia.

The data are not recent, but it reflects a rather longitudinal research period.

This paper extends the literature of tax research in Malaysia which is in its’ infancy stage. Furthermore, it investigates the role of political connections in tax-planning research.

This paper aims to provide an empirical analysis of the effects of regulatory enforcement and customer risk determinants on money laundering risk judgment. The study further explores the moderating impact of regulatory enforcement on compliance officers in the banking and money service business (MSB) sectors. The analysis is conducted to find the important factors that contribute to the issues of risk judgement among compliance officers to establish effective anti-money laundering (AML) and countering financing of terrorism compliance at the financial institutions, as highlighted in the National Risk Assessment Report 2017 by the Central Bank of Malaysia.

An experimental study with four different scenarios of case studies distributed to 124 compliance officers at the banking and MSB sectors was conducted via online platforms. The paper uses a quantitative approach via structural equation modelling.

The result shows a significant effect of customer risk determinants and regulatory enforcement on money laundering risk judgement, taking into account competency as the control measure. A further test on the interaction effects of both determinants shows a significant result on the money laundering risk judgement. The empirical evidence indicated that regulatory enforcement influenced compliance officers’ money laundering risk judgement and suspicious transaction report submission. In other words, the banking and MSB sectors’ AML compliance significantly depends on the regulators’ enforcement activity.

This study is limited to two independent variables: regulatory enforcement and customer risk determinants. Future studies may consider other factors affecting compliance officers’ money laundering risk judgement, such as technical competency, knowledge management, digitalization and technology and ethical issues.

This study provides several theoretical and practical implications. Emphasizing the excellent quality of judgement and, eventually, good quality of reporting the suspicious transactions will not be achieved merely from enforcing fines and punishment, but comprehensive measures must be taken. Increasing the competency and training, educating the compliance officers, supporting the industry and practitioners with incentives and digitalization, enhancing the campaign and awareness among the public and standardizing the policy shall be the good initiatives for the regulatory enforcement to establish.

This paper provides a valuable contribution to the body of knowledge and fulfills the significant gaps in the literature on money laundering, not to mention, the integration between behavioural studies and anti-money laundering compliance, which has scarcely been statistically evident from the research studies.

This study aims to examine the three dimensions of market orientation, namely, customer orientation, competitor orientation and inter-function coordination, which influence the accountability in the financial and social performance of tourism operators in large touristic cities.

In total, 95 usable questionnaires as the required data were collected from the top managers of four- and five-star hotels in Iran.

Partial least squares (PLS) results confirm that customer orientation and inter-function coordination influence both the financial and social performance of the hospitality sector yet reveal that competitor orientation has no significant relationship with social performance.

These findings not only highlight the compatibility of PLS with various forms of statistical analyzes but also furthers the current understanding of hospitality networks in megacity economies, where literature are scarce.

The findings of this study can help policymakers, tourism associations and practitioners enhance the accountability and sustainable financial and social performance of the hospitality industry in megacities. This study proposes some unique measurements for the social and financial performance of the hospitality sectors.

The paper states some new measurements for the social performance of the hospitality sectors. In addition, measuring the impacts of market orientation on the financial and social aspects of hotels is totally unique.

The purpose of this study is to examine the mediating effect of effort on the relationship between both accountability pressure and self‐efficacy, and auditors' performance.

The paper uses a between‐subjects experimental research design with accountability pressure manipulated randomly to two groups, accountable and non‐accountable. Each participant is required to perform internal control tasks.

Based on partial least square (PLS) analysis, results indicate that both variables, i.e. accountability pressure and self‐efficacy, are positively related to audit judgment performance through the process of high level of effort. High self‐efficacious participants who received accountability pressure would have high levels of effort, which in turn increase audit judgment performance.

This study provides further evidence on the effect of motivational factors on auditors' performance. Understanding the mediating role of some motivational variables is crucial in designing a continuous development program to enhance auditors' performance. The proposed framework of effort as a mediating variable is consistent with Libby and Lipe and Chang et al., who argue that accountability pressure and self‐efficacy would cause individuals to increase their effort in order to perform better.

The paper contributes to the literature on motivational factors that would explain the variability in audit judgments in coping with complex audit tasks.

This study examines the carbon emissions of Australia’s agriculture, forestry and fishing sectors from a consumption perspective to develop effective policy frameworks. The objective is to identify key supply chains, industries and products contributing to these emissions and provide recommendations for sustainable development.

A multiregional input-output lifecycle assessment was conducted using the Australian Industrial Ecology Virtual Laboratory (IELab) platform to disaggregate sectors and enable benchmarking against other economic sectors.

In 2018, the “agriculture, forestry, and fishing” sector was responsible for 12.15% of Australia’s carbon footprint. Major contributors included the “electricity, gas, water, and waste” category (26.1%) and the sector’s activities (24.3%). The “transport, postal, and warehousing” sector also contributed 18.4%. Within the industry, the agriculture subsector had the highest impact (71.3%), followed by forestry and logging (15%) and fishing, hunting and trapping (7.6%). Aquaculture and supporting services contributed 6.1%.

The principal constraint encountered by the present study pertained to the availability of up-to-date data. The latest accessible data for quantifying the carbon footprint within Australia’s agriculture, forestry and fishing sector, utilizing the Input-Output analysis methodology through the Australian Industrial Ecology Virtual Laboratory (IELab) platform, about 2018.

The findings of this study provide policymakers with detailed insights into the carbon footprints of key sectors, highlighting the contributions from each subsector. This information can be directly used to develop effective emission-reduction policies, with a focus on reducing emissions in utility services, transport and warehousing.

The study, by raising public awareness of the significant role of industrial agricultural methods in Australia’s carbon footprint and emphasizing the importance of renewable energy and sustainable fuels for electricity generation and road transport, underscores the urgent need for action to mitigate climate change.

This study stands out by not only identifying the most impactful industries but also by providing specific strategies to reduce their emissions. It offers a comprehensive breakdown of specific agricultural activities and outlines mitigation strategies for utility services, agricultural operations and transport, thereby adding a unique perspective to the existing knowledge.

This paper investigates the relationship between risk management practices and potential fraudulent financial reporting in Malaysia by considering recent regulatory reforms of the Malaysian government on risk management practices.

The sample of this study was based on 257 firm-year observations during the 2012–2017 period. This study employed panel-least square regressions with period fixed effects.

This study found a significant association between risk management activities in the disclosure and potential fraudulent financial reporting. Nevertheless, this study found there is insignificant effect of the risk-management committee in reducing potential of fraudulent financial reporting.

This study is a pioneer research that relates firms’ risk management practices with potential fraudulent financial reporting measured by F-score. Thus, this study provides an insight to regulators on the extent of risk-management practices in deterring potential fraudulent financial reporting which can be used as an input for greater enforcement of risk-management regulations.

In the previous study of financial distress prediction, deep learning techniques performed better than traditional techniques over time-series data. This study investigates the performance of deep learning models: recurrent neural network, long short-term memory and gated recurrent unit for the financial distress prediction among the Malaysian public listed corporation over the time-series data. This study also compares the performance of logistic regression, support vector machine, neural network, decision tree and the deep learning models on single-year data.

The data used are the financial data of public listed companies that been classified as PN17 status (distress) and non-PN17 (not distress) in Malaysia. This study was conducted using machine learning library of Python programming language.

The findings indicate that all deep learning models used for this study achieved 90% accuracy and above with long short-term memory (LSTM) and gated recurrent unit (GRU) getting 93% accuracy. In addition, deep learning models consistently have good performance compared to the other models over single-year data. The results show LSTM and GRU getting 90% and recurrent neural network (RNN) 88% accuracy. The results also show that LSTM and GRU get better precision and recall compared to RNN. The findings of this study show that the deep learning approach will lead to better performance in financial distress prediction studies. To be added, time-series data should be highlighted in any financial distress prediction studies since it has a big impact on credit risk assessment.

The first limitation of this study is the hyperparameter tuning only applied for deep learning models. Secondly, the time-series data are only used for deep learning models since the other models optimally fit on single-year data.

This study proposes recommendations that deep learning is a new approach that will lead to better performance in financial distress prediction studies. Besides that, time-series data should be highlighted in any financial distress prediction studies since the data have a big impact on the assessment of credit risk.

To the best of authors' knowledge, this article is the first study that uses the gated recurrent unit in financial distress prediction studies based on time-series data for Malaysian public listed companies. The findings of this study can help financial institutions/investors to find a better and accurate approach for credit risk assessment.