PurposeThe increasing provision of timely financial information through web‐based technology is expected to improve the quality of communication between a company and its stakeholders. However, the information asymmetry problem still exists since almost all “web‐releases” usually remain unaudited. The purpose of this paper is to propose conceptual and technical frameworks of continuous auditing to provide a solution for this problem. This solution could also move the traditional auditing forward to the new e‐auditing generation.Design/methodology/approachThis paper develops a conceptual framework to present why continuous auditing would dominate other auditing approaches in examining web‐based financial information. Using a 3 × 2 × 2 × 1 design, this study compares the economic efficiency of three auditing approaches under the joint‐combination of various disclosure types, materiality perceptions and information environments. A technical framework, the external continuous auditing machine, is derived from the conceptual framework to specify the generic procedures to perform the online control testing and the continuous substantive testing over web‐releases.FindingsContinuous auditing issues are scrutinized both theoretically and technically. Two main conclusions arise. First, the behavior model simulates various information disclosing and auditing environment and argues that the continuous auditing would be the most appropriate approach for web‐releasing assurance. Although the hypothesis derived from that model still needs further empirical supports, the anticipated sustaining is quite reasonable under the emergent web‐release practice.Originality/valueGiven the new era of online, real‐time business reporting, constructing a theoretical model and applying it to develop a technical model for implementing continuous audits for web‐releases provide significant contributions to the accounting/auditing professionals as well as researchers.
This study uses a design science approach to examine the consistency between quantitative financial ratios and qualitative narrative disclosures in the annual reports. To extract information on the tone of unstructured qualitative textual data, we first use the term frequency/inverse document frequency (TFIDF) text mining technique to classify each company's narrative disclosure as either “Positive” or “Negative.” For the quantitative information, we use the K-means method to cluster each company's financial performance data into “Good” or “Poor” groups. Consistency is said to occur when the textual and numerical data form either a “Positive-Good” pair or a “Negative-Poor” pair. The design model is presented in a stepwise fashion and therefore is transparent for evaluation and validation. Our evaluation process demonstrates the feasibility of the design model. The evaluation was conducted using listed semiconductor companies in countries with different levels of market development. The results show that U.S. firms are less likely to exaggerate in their narrative disclosures and are more likely to understate their performance in MD&As compared to companies in other markets such as China and Taiwan.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.