International Journal of Accounting Information Systems最新文献

Accounting fraud is a widespread problem that causes significant damage in the economic market. Detection and investigation of fraudulent firms require a large amount of time, money, and effort for corporate monitors and regulators. In this study, we explore how textual contents from financial reports help in detecting accounting fraud. Pre-trained contextual language learning models, such as BERT, have significantly advanced natural language processing in recent years. We fine-tune the BERT model on Management Discussion and Analysis (MD&A) sections of annual 10-K reports from the Securities and Exchange Commission (SEC) database. Our final model outperforms the textual benchmark model and the quantitative benchmark model from the previous literature by 15% and 12%, respectively. Further, our model identifies five times more fraudulent firm-year observations than the textual benchmark by investigating the same number of firms, and three times more than the quantitative benchmark. Optimizing this investigation process, where more fraudulent observations are detected in the same size of the investigation sample, would be of great economic significance for regulators, investors, financial analysts, and auditors.

This study analyses the organisational reengineering processes developed following the adoption of an Enterprise Resource Planning (ERP) system in a public university hospital. Interestingly, a public university hospital was obliged to adhere to a regional ERP system owing to regulatory requirements. This caused the elimination of the pre-existing information flow architecture and the need for further reengineering. The analysis examines a case study in which reengineering information flow is conducted with ERP and business intelligence (BI) systems, highlighting the positive results. Using a data warehouse based on the ERP and BI systems allowed it to transform data into useful information, providing a global and detailed vision of the university hospital’s performance through Key Performance Indicators (KPIs). Indeed, reengineering processes have made it possible to reorganise information flows consistent with the needs of new information systems.

Top managers have a decisive role in managing business organizations, including linking the effective use of their organizational resources with the corporate strategy. This study aims to analyze the influence of top management support on the utilization of management accounting systems (MAS) in SMEs. For this purpose, we explored the mediating role of four variables related to organizational decision-making orientation and user issues and concerns. The findings of our study suggest that top management can use these variables to improve the utilization of the information provided by MAS in the SMEs’ decision processes. They also show the decisive role of user satisfaction and training in improving MAS utilization. We discuss these findings and present theoretical and practical contributions to the management accounting literature.

Based on stakeholder theory, resource-based theory, and decision theory, this study explores the relationship between information technology governance and internal information quality in determining business entity tax savings. Specifically, we examine the effects of internal information quality and information technology governance on corporate tax savings. We also investigate the joint effect of information technology governance and internal information quality on tax savings. This study used a quantitative method with panel data regression using a sample of the top 500 listed companies on the Australian Stock Exchange (ASX) from 2017 to 2021. Our dataset consists of 1,295 firm-year observations. We collected data manually to assess the proxy for information technology governance. Our analysis yielded three main findings. Firstly, we found no significant effect of the quality of internal information on tax savings on the ASX. However, we did observe a positive relationship between the construct of information technology governance and tax savings. Additionally, the presence of information technology governance was found to moderate this relationship. These findings remained consistent when subjected to various robustness checks and additional tests. Our findings highlight the importance of the information technology governance construct as a powerful catalyst for activating the link between internal information quality and optimal tax performance. This study has theoretical and practical implications for businesses, regulators, investors, and tax authorities, which are discussed further.

Prior research foresees that advancing digital technologies call for increasing competency levels of controllers. Competency theory predicts that achieving this will require increasing knowledge of these technologies and the ability to task-specifically use it. Empirical evidence of the recognition of these necessary conditions is missing. Drawing on competency literature and extant research on influences of nine technologies, we survey 453 senior controllers. We find for all technologies that they perceive their current knowledge and competency levels lower than required and that their expectations of the required competency growth correlate positively with perceived current knowledge at any current competency level, even for task-specific technologies that have the highest current and future competency scores (big data, analytics, visualization). However, their expectations may underestimate the future digital competency levels required for staying relevant. Our evidence urges controllers to work on their digital competencies and put task-specific knowledge first for each new competency.

We use Accounting Information Systems (AIS) meta-theory to develop a framework for analyzing and using machine learning in accounting research, emphasizing 1) specific accounting research tasks, 2) supervised and unsupervised models, and 3) inductive vs. deductive research designs. We apply our framework to organize AIS and accounting research and highlight opportunities for future AIS research using machine learning. We discuss the changes in technology that have made machine learning more feasible in practice and research and how these changes might motivate and influence future research projects. We conclude by providing directions for future work in machine learning in AIS research and discussing the potential application to practice.

Businesses are increasingly using artificial intelligence (AI) in accounting systems to reduce uncertainty and improve accuracy. However, algorithm aversion (Dietvorst et al., 2015) indicates that individuals often avoid information provided by automated systems as compared to that provided by humans. This paper is an exploratory step towards documenting an emotional response to AI. We experimentally investigate how disclosing the use of AI rather than human staff for estimating the fair value of an asset influences investment decisions through lower levels of emotional response, particularly in pleasantness and attentiveness. Consistent with algorithm aversion, we find that disclosing the use of AI to estimate the asset’s fair value reduces the effect of information valence on nonprofessional investor responses. Specifically, when a company’s AI usage is disclosed, investors make smaller additional investments when fair value information is positive and smaller investment withdrawals when fair value information is negative, as compared to when human staff usage is disclosed. Importantly, we also find that emotions mediate the effect of information source (AI versus human staff) and moderate the effect of information valence on investment decisions.

This article aims to shed light on digital transformation in the accounting sector from the perspective of institutional change. We performed a systematic literature review to understand how institutional change could be applied to research on Accounting Information Systems (AIS), using a quantitative method to categorize data through LSA (Latent Semantic Analysis) technique; and a qualitative method performed by hierarchical categorical content analysis as a basis for inferences. We reviewed 309 articles using Institutional Theory in accounting and AIS. The LSA results presented the main topics of study, subfields of research, and uses of Institutional Theory. Regarding accounting research, it is possible to identify the role of institutional forces in the IFRS adoption (in private and public sectors), XBRL adoption, and disclosure of information on organizations’ environmental, social, and governance, with a focus on analyses at the organizational level and regulatory pressures. A deep dive into AIS articles showed that Institutional Theory was applied to analyze the adoption of systems/technologies (mostly ERP and XBRL) through isomorphic processes (coercive, mimetic, and normative). We proposed to analyze digital technologies as an exogenous change for the digital transformation in accounting business using another perspective of Institutional Theory: institutional change. Institutional change analyzes the role of destabilizing elements in the change of an organizational field as a whole and not just in a few organizations. In this perspective, technology is the agent of change on a broader level, transforming organizations, individuals, customers, suppliers, and governments. The main contribution of this article is presenting suggestions for future AIS. This research agenda contributes to advancing Institutional Theory in AIS, using the lens of institutional change to analyze the new realities shaping accounting organizations, such as digital transformation.