Maximizing positive influence of live-campaign promotion in live social E-commerce networks

Peikun Ni, Jianming Zhu, Yuxin Gao and Guoqing Wang

European Journal of Operational Research, 2026, vol. 331, issue 3, 960-972

Abstract: Live-campaign promotion is a form of real-time scenario-based marketing initiated in live social E-commerce networks (LSE-Ns), and its market size and influence are continuously expanding. This immersive marketing scenario greatly enhances individuals’ (i.e., consumers’) emotional resonance, which may exacerbate problems such as the overexposure of physical products and counteract the positive influence of live-campaign promotion. To alleviate this deficiency, we formulate and tackle the live-campaign promotion problem with practical constraints under two scenarios, unit cost and differentiated cost, by selecting a set of initiators in LSE-Ns to launch live-campaigns that maximize the positive influence. Specifically, we first construct a live-campaign promotion model and introduce two parameters to quantify the positive influence: a static parameter representing the actual value of the physical product, and a dynamic parameter reflecting individual’s expected value. Then, we prove that the problem of maximizing the positive influence of live-campaign promotion is NP-hard, and its objective function is non-monotonic and non-submodular. Considering the properties of the objective function, a DS-extension decomposition method is devised, upon which a pseudo-subgradient ascent algorithm is further developed. The effectiveness of the constructed algorithms in achieving positive influence is theoretically proven, and empirical tests on four network datasets validate their practical performance. The experimental results show that our approach outperforms other off-the-shelf methods, achieving at least 5.3 % improvement in positive influence while maintaining the associated negative influence rate below the average level of 18.2 %. Moreover, it reveals some key marketing insights for live-campaign promoters.

Keywords: Computing science; Positive influence maximization; Non-submodular optimization; Pseudo-subgradient ascent algorithm (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:331:y:2026:i:3:p:960-972

DOI: 10.1016/j.ejor.2025.10.020

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