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The use of algorithms is beginning to replace human activities in the news business, and the presence of this technique will only continue to grow. The ways in which public news readers perceive the quality of news articles written by algorithms and how this perception differs based on cultural conditioning remain issues of debate. Informed by the heuristic-systematic model (HSM) and the similarity-attraction theory, we attempted to answer these questions by conducting a three-way one-way analysis of variance (ANOVA) test with a 2 (author: algorithm vs. human journalist) × 2 (media: traditional media vs. online media) × 2 (cultural background: the US vs. South Korea) between-subjects experiment (N = 360). Our findings revealed that participants perceived the quality of news articles written by algorithms to be higher than those written by human journalists. We also found that when news consumption occurs online, algorithm-generated news tends to be rated higher than human-written news in terms of quality perception. Further, we identified a three-way interaction effect of media types, authors, and cultural backgrounds on the quality perception of news articles. As, to the best of our knowledge, this study is the first to theoretically examine how news readers perceive algorithm-generated news from a cultural point of view, our research findings may hold important theoretical and practical implications.

Towards a Sustainable News Business: Understanding Readers’ Perceptions of Algorithm-Generated News Based on Cultural Conditioning