Examining the Influence of Algorithmic Message Personalization on Source Credibility and Reputation

Abstract

Digital technologies such as AI (Artificial Intelligence) and big data analytics are altering public relations practices such as scanning social media and posting during crises. Crisis scholarship found these practices could potentially yield positive crisis outcomes such as increased reputational measures. However, research has not yet fully outlined the potential risks associated with using these tactics in crisis communication. Guided by situational crisis communication theory (SCCT), this study utilized a 2 (reputation repair strategy: Deny/Apology) x 2 (presence vs. absence of message personalization notification) factorial design experiment to determine how the utilization of algorithmic message personalization might influence reputational and source credibility evaluations during an athlete reputational crisis (ARC). Results revealed that reputation repair strategy selection indirectly influenced stakeholders’ reputation evaluations via source credibility. This relationship was moderated by a notification of personalization. Theoretical and practical insights elucidate how the increased usage of algorithmic-based message personalization influences crisis communication outcomes.