Welcome to IFIP 8.2!

The IFIP 8.2 working group focuses on the development and use of information technologies in organizational contexts, both broadly defined. The group seeks to generate and disseminate knowledge about and improve understand of the role and impact of information technology, and to improve the design and application of information technologies that are both useful and effective for individuals, groups, organizations and society at large.


Matthew JONES and Benjamin MUELLER welcomed all attending members, friends, and guests to the WG’s annual business meeting. Both shared their appreciation for the first in-person meeting in a while. The complementary option to dial-in online had to be canceled, however, because of a last-minute organizational issue.

The WG’s secretary Yeliz ESERYEL was absent.

EJIS Special Issue - Embracing contrarian thinking: value-reflexive research for a digital world

In continuance of the OASIS pre-ICIS workshop 2022 in Copenhagen, Denmark, on “Criticality and Values in Digital Transformation Research,” we share the call for papers for a special issue in the European Journal of Information Systems. We look forward to receiving submissions from the IFIP community and particularly, the OASIS 2022 workshop attendees.

+++ Short Call for Papers +++

Digital technologies and our world are deeply enmeshed shaping our lives into a digital world.

Joint Conference of IFIP Working Groups

After Latour: Globalisation, Inequity and Climate Change

See the conference website for full details including submission instructions.

Event date: 
2023-12-07 to 2023-12-08

Criticality and Values in Digital Transformation Research—Proceedings

Proceedings from the OASIS 2022 workshop that took place as a pre-ICIS workshop in Copenhagen, Denmark, on December 10. Since the proceedings were optional, they contain only a limited number of the manuscripts presented and discussed at the workshop. These are:

Call for Papers: Criticality and Values in Digital Transformation Research

A Value Analysis of Machine Learning-based Usable Privacy