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Information Systems Research Themes: A Seventeen-year Data-driven Temporal Analysis
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2018 | Communications of the Association for Information Systems | Citations: 0
Authors: Goyal, Sandeep; Ahuja, Manju; Guan, Jian

Abstract: Extending the research on our discipline’s identity, we examine how the major re ... Expand

Abstract: Extending the research on our discipline’s identity, we examine how the major research themes have evolved in four top IS journals: Management Information Systems Quarterly (MISQ), Information Systems Research (ISR), Journal of the Association for Information Systems (JAIS), and Journal of Management Information Systems (JMIS). By doing so, we answer Palvia, Daneshvar Kakhki, Ghoshal, Uppala, and Wang’s (2015) call to provide continuous updates to the research trends in IS due to the discipline’s dynamism. Second, building on Sidorov, Evangelopoulos, Valacich, and Ramakrishnan (2008) we examine temporal trends in prominent research streams over the last 17 years. We show that, as IS research evolves over time, certain themes appear to endure the test of time, while others peak and trough. More importantly, our analysis identifies new emergent themes that have begun to gain prominence in IS research community. Further, we break down our findings by journal and show the type of content that they may desire most. Our findings also allow the IS research community to discern the specific contributions and roles of our premier journals in the evolution of research themes over time. Collapse

Semantic filters: expectation maximization clustering

Topics: electronic commerce privacy IS discipline data security technology adoption

Methods: natural language processing cluster analysis data transformation literature sample descriptive statistic

Theories: technology acceptance model
A computational model for financial reporting fraud detection
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2011 | Decision Support Systems | Citations: 18
Authors: Glancy, Fletcher H.; Yadav, Surya B.

Abstract: A computational fraud detection model (CFDM) was proposed for detecting fraud in ... Expand

Abstract: A computational fraud detection model (CFDM) was proposed for detecting fraud in financial reporting. CFDM uses a quantitative approach on textual data. It incorporates techniques that use essentially all of information contained in the textual data for fraud detection. Extant work provides a foundation for detecting deception in high and low synchronicity computer-mediated communication (CMC). CFDM provides an analytical method that has the potential for automation. It was tested on the Management's Discussion and Analysis from 10-K filings and was able to distinguish fraudulent filings from non-fraudulent ones. CFDM can serve as a screening tool where deception is suspected. Collapse

Semantic filters: expectation maximization clustering

Topics: accounting fraud detection process automation system database system deception detection

Methods: natural language processing cluster analysis hierarchical clustering qualitative content analysis expectation maximization clustering

Theories: interpersonal deception theory media richness theory

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Information Systems Research Themes: A Seventeen-year Data-driven Temporal Analysis

A computational model for financial reporting fraud detection