Hawaii International Conference on Systems Sciences (HICSS-52)

Internet of Things (IoT)

Published 30. January 2019 by Prof. Dr. Susan P. Williams

Team members of IndustryConnect 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.

This post was originally published by Prof. Dr. Susan P. Williams on the website of the Enterprise of Things Lab on the 12th of January, 2019: https://eotlab.org/hicss-52/

Published by

Prof. Dr. Susan P. Williams


Sue Williams is a Professor in the Faculty of Computer Science at the University of Koblenz. She is head of the Enterprise Information Management Research Group and its two research labs, the Information Design Lab and the Enterprise of Things Lab and is co-founder of IndustryConnect. She is an interdisciplinary researcher with expertise in the areas of social and organisational informatics. Her research examines complex socio-technical change (STC) and the use-centred design of information artefacts and the digital workplace.

Additional contacts

Prof. Dr. Petra Schubert


Petra Schubert is a Professor in the Faculty of Computer Science at the University of Koblenz. She is the Director of the Competence Centre for Collaboration Technologies (UCT) and is co-founder of IndustryConnect. She is an expert in conducting industry research and co-developed the eXperience case study methodology. Her research interests are in the area of Enterprise Systems with a specific focus on large-scale software systems such as enterprise resource planning and collaboration systems.

Florian Schwade

Research Assistant

Since April 2015 Florian Schwade has been a Research Assistant in the Business Software Research Group at the University of Koblenz-Landau under the leadership of Prof. Dr. Petra Schubert. He holds a Master’s degree in business information systems from the University of Koblenz-Landau with stays abroad at the Auckland University of Technology (Auckland, New Zealand). His current research focuses is on Social Collaboration Analytics. During his studies Florian Schwade was already a student research assistant in the Software Research Group for several years.

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