Journal of management information systems. (Philadelphia, PA : Taylor & Francis Group, LLC, 2021)
The Roles of IT Strategies and Security Investments in Reducing Organizational Security Breaches He Li, Sungjin Yoo & William J. Kettinger Pages 222-245 ABSTRACT This research examines the joint effects of information technology (IT) strategies and security investments on organizational security breaches. We focus on two forms of IT strategies: digitalization and embeddedness in IT outsourcing networks. Our longitudinal analysis of U.S. hospitals demonstrates that IT security investments reduce security breaches in less digitalized organizations but increase security breaches for highly digitalized organizations. Investing in technical network control security systems such as anti-virus and intrusion detection systems reduces external breaches. Implementing identity and access management security systems such as biometric scanning and user authentication decreases internal breaches but increases external breaches. However, organizations' embeddedness in IT outsourcing networks weakens the impacts of these technologies investments on external breaches but amplifies the negative relationship between identity and access management security systems and internal breaches. Our results offer an alternative understanding of organizational IT security investments and explain contrary results found in prior studies. Practical guidelines on organizational IT security strategies are discussed.
Unveiling the Hidden Truth of Drug Addiction: A Social Media Approach Using Similarity Network-Based Deep Learning Jiaheng Xie , Zhu Zhang, Xiao Liu & Daniel Zeng Pages 166-195 ABSTRACT Opioid use disorder (OUD) is an epidemic that costs the U.S. healthcare systems $504 billion annually and poses grave mortality risks. Existing studies investigated OUD treatment barriers via surveys as a means to mitigate this opioid crisis. However, the response rate of these surveys is low due to social stigma around opioids. We explore user-generated content in social media as a new data source to study OUD. We design a novel IT system, SImilarity Network-based DEep Learning (SINDEL), to discover OUD treatment barriers from patient narratives and address the challenge of morphs. SINDEL significantly outperforms state-of-the-art NLP models, reaching an F1 score of 76.79 percent. Thirteen types of treatment barriers were identified and verified by domain experts. This work contributes to information systems with a novel deep-learning-based approach for text analytics and generalized design principles for social media analytics methods. We also unveil the hurdles patients endure during the opioid epidemic.