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Dynamic property of consumer-based brand competitiveness (CBBC) in human interaction behavior

Meihua Zuo (Guangdong University of Technology, Guangzhou, China) (Huizhou University, Huizhou, China)
Hongwei Liu (Guangdong University of Technology, Guangzhou, China)
Hui Zhu (School of Management, Guangzhou University, Guangzhou, China)
Hongming Gao (Guangdong University of Technology, Guangzhou, China)

Industrial Management & Data Systems

ISSN: 0263-5577

Article publication date: 10 July 2019

Issue publication date: 7 August 2019




The purpose of this paper is to identify potential competitive relationships among brands by analyzing the dynamic clicking behavior of consumers.


Consumer sequential online click data, collected from, is used to analyze the dynamic competitive relationship between brands. It is found that the competition intensity across categories of products can differ considerably. Consumers exhibit big differences in purchasing time of durable-like goods, that is, the purchasing probability of such products changes considerably over time. The local polynomial regression model (LPRM) is used to analyze the relationship between brand competition of durable-like goods and the purchasing probability of a particular brand.


The statistical results of collective behaviors show that there is a 90/10 rule for the category durable-like goods, implying that ten percent of the brands account for 90 percent market share in terms of both clicking and purchasing behavior. The dynamic brand cognitive process of impulsive consumers displays an inverted V shape, while cautious consumers display a double V shaped cognitive process. The dynamic consumers’ cognition illustrates that when the brands capture a half of the click volume, the brands’ competitiveness reaches to its peak and makes no significant different from brands accounting for 100 percent of the click volume in terms of the purchasing probability.

Research limitations/implications

There are some limitations to the research, including the limitations imposed by the data set. One of the most serious problems in the data set is that the collected click-stream is desensitized severely, restricting the richness of the conclusions of this study. Second, the data set consists of many other consumer behavioral data, but only the consumer’s clicking behavior is analyzed in this study. Therefore, in future research, the parameters brand browsing by consumers and the time of browsing in each brand should be added as indicators of brand competitive intensity.

Practical implications

The authors study brand competitiveness by analyzing the relationship between the click rate and the purchase likelihood of individual brands for durable-like products. When the brand competitiveness is less than 50 percent, consumers tend to seek a variety of new brands, and their purchase likelihood is positively correlated with the brand competitiveness. Once consumers learn about a particular brand excessively among all other brands at a period of time, the purchase likelihood of its products decreases due to the thinner consumer’s short-term loyalty the brand. Till the brand competitiveness runs up to 100 percent, consumers are most likely to purchase a brand and its product. That indicates brand competitiveness maintain 50 percent of the whole market is most efficient to be profitable, and the performance of costing more to improve the brand competitiveness might make no difference.


There are many studies on brand competition, but most of these research works analyze the brand’s marketing strategy from the perspective of the company. The limitation of this research is that the data are historical and failure to reflect time-variant competition. Some researchers have studied brand competition through consumer behavior, but the shortcoming of these studies is that it does not consider sequentiality of consumer behavior as this study does. Therefore, this study contributes to the literature by using consumers’ sequential clicking behavior and expands the perspective of brand competition research from the angle of consumers. Simultaneously, this paper uses the LPRM to analyze the relationship between consumer clicking behavior and brand competition for the first time, and expands the methodology accordingly.



This research was funded by the National Natural Science Foundation of China (NSFC): Research of Evolutional Decision-making Behavior based on Interactive Decision Support Aids in E-commerce (71,671,048), ¥48,0000 (2016–2019), the National Social Science Fund of China (NSSFC): Research of Public Choice Based on Arrow Axiom System and Arrow Impossibility Theorem (17BJL025),¥20,0000, (2017–2019), and the Science Foundation of Ministry of Education of China (SFMEC): research on the influence mechanism of social trust based on multi-modal relationship of sharing economy (19C11078031), ¥8,0000 (2019–2021).


Zuo, M., Liu, H., Zhu, H. and Gao, H. (2019), "Dynamic property of consumer-based brand competitiveness (CBBC) in human interaction behavior", Industrial Management & Data Systems, Vol. 119 No. 6, pp. 1223-1241.



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