International Journal of Accounting Information Systems最新文献

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.

Inadequate information technology (IT) management can lead to system ineffectiveness and operational stagnation within enterprises. While the application of IT governance provides a means for companies to validate IT functionality, oversee IT operations, and mitigate IT-associated risks, there is a paucity of research examining its implications of IT governance on IT controls, particularly within the context of a firm’s Internal Audit Function (IAF). Addressing this gap in the literature, this research delves into the relationship between the characteristics of the IAF and IT governance within the IAF. It further probes the linkage between IT governance associated with the IAF and IT control activities. We analyze survey data from 414 internal auditors across various Taiwanese companies using partial least squares regression. The findings suggest that IT knowledge and internal auditing roles have a significantly positive relationship with the quality of the IAF-IT relationship and IT governance processes. Similarly, IT audit competencies exhibit a significantly positive relationship with IT governance processes. Furthermore, properly structured IT governance processes and a high-quality IAF-IT relationship demonstrate a positive association with the effectiveness of general controls. This research amalgamates and extends prior investigations into IT governance and internal auditing, underlining their critical role in successfully implementing superior IT controls.

Although the benefits of technological innovation are widely recognised, the accounting profession continues to undervalue and inadequately leverage technologies such as artificial intelligence, robotic process automation and blockchain. This study builds on earlier work on the antecedents and barriers to technology adoption and considers the role of technological, organisational and environmental factors in the accounting context. The mixed methods design involved surveying 585 accounting managers in Australia and parts of South-East Asia and interviewing 20 Australian accounting managers. Using the Technology–Organisation–Environment model, the study explored differences in the importance of factors by region and organisational type and investigated manager perspectives on strategies to support adoption. Findings indicated that security and privacy concerns are paramount to decision-making, while environment-related factors were of less importance. Prioritised strategies included staying informed of technological innovation, encouraging staff engagement and support, and implementing effective project management. The study illuminates the critical role of professional associations and industry bodies in providing tailored support for members to foster greater technological orientation and advance in the accounting profession, including advocating for high-level, technology-related strategies to drive organisational transformation.

This study examines factors that influence auditors’ intention to use blockchain. From the dual-factor perspective, it argues that auditors’ knowledge about the implications of blockchain for auditing enables their intention to use the technology. However, professional skepticism and perceived adequacy of accounting standards present barriers to the use, which reduce the effect of knowledge on use intention. These claims are tested with a survey of 118 auditors in the U.S. The results show that auditors’ knowledge about blockchain is positively associated with their use intention, indicating their optimism about this technology. Their professional skepticism has no effect on intention, but perceived adequacy of accounting standards has a negative effect, which also weakens the effect of blockchain knowledge on intention, revealing the status quo bias in blockchain use. This study extends the literature on the acceptance of blockchain technology by accounting and auditing professionals by showing how the need to improve accounting standards, which are important attributes of their professional judgments, may influence their blockchain use. It has implications for promoting blockchain among auditors.

The Theory of Technology Dominance (TTD) provides a theoretical foundation for understanding how intelligent systems impact human decision-making. The theory has three phases with propositions related to (1) the foundations of reliance, (2) short-term effects on novice versus expert decision-making, and (3) long-term epistemological effects related to individual deskilling and profession-wide stagnation. In this theory paper, we propose an extension of TTD, that we refer to as TTD2, primarily to increase our theoretical understanding of how, why, and when the short-term and long-term effects on decision-making occur and why advances in technology design have exacerbated some weaknesses and eroded some benefits. Recently, researchers have called for reconsideration of how we design intelligent systems to mitigate the detrimental effects of technology; in TTD2 we provide a theory-based understanding for capturing the complexity underlying the occurrence of the effects.