Purpose This paper aims to solve the problem of information overload and reduce search costs. It proposes a social e-commerce online reputation formation model and community state-introduced model. A system dynamics trend simulation has been run to capture the relationship among the sellers, buyers, social e-commerce platforms and external environment to obtain an online reputation. Design/methodology/approach Empirical research relating to social e-commerce reputation has been used to confirm the influencing factors in social e-commerce, and a conceptual framework is developed for social e-commerce reputation formation. Thereafter, a trend simulation is generated to classify the relationship among the factors based on system dynamics. Also, the improved algorithm for community detection and a state-introduced model based on a Markov network are proposed to achieve better network partition for better online reputation management. Findings The empirical model captures the interaction effect of social e-commerce reputation and the state-introduced model to guide community public opinion and improve the efficiency of social e-commerce reputation formation. This helps minimize searching cost thereby improving social e-commerce reputation construction and management. Research limitations/implications There is no appropriate online reputation system to be constructed to test the relationship proposed in the study for a field experiment. Also, deeper investigation for the nodes’ attributes in social networks should be made in future research. Besides, researchers are advised to explore measurement for the reputation of a given seller by using social media data as from Twitter or micro blogs. Originality/value Investigations that study online reputation in the social e-commerce are limited. The empirical research figured out the factors which can influence the formation of online reputation in social e-commerce. An SD model was proposed to explain the factors interaction and trend simulation was run. Also, a state-introduced model was proposed to highlight the effect of nodes’ attributes on communities’ detection to give a deeper investigation for the online reputation management.

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With the advent of the era of web 2.0, network information technology tends to interact with human beings in real time, which makes the interaction in the network community more frequent, real-time and closer. As an external clue, the interaction in the network community plays a very important reference role in influencing consumers' purchase decisions. This paper mainly focuses on the relationship between the two interactions (customer-customer, enterprise-customer) and customer engagement, and explores its internal relationship. The research finds that the interaction in the network community is positively affecting the formation of customer engagement, and puts forward relevant marketing suggestions based on the research conclusions, which provides theoretical basis for enterprise decision-making and follow-up related research.