Influence Model of Enterprise Microblogging Marketing Based on Social Network

Abstract

Abstract:

Measuring microblog influence and identifying marketing effect can help enterprises to develop effective microblogging operations. We focus on microblog marketing social network generated by information flow when e-commerce business introduces marketing shopping links. Microblogging interactive information flow diagram is analyzed and defined based onthe consideration of mutual influence relationship between users and microblogs and link structure which is a true reflection of corporate microblogging social network interaction and information flow. With the foundation of interactive information flow diagram, formulas computing influence scores of business users and microblogs content are proposed respectively. The optimal weight values are obtained by experiments using microblog data from dangdang.com, and then formulas are determined. Experimental results show that in the situation that user activity level of microblog social network is generally low, ongoing series microblog marketing activities with opportunity marketing or stimulus marketing contribute to the expansion and accumulation of microblog business users' influence.

Key words: social network, social commerce, microblog marketing, influence

Zuo Wenming, Huang Qiuping, Chen Huaqiong, Mo Xiaohua. Influence Model of Enterprise Microblogging Marketing Based on Social Network[J]. , 2016, 28(9): 163-172.

References

[1] Gaedke Y., Robra-bissantz S., Christian S., et al. An Analyse of Variables Influencing E-Readiness 2.0[C].Proceedings of the IADIS International Conference on e-Commerce, 2010
[2] Liang T. P., Turban E. Introduction to the Special Issue Social Commerce:.Research Framework for Social Commerce[J].International Journal of Electronic Commerce, 2011,16(2):5-14
[3] Tang J., Sun J., Wang C., et al. Social Influence Analysis in Large-Scale Networks[C].Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2009
[4] Yamaguchi Y., Takahashi T. TURank: Twitter User Ranking Based on User-Tweet Graph Analysis[C].Web Information Systems Engineering.WISE 2010, 2010
[5] Cha M., Haddadi H., Benevonuto F., et al. Measuring User Influence in Twitter: The Million Follower Fallacy[C].Proceedings of the Fourth International AAAI Conference on Weblogs and Social Media, 2010
[6] Goyal A., Bonchi F., Lakshmanan L. V. S. Learning Influence Probabilities in Social Networks[C].Proceedings of the Third ACM International Conference on Web Search and Data Mining -WSDM '10, 2010
[7] Ye S., Wu S. F. Measuring Message Propagation and Social Influence on Twitter.com[M].Heidelberg: Springer Berlin Heidelberg, 2010
[8] Zwass V. Co-Creation: Toward.Taxonomy and an Integrated Research Perspective[J].International Journal of Electronic Commerce, 2010,15(1):11-48
[9] Cheung M. Y., Luo C., Sia C. L., et al. Credibility of Electronic Word-of-Mouth: Informational and Normative Determinants of On-Line Consumer Recommendations[J].International Journal of Electronic Commerce, 2009,13(4):9-38
[10] Park D., Lee J., Han I. The Effect of On-Line Consumer Reviews on Consumer Purchasing Intention: The Moderating Role of Involvement [J].International Journal of Electronic Commerce, 2007,11(4):125-148
[11] Jansen B. J., Zhang M., Sobel K, et al. Twitter Power: Tweets as Electronic Word of Mouth[J].Journal of the American Society for Information Science and Technology, 2009,60(11):2169-2188
[12] Java A., Song X., Finin T., et al. Why We Twitter: Understanding Microblogging Usage and Communities[C].Proceedings of the 9th WebKDD and 1st SNA-KDD 2007 Workshop on Web Mining and Social Network Analysis, 2007
[13] Huberman B. A., Romero D. M., Wu F. Social Networks That Matter: Twitter Under the Microscope[J].First Monday, 2009,14(1):1-9
[14] Asur S., Huberman B. A. Predicting the Future with Social Media[C].IEEE/ACM International Conference on Web Intelligence-Intelligent Agent Technology (WI-IAT), 2010
[15] 2012企业微博白皮书[R].新浪微博,CIC,2012
[16] 金永生,王睿,陈祥兵.企业微博营销效果和粉丝数量的短期互动模型[J].管理科学, 2011,24(4):71-83
[17] 杨铭,祁巍,闫相斌,李一军.在线商品评论的效用分析研究[J].管理科学学报, 2012,15(5):65-75
[18] Liang T. P., Ho Y. T., Li Y. W., et al. What Drives Social Commerce: The Role of Social Support and Relationship Quality[J].International Journal of Electronic Commerce, 2011,16(2):69-90
[19] Geierhos M. Customer Interaction 2.0: Adopting Social Media as Customer Service Channel[J].Journal of Advances in Information Technology, 2011,2(4):169-174
[20] 元世娇.微博营销的特点与功能初探[J].江苏商论, 2011,(5):97-98
[21] Hanna R., Rohm A., Crittenden V. We're All Connected: The Power of the Social Media Ecosystem[J].Business Horizons, 2011,54(3):265-273
[22] Kaplan A. M., Haenlein M. The Early Bird Catches the News: Nine Things You Should Know About Micro-Blogging[J].Business Horizons, 2011,54(2):105-113
[23] 袁毅.微博客信息传播结构、路径及其影响因素分析[J].图书情报工作, 2011,55(12):26-30
[24] 王霞,牛海鹏.企业微博营销中品牌曝光度对网络口碑的影响研究[J].管理评论, 2013,25(5):116-122
[25] Burton S., Soboleva A. Interactive or Reactive? Marketing with Twitter[J].Journal of Consumer Marketing, 2011,28(7):491-499
[26] 郭秋艳,何跃.新浪微博名人用户特征挖掘及效应研究[J].情报杂志, 2013,32(2):112-116
[27] 庄贵军,徐文,周筱莲.关系营销导向对企业使用渠道权力的影响[J].管理科学学报, 2008,11(3):114-124
[28] 唐亦之.跨界联姻:社会化网络与电子商务的共赢之道[J].传媒, 2011,(6):57-58
[29] 彭建平,张业军.电信社群网络用户行为特征及演化分析[J].管理工程学报, 2012,26(3):88-95
[30] Balmin A., Hristidis V., Papakonstantinou Y. ObjectRank: Authority-Based Keyword Search in Databases[C].Proceedings of the 30th VLDB Conference, 2004
[31] 赵爱琴,朱景焕.企业微博营销效果评估研究[J].江苏商论, 2012,(1):89-92
[32] 科特勒,凯勒.营销管理(第13版)[M].上海: 格致出版社,上海人民出版社, 2009