How does collectively seeking social media community support help employees solve organizational injustice-related problems? : An analysis of Twitter Big Data
The current job market is characterized by turbulence and uncertainty, and employees seem to become highly sensitive to issues related to injustice. When employees perceive organizational injustice (i.e., distributive, procedural, interactional, and informational), they may seek social support. We suggest that employees, instead of approaching their supervisor/manager, colleagues, family, or friends as support providers, resort to social media to seek support from the network community. The communal nature of social media that embodies the possibility of interacting with individuals with similar characteristics makes it possible for those seeking collective support to benefit from the available mechanisms. Seeking community support seems to play a pivotal role, in that it can be focused on actions or emotions depending on what individuals actually need and look for when they seek social support. When seeking community-social support, employees may plan and act collectively. In response to their collective action, the social media community may engage in an intervention either by taking necessary actions (instrumental support) to help employees solve their problems related to organizational injustice or through supporting employees in managing their emotions (emotional support). Emotional community support is most likely as consequential as the instrumental one, given that providing emotional support is a strong indicator of choosing sides. Companies use social media to understand their reputation within society; therefore, when employees reach out to social media to seek support, that community support becomes eligible to help them out. Twitter, a widely accessible social media that allows exploring different social phenomena, will be utilized for event-specific targeted data collection. Streamed Twitter posts shared by publicly available accounts will be included in the dataset. A broad enough time period to encompass pre-, intra-, and post-event periods will be chosen. The analysis will consist of data cleaning, and structuring, where initial auto-coding will be completed by manually proofing. Plans for the initial analysis involve word frequency, polarization, network analysis, centrality, positive/negative sentiment, and sociogram. Findings are expected to be explorative for a better understanding of the above-detailed mechanisms about the capacity and eligibility of social-media community support that may help employees solve their organizational problems related to injustice. Ej belagd</p
