@conference {1533,
	title = {Call for Papers: Criticality and Values in Digital Transformation Research},
	booktitle = {IFIP 8.2 pre-ICIS OASIS Workshop 2022},
	year = {2022},
	publisher = {IFIP Working Group 8.2},
	organization = {IFIP Working Group 8.2},
	address = {Copenhagen, Denmark},
	abstract = {The OASIS pre-ICIS 2022 workshop calls for submissions that take a value-reflective and critical stance on digital transformation. While researchers have paid great attention to understanding how digital transformation can uphold and better the status-quo, there is an emerging awareness of negative consequences and new socioeconomic challenges triggered by digital technologies. Indeed, we face a multitude of societal and organizational issues that digital technologies have the potential to amplify or alleviate; depending on which values we value. The ubiquity of computing blurs boundaries between work and life. Always-on engenders a contorted reality of self and others, yet it also enables flexibility and autonomy. Artificial intelligence (AI) can fuel discrimination, undermine freedom of speech or disassemble entire democracies, while it can also drive progress in society and economy for instance, by helping people with disabilities, optimizing energy generation and consumption, or supporting the development of new drugs. All too often, digital technologies are built on the principle of privacy violation rather than of privacy as a basic human right, and security is used as an argument for enhanced surveillance and control rather than for human wellbeing and protection in the digital sphere. At the same time, we have to tackle social and environmental sustainability issues such as climate change, poverty and inequality in which digital technologies can take a positive role. All this calls for a value-reflective stance and exploration of the criticality of digital transformation. After all, we must acknowledge that any attempt to leverage technology for good can also have negative corollary effects. Hence, the OASIS pre-ICIS 2022 workshop aims to promote research that engages and reflects the value positions and criticality in digital transformation research. We propose an inclusive agenda embracing the more traditional IFIP 8.2 community while being open towards other relevant topics outside IFIP 8.2.},
	keywords = {Criticality, Digital transformation, Information infrastructure, Responsible Artificial Intelligence, values},
	author = {Markus P. Zimmer and Polyxeni Vasilakopoulou and Miria Grisot and Marko Niemimaa}
}
@proceedings {1534,
	title = {Digital Platform-Enabled Organizational Resilience in Major Exogeneous Shocks: A Multimethod Exploration Study of Platform{\textquoteright}s Network Effect on SMEs{\textquoteright} Digital Resilience during COVID-19},
	year = {2022},
	publisher = {IFIP Working Group 8.2},
	address = {Copenhagen, Denmark},
	author = {Hoang Ton Nu Huong Giang and Ahmad Asadullah and Alfred Ong and Teo Hock-Hai}
}
@conference {1535,
	title = {Digital Transformations in Non-Governmental Organizations {\textendash} A Case Study on the Effect of Power Imbalances},
	booktitle = {IFIP 8.2 pre-ICIS OASIS Workshop 2022},
	year = {2022},
	publisher = {IFIP Working Group 8.2},
	organization = {IFIP Working Group 8.2},
	address = {Copenhagen, Denmark},
	author = {Marie E. Godefroid and Ralf Plattfaut and Bj{\"o}rn Niehaves}
}
@proceedings {1536,
	title = {A Value Analysis of Machine Learning-based Usable Privacy},
	year = {2022},
	publisher = {IFIP Working Group 8.2},
	address = {Copenhagen, Denmark},
	abstract = {With the ubiquitous presence of machine learning based decision making in the digitalization of various processes, workflows and solutions across every functional area of our society, it is important to understand the value of such systems. Especially, to understand the value of them in the light of end user{\textquoteright}s privacy because machine learning programs learn accurate inferences by inductively extracting information from various pieces of data. We aim to study the relationship of a machine learning based detection of anti-privacy user interface design patterns (dark patterns) and self-determination of the end users, in order to analyze the value of the machine learning technology. To this end, we are in the process of i) investigating the perspectives of practitioners in the field of machine learning-based usable privacy and ii) developing a machine learning program that detects dark patterns in website{\textquoteright}s consent form designs. Following which, we plan to conduct user studies to empirically study end users perspectives for example on machine learning assisted decision on one{\textquoteright}s online privacy.},
	author = {Jenni Reuben and Ala Sarah Alaqra}
}
