Title: Examining the role of likes in follower network evolution based on a dynamic panel data model

Authors: Tao Wang; Shuang Fu; Zhiyi Wu

Addresses: School of Business, Putian University, Putian, 351100, China ' School of Information Science and Technology, Xiamen University Tan Kah Kee College, Zhangzhou, 363105, China ' School of Information Management and Engineering, Shanghai University of Finance and Economics, Shanghai, 200433, China

Abstract: Posting product recommendation articles by content creators from the consumer group in social shopping communities has become an effective way to connect consumers to products. Content creators with larger follower counts have higher levels of influence. However, little is known about the causes of the evolution of their follower networks. Therefore, we examined the impact of social media likes that content creators received on the follower count and the moderating effect of the previous follower count on the role of likes. We achieve that by crawling real data from China's leading social shopping community. We empirically tested a dynamic panel data model and found that more likes are positively associated with the growth of the follower network size, while the previous follower count negatively moderates this effect. These findings have implications for researchers seeking to understand the antecedents of follower network evolution and for practitioners seeking to attract more followers.

Keywords: content creator; social media like; follower network evolution; social shopping community; dynamic panel data model.

DOI: 10.1504/IJCSE.2023.135296

International Journal of Computational Science and Engineering, 2023 Vol.26 No.6, pp.641 - 649

Received: 10 Nov 2022
Accepted: 06 Feb 2023
Published online: 04 Dec 2023 *

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