Generative AI and deep fakes in Media Industry - An Innovation Resistance Theory Perspective

This empirical study intends to study users resistance towards emerging technologies like generative artificial intelligence (AI) and deep fakes affecting the media industry. The research's basic framework, the Innovation Resistance Theory, aids in making sense of the challenges and obstacles associated with adopting new technology. This theory can be used in research on generative AI and deepfakes to empirically analyze resistance variables and provide mitigation strategies for unfavorable impacts. A structured questionnaire was administered to gather information from media industry users (N= 283). Data analysis was performed using Smart-PLS 4.0 software, and the technique used was partial least square-structural equation modeling. Findings indicate that consumers’ functional barrier like value barrier and risk barrier, along with psychological barriers like traditional barriers, and image barrier were significantly impacting generative AI and deepfakes usage intentions. However, usage barrier did not have significant influence on deepfake usage. sThis paper adds to the ongoing discourse on responsible innovation in media. As deepfake technology becomes more widespread, it's relevant for policymakers, media professionals, and the public to know about associated risks.