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The social influencer integrated marketing strategy, which builds social influencers through potential users, has gained widespread attention in the industry. Traditional Scoring Link Prediction Algorithms (SLPA) mainly rely on homogeneous network indicators to predict friend relationships, which cannot provide accurate link prediction results in cold-start situations. To overcome these limitations, the Closeness Heterogeneous Link Prediction Algorithm (CHLPA) is proposed, which uses node closeness centrality to describe the social intimacy of nodes and provides a heterogeneous measure of a network based on this. Three types of heterogeneous indicators of social intimacy were proposed based on the principle of three-degree influence. Due to scarce overlapping node sample data, CHLPA uses gradient boosting trees to select the most suitable index, the second most suitable index, and the third most suitable index from Social Intimacy Heterogeneous Indexes (SIHIs) and SLPAs. Then, these indicators are weighted and combined to predict the likelihood of other node users in the two product circles in an online brand community becoming friends with overlapping node users. Finally, a hill-climbing algorithm is designed based on this to build integrated marketing social influencers, and the effectiveness and robustness of the algorithm are validated. [ABSTRACT FROM AUTHOR]

Customer engagement behavior is a key factor in promoting the sustainable growth of virtual brand communities. Despite the extensive research on the antecedents of customer engagement behaviors, the influence of intercustomer social support remains a neglected area in the context of virtual brand communities. Based on a total of 293 valid questionnaires collected via an online survey, a structural equation model and hierarchical regression analysis are used to examine the effects of intercustomer social support (informational/emotional support) on customer engagement behaviors (customer-oriented/community-oriented engagement behaviors) in virtual brand communities, as well as consider the mediating role of self-efficacy and the moderating role of interdependent self-construal in the above relationships. The empirical finding shows that informational/emotional support significantly affects customer-oriented and community-oriented engagement behaviors. Self-efficacy plays a mediating role in the relationship between informational/emotional support and customer-oriented/community-oriented engagement behaviors. Interdependent self-construal positively moderates the relationship between informational/emotional support and customer-oriented engagement behaviors and positively moderates the relationship between informational support and community-oriented engagement behaviors. This article provides a more comprehensive understanding of the relationships between intercustomer social support and customer engagement behaviors in the context of virtual brand communities, and improves the existing customer engagement behaviors management practices that are beneficial for the companies. [ABSTRACT FROM AUTHOR]

One of the main problems encountered by social networks is the cold start problem. The term "cold start problem" refers to the difficulty in predicting new users' friendships due to the limited number of links those users have with existing nodes. To fill the gap, this paper proposes a Fully Integrated Link Prediction Algorithm (FILPA) that describes the social distance of nodes by using "betweenness centrality," and develops a Social Distance Index (SDI) based on micro- and macro-network structure according to social distance. With the aim of constructing adaptive SDIs that are suitable for the characteristics of a network, a naive Bayes (NB) method is firstly adopted to select appropriate SDIs according to the density and social distance characteristics of common neighbors in the local network. To avoid the risk of algorithm accuracy reduction caused by blind combination of SDIs, the AdaBoost meta-learning strategy is applied to develop a Fully Integrated Social Distance Index (FISDI) composed of the best SDIs screened by NB. The possible friendships among nodes will then be comprehensively presented using high performance FISDI. Finally, in order to realize the "products rapidly attracting users" in new user marketing, FILPA is used to predict the possible friendship between new users in an online brand community and others in different product circles. [ABSTRACT FROM AUTHOR]

This research proposes a framework for the fashion brand community to explore public participation behaviors triggered by brand information and to understand the importance of key image cues and brand positioning. In addition, it reviews different participation responses (likes, comments, and shares) to build systematic image and theme modules that detail planning requirements for community information. The sample includes luxury fashion brands (Chanel, Hermès, and Louis Vuitton) and fast fashion brands (Adidas, Nike, and Zara). Using a web crawler, a total of 21,670 posts made from 2011 to 2019 are obtained. A fashion brand image model is constructed to determine key image cues in posts by each brand. Drawing on the findings of the ensemble analysis, this research divides cues used by the six major fashion brands into two modules, image cue module and image and theme cue module, to understand participation responses in the form of likes, comments, and shares. The results of the systematic image and theme module serve as a critical reference for admins exploring the characteristics of public participation for each brand and the main factors motivating public participation. [ABSTRACT FROM AUTHOR]