In this paper,it investigates the factors affecting successive brand alliance in which two brands from different product categories are featured together to introduce a co-brand.Based on the brand alliance theories,th...In this paper,it investigates the factors affecting successive brand alliance in which two brands from different product categories are featured together to introduce a co-brand.Based on the brand alliance theories,the degree of association,similarity,and complimentary fit between parents brands are important factors in determining successive brand alliance.By using the 5P's,which are premium price,perceived quality,product features,performance,and perceived image,as the measuring scale in brand attributes for each brand,it has reflected the change of brand attributes of the brands before and after brand alliance.400 subjects participate in the research,and 360 of which are valid.From this study,we conclude that when association between parents turns from "-" to "+" and similarity from "-" to "0",there is a trend of descending of co-brand attributes.When fit between parents turns from "-" to "0",there is also a trend of descending of co-brand attributes.While fit between parents turns from "+" to "0",there is a trend of increasing of co-brand attributes.The results show an interesting pattern of interactions among factors,which has important implications for managers in co-brand marketing.These also provide researchers with promising avenues for further study in brand alliance.展开更多
We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, w...We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, we take a different formulation to this problem by noting that these existing approaches fail to consider the potential relationships both among the consumers （consumer influence） and among the shops （competitions or collaborations）. Taking this observation into consideration, we propose a new method for e-commerce business model mining and prediction, called EBMM, which combines regression with community analysis. The challenge is that the links in the network are typically not directly observed, which is addressed by applying information diffusion theory through the consumer-shop network. Extensive evaluations using Alibaba Group e-commerce data demonstrate the promise and superiority of EBMM to the state-of-the-art methods in terms of business model mining and prediction.展开更多
文摘In this paper,it investigates the factors affecting successive brand alliance in which two brands from different product categories are featured together to introduce a co-brand.Based on the brand alliance theories,the degree of association,similarity,and complimentary fit between parents brands are important factors in determining successive brand alliance.By using the 5P's,which are premium price,perceived quality,product features,performance,and perceived image,as the measuring scale in brand attributes for each brand,it has reflected the change of brand attributes of the brands before and after brand alliance.400 subjects participate in the research,and 360 of which are valid.From this study,we conclude that when association between parents turns from "-" to "+" and similarity from "-" to "0",there is a trend of descending of co-brand attributes.When fit between parents turns from "-" to "0",there is also a trend of descending of co-brand attributes.While fit between parents turns from "+" to "0",there is a trend of increasing of co-brand attributes.The results show an interesting pattern of interactions among factors,which has important implications for managers in co-brand marketing.These also provide researchers with promising avenues for further study in brand alliance.
基金supported by the National Basic Research Program(973)of China(No.2012CB316400)Zhejiang University-Alibaba Financial Joint Lab,Zhejiang Provincial Engineering Center on Media Data Cloud Processing and Analysis,Chinathe US National Science Foundation(No.CCF-1017828)
文摘We study the problem of business model mining and prediction in the e-commerce context. Unlike most existing approaches where this is typically formulated as a regression problem or a time-series prediction problem, we take a different formulation to this problem by noting that these existing approaches fail to consider the potential relationships both among the consumers （consumer influence） and among the shops （competitions or collaborations）. Taking this observation into consideration, we propose a new method for e-commerce business model mining and prediction, called EBMM, which combines regression with community analysis. The challenge is that the links in the network are typically not directly observed, which is addressed by applying information diffusion theory through the consumer-shop network. Extensive evaluations using Alibaba Group e-commerce data demonstrate the promise and superiority of EBMM to the state-of-the-art methods in terms of business model mining and prediction.
