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The purpose of this study is to explore how the risk of management motives for fraud can be assessed in external audits.

Semi-structured interviews were conducted with 26 experienced external auditors to explore their perspectives on the methods they employ to assess the risk of management motives for fraud.

The study identifies six methods external auditors can use to assess management motives for fraud. It emphasises that assessing management motives requires auditors to go beyond understanding these motives and necessitates a sceptical and analytical mindset. Auditors need to identify the accounts most vulnerable to management manipulations, observe management attitudes and assess the credibility of management assertions. The auditors in this study highlight specific accounts frequently manipulated by management. Still, manual year-end journal entries are the most vulnerable to management manipulations as they are subject to fewer controls. They recommend increasing the sample size to 100% and assigning more experienced staff, particularly, those with qualifications in fraud examination or anti-fraud training, to audit these vulnerable accounts thoroughly. They also provided examples of how auditors can identify management motives for fraud, observe management attitudes and assess the credibility of management assertions.

Audit standards (e.g. ISA 240, SAS99) lack explicit guidance on assessing management motives for fraud, but auditors are required to consider it in fraud risk assessment. This study proposes guidance recommendations to improve auditors' ability to assess this risk, which could be integrated into professional audit standards and training materials to improve auditors' professional scepticism, ability to challenge management and skills in fraud risk assessment.

Assessing the risk of management motives for fraud in external audits has received limited attention in the literature. To the best of the authors’ knowledge, this study is the first to address this knowledge gap.

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

The primary purpose of this exploratory paper is to propose a novel analytical framework for examining corruption from a behavioral perspective by highlighting multiple issues associated with consumerism.

This paper examines the relationship between excessive consumption activities and corrupt acts, drawing upon existing literature on corruption, consumerism and consumption, as well as multiple reports and cases of corruption and money laundering in Indonesia. With regard to corruption networks, this paper analyses the associated behavioral patterns and social dynamics by using the Fraud Triangle and the Fraud Elements Triangle frameworks to examine the phenomenon of living beyond one’s means. This paper also addresses the notion of sacredness in the context of consumer activities and how such sacredness plays a role in causing otherwise honest individuals to engage in corrupt acts.

The author established that corruption represents a complex societal issue that extends across several dimensions of society, encompassing both horizontal and vertical aspects. Consequently, addressing this problem poses significant challenges. Excessive consumption has been identified as one of the various behavioral concerns that are implicated in the widespread occurrence of corruption in many nations. Individuals who partake in excessive consumption play a role in shaping ethical norms that serve to legitimize and rationalize immoral behavior, therefore fostering a society marked by corruption. The act of engaging in excessive consumption is also associated with cases of money laundering offenses that are connected to corruption and several other illicit activities. The lifestyle of corrupt individuals is one of the primary behavioral concerns associated with corruption, as “living beyond means” is the most common behavioral red flag among occupational fraud offenders worldwide. The phenomenon of consumerism may also shape the minds of individuals as if it were an “implicit religion” due to the fact that it may generate human experiences that elicit highly positive emotions and satisfy certain sacredness-associated characteristics. The pursuit of transcendental experiences through the acquisition and consumption of sacred consumption objects may heighten the incentive to commit fraudulent acts such as corruption.

This self-funded exploratory study uses document analysis to examine the corruption phenomenon in Indonesia. Future studies will benefit from in-depth interviews with former offenders and investigators of corruption.

This exploratory study contributes to advancing corruption prevention strategies. It does this by introducing a novel analytical framework that allows for the examination of several behavioral issues associated with consumerism, which have the potential to foster the proliferation of corruption.

This exploratory study highlights the importance of comprehending the intricacies of consumerism, namely, its adverse effects on the proliferation of corruption.

This study aims to synthesize the existing literature with insights gained from interviews conducted with regulatory experts. The objective is to analyse the challenges associated with incorporating cryptocurrencies into regulatory frameworks and to explore constraints in the regulatory institutionalization of cryptocurrencies.

The study methodology consists of two steps. The first step is to identify regulatory constraints in the literature review and in the next step, interviews are conducted with officials of the State Bank of Pakistan (SBP). The study used a qualitative case study methodology, in which a single case (regulatory constraint) was selected as a unit of analysis.

The findings show that lack of traceability, legal status, lack of governmental control due to decentralization, difficulty enforcing laws, volatility, lack of skills with regulators and difficulty integrating cryptocurrencies into the current financial system are the main obstacles to the introduction of a regulatory framework. Thus, on a broader conceptual level, the findings can be grouped into opportunism, lack of strategic capability and fragmented global laws.

This study could inform global cryptocurrency regulation discussions, sharing a developing country’s views on balancing the government, central banks, the financial sector and public interests. This could guide countries to consider cryptocurrency adoption in similar situations. This could affect the cryptocurrency market, impacting demand, supply and investor trust in Pakistan.

The study has implications for policy making officials. The research aims to offer valuable insights to the SBP and other regulatory authorities, helping them identify potential risks and create an effective regulatory framework for cryptocurrencies.

The study has implications for society in knowing about the volatile nature of cryptos and anonymity of their issuers, which poses regulatory constraints. This then implies its harmfullness to its traders and the huge losses that may arise from their trading due to its volatile nature.

This study contributes to the literature on the constraints, responsibilities and consultation framework of cryptocurrency regulations.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.

The purpose of our study is to investigate the impact of corporate culture on sales order backlog.

The authors use regression analysis to examine the relation between corporate culture and the level of sales order backlog, an important leading indicator of firm performance.

Using a large panel sample of US firms for the period of 2003–2021, the authors find a significant and positive relation, suggesting that firms with strong corporate culture have a higher level of sales order backlog.

The study findings contribute to two separate areas of research: corporate culture in management literature and sales order backlog in accounting literature. Prior study has focused on the impact of corporate culture on current firm performance. This study extends prior research by investigating the impact of corporate culture on order backlog, an important leading indicator of future performance.

A pronounced rise in postpandemic immigration is creating consumption opportunities and challenges for countries worldwide. Past research has shown that immigrant homeownership indicates advanced consumer acculturation. However, critical factors which differentiate immigrant decisions to purchase a home remain underexplored. This study aims to examine the importance of different identity resources in determining homeownership gaps between immigrant groups in Spain during a dynamic decade.

A mixed methods research design with triangulation was used. First, the critical “historical research method” is used to empirically assess 15,465 household-level microdata files from the National Immigrant Survey of Spain. Second, the analysis is corroborated through informant interviews, an evaluation of digital news archives and other historical traces such as relevant advertisements in Spain from 2000 to 2009.

Results provided an account of immigrant homeownership whereby foreign-born consumers leveraged resources to promote social identities aligned with an advanced level of acculturation through housing investment during this period. Furthermore, marketing focused on specific targets of ethnic minority consumers coupled with government policies to promote immigrant homeownership reinforced the “Spanish Dream” as a new paradigm for housing market integration.

Spain provides an unprecedented historical context to explain marketing-related phenomena due to a perfect storm of immigration, job availability and integration supports. Contrary to popular wisdom, immigrant consumer homeownership gaps are not solely a result of differences in income and economic mobility, but rather an advanced acculturation outcome driven by personal and social investments in resources that lead to consumer identities.

This study reviews and discusses the accounting literature that analyzes the role of auditors and enforcers in the context of fraud.

This literature review includes both qualitative and quantitative studies, based on the idea that the findings from different research paradigms can shed light on the complex interactions between different financial reporting controls. The authors use a mixed-methods research synthesis and select 64 accounting journal articles to analyze the main proxies for fraud, the stages of the fraud process under investigation and the roles played by auditors and enforcers.

The study highlights heterogeneity with respect to the terms and concepts used to capture the fraud phenomenon, a fragmentation in terms of the measures used in quantitative studies and a low level of detail in the fraud analysis. The review also shows a limited number of case studies and a lack of focus on the interaction and interplay between enforcers and auditors.

This study outlines directions for future accounting research on fraud.

The analysis underscores the need for the academic community, policymakers and practitioners to work together to prevent the destructive economic and social consequences of fraud in an increasingly complex and interconnected environment.

This study differs from previous literature reviews that focus on a single monitoring mechanism or deal with fraud in a broadly manner by discussing how the accounting literature addresses the roles and the complex interplay between enforcers and auditors in the context of accounting fraud.

The growing availability of naturalistic driving datasets (NDDs) presents a valuable opportunity to develop various models for autonomous driving. However, while current NDDs include data on vehicles with and without intended driving behavior changes, they do not explicitly demonstrate a type of data on vehicles that intend to change their driving behavior but do not execute the behaviors because of safety, efficiency, or other factors. This missing data is essential for autonomous driving decisions. This study aims to extract the driving data with implicit intentions to support the development of decision-making models.

According to Bayesian inference, drivers who have the same intended changes likely share similar influencing factors and states. Building on this principle, this study proposes an approach to extract data on vehicles that intended to execute specific behaviors but failed to do so. This is achieved by computing driving similarities between the candidate vehicles and benchmark vehicles with incorporation of the standard similarity metrics, which takes into account information on the surrounding vehicles' location topology and individual vehicle motion states. By doing so, the method enables a more comprehensive analysis of driving behavior and intention.

The proposed method is verified on the Next Generation SIMulation dataset (NGSim), which confirms its ability to reveal similarities between vehicles executing similar behaviors during the decision-making process in nature. The approach is also validated using simulated data, achieving an accuracy of 96.3 per cent in recognizing vehicles with specific driving behavior intentions that are not executed.

This study provides an innovative approach to extract driving data with implicit intentions and offers strong support to develop data-driven decision-making models for autonomous driving. With the support of this approach, the development of autonomous vehicles can capture more real driving experience from human drivers moving towards a safer and more efficient future.