Hawaii International Conference on Systems Sciences (HICSS-52)

Posted by Susan Williams on 11 January 2019

Lisbon Expo

8-11 January 2019, Grand Wailea, Maui, Hawaii

This week CEIR team members presented two papers on the topics of IoT and Enterprise Collaboration Systems at the 52nd Hawaii International Conference on Systems Sciences in Maui, Hawaii.

The paper written by Sue Williams, Catherine Hardy and Patrick Nitschke entitled “Configuring The Internet of Things (IoT): A Review and Implications for Big Data Analytics” was presented by Catherine Hardy, our colleague from the University of Sydney. The paper examines the relationship between IoT and big data analytics and the characteristics configuring and shaping the discourses around IoT.  Through our analysis we characterise IoT as a complex, (more than) technological, multi-scale and multi-level information infrastructure that is emergent and uncertain and explore these characteristics and the ways they are challenging governance capabilities in big data analytics. The paper concludes with an overview of the impact of IoT and big data analytics for building ‘sustainable futures’ and raise questions about responsible research and innovation.

The paper written by Florian Schwade and Petra Schubert entitled “Developing a User Typology for the Analysis
of Participation in Enterprise Collaboration Systems” was presented by Florian. The work proposes a user typology for Enterprise Collaboration Systems (ECS) and builds on previous research findings in the area of CSCW and Social Collaboration Analytics. The proposed typology includes: (1) a definition of user types, (2) dimensions of ECS use and (3) a classification of action (event) types and contains the user types: creator, contributor, lurker, inactive and non-user. These types are characterised by differences in the following dimensions: type of use, frequency of use, variety of use, choice of content type and platform preferences. The definition of user types along these dimensions facilitates the implementation of database queries (scripts) for Social Collaboration Analytics (SCA), with the aim of determining the distribution of types of users in an Enterprise Collaboration System.