An Examination of Not-For-Profit Stakeholder Networks for Relationship Management: A Small-Scale Analysis on Social Media

Using a small-scale descriptive network analysis approach, this study highlights the importance of stakeholder networks for identifying valuable stakeholders and the management of existing stakeholders in the context of mental health not-for-profit services. We extract network data from the social media brand pages of three health service organizations from the U.S., U.K., and Australia, to visually map networks of 579 social media brand pages (represented by nodes), connected by 5,600 edges. This network data is analyzed using a collection of popular graph analysis techniques to assess the differences in the way each of the service organizations manage stakeholder networks. We also compare node meta-information against basic topology measures to emphasize the importance of effectively managing relationships with stakeholders who have large external audiences. Implications and future research directions are also discussed

Strategic Self-presentation in the Sharing Economy: Implications for Host Branding

Peer-to-peer accommodation platform is a unique venue of commercial social exchanges where mixed-mode interactions (i.e., online first, then offline) occur between hosts and guests. With the continuous growth of sharing economy comes the importance to better understand the strategies that hosts use to communicate with and attract their prospective consumers. Using the framework of personal branding and self-presentation, this study explored the different ways hosts of peer-to-peer accommodation articulate their profile online. Using host descriptions from 12,785 Airbnb listings in New York, United States, five clusters of host self-presentation were identified: The Global Citizen, The Local Expert, The Personable, The Established, and The Creative. Honest and positive self-presentation, as well as competence strategies were identified from these clusters. The host profiles were further explored to identify differences in their behaviour, listing characteristics, and guest review ratings