Association Rules Analysis between Brand Post Characteristics and Consumer Engagement on Social Media

Zhijie Zhao; Yang Liu; Jiaying Wang; Biao Wang; Yiqi Guo

doi:10.5755/j01.ee.32.4.28060

Authors

Zhijie Zhao School of Computer and Information Engineering, Harbin University of Commerce; Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, China
Yang Liu School of Computer and Information Engineering, Harbin University of Commerce; Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, China
Jiaying Wang School of Computer and Information Engineering, Harbin University of Commerce, China
Biao Wang School of Business, Harbin University of Commerce, China
Yiqi Guo School of Computer and Information Engineering, Harbin University of Commerce; Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, China

DOI:

https://doi.org/10.5755/j01.ee.32.4.28060

Keywords:

Consumer engagement, Brand posts characteristics, Association rule analysis, Rough set method, Social media

Abstract

Brands are increasingly using social media to create and manage posts to initiate and maintain consumer engagement. Based on the theory of consumer engagement, a perspective of brand post content, form and posting time is introduced to construct a conceptual model of consumer engagement for Sina Weibo. Rough set method and reduction algorithm of Holte 1 R are used to automatically generate the optimal decision rules. Rough set method does not need any prior knowledge and assumptions, which could effectively overcome the disadvantages of traditional statistical methods. The results show that entertainment content is easy to trigger moderate level of consumer engagement, the effect of information content on shares is significantly stronger than that of comments and likes, and the promotion content has an impact on liking. As the most vivid and the most interactive characteristic respectively, videos and questions significantly affect the mid-level consumer engagement. Keeping post length in the range of 16–50 characters stimulates the medium degree of sharing. Posts created on weekend promote the medium level of sharing and the low level of liking, while published at the peak or low peak period trigger the same level of sharing, but do not affect comments or likes. The study detects the characteristics that affect consumer engagement and define the scope of its role. The relationship and intensity of different characteristics on different levels of consumer engagement are effectively evaluated and identified by refining the association rules of consumer engagement, which are available for providing reference for brand managers to formulate social media marketing strategies.

Author Biographies

Zhijie Zhao, School of Computer and Information Engineering, Harbin University of Commerce; Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, China

Zhijie Zhao received the B.S. and M.S. degrees from the Harbin University of Science and Technology, China, in 1985 and 1988, respectively, and the Ph.D. degree from the Harbin Institute of Technology in 2008. He is currently a Professor at the School of Computer and Information Engineering, Harbin University of Commerce, China. His research interests include image processing and intelligence information processing

Yang Liu, School of Computer and Information Engineering, Harbin University of Commerce; Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, China

Yang Liu is currently pursuing a Ph.D. degree in e-commerce from the School of Computer and Information Engineering, Harbin University of Commerce, China. Her research interests include the cross-application of big data technology in the field of e-commerce, consumer engagement behavior, and social media

Jiaying Wang, School of Computer and Information Engineering, Harbin University of Commerce, China

Jiaying Wang is a Ph.D. candidate in the Department of Computer and Information Engineering, Harbin University of Commerce, and a lecturer of Zaozhuang University, Harbin, China. His research interests include consumer behavior, electronic word-of-mouth (eWOM), e-commerce, and social media.

Biao Wang, School of Business, Harbin University of Commerce, China

Biao Wang received a B.S from the Shanxi University of Finance and Economics, China, in 1986, and M.S. degrees from the Jilin University, China, in 2001. He is currently a Professor at the Business School, Harbin University of Commerce, China. His research interests include statistics and quantitative economic analysis.

Yiqi Guo, School of Computer and Information Engineering, Harbin University of Commerce; Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing, China

Yiqi Guo is a master candidate in electronic commerce in the School of Computer and Information Engineering of Harbin University of Commerce, China. Her research interests include consumer behavior and social media.