Social media capital: Conceptualizing the nature, acquisition, and expenditure of social mediabased organizational resources Abstract   The nearuniversal organizational participation in social media is predicated on the belief there are some tangible or intangible new resources to be had through tweeting, pinning, posting, friending, and sharing. We argue the linchpin of any payoff from engagement in social media is a special form of social capital we refer to as social media capital, and offer a conceptual framework for understanding its nature, acquisition, and expenditure. This paper contributes to existing literature by elaborating a new type of organizational resource and then synthesizing and extending research on the processes through which organizations can translate social media efforts into meaningful organizational outcomes. Understanding this causal chain is critical not only for measuring the return on investment from social media use but also for developing accounting information systems that are both adaptable to social resources and better able to exploit the data analytic and forecasting capabilities of realtime social media data.

Tweeting the United Nations Climate Change Conference in Paris (COP21): An analysis of a social network and factors determining the network influence Abstract   To understand the Twitter network of an environmental and political event and to extend the network theory of social capital, we first performed a network analysis of the English tweets during the first 10 days of the United Nationsxe2x80x99 Conference of the Parties in Paris in 2015. Accounts for nonprofit and government agencies were more likely to be influential in the Twitter network and be retweeted, whereas individual accounts were more likely to retweet others. Based on a quota sample of 133 Twitter accounts and using both manual and machine coding, we further found that the number of followers (but not the size of following) and the commongoal frame (i.e., mitigationadaptation) positively predicted an accountu0027s influence in the Twitter network, whereas the conflict frame negatively predicted an accountu0027s network influence.