Dynamic property of consumer-based brand competitiveness (CBBC) in human interaction behavior
| Published date | 08 July 2019 |
| Date | 08 July 2019 |
| DOI | https://doi.org/10.1108/IMDS-09-2018-0403 |
| Pages | 1223-1241 |
| Author | Meihua Zuo,Hongwei Liu,Hui Zhu,Hongming Gao |
| Subject Matter | Information & knowledge management |
Dynamic property of
consumer-based brand
competitiveness (CBBC) in
human interaction behavior
Meihua Zuo
Guangdong University of Technology, Guangzhou, China and
Huizhou University, Huizhou, China
Hongwei Liu
Guangdong University of Technology, Guangzhou, China
Hui Zhu
School of Management, Guangzhou University, Guangzhou, China, and
Hongming Gao
Guangdong University of Technology, Guangzhou, China
Abstract
Purpose –The purpose of this paper is to identify potential competitive relationships among brands by
analyzing the dynamic clicking behavior of consumers.
Design/methodology/approach –Consumer sequential online click data, collected from JD.com, 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.
Findings –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 likelihoodof 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.
Originality/value –There are many studies on brand competition, bu t most of these researc h works
analyze the brand’s market ing strategy from the perspect ive of the company. The limitati on of this
research is that the data a re historical and failure to ref lect time-variant competiti on. Some researchers have
studied brand competi tion through consumer behavior, but t he shortcoming of these studies is tha t it does
not consider sequentia lity of consumer behavior as this stu dy does. Therefore, this study cont ributes to the
literature by using cons umers’sequential cli cking behavior and expands t he perspective of brand
Industrial Management & Data
Systems
Vol. 119 No. 6, 2019
pp. 1223-1241
© Emerald PublishingLimited
0263-5577
DOI 10.1108/IMDS-09-2018-0403
Received 17 September 2018
Revised 30 January 2019
Accepted 18 March 2019
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0263-5577.htm
1223
Dynamic
property of
CBBC
competition research from the angle of consumers. Simult aneously, this paper uses the LPRM to analyze
the relationship betwe en consumer clicking behavior a nd brand competition for the first t ime, and expands
the methodology accord ingly.
Keywords Brand competitiveness, Consumer clicking behaviour, Durable-like goods, Dynamic process,
Local polynomial regression model
Paper type Research paper
1. Introduction
Brand competition is an important component of the competitive strategy of mostfirms and is
commonly observed in practice through sales volume. The concept of brand power can be
traced back to the article “The Product and the Brand”in the Harvard Business Review
(Gardner and Levy, 1955). Brand competitiveness has been of interest to academics and
practitioners ever since. Most of the previous research analyzes brand competition from the
perspective of marketing strategy, such as, differentiated marketing strateg y (Clemons, 2008;
Gallego and Wang, 2014), experiential marketing strategy (Vila-López and Rodríguez-Molina,
2013), entertainment marketing strategy (Hackley and Tiwsakul, 2006) , increasing brand
market share (Ketels, 2006; Winzar et al., 2017) or enhancing brand equity (Keller and
Lehmann, 2003; Cho, 2018). The data used to study b rand competition from a marketing
perspective are historical, which are relatively lagging and not dynamic enough to capture
consumers’dynamic behavior. Simultaneously, thanks to the popularity of the internet
technology,the growth in mobile internet users (Peng et al., 2014) and the development of the
logistics industry, the number of consumer groups using e-commerce has increased rapidly.
At the same time, due to the low entry barrier to e-commerce platforms, low operation and
maintenance costs, low-product display costs (Bailey, 1998; Brynjolfsson and Smith, 2000)
and low search costs (Clemons et al., 2002; Ghose and Yao, 2010), more and more brand
manufacturers use e-commerce platforms. As a result,product offerings aremore concentrated,
and consumers can compare similar brands of products they want to purchase online at a
lower search cost. This results in more intense competition for online products than offline
ones. In orderto study the dynamic onlinebrand competitive relationship, westudy this from
the perspective of consumer clicking behavior, using the consumer’s click rate for a particular
brand as the market competitiveness of this brand.
Prior research has documented the usefulness of consumers’clicking behaviors as
proxies for user interests or preferences (Andrei et al., 2017). Here the use of clicking
behaviors is extended by considering consumers’sequential clicks on brand behavior, that
is, considering several brands in the same browsing sequence. The number of brands
clicked in a browsing sequence is used as an indicator for dynamic brand competition. In
fact, previous studies have used web data of other sources such as user-centric panel data
(Padmanabhan et al., 2001) or site-centric data (Bucklin and Sismeiro, 2009) to predict
consumer choices using co-occurrences of products. This research focuses on consumer
searching behavior. Specifically, consumers often click two or more brands together in the
same browsing sequence for comparison. In this study, we further extend prior research of
brand competition by using the co-occurrences of brands in one session to reveal the
potential competitive relationships among brands, to understand the competitive structure
of the market and to demonstrate the usefulness of brand competition via consumer
co-occurrence clicking behavior.
Sequential clicking behavior on brands potentially represents dynamic preferences.
Through the co-occurrences of brands in clicking behavior, the focus gradually is on one
brand among other brands (Erickson, 2009), and it eventually impacts the consumer’s
purchase decision making (Baumann and Winzar, 2017; Euibang et al., 2017). A shopping
process producedby a consumer over time on an online retailingwebsite is shown in Figure 1.
If a consumer searches a product, he or she might reveal brand inertia through the memory
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