Abstract

Background: The cost-effectiveness and brand impact of viral marketing have driven extensive research across disciplines. With the proliferation of online social networks over the past decade, viral marketing in online social networks (OSNVM) has emerged as a transformative yet fragmented research domain.

Method: We integrate the socio-technical systems (STS) perspective to categorize these studies into two streams: behavioral and data science, and use thematic analysis to analyze the articles further. The behavioral stream focuses on the behavioral aspects of marketing content, senders, receivers, and their impact on the effectiveness of viral marketing campaigns. The data science stream views viral marketing campaigns from a data network perspective, focusing mainly on network structure, scope, algorithms and pattern of dissemination.

Results: Although both streams are potentially complementary and can benefit from each other, prior studies tend to take a blinkered perspective, drawing mainly from one or the other research tradition, thus missing opportunities for synergy and a better understanding of the phenomenon of interest. This study develops an integrative framework bridging these paradigms through thematic analysis, specifically identifying research gaps and future directions for information systems scholarship.

Conclusion: This study bridges the disciplinary divide through a thematic framework enabling integrated OSNVM analysis. The framework systematically integrates behavioral and data science perspectives, enhancing conceptual clarity while establishing interdisciplinary connections. It identifies critical research gaps at this intersection, guiding future model development. Practically, it equips marketers with dual optimization strategies combining behavioral insights and data science analytics. These dual advances bridge theoretical and applied domains, establishing new standards for evidence-based campaign design.

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