NDLI: A Hybrid Approach to Discover MEC Interview Data with the Hierarchical Value Map of Social Networking Sites as an Example

Content Provider	IEEE Xplore Digital Library
Author	Yu-Chin Liu Ti-Lin Chueh Yun-Shan Cheng
Copyright Year	2011
Abstract	As the booming of social network sites (SNSs), people adapt to communicate and share information via internet recently. According to great business opportunities emerging in SNSs, entrepreneurs strive to explore the potential needs inside users and then provide interesting feature functions on SNS platforms. The Means-End Chain (MECs) research method has been widely used to explore customers¡¦ perceived values in selecting products. It is a good approach to help entrepreneurs finding the most appreciated product features. But however, while adopting MECs, researchers suffer the hassle of defining Attribute, Consequence and Value elements (ACV elements) from interview data. In addition, such context analyzing work heavily relies on researchers¡¦ subjective opinions, so that the research conclusions might be difficult to replicate and the contributions are limited. Therefore, this paper aims to propose hybrid miming techniques to automatically discover Attribute, Consequence and Value elements which are the most essential components in MEC approach. A case on studying customers¡¦ perceived values of social network cites is conducted by the proposed hybrid approach, and the experimental results show our method can discover the ACV elements effectively.
Starting Page	373
Ending Page	376
File Size	408063
Page Count	4
File Format	PDF
ISBN	9781612847580
DOI	10.1109/ASONAM.2011.27
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-07-25
Publisher Place	Taiwan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Context Means-End Chain Social network services Semantics Social Network Sites Information management Text Mining Reliability Interviews
Content Type	Text
Resource Type	Article

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