From Thematic Analysis and Meta-Synthesis to Integrated Model Design: An In-Depth Exploration of Knowledge Architecture in Influencer Ecosystem Typologies

Objectives: While the creator economy, particularly the influencer ecosystem, has become a potent cultural and economic force, scholarly understanding of it remains fragmented and one-dimensional. Previous studies have predominantly focused on isolated aspects such as marketing strategies or the sociology of digital fame, failing to provide a holistic perspective that explains the complex and dynamic interplay between actors, technologies, and economic structures. This research aims to fill this significant conceptual gap by designing and presenting an integrated and dynamic conceptual model. The primary objective is to move beyond linear analyses and offer a framework that systematically reveals the underlying logic, driving forces, and evolutionary pathways of the influencer ecosystem, demonstrating how human actors, technological structures, and economic logics co-evolve and mutually shape one another.
Methods: Adopting a qualitative, interpretive approach, this study utilizes a systematic two-stage strategy. In the first stage, following the PRISMA protocol for systematic search in reputable academic databases (e.g., Scopus, Web of Science), 28 in-depth qualitative studies published between 2020 and 2025 were selected as the final sample. The main selection criterion was the studies’ focus on the internal dynamics of the ecosystem. Subsequently, qualitative data extracted from these articles were meticulously coded using Thematic Analysis to identify initial themes. In the second stage, employing a “meta-synthesis” (meta-ethnography) strategy, which emphasizes the interpretation and re-interpretation of concepts to achieve a new, higher-level conceptualization, the extracted themes were integrated and synthesized. This process led to the development of a comprehensive conceptual model that transcends a mere aggregation of previous findings.
Findings: The main output of this research is the “Dynamic Three-Dimensional Architectural Model,” which conceptualizes the influencer ecosystem not as a static structure but as a living, co-evolutionary organism operating through three interconnected and feedback-looped layers. The first layer, the Human Core, focuses on complex relational dynamics, including concepts such as “perceived authenticity” as a strategic performance, the accumulation of “social capital” through interaction, and the “precarious labor” nature of influencing. The second layer, the Technological Scaffolding, addresses the foundational role of platforms, encompassing “algorithmic governance” as an invisible force shaping content and visibility, “platform architecture” (e.g., likes, stories) that enables or constrains actions, and “analytical tools” for performance optimization. The third layer, the Economic Engine, details the ecosystem’s commercial logic, including the “evolution of business models” from simple advertising towards the subscription and direct-sale economy, and the “professionalization” of the industry. A key finding is the cyclical dynamic among these layers; for instance, algorithmic pressure (Technological Scaffolding) for constant content creation intensifies the precarious nature of work (Human Core), which in turn drives the emergence of new business models (Economic Engine) aimed at mitigating this instability
Conclusion: By presenting the Dynamic Three-Dimensional Architectural Model, this research moves beyond one-dimensional analyses to provide a systemic analytical lens for understanding and predicting future trends in the creator economy. This framework offers significant theoretical and practical implications. Theoretically, it contributes to the literature on digital media and platform studies by reframing influencers as economic actors and digital laborers within a complex techno-economic structure. Practically, it provides influencers with a tool to better understand the forces shaping their careers, offers brands deeper insights for more effective collaborations, and highlights for policymakers the urgent need to address issues such as digital labor rights and algorithmic transparency