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On Robust Estimates of Correlated Risk in Cyber-Insured IT Firms: A First Look at Optimal AI-Based Estimates under “Small” Data

In this article, we comment on the drawbacks of the existing AI-based Bayesian network (BN) cyber-vulnerability analysis (C-VA) model proposed in... (more)

Service-oriented Application Composition with Evolutionary Heuristics and Multiple Criteria

The need to create and deploy business application systems rapidly has sparked interest in using web services to compose them. When creating... (more)

ThumbStroke: A Virtual Keyboard in Support of Sight-Free and One-Handed Text Entry on Touchscreen Mobile Devices

The QWERTY keyboard on mobile devices usually requires users’ full visual attention and both hands, which is not always possible. We propose a thumb-stroke-based keyboard, ThumbStroke, to support both sight-free and one-handed text entry. Text entry via ThumbStroke completely relies on the directions of thumb strokes at any place on the... (more)

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Call for Papers Special Issue on Analytics for Cybersecurity and Privacy -- New Deadline November 15, 2019


About TMIS

ACM Transactions on Management Information Systems (TMIS) publishes the highest quality papers about the design, development, assessment, and management of information technology and systems within organizations, businesses, and societies. In addition to traditional management and behavioral MIS research, ACM TMIS strongly encourages submissions of high-quality system and design science research, as well as submissions in emerging MIS multidisciplinary research topics that may span several traditional academic disciplines. ACM TMIS is indexed by Ei Compendex (EI) and Emerging Sources Citation Index (ESCI).

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Forthcoming Articles
Interaction Models for Detecting Nodal Activities in Temporal Social Media Networks

Detecting nodal activities in dynamic social networks has strategic importance in many applications, such as online marketing campaigns and homeland security surveillance. How peerto-peer exchanges in social media can facilitate nodal activity detection is not well explored. Existing models assumes network nodes to be static in time and do not adequately consider features from social theories. This research developed and validated two theory-based models, Random Interaction Model (RIM) and Preferential Interaction Model (PIM), to characterize temporal nodal activities in social media networks of human agents. The models capture the network characteristics of randomness and preferential interaction due to community size, human bias, declining connection cost, and rising reachability. The models were compared against three benchmark models (abbreviated as EAM, TAM, and DBMM) using a social-media community consisting of 790,462 users who posted over 3,286,473 tweets and formed more than 3,055,797 links during 2013-2015. The experimental results show that both RIM and PIM outperformed EAM and TAM significantly in accuracy across different dates and time windows. Both PIM and RIM scored significantly smaller errors than DBMM did. Structural properties of social networks were found to provide a simple and yet accurate approach to predicting model performances. These results indicate the models' strong capability of accounting for user interactions in real-world social media networks and temporal activity detection. The research should provide new approaches for temporal network activity detection, develop relevant new measures, and report new findings from large social media datasets.

Helpfulness Assessment of Online Reviews Using a Semantic Hierarchy of Product Features

Effective use of online consumer reviews is hampered by uncertainty about their helpfulness. Despite emerging efforts in identifying antecedents of review helpfulness, they have largely overlooked rich semantic relationships embedded in online reviews. To address the literature gap, this study probes review text by uncovering semantic relationships among product features. We introduce three novel factors - breadth, depth, and redundancy, to gain a deep understanding of review helpfulness. Drawing on product uncertainty and information quality theories, we conceptualize and operationalize the proposed factors based on a semantic hierarchy of product features. The evaluation results on both experience and search goods lend strong support to those factors in improving both theoretical understanding and practical assessment of review helpfulness. Breadth and depth also offer new lens for explaining mixed findings about some other factors in the literature.

The Economics of Cybercrime: The Role of Broadband and Socioeconomic Status

Under what conditions is the Internet more likely to be used maliciously for criminal activity? This study examines the conditions under which the Internet is associated with cybercriminal offenses. Using comprehensive state-level data in the United States during 2004-2010, our findings show that there is no clear empirical evidence that the Internet penetration rate is related to the number of Internet crime perpetrators; however, cybercriminal activities are contingent upon socioeconomic factors and connection speed. Specifically, a higher income, more education, a lower poverty rate, a lower unemployment rate, and a lower inequality are likely to make the Internet penetration be more positively related with cybercrime perpetrators, which are indeed different from the conditions of terrestrial crime in the real world. In addition, broadband connections are significantly and positively associated with Internet crime perpetrators, though narrowband connections are not. Taken together, cybercrime requires more than just a skilled perpetrator, and it requires an infrastructure to facilitate profiteering from the act. A relevant discussion is provided.

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