English  |  正體中文  |  简体中文  |  全文筆數/總筆數 : 60984/93521 (65%)
造訪人次 : 1574394      線上人數 : 11
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜尋範圍 查詢小技巧:
  • 您可在西文檢索詞彙前後加上"雙引號",以獲取較精準的檢索結果
  • 若欲以作者姓名搜尋,建議至進階搜尋限定作者欄位,可獲得較完整資料
  • 進階搜尋
    請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/113074

    題名: Antecedents and Optimal Industrial Customers on Cloud Services Adoption
    作者: Shui-Lien Chen;June-Hong Chen
    關鍵詞: Cloud services;technology acceptance theory;optimal industrial customer;structural equation modeling;technique for order performance by similarity to ideal solution
    日期: 2018-02-16
    上傳時間: 2018-04-12 12:10:43 (UTC+8)
    出版者: Taylor & Francis
    摘要: The rapid flourishing of the cloud service market necessitates investigating the underlying determinants of cloud services adoption and identifying optimal industrial customers for business-to-business (B2B) service encounters. Many studies have addressed technical and operational concerns related to cloud services. However, only a few studies have addressed the adoption of cloud computing from an organizational perspective, and none of them have considered the practical application of cloud computing in society. Therefore, in this paper, a research model is constructed to understand an industrial organization’s acceptance of cloud services and apply the results in order to explore optimal industrial customers. A questionnaire-based survey was used to collect data from the population, 227 firms in the manufacturing and services industries in Taiwan. Causal relationships were tested through structural equation modeling and the ordering of optimal industrial customers was evaluated by using the Technique for Order of Preference by Similarity to Ideal Solution method.
    關聯: The Service Industries Journal 41(9-10)
    DOI: 10.1080/02642069.2018.1437907
    顯示於類別:[管理科學學系暨研究所] 期刊論文


    檔案 描述 大小格式瀏覽次數



    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - 回饋