Background: This study explores consumer attitudes towards AI-generated videosusing the ABC model of attitudes, which includes Affective, Behavioral, and Cognitive components. AI-generated videos represent a significant evolution in content creation,incorporating advanced technologies that evoke a range of emotional, behavioural, andcognitive responses from consumers. Understanding these responses is crucial as AIbecomes increasingly integrated into media production. Purpose: The purpose of this study is to explore consumers’ attitudes towardsAI-generated videos. Methodology: Data was collected through semi-structured interviews with participantsfrom diverse backgrounds and professions. A purposive sampling method wasemployed to select participants familiar with generative AI technology, ensuring a richand diverse set of insights. Findings: Findings reveal a spectrum of emotional responses, with negative emotionsoften tied to perceptions of artificiality and lack of authenticity, while positive emotionsare driven by the novelty and potential of AI technology. Behavioural intentions varied,ranging from reluctance due to trust issues to openness influenced by perceived benefitsand professional interests. Cognitive evaluations highlighted knowledge disparities andethical concerns, with deeper understanding correlating with cautious optimism orscepticism. Conclusion: The study contributes to the literature by providing a nuancedunderstanding of how affective, behavioural, and cognitive dimensions interact to shapeoverall consumer attitudes towards AI-generated videos. Practical implications suggestthat companies and marketers should address trust and ethical concerns while enhancingthe authenticity and transparency of AI-generated videos to foster positive consumerengagement.
Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-130206 |
Date | January 2024 |
Creators | Charfou, Abdul Rahman, Naji, Jumana |
Publisher | Linnéuniversitetet, Institutionen för marknadsföring och turismvetenskap (MTS) |
Source Sets | DiVA Archive at Upsalla University |
Language | English |
Detected Language | English |
Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
Format | application/pdf |
Rights | info:eu-repo/semantics/openAccess |
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