Research on Identifying Brand Competition Relationships with Consumer Browsing Behavior

Liu Hong-wei; Liang Zhou-yang; Zuo Mei-hua; Lu Dan; Fan Meng-ting; He Rui-chao

doi:10.12052/gdutxb.190063

English Version Journal Login

您当前的位置：

首页 >

文章列表页 >

Research on Identifying Brand Competition Relationships with Consumer Browsing Behavior

更新时间：2026-05-27

- Research on Identifying Brand Competition Relationships with Consumer Browsing Behavior
- Vol. 36, Issue 5, Pages: 1-6, 13(2019)
- 作者机构：
  
  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
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.190063
  CLC： TP391.4
- Online First：21 August 2019，
  
  Published：2019
- 稿件说明：
移动端阅览
刘洪伟, 梁周扬, 左妹华, 陆丹, 范梦婷, 何锐超. 利用消费者浏览行为识别品牌竞争关系研究[J]. 广东工业大学学报, 2019, 36(5): 1-6, 13.

Liu Hong-wei, Liang Zhou-yang, Zuo Mei-hua, et al. Research on Identifying Brand Competition Relationships with Consumer Browsing Behavior[J]. 2019, 36(5): 1-6, 13.
刘洪伟, 梁周扬, 左妹华, 陆丹, 范梦婷, 何锐超. 利用消费者浏览行为识别品牌竞争关系研究[J]. 广东工业大学学报, 2019, 36(5): 1-6, 13. DOI： 10.12052/gdutxb.190063.

Liu Hong-wei, Liang Zhou-yang, Zuo Mei-hua, et al. Research on Identifying Brand Competition Relationships with Consumer Browsing Behavior[J]. 2019, 36(5): 1-6, 13. DOI： 10.12052/gdutxb.190063.

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.

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.

关键词

Keywords

references

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.

BAILEY J P. Intermediation and electronic markets: aggregation and pricing in internet commerce[D]. Cambridge, MA: Massachusetts Institute of Technology, 1998.

BRYNJOLFSSON E, SMITH M D. Frictionless commerce? a comparison of internet and conventional retailers [J]. Management Science, 2000, 46(4): 563-585.

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.

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.

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.

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.

ERDEM T. A dynamic analysis of market structure based on panel data [J]. Marketing Science, 1996, 15(4): 359-378.

GREWAL D R. An alternative efficient representation of demand-based competitive asymmetry [J]. Strategic Management Journal, 2007, 28(7): 755-766.

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.

KIM J B, ALBUQUERQUE P, BRONNENBERG B J. Mapping online consumer search [J]. Journal of Marketing Research, 2011, 48(1): 13-27.

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.

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.

YAO W M, FAHMY S. Flow-based partitioning of network testbed experiments [J]. Computer Networks, 2014, 58(15): 141-157.

RAPPUOLI R, ADEREM A. A 2020 vision for vaccines against HIV, tuberculosis and malaria [J]. Nature, 2011, 473(7348): 463-471.

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.

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.

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.

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.

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.

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.

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.

CHO S, CHONG A. Co-query volume as a proxy for brand relatedness [J]. Industrial Management Data Systems, 2018, 118(4): 930-944.

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.

FORTUNATO S, BARTHELEMY M. Resolution limit in community detection [J]. Proceedings of the National Academy of Sciences, 2007, 104(1): 36-41.

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Market Structure of Product Asymmetric Competition Based on Online User Behavior

An Reliability Evaluation of Distribution Network Based on Graph Model Integration Technology

Construction and Application of the Production Operation & Decision-making System Based on Big Data for Transport Group

Parallel Minimal Cut Set Algorithm and Its Application in Financial Social Networks

A Research on Forward Trends of Mobile E-commerce

Related Author

Yan Yang-fan

Liu Hong-wei

Fan Meng-ting

Mo Zan

Peng Xian-gang

Li Wang-jun

Huang Yuan-fang

Lin Ze-xin

Related Institution

Zhanjiang Power Supply Company, Guangdong Power Grid Co., Ltd.

School of Automation, Guangdong University of Technology

School of Information Science and Technology, Huizhou University

Guangdong Yunwei for Big Data Technology Consulting Limited Company

School of Computers, Guangdong University of Technology

AI问答

⁰