Does big data mean big knowledge? Integration of big data analysis and conceptual model for social commerce research

被引:16
|
作者
Tian, Xuemei [1 ]
Liu, Libo [1 ]
机构
[1] Swinburne Univ Technol, Fac Business & Law, Dept Business Technol & Entrepreneurship, Melbourne, Vic, Australia
关键词
Big data; Business intelligence; Customer knowledge management; Customer purchase behavior; BUSINESS INTELLIGENCE; REVIEWS;
D O I
10.1007/s10660-016-9242-7
中图分类号
F [经济];
学科分类号
02 ;
摘要
The Big Data era has descended on many communities, from governments and e-commerce to health organizations. Information systems designers face great opportunities and challenges in developing a holistic big data research approach for the new analytics savvy generation. In addition business intelligence is largely utilized in the business community and thus can leverage the opportunities from the abundant data and domain-specific analytics in many critical areas. The aim of this paper is to assess the relevance of these trends in the current business context through evidence-based documentation of current and emerging applications as well as their wider business implications. In this paper, we use BigML to examine how the two social information channels (i.e., friends-based opinion leaders-based social information) influence consumer purchase decisions on social commerce sites. We undertake an empirical study in which we integrate a framework and a theoretical model for big data analysis. We conduct an empirical study to demonstrate that big data analytics can be successfully combined with a theoretical model to produce more robust and effective consumer purchase decisions. The results offer important and interesting insights into IS research and practice.
引用
收藏
页码:169 / 183
页数:15
相关论文
共 50 条
  • [41] E-Commerce Security Research in Big Data Environment
    Zhang, Mei
    Liu, Huan
    Wen, Jinghua
    INTERNATIONAL JOURNAL OF ENTERPRISE INFORMATION SYSTEMS, 2018, 14 (01) : 63 - 76
  • [42] Research on E-commerce Credit Information Evaluation Based on Social Big Data
    Shuang, Huang
    2020 5TH INTERNATIONAL CONFERENCE ON SMART GRID AND ELECTRICAL AUTOMATION (ICSGEA 2020), 2020, : 510 - 514
  • [43] The Big Data Analysis
    Burunova, Anna V.
    PROCEEDINGS OF THE 2018 IEEE CONFERENCE OF RUSSIAN YOUNG RESEARCHERS IN ELECTRICAL AND ELECTRONIC ENGINEERING (EICONRUS), 2018, : 285 - 286
  • [44] Research on Big Data Reference Architecture Model
    Luo Xiaofeng
    Luo Jing
    2020 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE AND BIG DATA (ICAIBD 2020), 2020, : 205 - 209
  • [45] Big Data, the Internet of Things, and the Revised Knowledge Pyramid
    Jennex, Murray E.
    DATA BASE FOR ADVANCES IN INFORMATION SYSTEMS, 2017, 48 (04): : 69 - 79
  • [46] Big data text analytics: an enabler of knowledge management
    Khan, Zaheer
    Vorley, Tim
    JOURNAL OF KNOWLEDGE MANAGEMENT, 2017, 21 (01) : 18 - 34
  • [47] Big data systems: knowledge transfer or intelligence insights?
    Rothberg, Helen N.
    Erickson, G. Scott
    JOURNAL OF KNOWLEDGE MANAGEMENT, 2017, 21 (01) : 92 - 112
  • [48] Big Data for Social Transportation
    Zheng, Xinhu
    Chen, Wei
    Wang, Pu
    Shen, Dayong
    Chen, Songhang
    Wang, Xiao
    Zhang, Qingpeng
    Yang, Liuqing
    IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS, 2016, 17 (03) : 620 - 630
  • [49] Big data and Wikipedia research: social science knowledge across disciplinary divides
    Schroeder, Ralph
    Taylor, Linnet
    INFORMATION COMMUNICATION & SOCIETY, 2015, 18 (09) : 1039 - 1056
  • [50] Big data and explanation: Reflections on the uses of big data in media and communication research
    Helles, Rasmus
    Ormen, Jacob
    EUROPEAN JOURNAL OF COMMUNICATION, 2020, 35 (03) : 290 - 300