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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.

Are financially illiterate individuals all the same? This study aims to answer this question. Specifically, the authors investigate whether people answering incorrectly and “do not know” to the big five questions about financial knowledge (FK), all identified by previous literature as financially illiterate, are two sides of the same coin, or rather individuals with different socio-economic and demographic characteristics, and whether this leads to different levels of risk of falling victim to financial fraud.

Using a large and representative sample of Italian adults, the authors run both ordered probit and probit regressions to test the determinants of financially illiterate individuals, distinguishing between those answering FK questions incorrectly and those answering “do not know”. The authors also measure the probability of falling victim to financial fraud for the two groups. To check the robustness of our results, the authors run a multinomial regression, a structural equation model and an instrumental variable regression model.

The authors demonstrate that the socio-demographic and socio-economic characteristics of individuals selecting incorrect responses to FK questions are different from those of individuals selecting the “do not know” option. Moreover, the results show that the former are more likely to be victims of financial frauds.

The “one-size-fits-all” approach is not suitable for financial education. It is important to consider socio-demographic and socio-economic characteristics of individuals in order to identify specific targets of education programmes aiming to reduce insecurity and excessive self-confidence as well as to increase objective FK. The study’s findings also identify vulnerable groups to which financial fraud prevention schemes should be targeted.

To date, financial illiteracy has been measured as the sum of incorrect and “do not know” responses given to FK questions. This approach does not allow to observe the socio-demographic and socio-economic differences between people choosing the “do not know” option and those answering incorrectly. The paper aims to overcome this limit by investigating the socio-demographic and socio-economic characteristics of individuals selecting “do not know” and incorrect responses, respectively. The authors also investigate whether the two groups have different probabilities of being victims of financial fraud.

This study aims to examine the most influential drivers, both product-specific and consumer-specific, affecting Asian consumers' willingness to pay (WTP) for organic olive oil.

To individuate the most influential drivers of WTP for organic products and to assess their effect, in terms of configurational paths and consumer profiles, this study sequentially employs explorative factor analysis approach and a fuzzy-set qualitative comparative analysis method. The survey is carried out in different areas of Asia (e.g. Pakistan, Vietnam and China).

The results suggest that Asian consumers' WTP for organic products is described by consumer-specific drivers (gender, occupation and household size) as well as product-specific drivers (product authenticity and sustainability, consumer ethnocentrism and food fraud risk perception).

The findings of the study permit the identification of different drivers that move consumers' WTP for organic olive oil. The study contributes to setting the ground for companies to propose and implement efficacious marketing strategies for organic olive oil in importing countries, such as Asia.

Benford's Law is an empirical observation about the frequency of digits in a variety of naturally occurring data sets. Auditors and forensic scientists have used Benford's Law to detect erroneous data in accounting and legal usage. One well-known limitation is that Benford's Law fails when data have clear minimum and maximum values. Many kinds of education data, including assessment scores, typically include hard maximums and therefore do not meet the parametric assumptions of Benford's Law. This paper implements a transformation procedure which allows for assessment data to be compared to Benford's Law. As a case study, a data quality assessment of oral language scores from the Early Childhood Longitudinal Study, Kindergarten (ECLS-K) study is used and higher risk data segments detected. The same method could be used to evaluate other concerns, such as test fraud, or other bounded datasets.

The purpose of this study is to examine how companies integrate cyber risk into their enterprise risk management practices. Data breaches have become commonplace, with thousands occurring each year, and some costing hundreds of millions of dollars. Consequently, cyber risk has become one of the gravest risks facing organizations, and has attracted boardroom-level attention. On the other hand, companies already manage many kinds of difficult and growing risks, and that firms lose less than 1% of annual revenues as a result of cyber incidents. Therefore, how should firms appropriately address cyber risk? Is it indeed a materially different kind of risk area, or is it simply just one more risk that can seamlessly be integrated into existing enterprise risk management (ERM) practices?

The authors performed thematic analysis based on semi-structured interviews, with non-probabilistic, purposive sampling, to answer two main questions. First, how do firms manage enterprise risks, generally? And second, how are they integrating cyber risk into these existing processes?

The authors find that there is considerable variation in the approach and sophistication in ERM practices, such as whether they are driven more like an auditing function, or as a risk champion. The authors also find that despite the novelty of cyber risk, it can be integrated like other enterprise risks, and that cyber risk is most often seen as an operational risk (similar to workplace accidents or fraud), rather than a strategic risk, emerging from, for example, technology innovation and R&D.

The generalization of the results is limited by the sample size and variation of firms interviewed. While the authors attempted to interview enterprise risk managers across a wide variation of firms, there were clear limitations in the scope. That being said, the authors were fortunate to be able to examine ERM and cyber risk practices across small and large, private and publicly traded companies, from a variety of business sectors.

The authors believe these finding are important because they present evidence that while cyber risk may be new, it does not require specialized handling or processes to track it at the enterprise level. While some firms may choose to provide special accommodations or attention because of their data collection or business practices, this approach is neither necessary nor required of all firms in all situations.

This research is one of the only papers that, to the best of the authors’ knowledge, examines how cyber risk is integrated at an enterprise level.

The emergence of machine learning has opened a new way for researchers. It allows them to supplement the traditional manual methods for conducting a literature review and turning it into smart literature. This study aims to present a framework for incorporating machine learning into financial statement fraud (FSF) literature analysis. This framework facilitates the analysis of a large amount of literature to show the trend of the field and identify the most productive authors, journals and potential areas for future research.

In this study, a framework was introduced that merges bibliometric analysis techniques such as word frequency, co-word analysis and coauthorship analysis with the Latent Dirichlet Allocation topic modeling approach. This framework was used to uncover subtopics from 20 years of financial fraud research articles. Furthermore, the hierarchical clustering method was used on selected subtopics to demonstrate the primary contexts in the literature on FSF.

This study has contributed to the literature in two ways. First, this study has determined the top journals, articles, countries and keywords based on various bibliometric metrics. Second, using topic modeling and then hierarchy clustering, this study demonstrates the four primary contexts in FSF detection.

In this study, the authors tried to comprehensively view the studies related to financial fraud conducted over two decades. However, this research has limitations that can be an opportunity for future researchers. The first limitation is due to language bias. This study has focused on English language articles, so it is suggested that other researchers consider other languages as well. The second limitation is caused by citation bias. In this study, the authors tried to show the top articles based on the citation criteria. However, judging based on citation alone can be misleading. Therefore, this study suggests that the researchers consider other measures to check the citation quality and assess the studies’ precision by applying meta-analysis.

Despite the popularity of bibliometric analysis and topic modeling, there have been limited efforts to use machine learning for literature review. This novel approach of using hierarchical clustering on topic modeling results enable us to uncover four primary contexts. Furthermore, this method allowed us to show the keywords of each context and highlight significant articles within each context.

This paper aims to examine the association between the working relationship between internal and external auditors and the moral courage of internal auditors to report management fraud in the Tunisian setting.

Data are gathered from 163 internal auditors working in Tunisian companies and a partial least squares–structural equation model (PLS-SEM) is used to test the hypothesis regarding the effect of the cooperation between internal and external auditors on internal auditors’ moral courage.

The results of this study provide strong empirical support for the positive impact of the working relationship between internal and external auditors on internal auditors’ moral courage to report management fraud and unethical behaviors.

The reported results increase the awareness of Tunisian regulators to enact regulations that strengthen the collaboration between internal and external auditors to promote internal auditors’ moral courage and then limit fraud and improve organizational performance in the Tunisian setting.

This paper fills one of the major research gaps in internal audit and moral courage research streams by revealing that the courageous behavior of internal auditors can be fostered by specific means efficacy such as the working relationship between internal and external auditors.

This study aims to understand the behavior of internal auditors towards quality and analyze if some organizational and individual factors influence internal audit quality.

The sample is constituted by Portuguese internal auditors, and the methodology includes the use of partial least squares – structuring equation model (PLS-SEM) to test the hypothesis under study.

The results show that there is a negative relationship between reduced audit quality practices (RAQP) and organizational commitment and independence. The results found that time pressure positively affect RAQP. There is no evidence that perceived organizational support (POS) and risk profile are determinants of RAQP.

This work contributes by extending the literature about the determinants of internal audit quality, but also to the practice by understanding the factors that influence the behavior of internal auditors and by making recommendations that allow an improvement of the quality of internal auditing.

本研究擬瞭解內部審計師就保持內部審計質量所作出的行為，並探討是否有些組織或個人因素、會影響內部審計的質量。

研究的樣本由葡萄牙內部審計師構成，而研究方法則包括偏最小平方法的結構方程模型 (PLS-SEM) 的使用，以測試正在研究中的假設。

研究結果顯示、降低審計質量的慣常做法與組織承諾及獨立性是成負相關的; 研究結果亦顯示、時間壓力會對降低審計質量的慣常做法產生正面影響，沒有證據證實、組織支持感和風險概況是降低審計質量慣常做法的決定因素。

本研究的貢獻為、就研究內部審計質量決定因素的文獻方面、擴展了有關的學術探討; 研究亦對內部審計作出實務方面的貢獻，因研究結果幫助我們更瞭解影響內部審計師行為的因素，而且，研究人員提出了提升內部審計質量的建議。

內部審計質量、降低審計質量的慣常做法、組織決定因素、個人決定因素.

This study aims to measure the effects of moderation by age and income in mobile payment systems' intended use as predictors of performance expectation, effort expectation, social influence, risk and perceived costs.

This study is based on a survey that generated a sample of 1,742 Brazilian users that responded to the measurement scale. The research data were analyzed using the partial least squares structural equation model.

All proposed latent variables were significant, with income positively moderating the performance expectation and negatively moderating the perceived cost and perceived risk. In addition, age positively moderates performance expectation and negatively moderates cost perception.

The findings evolved previous literature by understanding moderating effects that make it possible for companies operating in mobile payments to generate segmented communication and engagement plans for users of different income and age brackets.