Data Analytics in Behavioral Research

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    Your Privacy Is Your Friend's Privacy: Examining Interdependent Information Disclosure on Online Social Networks
    ( 2018-01-03) Alsarkal, Yaqoub ; Zhang, Nan ; Xu, Heng
    The highly interactive nature of interpersonal communication on online social networks (OSNs) impels us to think about privacy as a communal matter, with users' private information being revealed by not only their own voluntary disclosures, but also the activities of their social ties. The current privacy literature has identified two types of information disclosures in OSNs: self-disclosure, i.e., the disclosure of an OSN user's private information by him/herself; and co-disclosure, i.e., the disclosure of the user's private information by other users. Although co-disclosure has been increasingly identified as a new source of privacy threat inherent to the OSN context, few systematic attempts have been made to provide a framework for understanding the commonalities and distinctions between self- vs. co-disclosure, especially pertaining to different types of private information. To address this gap, this paper presents a data-driven study that builds upon an innovative measurement for quantifying the extent to which others' co-disclosure could lead to actual privacy harm. The results demonstrate the significant harm caused by co-disclosure and illustrate the differences between the identity elements revealed through self- and co-disclosure.
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    Understanding Mobile Banking Success Through User Segmentation
    ( 2018-01-03) Motiwalla, Luvai ; Albashrawi, Mousa ; Kartal, Hasan
    Mobile banking (MB) which involves the use of mobile devices to access bank accounts for conducting financial transactions has grown rapidly but unevenly with users. Banks realizes the strategic role of user’s satisfaction and the importance of MB systems in their business models. Yet, the diversity of users and disparity of system usage behaviors make difficult to measure MB success. This study segments the MB users on system use behavior of 4,478 users with objective measures by analyzing the MB system log files on various system usage metrics. Then, a subjective measures study surveys the same users on the system success factors of the information systems (IS) success model by using 445 responses. Results indicate that the influence of success factors significantly varies among user segments for intention to use, which makes an important contribution to enhance interpretation of the IS success model.
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    Value Chain Creation in Business Analytics
    ( 2018-01-03) Yoo, Dong ; Roh, James
    Firms are awash in big data and analytical technology as part of deriving values in the turbulent environment. The literature has somewhat reached a consensus that investments in technology only may not reap benefits from business analytics (BA). The main purpose of BA is not about how to install technical capabilities, but about how to make a process whereby a firm builds a value chain converting data into insights, leading to quality decisions. Drawing upon the theory of the information value chain, this study develops a BA value chain model and tests it with 268 data scientists. Results show that organizational resilience, absorptive capacity, and analytical IT capabilities are critical antecedents to analytical decision-making quality which in turn influences BA net benefits. Particularly, results illustrate that organizational resilience is a more significant variable impacting analytical decision-making quality than the influence of people and technology. Theoretical and practical implications are also discussed.
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    Multimodal Data Fusion and Behavioral Analysis Tooling for Exploring Trust, Trust-propensity, and Phishing Victimization in Online Environments
    ( 2018-01-03) Hefley, Michael ; Wethor, Gabrielle ; Hale, Matthew L.
    Online environments, including email and social media platforms, are continuously threatened by malicious content designed by attackers to install malware on unsuspecting users and/or phish them into revealing sensitive data about themselves. Often slipping past technical mitigations (e.g. spam filters), attacks target the human element and seek to elicit trust as a means of achieving their nefarious ends. Victimized end-users lack the discernment, visual acuity, training, and/or experience to correctly identify the nefarious antecedents of trust that should prompt suspicion. Existing literature has explored trust, trust-propensity, and victimization, but studies lack data capture richness, realism, and/or the ability to investigate active user interactions. This paper defines a data collection and fusion approach alongside new open-sourced behavioral analysis tooling that addresses all three factors to provide researchers with empirical, evidence-based, insights into active end-user trust behaviors. The approach is evaluated in terms of comparative analysis, run-time performance, and fused data accuracy.
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    A Systematic Analysis of Patient Portals Adoption, Acceptance and Usage: The Trajectory for Triple Aim?
    ( 2018-01-03) Al-Ramahi, Mohammad ; Noteboom, Cherie
    Personal Health Records (PHR), often known as patient portal, are consumer-centric tools that can strengthen consumers’ ability to actively manage their own health and healthcare. The incorporation of patient portals provides the promise to assist with Triple Aim and population health goals. Patient portals encourage patients to play a more active role in their healthcare by giving them more responsibility for maintaining a healthy lifestyle and managing chronic diseases and thus may provide a cost-effective way to improve quality of care. In this study, we extend the existing literature by using a data analytic approach to provide more insights in adopting mobile patient portals. Specifically, we aim to use topic modeling approach, LDA algorithm, to systematically analyze users’ feedback (i.e., online users’ reviews) from the actual use of a common mobile patient portal, Epic’s MyChart. To validate the extracted topics, we compared the results of LDA analysis with that of human analysis. Overall, the extracted topics revealed opportunities for improvement and to enhance the design of current basic portals to improve usage. Improved portal usage will move toward effective population health management and achievement of the triple aim goals.
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    Introduction to the Minitrack on Data Analytics in Behavioral Research
    ( 2018-01-03) Motiwalla, Luvai ; Deokar, Amit ; Sarnikar, Surendra