广东工业大学学报 ›› 2019, Vol. 36 ›› Issue (05): 1-6,13.doi: 10.12052/gdutxb.190063

• 综合研究 •    下一篇

利用消费者浏览行为识别品牌竞争关系研究

刘洪伟1, 梁周扬1, 左妹华1,2, 陆丹1, 范梦婷1, 何锐超1   

  1. 1. 广东工业大学 管理学院, 广东 广州 510520;
    2. 惠州学院 建筑与土木工程学院, 广东 惠州 516007
  • 收稿日期:2019-04-27 出版日期:2019-08-21 发布日期:2019-08-06
  • 通信作者: 左妹华(1986-),女,讲师,博士研究生,主要研究方向为信息系统.E-mail:zuomeihua123@126.com E-mail:zuomeihua123@126.com
  • 作者简介:刘洪伟(1962-),男,教授,博士,博士生导师,主要研究方向为信息系统、商务数据分析.
  • 基金资助:
    国家自然科学基金资助项目(71671048);国家社会科学基金资助项目(17BJL025);惠州学院社科资助项目(hzu201725)

Research on Identifying Brand Competition Relationships with Consumer Browsing Behavior

Liu Hong-wei1, Liang Zhou-yang1, Zuo Mei-hua1,2, Lu Dan1, Fan Meng-ting1, He Rui-chao1   

  1. 1. School of Management, Guangdong University of Technology, Guangzhou 510520, China;
    2. School of Architecture and Civil Engineering, Huizhou University, Huizhou 516007, China
  • Received:2019-04-27 Online:2019-08-21 Published:2019-08-06

摘要: 为了能在包含近百种同类品牌、几千种同品牌产品的电商平台中全面识别品牌间和品牌内产品的竞争性市场结构,根据共现理论,从消费者的在线浏览行为出发,将在线消费者同一浏览行为中共现的品牌和产品视为相互竞争关系,利用社交网络从消费者浏览行为的角度识别品牌间和品牌内产品间的竞争关系.分析结果显示,相对于品牌间的竞争强度而言,品牌内产品的同质化竞争更激烈.该结果对品牌内产品的生产线管理具有重要的实践意义,相对于着重关注品牌间竞争关系的企业而言,品牌生产企业更应将时间和精力放在品牌内生产线的长度设计上,以便快速提升品牌的市场竞争力.

关键词: 大数据, 可视化, 非对称竞争, 消费者浏览行为, 共现理论

Abstract: In order to completely identify the competitive market structure of products inter-brands and intra-brands on e-commerce platforms, which contain nearly 100 similar brands and thousands of products of the same brand. Based on the co-occurrence theory, a study is conducted on the brands and products appearing in the same browsing behavior of online consumers as competing relationships from the online browsing behavior of consumers, and social networks are used to visualize inter-brand and intra-brand competition from the perspective of consumer browsing behavior. The analysis results show that the homogenization competition of products intra-brands is more serious than that of inter-brands. This result has important practical significance for the production line management of products within the brand. Compared with focusing on the competitive relationship between inter-brands, brand manufacturers should put more time and energy into the design of the length of production line of intra-brands, so as to rapidly enhance the brand's market competitiveness.

Key words: big data, visualization, asymmetric competition, consumer browsing behavior, co-occurrence theory

中图分类号: 

  • TP391.4
[1] 中国互联网络信息中心. 第41次中国互联网络发展状况统计报告[R]. 北京:中国互联网络信息中心, 2018:63-64.
[2] BAILEY J P. Intermediation and electronic markets:aggregation and pricing in internet commerce[D]. Cambridge, MA:Massachusetts Institute of Technology, 1998.
[3] BRYNJOLFSSON E, SMITH M D. Frictionless commerce? a comparison of internet and conventional retailers[J]. Management Science, 2000, 46(4):563-585
[4] CLEMONS E K, HANN I H, HITT L M. Price dispersion and differentiation in online travel:an empirical investigation[J]. Management Science, 2002, 48(4):534-549
[5] GHOSE A, YAO Y. Using transaction prices to re-examine price dispersion in electronic markets[J]. Social Science Electronic Publishing, 2010, 22(2):269-288
[6] BRYNJOLFSSON E, SIMESTER D. Goodbye Pareto Principle, hello Long Tail:the effect of search costs on the concentration of product sales[J]. Management Science, 2006, 57(8):1373-1386
[7] LI B, LI X, LIU H. Consumer preferences, cannibalization, and competition:evidence from the personal computer industry[J]. MIS Quarterly:Management Information Systems, 2018, 42(2):661-678
[8] ERDEM T. A dynamic analysis of market structure based on panel data[J]. Marketing Science, 1996, 15(4):359-378
[9] GREWAL D R. An alternative efficient representation of demand-based competitive asymmetry[J]. Strategic Management Journal, 2007, 28(7):755-766
[10] DESARBO W S, GREWAL R, WIND J. Who competes with whom? a demand-based perspective for identifying and representing asymmetric competition[J]. Strategic Management Journal, 2006, 27(2):101-129
[11] KIM J B, ALBUQUERQUE P, BRONNENBERG B J. Mapping online consumer search[J]. Journal of Marketing Research, 2011, 48(1):13-27
[12] NETZER O, FELDMAN R, GOLDENBERG J, et al. Mine your own business:market-structure surveillance through text mining[J]. Marketing Science, 2012, 31(3):521-543
[13] KIM J B, ALBUQUERQUE P, BRONNENBERG B J, et al. The probit choice model under sequential search with an application to online retailing[J]. Social Science Electronic Publishing, 2016, 26(1):61-73
[14] YAO W M, FAHMY S. Flow-based partitioning of network testbed experiments[J]. Computer Networks, 2014, 58(15):141-157
[15] RAPPUOLI R, ADEREM A. A 2020 vision for vaccines against HIV, tuberculosis and malaria[J]. Nature, 2011, 473(7348):463-471
[16] RINGEL D M, SKIERA B. Visualizing asymmetric competition among more than 1000 products using big search data[J]. Marketing Science, 2016, 35(3):511-534
[17] 杨建梅, 周恋, 周连强. 中国汽车产业竞争关系与轿车社团企业对抗行动研究[J]. 管理学报, 2013, 10(1):49-55 YANG J M, ZHOU L, ZHOU L Q. Competitive relationships of auto industry and rivalry actions of car community enterprises in China[J]. Chinese Journal of Management, 2013, 10(1):49-55
[18] 谢逢洁, 崔文田, 武小平. 快递产业竞争关系网络模型构建及结构特性分析[J]. 系统工程, 2017, 35(7):101-106 XIE F J, CUI W T, WU X P. The model of competitive relationship network of express enterprises and its structural properties[J]. Systems Engineering, 2017, 35(7):101-106
[19] MOE W W. An empirical two-stage choice model with varying decision rules applied to internet clickstream data[J]. Journal of Marketing Research, 2006, 43(4):680-692
[20] IWANAGA J, NISHIMURA N, SUKEGAWA N, et al. Estimating product-choice probabilities from recency and frequency of page views[J]. Knowledge-Based Systems, 2016, 99(C):157-167
[21] URBAN G L, HULLAND J S, WEINBERG B D. Premarket forecasting for new consumer durable goods:model[J]. Journal of Marketing, 1993, 57(2):47-63
[22] DESARBO W S, MANRAI A K, MANRAI L A. Non-spatial tree models for the assessment of competitive market structure:an integrated review of marketing and psychometric literature[J]. Handbooks in Operations Research & Management Science, 1993, 5(5):193-257
[23] CHO S, CHONG A. Co-query volume as a proxy for brand relatedness[J]. Industrial Management & Data Systems, 2018, 118(4):930-944
[24] NEWMAN M E J. Fast algorithm for detecting community structure in networks[J]. Phys Rev E Stat Nonlin Soft Matter Phys, 2003, 69(6):066133
[25] FORTUNATO S, BARTHELEMY M. Resolution limit in community detection[J]. Proceedings of the National Academy of Sciences, 2007, 104(1):36-41
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