Effect of Seeding Strategy on the Efficiency of Brand Spreading in Complex Social Networks

Abstract

In social networks, consumers gather to form brand communities, and the community structure significantly impacts the dissemination of brand information. Which communication strategy is more conducive to information dissemination in different structured brand communities? Considering the above factors, we propose the word-of-mouth (WOM) agent model based on the traditional rumor model and bass model, in which the brand WOM spreading is affected by the user's psychological mechanisms, the network structure, and other factors. Through simulation experiments, the results showed the following: (1) the conclusion of the traditional bass model is no longer applicable to social marketing in brand information diffusion, that is, the effect of external marketing stimulation on information dissemination is limited. (2) The communication effect and the efficiency of information in different structures of the learning-community network are very different. (3) The strategy of hub nodes is not suitable for all types of networks, and the impact of different seeding strategies on the efficiency and effect of brand information dissemination was verified. Finally, the conclusion was verified again using the social network data on Facebook.

Research on the Influence of Socialization Strategy of Online Educating Platform on Users' Learning Behavior

“…To explore the mechanism of socialization strategy's influence on consumers' behavioral intention (punching frequency and learning duration), the authors examined the mediating role of community identity in socialization strategy with punching frequency and learning duration, respectively [21]. The test for mediation effects was conducted using the Bootstrap method proposed by Preacher et al In recent years, it is generally accepted that Bootstrap is more effective and reasonable for estimating mediating effects, and has been widely used in the fields of psychology, consumer behavior, and organizational behavior.…”

Online or offline? The impact of environmental knowledge acquisition on environmental behavior of Chinese farmers based on social capital perspective

“…This suggests that the main source of trust and reciprocity for farmers is offline interpersonal discussions, and that social media use is more for self-interest motives, to gain knowledge, rather than to provide benefits to other users. At the same time, it reflects the fact that offline interaction is still the main way of accessing information in rural areas, showing the practical utility of homogeneous interaction in rural areas (Shiyong Z et al, 2021).…”

“…Finally, we also found a positive correlation between environmental knowledge and environmental behavior (β = 0.19, p < 0.001), thus supporting Hypothesis 3. This suggests that environmental knowledge and awareness is necessary to improve farmers' environmental behavior (Shiyong Z et al, 2022).…”

“…At the 75th UN General Assembly General Debate and Climate Ambition Summit, President Xi Jinping proposed that China should strive to peak its carbon dioxide emissions by 2030 and work towards achieving carbon neutrality by 2060 (Shiyong et al, 2022). This major strategic development and systemic change will inevitably force a green development and low-carbon transformation of the way of economic development, promote high-quality development of China's economy, and profoundly change the lifestyles of members of society at large towards a green and low-carbon direction .…”

“…Thirdly, governance models, which mostly focus on socially interactive and participatory environmental protection models (Mallapaty, 2020). While academic research has focused on the government's leading role in environmental governance in terms of policy mechanisms, practical challenges, and governance models, which has contributed to the improvement of the rural environment at the macro level (Kang Y et al, 2020), there is little research on the participation of non-governmental actors in environmental governance at the micro level (Shiyong Z et al, 2022).…”