The Diffusion of Innovation Theory in the Digital Age: A Critical Analysis of Its Evolution, Application, and Reinterpretation from 2005 to 2025

For over 50 years, Everett Rogers’ Diffusion of Innovation (DOI) theory has been a cornerstone of understanding how new ideas and technologies spread through social systems. The period of 2000-2025 has ushered in an unprecedented revolution in communication brought about by the explosion of digital media, the emergence of social networking platforms, and the proliferation of mobile connectivity, which has fundamentally altered our human communications, social systems, and behaviors. This critical literature review investigates how DOI theory has been applied, adapted, and remains relevant in the digital media age. This paper utilizes a systematic review method to collect academic literature published in this time frame while synthesizing how the basic constructs of DOI theory—such as adopter categories, innovation attributes, communication channels, and the S-shaped adoption curve—have been developed, amended, or referenced. While DOI theory's tenets are surprisingly resilient, the digital media age has shifted dynamics and introduced substantial theoretical modifications. Digital platforms have collapsed distinctions between mass and interpersonal communication, diffusion processes have rapidly increased adoption, and network effects have increased social influence's role in adoption decisions. The rise of the digital influence altered what it means to be an opinion leader, and the algorithmic curation of content can even represent a robust non-human actor in generating diffusion. This review also identifies some critical limitations of the classic DOI model relating to the digital divide, complexities of information overload, and adoption dynamics associated with purely digital innovations, such as cryptocurrencies and AI/predictive services. Additionally, this review revealed some key gaps in the respective literature establishing the relationship between algorithmic influence and human social networks, and the long-term societal implications of algorithmically driven diffusion. This review concludes that although DOI theory is useful, it needs to be combined with network theory, technology acceptance models, and critical media studies to better grasp innovation diffusion today.