Johan Perols

Information Technology Continuance: A Theoretic Extension and Empirical Test

This paper proposes a theoretical extension of the information technology (IT) continuance model by linking continuance intention to behavior and elaborating the contingent factors that shape IT continuance intention and behavior. Drawing on recent findings in the cognitive psychology literature, it conceptualizes perceived behavioral control as consisting of two dimensions — IT self-efficacy and facilitating conditions — and links these two dimensions respectively to IT continuance intention and behavior. Field data from a longitudinal survey of document management system usage among administrators and staff personnel at a governmental agency in Ukraine provides empirical support for our extended model. Our study advances the emerging body of research on IT continuance by extending the theoretical boundaries of the IT continuance model, and contributes to IT acceptance and usage research by clarifying the conceptualization and effect of the PBC construct.

Information Market-Based Decision Fusion

Improved classification performance has practical real-world benefits ranging from improved effectiveness in detecting diseases to increased efficiency in identifying firms that are committing financial fraud. Multiclassifier combination (MCC) aims to improve classification performance by combining the decisions of multiple individual classifiers. In this paper, we present information market-based fusion (IMF), a novel multiclassifier combiner method for decision fusion that is based on information markets. In IMF, the individual classifiers are implemented as participants in an information market where they place bets on different object classes. The reciprocals of the market odds that minimize the difference between the total betting amount and the potential payouts for different classes represent the MCC probability estimates of each class being the true object class. By using a market-based approach, IMF can adjust to changes in base-classifier performance without requiring offline training data or a static ensemble composition. Experimental results show that when the true classes of objects are only revealed for objects classified as positive, for low positive ratios, IMF outperforms three benchmarks combiner methods, majority, average, and weighted average; for high positive ratios, IMF outperforms majority and performs on par with average and weighted average. When the true classes of all objects are revealed, IMF outperforms weighted average and majority and marginally outperforms average.

Information Fusion in Continuous Assurance

ABSTRACT : We extend continuous assurance research by proposing a novel continuous assurance architecture grounded in information fusion research. Existing continuous assurance architectures focus primarily on methods of monitoring assurance clients systems to detect anomalous activities and have not addressed the question of how to process the detected anomalies. Consequently, actual implementations of these systems typically detect a large number of anomalies, with the resulting information overload leading to suboptimal decision making due to human information processing limitations. The proposed architecture addresses these issues by performing anomaly detection, aggregation, and evaluation. Within the proposed architecture, artifacts developed in prior continuous assurance, ontology, and artificial intelligence research are used to perform the detection, aggregation, and evaluation information fusion tasks. The architecture contributes to the academic continuous assurance literature and has implicat...