International Journal of Accounting Information Systems最新文献

This study uses analytical models to investigate whether requiring cybersecurity assurance or a particular maturity level for vendors or contractors will help them improve their cybersecurity management. Our findings suggest that, if a supplier decides on its preferred cybersecurity maturity level without knowing what level a contract requires, the supplier is more likely to exert more effort to improve its cybersecurity management. We also show that a buyer can incentivize the supplier to engage in improving cybersecurity risk management by imposing a reduced contractual price or a fine when a breach occurs. Our findings reveal the role played by cybersecurity maturity level assurance and we discuss practical implications.

Digitization is fundamentally changing how organizations create and deliver business value, with information technology (IT) leveraged to improve business processes and controls. Its pervasive effects upon organizations’ risk exposures and performance requires boards’ prudent and integrated consideration of the resultant IT opportunities and risk management. However, IT governance research suggests that boards’ governance of IT is more commonly delegated and relegated to management and committees than integrated as part of their corporate governance practices. In response, our study contributes timely and structured understanding of boards’ roles and the mechanisms required for IT governance to become an integrated component of corporate governance.

Governments shoulder the responsibility of pursuing a variety of sustainability objectives, the consequences of which may not be discernable in traditional reporting frameworks. Environmental, Social, and Governance (ESG) reporting would be a valuable addition to the existing financial, service, and infrastructure aspects of government reporting. While reportable data may be difficult to measure, smart city development strategies dovetail with technologies that facilitate sustainability reporting. Smart city strategies utilize technologies to support and inform municipal governance. This paper proposes a framework of government ESG reporting based on smart city initiatives, details potential steps in the process of government ESG reporting, discusses how smart cities could facilitate ESG reporting, and illustrates potential avenues of analysis using New York City vehicular mobility data as an example. This research sheds light on an under-examined topic, offers a perspective on adding ESG to government reporting, explores how sustainability data can inform government ESG reporting, and demonstrates the utility of smart city data for reporting, assurance, and monitoring.

This study applies the methodology of the SEC (2018) to empirically determine thresholds for liquidity of crypto assets, utilizing two metrics for assessing liquidity: the Average Daily Volume (ADV) calculated by the number of units of crypto assets traded (ADV#) and by the traded dollar amounts (ADV$). Our findings reveal that the liquidity distribution patterns for both actively and thinly traded National Market System (NMS) stocks, alongside crypto-USD pairs, exhibit comparable trends. Notably, the liquidity threshold distributions remain stable despite the inclusion of crypto assets with very low unit prices; however, the volume of units traded does affect the distribution when ADV$ is used. This research contributes to the accounting field by offering a new approach to determine liquidity benchmarks for crypto assets, potentially guiding the assessment of whether a crypto asset is traded in an active or inactive market for fair value accounting purposes.

Leveraging ubiquitous digital data, advanced hardware, and sophisticated software, Big Data Analytics (BDA) enables unprecedented in-depth examination of business processes. This paper investigates how the Big 4 accounting firms promote their use of technology-enabled analytics in auditing practices on their official websites. We find that all the Big 4 market their audit analytics as offering operational insights that add value for clients. Historically, auditors have sought to share valuable perspectives gained during engagements beyond verifying that clients are in compliance with accounting principles. However, providing these value-adding insights is transitioning from a supplementary benefit to an essential selling point of audits. This behavior likely stems from BDA technology’s inherent duality–facilitating regulatory compliance and business intelligence. Incorporating artificial intelligence into audit firms’ technological arsenal will further enhance their ability to deliver client-valued insights. These developments necessitate further research into how the BDA application impacts auditor independence.

This study aims to analyze whether non-financial (NF) performance is influenced by the quality of accounting information system (AIS) and the quality/usefulness of financial information (FI) and whether this influence is more pronounced in companies managed by women. Data of 381 Portuguese companies were subjected to structural equations model analysis. The results reveal that (1) companies managed by women exhibit the highest quality in terms of AIS and FI, (2) the quality of the FI determines its usefulness in the decision-making process, and (3) quality and usefulness of the FI has a significant impact on NF performance. Within the framework of contingency theory, the research provides empirical evidence that the NF performance of Portuguese firms is associated with the presence of women in management, as they enhance the quality of financial reporting, essential for the success of any organization. This research is pertinent for accounting and management professionals, as it underscores the importance of quality financial reporting for the survival and growth of companies, while also emphasizing the significance of gender diversity in company management. © 2021 Published by Elsevier Inc. All rights reserved.

We investigate how graphical vividness and interactivity in displays of non-financial information following poor financial performance affect the judgments of nonprofessional investors. Leveraging the Hamilton and Winchel (2019) model of dual-process theories of persuasion in financial reporting, we hypothesize and find that graphical vividness and interactivity jointly influence nonprofessional investors. Despite negative news in financial information, investors perceive a firm’s performance more positively when favorable non-financial information is presented more vividly and with a user interactivity function. Finally, contributing to theory, we find that effort positively mediates the effects of user interactivity on nonprofessional investors’ judgments and decisions. The results lend empirical support to the Hamilton and Winchel (2019) model of dual-process theories of persuasion in financial reporting and have implications for designers of investor relations websites, investor groups consuming this information, and regulators concerned about the need for standards in the presentation of non-financial information in annual reports.

This study seeks to explore the potential of data-driven methods for developing a financial statement fraud prediction model. We emphasize that building a fraud prediction model that can be used to detect fraud in real-world applications should receive attention from researchers. However, the severe class imbalance issue and the complex nature of fraudulent activities make it a rather challenging task. To address these problems, we apply the combinations of different sampling techniques and tree-based ensemble classifiers to an extensive set of raw financial statement data. The results show that the models using an extensive set of raw financial data, undersampling techniques and boosting tree classifiers are superior in fraud detection. Moreover, several features without a priori knowledge are identified to be important for fraud prediction models by feature importance evaluation. Accordingly, this study provides a methodological guide for designing fraud prediction models for real-world applications and serves as a preliminary step of the knowledge discovery process to complement fraud detection knowledge systems.

Mobile payment has become increasingly popular in recent years. However, concerns remain about the information security risk management practices implemented by non-financial-institution mobile payment service providers, such as mobile phone carriers and technology companies, using tokenization systems and encryption mechanisms. Using the modified Delphi method and building on the COBIT 2019 framework, this study explores and suggests how these non-financial-institution mobile payment service providers can consider a more holistic list of information security risk items and their corresponding management practices. We believe the proposed practices will help non-financial-institution mobile payment service providers focus on the valuable aspects of information security risks.

Intelligent Information Technology (IIT) applications are crucial in the audit process, enhancing quality, effectiveness, and efficiency. The client acceptance process (CAP), one of the critical audit steps, involves subjective evaluations where business managers' claims intersect with independent audit firm managers' expectations. This subjective nature introduces the potential for errors or misjudgments, impacting audit time and costs. In this paper, therefore, we propose a decision support system considering both auditors' subjective judgments and financial data variations for accepting or rejecting a client enterprise. The decision support system consisting of the Fuzzy Analytic Hierarchy Process (AHP), the logistic regression model, and the fuzzy inference system comprises four phases. In the first phase, a logistic regression model is developed using financial ratios to determine the client's probability of being in a close monitoring market (CMM) which represents publicly traded firms that are struggling to meet specific financial indicators or that are exposed to certain risks. In the second phase, the evaluation criteria used by the audit firm to measure the market reputation of the client enterprise are defined, and the weights of the evaluation criteria are obtained by using Fuzzy AHP. In the third phase, the Client Acceptance Score (CAS) representing market reputation of the client is calculated by incorporating the results of a reputation survey and applying the weights assigned to the evaluation criteria obtained in the second phase. Finally, client acceptance risk level (CARL) is obtained by using a fuzzy inference system and a rule-based defined by auditors. The CMM probability value and CAS score obtained in previous phases are used as input values of the fuzzy inference system. The CARL score guides the audit firm in deciding whether to engage with the client. To illustrate the applicability of the proposed model, a case study has been given in the paper.