A CROSS-PLATFORM MARKET STRUCTURE ANALYSIS METHOD USING ONLINE PRODUCT REVIEWS

被引：18

作者：

Kou, Gang ^{[1
]}

Yang, Pei ^{[2
]}

Peng, Yi ^{[3
]}

Xiao, Hui ^{[4
]}

Xiao, Feng ^{[1
]}

Chen, Yang ^{[1
]}

Alsaadi, Fawaz E. ^{[5
]}

机构：

[1] Southwestern Univ Finance & Econ, Sch Business Adm, Chengdu 611130, Peoples R China

[2] Chengdu Univ Technol, Coll Management Sci, Chengdu 610059, Peoples R China

[3] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 610054, Peoples R China

[4] Southwestern Univ Finance & Econ, Sch Stat, 555 Liutai Ave, Chengdu 611130, Peoples R China

[5] King Abdulaziz Univ, Fac Comp & IT, Dept Informat Technol, Jeddah, Saudi Arabia

来源：

TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY | 2021年 / 27卷 / 05期

基金：

中国国家自然科学基金;

关键词：

market structure analysis; online product reviews; multi-attribute group decision making; DYNAMIC-ANALYSIS; ALGORITHMS;

D O I：

10.3846/tede.2021.12005

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

Studies have shown that online product reviews can indicate the position of a competitive brand. Even though reviews on different platforms may express different opinions, most studies are based on only one platform. This may lead to an inaccurate analysis of market structure. To solve this problem, we develop a novel market structure analysis based on multi-attribute group decision-making which can integrate reviews from different platforms. Multiple platforms more comprehensively reflect the market than single platforms do. To verify the effectiveness of the proposed method, we conduct a case study of mobile phone reviews across three top e-commerce platforms in China. In addition, we propose a process to generate priorities for product-attribute improvements using a cross-platform market structure analysis method. Our experiments demonstrate the effectiveness of the proposed method.

引用

页码：992 / 1018

页数：27

共 41 条

[1] BEHAVIOR MONITORING METHODS FOR TRADE-BASED MONEY LAUNDERING INTEGRATING MACRO AND MICRO PRUDENTIAL REGULATION: A CASE FROM CHINA [J].

Chao, Xiangrui ;

Kou, Gang ;

Peng, Yi ;

Alsaadi, Fawaz E. .

TECHNOLOGICAL AND ECONOMIC DEVELOPMENT OF ECONOMY, 2019, 25 (06) :1081-1096

[2] Encoding candlesticks as images for pattern classification using convolutional neural networks [J].

Chen, Jun-Hao ;

Tsai, Yun-Cheng .

FINANCIAL INNOVATION, 2020, 6 (01)

[3] Visualizing market structure through online product reviews: Integrate topic modeling, TOPSIS, and multi-dimensional scaling approaches [J].

Chen, Kun ;

Kou, Gang ;

Shang, Jennifer ;

Chen, Yang .

ELECTRONIC COMMERCE RESEARCH AND APPLICATIONS, 2015, 14 (01) :58-74

[4] Building market structures from consumer preferences [J].

Cooper, LG ;

Inoue, A .

JOURNAL OF MARKETING RESEARCH, 1996, 33 (03) :293-306

[5] Inferring market structure from customer response to competing and complementary products [J].

Elrod, T ;

Russell, GJ ;

Shocker, AD ;

Andrews, RL ;

Bacon, L ;

Bayus, BL ;

Carroll, JD ;

Johnson, RM ;

Kamakura, WA ;

Lenk, P ;

Mazanec, JA ;

Rao, VR ;

Shankar, V .

MARKETING LETTERS, 2002, 13 (03) :221-232

[6] A dynamic analysis of market structure based on panel data [J].

Erdem, T .

MARKETING SCIENCE, 1996, 15 (04) :359-378

[7] A hybrid Bayesian-network proposition for forecasting the crude oil price [J].

Fazelabdolabadi, Babak .

FINANCIAL INNOVATION, 2019, 5 (01)

[8] An experimental comparison of performance measures for classification [J].

Ferri, C. ;

Hernandez-Orallo, J. ;

Modroiu, R. .

PATTERN RECOGNITION LETTERS, 2009, 30 (01) :27-38

[9]

FRAAS AG, 1977, J IND ECON, V26, P21

[10] COMPETITIVE MARKET-STRUCTURE ANALYSIS - PRINCIPAL PARTITIONING OF REVEALED SUBSTITUTABILITIES [J].

FRASER, C ;

BRADFORD, JW .

JOURNAL OF CONSUMER RESEARCH, 1983, 10 (01) :15-30

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