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Analyzing Social Media for Corporate Reputation Management: How Firms Can Improve Business Agility

Analyzing Social Media for Corporate Reputation Management: How Firms Can Improve Business Agility

Christoph Seebach, Roman Beck, Olga Denisova
Copyright: © 2013 |Volume: 4 |Issue: 3 |Pages: 17
ISSN: 1947-3591|EISSN: 1947-3605|EISBN13: 9781466633742|DOI: 10.4018/ijbir.2013070104
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MLA

Seebach, Christoph, et al. "Analyzing Social Media for Corporate Reputation Management: How Firms Can Improve Business Agility." IJBIR vol.4, no.3 2013: pp.50-66. http://doi.org/10.4018/ijbir.2013070104

APA

Seebach, C., Beck, R., & Denisova, O. (2013). Analyzing Social Media for Corporate Reputation Management: How Firms Can Improve Business Agility. International Journal of Business Intelligence Research (IJBIR), 4(3), 50-66. http://doi.org/10.4018/ijbir.2013070104

Chicago

Seebach, Christoph, Roman Beck, and Olga Denisova. "Analyzing Social Media for Corporate Reputation Management: How Firms Can Improve Business Agility," International Journal of Business Intelligence Research (IJBIR) 4, no.3: 50-66. http://doi.org/10.4018/ijbir.2013070104

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Abstract

The business agility concept reflects an organization’s need to develop sensing capabilities for being able to respond to changes in the business environment. Therefore, intelligent information systems are needed to support decision makers with accurate and timely information. Since corporate reputation is among the most valuable assets, organizations need efficient measuring techniques to manage it. Recently, due to the advent of social media new reputational challenges have emerged for firms, since such technologies significantly increase the risk for being associated with negative issues. Therefore, organizations should utilize there IT-systems for actively sensing social media content as a basis for a quick response to reputational threats. Accordingly, the authors provide an empirical example on how firms might improve reputation management through sensing social media. Specifically, the authors analyze a dataset of 271,207 messages about an American Bank collected on Twitter. For their empirical investigation, the applied automated sentiment analysis and manual content analysis.

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