The DOI matches its Crossref record; 117 of the 117 references it cites were found in a registry or an index.
Measuring Organizational Legitimacy in Social Media: Assessing Citizens’ Judgments With Sentiment Analysis
Open accessAbstract
Conventional quantitative methods for the measurement of organizational legitimacy consider mainly three sources that make judgments about organizations visible: news media, accreditation bodies, and surveys. Over the last decade, however, social media have enabled ordinary citizens to bypass the gatekeeping function of these institutional evaluators and autonomously make individual judgments public. This inclusion of voices beyond functional and formally organized stakeholder groups potentially pluralizes the ongoing discussions about organizations. The individual judgments in blogs, tweets, and Facebook posts give indication about the broader fit between an organization’s perceived behavior and heterogeneous social norms and therefore constitute an indicator of organizational legitimacy that can be accessed and measured. We propose the use of social media data and sentiment analysis to study the affect-based responses to organizational actions by citizens. We critically discuss and compare the method with existing quantitative methods for legitimacy measurement and apply them to a recent case in the banking industry. We discuss the value of the method for studying the process of legitimacy construction as the expression and negotiation of normative judgments about organizations by various evaluators.
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Cites
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“We then triangulate (Jick, 1979) these results with those from conventional quantitative measures that are applied to the same case and time period.”
ResultThumbs up?: sentiment classification using machine learning techniques
Pang, B., Lee, L., & Vaithyanathan, S. (2002) · Proceedings of the ACL-02 conference on Empirical methods in natural language processing - EMNLP '02 · doi:10.3115/1118693.1118704 · Cited by 7,024
Convergence Culture: Where Old and New Media Collide
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Opinion Mining and Sentiment Analysis
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Cited by
A Survey of the Usages of Deep Learning for Natural Language Processing
Otter, D. W., Medina, J. R., & Kalita, J. K. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2979670 · Cited by 1,836
Authenticity under threat: When social media influencers need to go beyond self-presentation
Audrezet, A., de Kerviler, G., & Guidry Moulard, J. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2018.07.008 · Cited by 1,452
Social media and the formation of organizational reputation
Etter, M., Ravasi, D., & Colleoni, E. (2019) · Academy of Management Review · doi:10.5465/amr.2014.0280 · Cited by 428
Self-citationMamo, Y., Crompton, H., Burke, D., & Nickel, C. (2024) · TechTrends · doi:10.1007/s11528-024-00954-1 · Cited by 65
Metadata
- Authors
- Etter, M., Colleoni, E., Illia, L., Meggiorin, K., & D’Eugenio, A.
- Venue
- Business & Society
- Publisher
- SAGE Publications
- Published
- 2016-12-20
- Volume
- 57
- Issue
- 1
- Pages
- 60-97
- ISSN
- 0007-6503 (ISSN-L)
- 1552-4205
- OpenAlex
- W2620411403
- Cited by
- 257
- References
- 117
- FWCI
- 31.22
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Etter, M., Colleoni, E., Illia, L., Meggiorin, K., & D’Eugenio, A. (2018). Measuring Organizational Legitimacy in Social Media: Assessing Citizens’ Judgments With Sentiment Analysis. Business & Society. https://doi.org/10.1177/0007650316683926
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