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Issue title: Special Section: Green and Human Information Technology
Guest editors: Seong Oun Hwang
Article type: Research Article
Authors: Lee, Duckkia; * | Helal, Sumib | Jung, Eunsungc
Affiliations: [a] Department of Smart Software, Yonam Institute of Technology, South Korea | [b] School of Computing and Communications, Lancaster University, U. K. | [c] Department of Computer and Information Communications Engineering, Hongik University, South Korea
Correspondence: [*] Corresponding author. Duckki Lee, Department of Smart Software, Yonam Institute of Technology, South Korea. E-mail: [email protected].
Abstract: With the emergence of mobile and pervasive computing, digital gadgets and products have become a pervasive and of our lives. Everyday, they influence us explicitly or implicitly and are changing our way of life as well as our behaviors, either intentionally or unintentionally. Throughout the past decade, research has been conducted to make persuasion interactive rather than unilateral. The outcome is known today as persuasive computing technology. There have been considerable efforts to apply persuasive technology in many domains, especially healthcare. Although these efforts to are gradually coming to fruition, several problems and challenges remain. Most currently available persuasive systems were not based on explicit theoretical foundations of persuasion theories and models when they were developed. There are two major reasons: (1) a general lack of theoretical frameworks and practical methodologies for building persuasive systems or applications, and (2) it is difficult for computer scientists and developers to understand and harness persuasion theories and models. In response, we have developed an Action-based Behavior Model (ABM) that can be utilized by computer scientists and developers. In this paper, we validate ABM through a scenario-based case, describing how it can be utilized in participatory telehealth to enable persuasion.
Keywords: Persuasion-enabled telehealth, persuasive system, persuasive computing, smart health
DOI: 10.3233/JIFS-169853
Journal: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6133-6140, 2018
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