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Purpose

The purpose of this paper is to introduce and investigate social brokers who belong to and connect multiple groups in a social network. This paper also reveals the differential effects of innovative and following social brokers on content diffusion in terms of adoption timing, speed and size.

Design/methodology/approach

The paper collected field data related to 69,086 users on the largest social network platform in China and analysed their adoption behaviours of 2,492 pieces of content.

Findings

The analysis reveals that social brokers encourage content diffusion and accelerate the speed of content adoption in a social network. Specifically, following social brokers play a greater role than innovative social brokers in accelerating the speed of content adoption and expanding the size of content adoption. However, in the early stage of content diffusion within the social network, innovative social brokers could predict the success of content adoption more effectively than following social brokers.

Research limitations/implications

This research extends the current diffusion literature by introducing the social broker and examining the effect of social brokers on the process of content adoption.

Practical implications

The findings provide suggestions to marketing managers on how to improve the diffusion of marketing-related content, such as by seeding specific people – that is, social brokers – with content, so they can serve as content transmitters in marketing campaigns. In addition, the findings suggest that to optimise content adoption in a social network, managers should strategically target innovative social brokers or following social brokers at various stages of content seeding-based marketing campaigns.

Originality/value

To the best of the authors’ knowledge, this research is the first to test the effects of social brokers on content adoption and identify innovative and following social brokers. The findings enrich the literature on content marketing by providing new perspectives on social structures in social networks.

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