10/ In conclusion, our study sheds light on the complex interplay between cognitive and affective involvement in shaping perceptions of media bias in the age of AI journalism. Exciting times ahead as we navigate the evolving landscape! 🚀 📊 #MediaResearch

9/ While our research provides valuable insights, it has limitations. Future studies should explore participants’ awareness of source incongruity and test different controversial topics 📚🔍#FutureResearch

8/ Implications for the broader digital ecosystem: As AI becomes more prevalent, understanding how AI source cues are evaluated is crucial. Our study initiates this exploration, particularly relevant in the realm of AI chatbots and digital interfaces 🤖 💬 #AIInfluence

7/ Beyond AI journalism, our findings suggest that strategic manipulation of source cues can mitigate bias, considering audience engagement. Relevant in today’s digital landscape where quick judgements are made about information credibility 🌐🤔

6/ Managerial implications: AI source cues can influence audience perceptions, potentially altering bias and credibility perceptions. Media organizations can strategically use AI journalism to temper bias associated with human authorship 🤖 🔍 #MediaStrategy #AIJournalism

5/ Our study advances theoretical perspectives in bias judgements toward machines in journalism. Both affective and cognitive involvements are key moderators in the impact of the machine heuristic on HMB.

4/ Affective and cognitive involvements play a crucial role. AI cues considerably lowered HMB for those with high positive emotional affect. Positive emotions enhance the mitigating effect, while negative emotions have a counterintuitive impact. (Nuanced theoretical contribution)

3/ Political ideology matters! Participants’ congruence with media sources affects the mitigating effect of AI cues on biased perceptions. HMB is higher for incongruent media sources, or at least the context for our study: COVID-19 vaccination opinions 📊