The research presented in this paper was partially funded by the German Federal Ministry of Education and Research in the context of the project KoLeArn (www.KoLeArn.de), Grant No. 01BE17008A. The authors are responsible for the content of this publication. We express our gratitude to the students of the University of Kassel who took part in this study. We would also like to thank Marian Thiel de Gafenco for his work and ideas in the early phases of this research project. Furthermore, this research builds on a paper that has been presented at the Academy of Management Annual Meeting 2017 in Atlanta (Janson & Söllner, 2017). We thank the reviewers and attendees as well as the mentors of the Management Education and Learning Writers Workshop for their valuable feedback that helped us to improve our research and to write this paper. Last but not least, we thank the Associate Editor for his guidance as well as the three anonymous reviewers for their constructive feedback and openness during the review process.

The digital age has yielded systems that increasingly reduce the complexity of our everyday lives. As such, smart personal assistants such as Amazon's Alexa or Apple's Siri combine the comfort of intuitive natural language interaction with the utility of personalized and situation-dependent information and service provision. However, research on SPAs is becoming increasingly complex and opaque. To reduce complexity, this paper introduces a classification system for SPAs. Based on a systematic literature review, a cluster analysis reveals five SPA archetypes:

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.

Technology-mediated learning (TML) is a major trend in education, since it allows to integrate the strengths of traditional-and IT-based learning activities. However, TML providers still struggle in identifying areas for improvement in their TML offerings. One reason for their struggles is inconsistencies in the literature regarding drivers of TML performance. Prior research suggests that these inconsistencies in TML literature might stem from neglecting the importance of considering the process perspective in addition to the input and outcome perspectives. This gap needs to be addressed to better understand the different drivers of the performance of TML scenarios. Filling this gap would further support TML providers with more precise guidance on how to (re-)design their offerings toward their customers' needs. To achieve our goal, we combine qualitative and quantitative methods to develop and evaluate a holistic model for assessing TML performance. In particular, we consolidate the body of literature, followed by a focus group workshop and a Q-sorting exercise with TML practitioners, and an empirical pre-study to develop and initially test our research model. Afterward, we collect data from 161 participants of TML vocational software trainings and evaluate our holistic model for assessing TML performance. The results provide empirical evidence for the importance of the TML process quality dimension as suggested in prior literature and highlighted by our TML practitioners. Our main theoretical as well as practical contribution is a holistic model that provides comprehensive insights into which constructs and facets shape the performance of TML in vocational software trainings.

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