D
Dong-Hee Shin
Researcher at Zayed University
Publications - 278
Citations - 11240
Dong-Hee Shin is an academic researcher from Zayed University. The author has contributed to research in topics: User experience design & Technology acceptance model. The author has an hindex of 49, co-authored 260 publications receiving 8730 citations. Previous affiliations of Dong-Hee Shin include Sungkyunkwan University & Pennsylvania State University.
Papers
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Towards an understanding of the consumer acceptance of mobile wallet
TL;DR: The proposed model brings together extant research on mobile payment and provides an important cluster of antecedents to eventual technology acceptance via constructs of behavioral intention to use and actual system usage.
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The effects of trust, security and privacy in social networking: A security-based approach to understand the pattern of adoption
TL;DR: An SNS acceptance model is proposed by integrating cognitive as well as affective attitudes as primary influencing factors, which are driven by underlying beliefs, perceived security, perceived privacy, trust, attitude, and intention.
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An acceptance model for smart watches: Implications for the adoption of future wearable technology
Ki Joon Kim,Dong-Hee Shin +1 more
TL;DR: The AQ and RA of smart watches were found to be associated with perceived usefulness, while the sense of MB and AV induced by smart watches led to a greater perceived ease of use.
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Empathy and embodied experience in virtual environment
TL;DR: The findings of this study suggest that the cognitive processes by which users experience quality, presence, and flow determine how they will empathize with and embody VR stories.
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The effects of explainability and causability on perception, trust, and acceptance: Implications for explainable AI
TL;DR: This study examines the effect of explainability in AI on user trust and attitudes toward AI by conceptualizing causability as an antecedent of explainable and as a key cue of an algorithm and examines them in relation to trust by testing how they affect user perceived performance of AI-driven services.