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Mining the Adoption Intention of Location-Based Services Based on Dominance-Based Rough Set Approaches

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Modelling, Computation and Optimization in Information Systems and Management Sciences

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 360))

Abstract

Faced with the excessive opportunity, mobile service providers have unconstrained a variability of services, such as mobile instant messaging, mobile shopping and location-based services (LBS). Location-based service is a social media tool that allows users to write short text messages to public and private networks. This research focuses specifically on the mobile services on LBS. The main purposes of this study are to investigate and compare what recommendation sources influence the intention to use LBS and to combine gender, daily internet hour usage and past use experience to infer the usage of LBSs decision rules using a dominace-based rough-set approach. Data for this study were collected from 398 users and potential users. The analysis is grounded in the taxonomy of induction-related activities using a domiance-based rough set approach to infer the usage of LBSs decision rules. Finally, the study of the nature of LBS reflects essential practical and academic value.

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Authors and Affiliations

  1. Department of International Business, Asia University, 500, Lioufeng Rd., Taichung, Wufeng, 41354, Taiwan

    Yang-Chieh Chin

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  1. Yang-Chieh Chin
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Correspondence to Yang-Chieh Chin .

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Editors and Affiliations

  1. Laboratory of Theoretical and Applied Computer Science, University of Lorraine, Metz, France

    Hoai An Le Thi

  2. Laboratory of Mathematics, National Institute for Applied Sciences, Rouen, France

    Tao Pham Dinh

  3. Division of Knowledge Management Systems, Wroclaw University of Technology, Poland

    Ngoc Thanh Nguyen

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Chin, YC. (2015). Mining the Adoption Intention of Location-Based Services Based on Dominance-Based Rough Set Approaches. In: Le Thi, H., Pham Dinh, T., Nguyen, N. (eds) Modelling, Computation and Optimization in Information Systems and Management Sciences. Advances in Intelligent Systems and Computing, vol 360. Springer, Cham. https://doi.org/10.1007/978-3-319-18167-7_6

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  • DOI: https://doi.org/10.1007/978-3-319-18167-7_6

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-18166-0

  • Online ISBN: 978-3-319-18167-7

  • eBook Packages: EngineeringEngineering (R0)

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