[1]陈星宇,周展,黄俊文,等.基于参与-情感-认知框架的用户创新性预测[J].深圳大学学报理工版,2018,35(4):426-431.[doi:10.3724/SP.J.1249.2018.04426]
 CHEN Xingyu,ZHOU Zhan,HUANG Junwen,et al.Predicting user innovativeness based on an involvement-emotion-cognition framework[J].Journal of Shenzhen University Science and Engineering,2018,35(4):426-431.[doi:10.3724/SP.J.1249.2018.04426]
点击复制

基于参与-情感-认知框架的用户创新性预测()
分享到:

《深圳大学学报理工版》[ISSN:1000-2618/CN:44-1401/N]

卷:
第35卷
期数:
2018年第4期
页码:
426-431
栏目:
数学与应用数学
出版日期:
2018-07-10

文章信息/Info

Title:
Predicting user innovativeness based on an involvement-emotion-cognition framework
作者:
陈星宇1周展1黄俊文1陶达2
1) 深圳大学管理学院,广东深圳 518060;2) 深圳大学人因工程研究所,广东深圳 518060
Author(s):
CHEN Xingyu1 ZHOU Zhan1 HUANG Junwen1 and TAO Da2
1) College of Management, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China 2) Institute of Human Factors and Ergonomics, Shenzhen University, Shenzhen 518060, Guangdong Province, P.R.China
关键词:
市场营销用户研究创新用户参与-情感-认知框架关键词提取创新等级
Keywords:
marketing user research innovative users involvement-emotion-cognition framework keyword extraction innovation index
分类号:
F 204
DOI:
10.3724/SP.J.1249.2018.04426
文献标志码:
A
摘要:
随着近年来互联网行业的兴起,新兴企业对产品创新的需求逐渐增加. 从海量潜在在线用户中识别创新用户并获取其对新产品的需求,是企业进行产品创新的一个有效途径.然而基于现有的用户特征理论和市场细分模型维度并不能有效的自动识别出创新用户.因此,本研究基于用户创新特征的3个维度,即参与、情感和认知,构建一个可预测在线社区用户创新性的综合细分框架.此框架通过提取与用户创新特征相关联的维度变量,结合本研究挖掘方法处理海量在线用户数据,并通过计算用户的创新等级预测用户创新性,从而有效识别不同创新能力的在线用户群体. 最后,通过基于国内某知名在线社区的例证分析,证实了此框架的有效性.
Abstract:
Identifying innovative users and figuring out their requirements for new products are one of effective approaches for product innovation. However, existing market theories and segmentation models fail to achieve such purpose. This study propose an integrated customer segmentation framework for the prediction of innovative users based on three user innovation characteristics (i.e., involvement, emotion and cognition). Combining text mining methods, this framework is able to predict innovative users from massive online user data by calculating the users’ innovation level based on extracted users’ innovation features. A case study with data from a well-known Chinese online user-generate-content community is conducted and verifies the effectiveness of the proposed framework.
更新日期/Last Update: 2018-06-20