English  |  正體中文  |  简体中文  |  全文笔数/总笔数 : 58317/91854 (63%)
造访人次 : 14031587      在线人数 : 70
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
搜寻范围 查询小技巧:
  • 您可在西文检索词汇前后加上"双引号",以获取较精准的检索结果
  • 若欲以作者姓名搜寻,建议至进阶搜寻限定作者字段,可获得较完整数据
  • 进阶搜寻

    jsp.display-item.identifier=請使用永久網址來引用或連結此文件: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/113074

    题名: Antecedents and Optimal Industrial Customers on Cloud Services Adoption
    作者: Shui-Lien Chen;June-Hong Chen
    关键词: 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
    上传时间: 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: Service Industries Journal
    DOI: 10.1080/02642069.2018.1437907
    显示于类别:[管理科學學系暨研究所] 期刊論文


    档案 描述 大小格式浏览次数



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