A preliminary study on the adoption of AIGC tools by social media content creators

In recent years, the rapid development of artificial intelligence generated conten (AIGC) technologies has profoundly reshaped the ecology of digital content production. With the widespread application of tools such as ChatGPT, Midjourney, DALL-E, and AI-assisted video editing systems, content creation is increasingly characterized by automation, intelligence, and platform integration. At the same time, the expansion of the creator economy has made social media content creators an important group in contemporary digital labor systems. Unlike traditional media professionals working within formal organizations, creators usually operate as independent and highly platform-dependent workers whose production decisions are closely tied to audience engagement, personal branding, and unstable algorithmic environments. Against this background, the adoption of AIGC tools by social media content creators has become not only a technical issue, but also a question involving creative autonomy, labor transformation, authenticity, trust, and platform governance.

Literature Review on the Creator Economy, Creator Labor, and AIGC Adoption

The creator economy has gradually emerged as an independent field of research over the past decade, although its boundaries remain subject to debate. In essence, it describes an economic system in which individuals monetize their creativity, personality, and skills through digital platforms usually by first building an audience and then developing multiple revenue streams on that basis: advertising, sponsorships, subscriptions, and merchandise [1]. Unlike traditional media employment, creator work is often precarious, platform-dependent, and requires continuous personal branding and audience engagement.

Research on creator labor has highlighted several distinctive characteristics. Glat documented the cross-platform strategies adopted by creators to manage uncertainty, reducing their reliance on a single algorithm by maintaining a presence across multiple platforms [2]. Duffy and Ononye examined the politics of vulnerability in the influencer economy, demonstrating how shifts in platform policies and algorithmic changes can abruptly disrupt carefully built careers [3]. Bainotti explored the portfolio careers of content creators, emphasizing how labor, precarity, and identity intersect within this field [4]. Collectively, these literature present a picture of creators as entrepreneurial workers navigating an unstable environment, continuously adapting to platform changes while preserving authentic connections with their audiences.

This context is crucial for understanding the adoption of AIGC. Creators are no employees with stable job descriptions and dedicated technical support teams, bu independent operators who must decide for themselves which tools to invest their time and money in, often with limited information about potential returns. Their adoption decisions are shaped not only by the functional features of the tools but also by concerns such as authenticity, audience trust, and competitive positioning [5, 6].

A substantial body of research has explored why individuals adopt new technologies, and the Unified Theory of Acceptance and Use of Technology (UTAUT) has emerged as one of the most comprehensive frameworks [7]. This model identifies four key constructs: performance expectancy (the degree of benefits gained from using technology), effort expectancy (ease of use), social influence (the extent to which significant others believe an individual should use the technology), and facilitating conditions (the organizational and technical infrastructure that supports usage).

Recent applications of UTAUT to AI tools have yielded relevant insights. Menon and Shilpa applied the model to ChatGPT adoption and found that performance expectancy and effort expectancy exerted particularly significant effects [8]. Kim et al. investigated generative AI adoption in Korean enterprises, underlining the importance of social influence and facilitating conditions [9]. Ali et al. examined GenAI acceptance in media content creation across Arab Gulf states, validating the applicability of UTAUT while noting cultural specificities [10]. Li surveyed designers’ adoption of AIGC tools, introducing perceived anxiety and perceived risk as important extensions to the baseline model [11].

However, research specifically on AIGC in content creation is still emerging bu growing rapidly. Wei and Tyson examined the impact of AIGC on social media through the case of Pixiv, documenting how AI-generated content disrupts existing creative communities [12]. Wang and Xu explored the mechanisms of user interaction with AI-generated content, finding that transparency regarding AI involvement shapes audience acceptance [13]. Zhou and Lu investigated trust formation in AIGC adoption, highlighting the psychological barriers users face when engaging with machinegenerated content [14].

Several studies have focused on specific groups of creators. Zhu et al. examined AIGC adoption among designers and identified challenges related to workflow integration [15]. Zhang observed visual creative practitioners in China and noted concerns over skill depreciation [16]. Hussain et al. analyzed interactions around ChatGPT-related content on YouTube, offering insights into how audiences respond to AI-related topics [17]. Collectively, these fragmented studies paint a picture: creators are experimenting with AIGC tools and experiencing genuine productivity gains, but they are also struggling with concerns such as authenticity, creative identity, and uncertainty surrounding platform policies.

Materials and Methods

This study adopts a mixed exploratory and conceptual research approach. Through a systematic literature review and theoretical synthesis, it preliminarily outlines the key issues and influencing factors in the process of AIGC (Artificial Intelligence Generated Content) adoption among content creators. Given the rapid iteration of current AIGC technologies and the ongoing evolution of creators’ usage behaviors and application scenarios, this study is not positioned to pursue large-scale empirical data collection. Instead, at the initial stage of research, it prioritizes constructing a relatively comprehensive theoretical framework, focusing on capturing the diversity and complexity of the phenomenon.

In the literature review section, this study conducted extensive searches across multiple academic databases including Google Scholar, ACM Digital Library, and IEEE Xplore. The main keywords employed consisted of both Chinese and English combinations such as “AIGC adoption”, “content creators”, “generative AI”, “creator economy”, and “opinion leaders”. To accurately reflect the explosive development of generative AI technologies and their impact on content production in recent years, the literature covered is mainly concentrated between 2022 and 2025, with particular attention paid to academic discussions triggered after the advent of ChatGPT. Meanwhile, to maintain the integrity of the theoretical context, this study also appropriately incorporated foundational literature related to technology adoption theories and creator labor, ensuring that the investigation of cutting-edge phenomena remains grounded in existing academic achievements.

Results and Discussion