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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/117210

    Title: Big data analytics for investigating Taiwan Line sticker social media marketing
    Other Titles: Asia Pacific Journal of Marketing and Logistics
    Authors: Shu-Hsien Liao;Szu-Yu Hsu
    Keywords: Social marketing;E-marketing;Database marketing;Media (new media)
    Date: 2019-10-31
    Issue Date: 2019-10-03 12:11:28 (UTC+8)
    Publisher: Emerald
    Abstract: Purpose – Line sticker, a social media, it allows users to exchange multimedia files and engage in one-to-one
    and one-to-many communication with text, pictures, animation and sound. The purpose of this paper is to
    examine various Taiwan user experiences in the Line sticker use behaviors. Further, this research looks at
    how the situations of Line sticker proprietors and their affiliates are disseminated for formulating social
    media marketing (SMM) in its business model concerns.
    Design/methodology/approach – This study examines the experience of various Taiwanese Line stickers
    users utilizing a market survey, a total of 1,164 valid questionnaire data, and the questionnaire is divided into
    five sections with 30 items in terms of the database design. All questions use nominal and order scales. This
    study develops a big data analytics approach, including cluster analysis and association rules, based on a big
    data structure and a relational database.
    Findings – The authors divide Taiwan Line sticker users into three clusters by their profiles and then find
    each group’s social media utilization and online purchase behaviors for investigating the Line sticker SMM
    and business models.
    Originality/value – This is the first study to offer a big data analytics to investigate and analyze the
    varieties in the use of Line sticker by exploring users’ behaviors for further SMM and business model
    Relation: Asia Pacific Journal of Marketing and Logistics 32(2), p.589-606
    DOI: 10.1108/APJML-03-2019-0211
    Appears in Collections:[Department of Management Sciences] Journal Article

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