Mo Zan, Fan Meng-ting, Liu Hong-wei, et al. Market Structure of Product Asymmetric Competition Based on Online User Behavior[J]. 2023, 40(2): 111-119.DOI: 10.12052/gdutxb.220093.
Market Structure of Product Asymmetric Competition Based on Online User Behavior
The study of consumer involvement in products has become a hot issue
yet obtaining competitive intelligence from the structure of the product competitive market has been neglected. To gain a more comprehensive understanding of the competitive market structure of products
product consideration sets are constructed from an involvement theory perspective using online user behavior data as the object. Complex network clustering method is used to visualize the asymmetric competitive market of the product. The results show that the consideration set consisting of consumers' attention involvement of products is the most representative for mapping the product asymmetric competition market. The structure map of the product asymmetric competition market mapped by the complex network clustering method is concise and clear. This market structure map not only identifies the competitors with the focal product
but also maps the degree of asymmetric competition with the competitors of the focal product. In addition
it shows insights such as the popularity of different submarkets
the high level of product competition within the submarkets and the low level of product competition between the external ones. This study demonstrates that this research model is more interpretable than traditional models.
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HEATH T B, RYU G, CHATTERJEE S, et al . Asymmetric competition in choice and the leveraging of competitive disadvantages [J ] . Journal of Consumer Research, 2000, 27(3): 291-308.
WEI Q, QIAO D D, ZHANG J, et al . A novel bipartite graph based competitiveness degree analysis from query logs [J ] . ACM Transactions on Knowledge Discovery from Data, 2016, 11(2): 1-25.
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.
RINGEL D M, SKIERA B. Visualizing asymmetric competition among more than 1, 000 Products using big search data [J]. Marketing Science, 2016, 35(3): 511-34.
LIU Y Z, QIAN Y, JIANG Y C, et al . Using favorite data to analyze asymmetric competition: machine learning models [J ] . European Journal of Operational Research, 2020, 287(2): 600-15.
GAO H M, LIU H W, ZHAN M J, et al . Research on the impact of online reviews and product on virtual shopping-car decision-making based on consumer involvement [J ] . Chinese Journal of Management Science, 2019: 1-12.
DAY G S, SRIVASTAVA R K. Customer oriented approaches to identifying product markets [J]. Journal of Marketing, 1979, 43(4): 8-19.
DAMANGIR S, DU R Y, HU Y. Uncovering patterns of product co-consideration: a case study of online vehicle Price Quote Request Data [J]. Journal of Interactive Marketing, 2018, 42: 1-17.
LIU H W, ZHAN M J, GAO H M, et al. A product competitive market structure analysis based on consumer behavioral stream [J]. Journal of Guangdong University of Technology, 2021, 38 (2) : 26-33.
BIGNÉ J E, NATALIA V L. Competitive groups in the automobile industry: a compared supply-demand approach [J]. Journal of Strategic Marketing, 2002, 10(1): 21-45.
VOLETI S, KOPALLE P K, GHOSH P. An inter-product competition model incorporating branding hierarchy and product similarities using store-level data [J]. Management Science, 2015, 61(11): 2720-38.
DESARBO W S, GREWAL R. An alternative efficient representation of demand-based competitive asymmetry [J]. Strategic Management Journal, 2007, 28(7): 755-66.
KIM J B, ALBUQUERQUE P, BRONNENBERG B J. Mapping online consumer search [J]. Journal of Marketing Research, 2011, 48(1): 13-27.
YUAN Z Q, YI S P, WANG Y H, et al . Visualization of ERP operation based on user behavior analysis and Its application [J ] . Industrial Engineering and Management, 2022, 27(3): 127-138.
ZAICHKOWSKY J L. Measuring the involvement construct [J]. Journal of Consumer Research, 1985, 12(3): 341-52.
DAHLéN M, EKBORN Y, MöRNER N. To click or not to click: an empirical study of response to banner ads for high and low involvement products [J]. Consumption Markets Culture, 2000, 4(1): 57-76.
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): 101-29.
ELROD T, RUSSELL G J, SHOCKER A D, et al . Inferring market structure from customer response to competing and complementary products [J ] . Marketing Letters, 2002, 13(3): 221-32.
LI J, NETESSINE S, KOULAYEV S. Price to compete with many: how to identify price competition in high-dimensional space [J]. Management Science, 2018, 64(9): 4118-36.
PINTO T, FALCAO-REIS F. Strategic participation in competitive electricity markets: internal versus sectorial data analysis [J]. International Journal of Electrical Power Energy Systems, 2019, 108(Jun.): 432-444.
WANG J. A method of security management of early warning based on mean shift algorithm and data mining [J]. Journal of Intelligent and Fuzzy Systems, 2020, 39(8): 1-8.