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Accounting narratives and impression management on social media

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  • Jessica H. Yang
  • Siwen Liu

Abstract

In this paper, we examine the defensive and assertive impression management strategies and the impact of firm performance on accounting narratives by investigating the earnings disclosures of FTSE 100 companies on Twitter. Social media has become the prevailing venue for organisational self-presentation because it provides firms with more control over the image they intend to establish and maintain through the communication and content they deliver online. Our findings show that firms minimise the disclosures of negative information but employ various patterns and dissemination techniques to emphasise positive information. Specifically, improving performers are more willing to post and disseminate earnings-related tweets to achieve a higher degree of stakeholder engagement than declining performers. Based on these findings, we conclude that firms present themselves on social media opportunistically to construct a positive public image.

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  • Jessica H. Yang & Siwen Liu, 2017. "Accounting narratives and impression management on social media," Accounting and Business Research, Taylor & Francis Journals, vol. 47(6), pages 673-694, September.
    • Handle: RePEc:taf:acctbr:v:47:y:2017:i:6:p:673-694
    DOI: 10.1080/00014788.2017.1322936
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    7. Niamh M. Brennan & Doris M. Merkl-Davies, 2018. "Do firms effectively communicate with financial stakeholders? A conceptual model of corporate communication in a capital market context," Accounting and Business Research, Taylor & Francis Journals, vol. 48(5), pages 553-577, July.
    8. Jie Yang & Jie Wang & Xiaofeng Zhang & Chunqi Shen & Zhijuan Shao, 2022. "How Social Impressions Affect Public Acceptance of Nuclear Energy: A Case Study in China," Sustainability, MDPI, vol. 14(18), pages 1-23, September.
    9. Liu, Zhenyuan & Geng, Ruoqi & Tse, Ying Kei (Mike) & Han, Shuihua, 2023. "Mapping the relationship between social media usage and organizational performance: A meta-analysis," Technological Forecasting and Social Change, Elsevier, vol. 187(C).
    10. Xin Xu & Feng Xiong & Zhe An, 2023. "Using Machine Learning to Predict Corporate Fraud: Evidence Based on the GONE Framework," Journal of Business Ethics, Springer, vol. 186(1), pages 137-158, August.
    11. Gutiérrez‐Ponce Herenia & Chamizo González Julián & Manar Moffadi Awad Al‐mohareb, 2024. "Does corporate governance influence readability of the report by the chairman of the board of directors? The case of Jordanian listed companies," Corporate Social Responsibility and Environmental Management, John Wiley & Sons, vol. 31(4), pages 3535-3550, July.
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