Behaviour & Information Technology
Behaviour & Information Technology (BIT) puts people before technology. As such it deviates from other related journals. It is the primary scientific venue for peer-reviewed publications on human-centred IT.
About the Special Issue
The use of social networks and the Internet in general has become a habit for users to the point that there are millions of devices connected to the Internet, all of which constantly generate new data. Social network platforms have become a common forum for users seeking to share opinions and information about individual experiences. Facebook, Twitter, YouTube, and Instagram are the most popular social networks, but other platforms such as TripAdvisor, Booking, Amazon, and eBay are also considered social networks, because they allow users to share information. In a scenario where new users' habits have been generated in the digital environment, it is interesting to study how users have adapted habits on the Internet through the web or through social networks and digital platforms.
Several authors have focused on the analysis of users on social platforms through neuroscience experiments. The objective of these experiments is to better understand users who rely on social networks as well as to clarify the variables that affect their decision making. In turn, management can better understand current user behavior and predict future behavior. The study of user behavior in social networks is important as it can enable managers to generate meaningful insights that may in turn help to refine strategic responses or become the basis for further research. The comprehension of these new habits of digital behavior is relevant for executives who aim to develop their business projects in digital environments.
The purpose of this Special Issue is to understand user behavior in social networks with the application of neuroscience experiments applied to social networks.
For this Special Issue, we invite paper contributions related to any of the topics outlined above and which clearly relate to neuroscience experiments in social networks for users using research approaches such as neuromarketing, EEG, eye tracking, fMRI, user-generated content (UGC) analysis, social network analysis, sentiment analysis, big data, machine learning approaches, support vector machines, case studies or reviews of literature on this topic, as well as another quantitative, qualitative, or mixed/multimethod perspectives.
- Social networks
- Digital user behavior
- User neuroscience
|Closing date for submissions:||May 31, 2020|
|Review notification:||July 15, 2020|
|Revised papers due date:||September 15, 2020|
|Final decision notification:||October 15, 2020|