Enhancing social media engagement using AI-modified background music: examining the roles of event relevance, lyric resonance, AI-singer origins, audience interpretation, emotional resonance, and social media engagement

Abstract

Introduction: Drawing on the S-O-R model, this study aims to investigate the influence of three stimuli from AI-modified music (i.e., event relevance, lyric resonance, and AI-singer origins), two responses from social media content consumers (i.e., audience interpretation and emotional resonance) on the social media engagement of personalized background music modified by artificial intelligence (AI).

Methods: The structural equation modeling analyses of 467 social media content consumers' responses confirmed the role of those three stimuli and the mediating effect of audience interpretation and emotional resonance in shaping social media engagement.

Results: The findings shed light on the underlying mechanisms that drive social media engagement in the context of AI-modified background music created for non-professional content creators.

Discussion: The theoretical and practical implications of this study advance our understanding of social media engagement with AI-singer-originated background music and provide a basis for future investigations into this rapidly evolving phenomenon in the gig economy.

Keywords: AI-modified background music; AI-singer origins; audience interpretation; emotional resonance; event relevance; lyric resonance; social media engagement.