Abstract:We develop an information quality model based on a user-centric view adapted from Financial Accounting Standards Board1, Wang et al.2, and Wang and Strong3. The model consists of four essential attributes (or assertions): 'Accessibility,' 'Interpretability,' 'Relevance,' and 'Integrity.' Four sub-attributes lead to an evaluation of Integrity: 'Accuracy,' 'Completeness,' 'Consistency,' and 'Existence.' These sub-attributes relating to 'Integrity' are intrinsic in nature and relate to the process of how the information was created while the first three attributes: 'Accessibility,' 'Interpretability,' and 'Relevance' are extrinsic in nature. We present our model as an evidential network under the belief-function framework to permit user assessment of quality parameters. Two algorithms for combining assessments into an overall IQ measure are explored, and examples in the domain of medical information are used to illustrate key concepts. We discuss two scenarios, 'online-user' and 'assurance-provider,' which reflect two likely and important aspects of IQ evaluation currently facing information users -concerns about the impact of poor quality online information, and the need for information quality assurance. The model consists of four essential attributes (or assertions): 'Accessibility,' 'Interpretability,' 'Relevance,' and 'Integrity.' Four sub-attributes lead to an evaluation of Integrity: 'Accuracy,' 'Completeness,' 'Consistency,' and 'Existence.' These sub-attributes relating to 'Integrity' are intrinsic in nature and relate to the process of how the information was created while the first three attributes: 'Accessibility,' 'Interpretability,' and 'Relevance' are extrinsic in nature. We present our model as an evidential network under the belief-function framework to permit user assessment of quality parameters. Two algorithms for combining assessments into an overall IQ measure are explored, and examples in the domain of medical information are used to illustrate key concepts. We discuss two scenarios, 'online-user' and 'assurance-provider,' which reflect two likely and important aspects of IQ evaluation currently facing information users -concerns about the impact of poor quality online information, and the need for information quality assurance.

This research investigates the development of assured sustainability reports (SRs) during this century's first decade. More specifically, it presents basic descriptive data on a sample of 148 SRs published in 2006 and 2007 and contrasts this sample with the sample discussed in Mock et al. 2007. The prior study examined a sample of 130 assured SRs issued between 2002 and 2004. Both samples provide information about the nature of SRs, allowing us to investigate important questions such as which countries and industries are more likely to have an assurance statement, what levels of assurance are provided, and what factors affect the level of assurance provided.In addition to providing descriptive data relative to the above questions, we run logistic regressions where the dependent variable is whether a Big 4 firm provided the assurance for both periods being considered. Some important differences are observed related to whether the assurance provided applies to both the quantitative and qualitative assertions made in the report (significantly negatively associated with the Big 4 in the

This study develops an alternative methodology for the risk analysis of information systems security (ISS), an evidential reasoning approach under the Dempster-Shafer theory of belief functions. The approach has the following important dimensions. First, the evidential reasoning approach provides a rigorous, structured manner to incorporate relevant ISS risk factors, related counter measures and their interrelationships when estimating ISS risk. Secondly, the methodology employs the belief function definition of risk, that is, ISS risk is the plausibility of information system security failures. The proposed approach has other appealing features, such as facilitating cost-benefit analyses to help promote efficient ISS risk management. The paper both elaborates the theoretical concepts and provides operational guidance for implementing the method. The method is illustrated using a hypothetical example from the perspective of management and a real-world example from the perspective of external assurance providers. Sensitivity analyses are performed to evaluate the impact of important parameters on the model's results.

BELIEF-FUNCTION FORMULAS FOR AUDIT RISK SYNOPSIS AND INTRODUCTION: This article relates belief functions to the structure of audit risk and provides formulas for audit risk under certain simplifying assumptions. These formulas give plausibilities of error in the belief-function sense. We believe that belief-function plausibility represents auditors' intuitive understanding of audit risk better than ordinary probability. The plausibility of a statement, within belief-function theory, measures the extent to which we lack evidence against the statement. High plausibility for error indicates only a lack of assurance, not positive evidence that there is error. Before collecting, analyzing, and aggregating the evidence, an auditor may lack any assurance that a financial statement is correct, and in this case will attribute very high plausibility to material misstatement. This high plausibility does not necessarily indicate any evidence that the statement is materially misstated, and hence it is inappropriate to interpret it as a probability of material misstatement. The SAS No. 47 formula for audit risk is based on a very simple structure for audit evidence. The formulas we derive in this article are based on a slightly more complex but still simplified structure, together with other simplifying assumptions. We assume a tree-type structure for the evidence, assume that all evidence is affirmative and that each variable in the tree is binary. All these assumptions can be relaxed. As they are relaxed, however, the formulas become more complex and less informative, and it then becomes more useful to think in terms of computer algorithms rather than in terms of formulas (Shafer et al. 1988). In general, the structure of audit evidence corresponds to a network of variables. We derive formulas only for the case in which each item of evidence bears either on all the audit objectives of an account or on all the accounts in the financial statement, as in figure 1, so that the network is a tree. Usually, however, there will be some evidence that bears on some but not all objectives for an account, on some but not all accounts, or on objectives at different levels; in this case, the network will not be a tree.