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Utilizing Bots for Sustainable News Business: Understanding Users’ Perspectives of News Bots in the Age of Social Media

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  • Hyehyun Hong

    (Department of Advertising and Public Relations, Chung-Ang University, Seoul 06974, Korea)

  • Hyun Jee Oh

    (Department of Communication Studies, Hong Kong Baptist University, Kowloon Tong, Kowloon, Hong Kong SAR, China)

Abstract

The move of news audiences to social media has presented a major challenge for news organizations. How to adapt and adjust to this social media environment is an important issue for sustainable news business. News bots are one of the key technologies offered in the current media environment and are widely applied in news production, dissemination, and interaction with audiences. While benefits and concerns coexist about the application of bots in news organizations, the current study aimed to examine how social media users perceive news bots, the factors that affect their acceptance of bots in news organizations, and how this is related to their evaluation of social media news in general. An analysis of the US national survey dataset showed that self-efficacy (confidence in identifying content from a bot) was a successful predictor of news bot acceptance, which in turn resulted in a positive evaluation of social media news in general. In addition, an individual’s perceived prevalence of social media news from bots had an indirect effect on acceptance by increasing self-efficacy. The results are discussed with the aim of providing a better understanding of news audiences in the social media environment, and practical implications for the sustainable news business are suggested.

Suggested Citation

  • Hyehyun Hong & Hyun Jee Oh, 2020. "Utilizing Bots for Sustainable News Business: Understanding Users’ Perspectives of News Bots in the Age of Social Media," Sustainability, MDPI, vol. 12(16), pages 1-16, August.
  • Handle: RePEc:gam:jsusta:v:12:y:2020:i:16:p:6515-:d:398001
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    References listed on IDEAS

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    1. Matteo Cinelli & Emanuele Brugnoli & Ana Lucia Schmidt & Fabiana Zollo & Walter Quattrociocchi & Antonio Scala, 2020. "Selective exposure shapes the Facebook news diet," PLOS ONE, Public Library of Science, vol. 15(3), pages 1-17, March.
    2. repec:nas:journl:v:115:y:2018:p:12435-12440 is not listed on IDEAS
    3. Alexandre Bovet & Hernán A. Makse, 2019. "Influence of fake news in Twitter during the 2016 US presidential election," Nature Communications, Nature, vol. 10(1), pages 1-14, December.
    4. Andreas Veglis & Theodora A. Maniou, 2019. "Chatbots on the Rise: A New Narrative in Journalism," Studies in Media and Communication, Redfame publishing, vol. 7(1), pages 1-6, June.
    5. Chengcheng Shao & Giovanni Luca Ciampaglia & Onur Varol & Kai-Cheng Yang & Alessandro Flammini & Filippo Menczer, 2018. "The spread of low-credibility content by social bots," Nature Communications, Nature, vol. 9(1), pages 1-9, December.
    6. Yu-Hsi Yuan & Sang-Bing Tsai & Chien-Yun Dai & Hsiao-Ming Chen & Wan-Fei Chen & Chia-Huei Wu & Guodong Li & Jiangtao Wang, 2017. "An empirical research on relationships between subjective judgement, technology acceptance tendency and knowledge transfer," PLOS ONE, Public Library of Science, vol. 12(9), pages 1-22, September.
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    Cited by:

    1. Yunju Kim & Heejun Lee, 2021. "Towards a Sustainable News Business: Understanding Readers’ Perceptions of Algorithm-Generated News Based on Cultural Conditioning," Sustainability, MDPI, vol. 13(7), pages 1-14, March.

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