J
Jae-Hyeon Ahn
Researcher at KAIST
Publications - 65
Citations - 2231
Jae-Hyeon Ahn is an academic researcher from KAIST. The author has contributed to research in topics: Knowledge value chain & Telecommunications service. The author has an hindex of 21, co-authored 63 publications receiving 1916 citations. Previous affiliations of Jae-Hyeon Ahn include Bell Labs & Saint Petersburg State University.
Papers
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Customer churn analysis: Churn determinants and mediation effects of partial defection in the Korean mobile telecommunications service industry
TL;DR: Using customer transaction and billing data, the authors investigates determinants of customer churn in the Korean mobile telecommunications service market, and finds that call quality-related factors influence customer churn; however, customers participating in membership card programs are also more likely to churn.
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Attention to Banner Ads and Their Effectiveness: An Eye-Tracking Approach
Joowon Lee,Jae-Hyeon Ahn +1 more
TL;DR: The results suggest that animation in banner ads does not necessarily increase user attention, but that even if a user does not consciously notice a banner ad, the user's attitude toward the brand is influenced.
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Assessing the contribution of knowledge to business performance: the KP 3 methodology
Jae-Hyeon Ahn,Suk-Gwon Chang +1 more
TL;DR: The KP3 methodology developed in this paper assesses the contribution of knowledge to business performance by employing product and process as intermediaries between the two.
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Reward systems for intra-organizational knowledge sharing
Dong-Joo Lee,Jae-Hyeon Ahn +1 more
TL;DR: A formal model and analyze reward systems for intra-organizational knowledge sharing is developed and it is shown that several organizational factors can complement reward systems in increasing the performance of KM and can mitigate the productivity problem.
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Willingness to provide personal information: Perspective of privacy calculus in IoT services
TL;DR: Examination of factors affecting the willingness to provide privacy information based on the privacy calculus theory in several IoT services; healthcare, smart home and smart transportation indicates that people do not pay much attention to perceived privacy risk when providing privacy information for a better personalized service.