New political objectives, emerging regulatory regimes for the digital sphere, and higher penalties for violations have intensified the pressure to develop lawful IT artefacts. As the adaptation of existing IT artefacts to new regulations can be expensive and arduous, a more attractive approach would be to design IT artefacts lawfully from the beginning. A major challenge is that the law is generally technology-neutral, and lawful design requires legal expertise throughout the development, which is costly and time consuming due to communication challenges between legal experts and developers. One possible approach to proactively consider IT regulations in the systems development is design patterns that convey legal design knowledge and support developers in determining the appropriate design options. Consequently, we develop a framework for lawful design patterns and demonstrate their feasibility and advantages using the example of developing AI-based assistants and the regulation of the General Data Protection Regulation (GDPR). Using the design pattern framework, we develop design patterns for lawful AI-based assistants and evaluate them using (a) an experimental approach to show the usefulness of the patterns for developers and (b) rely on a legal simulation study to holistically evaluate how design patterns contribute to lawful IT.

This paper employs a longitudinal perspective to examine continued system use (CSU) by individuals in utilitarian, volitional contexts when alternative systems are present . We focus on two key behavioural antecedents of CSU – habit and continuance intention – and theorise how the relationships between CSU and these antecedents evolve over time. In addition, we hypothesise how the interaction effect of habit and intention on CSU evolves temporally. Our theorising differs from extant literature in two important respects: 1) In contrast to the widespread acceptance of the diminishing effect of continuance intention on CSU in the information systems (IS) literature, we hypothesise that in our context, its impact increases with time; and 2) In contrast to the negative moderation effect of habit on the relationship between intention and CSU proposed in the literature, we posit a positive interaction effect. We collect longitudinal survey data on the use of a higher education IS from students in a European university. Our results suggest that the impact of continuance intention on CSU as well as the interaction effect between habit and intention are increasing over time. We further introduce a methodological innovation – the permutation approach to conduct the multi-group analysis with repeated measures – to the literature.

Gamification is a well-known approach that refers to the use of elements to increase the motivation of information systems users. A remaining challenge in gamification is that no shared understanding of the meaning and classification of gamification elements currently exists. This impedes guidance concerning analysis and development of gamification concepts, and often results in non-effective gamification designs. The goal of our research is to consolidate current gamification research and rigorously develop a taxonomy, as well as to demonstrate how a systematic classification of gamification elements can provide guidance for the gamification of information systems and improve understanding of existing gamification concepts. To achieve our goal, we develop a taxonomic classification of gamification elements before evaluating this taxonomy using expert interviews. Furthermore, we provide evidence as to the taxonomy’s feasibility using two practical cases: First, we show how our taxonomy helps to analyse existing gamification concepts; second, we show how our taxonomy can be used for guiding the gamification of information systems. We enrich theory by introducing a novel taxonomy to better explain the characteristics of gamification elements, which will be valuable for both gamification analysis and design. This paper will help guide practitioners to select and combine gamification elements for their gamification concepts.

What if artificial intelligence (AI) machines became teammates rather than tools? This paper reports on an international initiative by 65 collaboration scientists to develop a research agenda for exploring the potential risks and benefits of machines as teammates (MaT). They generated 819 research questions. A subteam of 12 converged them to a research agenda comprising three design areas – Machine artifact, Collaboration, and Institution – and 17 dualities – significant effects with the potential for benefit or harm. The MaT research agenda offers a structure and archetypal research questions to organize early thought and research in this new area of study.

Technology acceptance research has shown that trust is an important factor fostering use of information systems (IS). As a result, numerous IS researchers have studied factors that build trust in I...