Single vs multiple collaborations in influencer-driven information dissemination: an evolutionary game model approach

Purpose - This paper aims to counteract the time-related decay of information dissemination in social commerce by proposing an evolutionary game model based on complex networks, analyzing how companies with limited budgets strategically select or re-select macro- and meso-influencers to maximize dissemination effectiveness. Design/methodology/approach - The model integrates utility and social environment updating mechanisms to simulate individual forwarding decisions, utilizing a purchase intention function influenced by environmental and personal factors, as well as the number of previous purchasers. A pre-experiment was conducted to determine the optimal time interval, followed by numerical simulations comparing single-stage versus two-stage influencer engagement strategies. Findings - Results demonstrate that companies with low brand popularity should prioritize investments in macro-influencers at the initial stage, particularly in risk-averse markets. Those with medium-to-high brand popularity benefit more from a two-stage strategy, where the initial investment in macro-influencers ranges from 0.5 to 0.8, while the subsequent investment in meso-influencers remains below 0.5. This approach is especially effective in markets with a higher proportion of risk-seeking consumers. Research limitations/implications - The study is constrained by simulation parameters and lacks validation with real-world data, which may affect the generalizability of the findings. Future research should explore empirical validation to strengthen these insights. Originality/value - This study introduces a novel evolutionary game model approach to optimizing influencer collaborations and information dissemination strategies, providing valuable insights for adapting influencer engagement based on brand popularity and communication stage.