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For the last 20 years, enterprise architecture management (EAM) was primarily an instrument for harmonizing and consolidating IT landscapes and is lived as a transformation and governance discipline. It, however, is rather related to IT strategy than aligned to the actual corporate strategy and the work of the enterprise architect is characterized by tasks like prescribing, monitoring, documenting, and controlling. As digital transformation continues apace, companies are facing new challenges that lead to a volatile, uncertain, complex, and ambiguous (VUCA) world. To face these challenges, vision, understanding, clarity and agility allow to anticipative and implement necessary changes. This, of course, has implications for the role of the enterprise architect. S/he needs to start actively supporting innovation and taking more of an advisory role instead of just being driven by the current state of the enterprise architecture. This paper investigates the role of the enterprise architect in the VUCA world. Based on current literature and expert interviews, a survey was conducted among consultants who work as (or with) enterprise architects. Survey results include the evaluation of statements on current tasks of enterprise architects, their influence on projects and companies as well as future requirements on the roles of the enterprise architect. The results from the survey were synthesized with the findings from literature to derive the roles, tasks and skills of enterprise architect in the VUCA world.
This study utilizes cross-country survey data to analyze differences in attitudes toward cryptocurrency as an alternative to traditional money issued by a central bank. Particularly, we investigate women’s general attitude toward cryptocurrency systems. Results suggest that women invest less into cryptocurrency, show less interest in the future cryptocurrency investment, and see less economic potential in these systems than men do. Further evidence shows that these attitudes are directly connected with lower literacy in cryptocurrency systems. These findings support theory on gender differences in investment behavior. We contribute to the existing literature by conducting a cross-country survey on cryptocurrency attitudes in Europe and Asia, and hence show that this gender effect is robust across these cultures.
I need to move it, move It!
(2024)
Purpose
Student interest and learning success is an important component of teaching learning research. However, while the impact of emotions and psychological needs on students' achievements has been a focus of research, the impact of their physiological needs has been under studied. In this explorative study, I examine what impact the physiological and psychological needs of student teachers have on their feelings, motivation, and interest in different learning settings.
Approach
The research method used was the daily reconstruction method and included the Felix-App, a new digital research and feedback tool that allows the measurement of feelings, needs, motivation, and interest in real time.
Findings
The results suggest the importance of physiological needs for perceived emotions, motivation, and interest in the learning subject. The psychological needs, on the other hand, are of less importance.
Originality
The Felix-App is an innovative tool to learn more about learners' emotions and needs in real learning settings. The importance of physiological needs has been known since Maslow, but should be considered much more in the context of teaching and learning research in the future. There is a need for further research on the importance of physical aspects in learning.
Artificial intelligence (AI)-based technologies can increasingly perform knowledge work tasks, such as medical diagnosis. Thereby, it is expected that humans will not be replaced by AI but work closely with AI-based technology (“augmentation”). Augmentation has ethical implications for humans (e.g., impact on autonomy, opportunities to flourish through work), thus, developers and managers of AI-based technology have a responsibility to anticipate and mitigate risks to human workers. However, doing so can be difficult as AI encompasses a wide range of technologies, some of which enable fundamentally new forms of interaction. In this research-in-progress paper, we propose the development of a taxonomy to categorize unique characteristics of AI-based technology that influence the interaction and have ethical implications for human workers. The completed taxonomy will support researchers in forming cumulative knowledge on the ethical implications of augmentation and assist practitioners in the ethical design and management of AI-based technology in knowledge work.
Observing inconsistent results in prior studies, this paper applies the elaboration likelihood model to investigate the impact of affective and cognitive cues embedded in social media messages on audience engagement during a political event. Leveraging a rich dataset in the context of the 2020 U.S. presidential elections containing more than 3 million tweets, we found the prominence of both cue types. For the overall sample, positivity and sentiment are negatively related to engagement. In contrast, the post-hoc sub-sample analysis of tweets from famous users shows that emotionally charged content is more engaging. The role of sentiment decreases when the number of followers grows and ultimately becomes insignificant for Twitter participants with a vast number of followers. Prosocial orientation (“we-talk”) is consistently associated with more likes, comments, and retweets in the overall sample and sub-samples.
During a crisis event, social media enables two-way communication and many-to-many information broadcasting, browsing others’ posts, publishing own content, and public commenting. These records can deliver valuable insights to approach problematic situations effectively. Our study explores how social media communication can be analyzed to understand the responses to health crises better. Results based on nearly 800 K tweets indicate that the coping and regulation foci framework holds good explanatory power, with four clusters salient in public reactions: 1) “Understanding” (problem-promotion); 2) “Action planning” (problem-prevention); 3) “Hope” (emotion-promotion) and 4) “Reassurance” (emotion-prevention). Second, the inter-temporal analysis shows high volatility of topic proportions and a shift from self-centered to community-centered topics during the course of the event. The insights are beneficial for research on crisis management and practicians who are interested in large-scale monitoring of their audience for well-informed decision-making.
Does a smile open all doors?
(2020)
Online photographs govern an individual’s choices across a variety of contexts. In sharing arrangements, facial appearance has been shown to affect the desire to collaborate, interest to explore a listing, and even willingness to pay for a stay. Because of the ubiquity of online images and their influence on social attitudes, it seems crucial to be able to control these aspects. The present study examines the effect of different photographic self-disclosures on the provider’s perceptions and willingness to accept a potential co-sharer. The findings from our experiment in the accommodation-sharing context suggest social attraction mediates the effect of photographic self-disclosures on willingness to host. Implications of the results for IS research and practitioners are discussed.
With larger artificial neural networks (ANN) and deeper neural architectures, common methods for training ANN, such as backpropagation, are key to learning success. Their role becomes particularly important when interpreting and controlling structures that evolve through machine learning. This work aims to extend previous research on backpropagation-based methods by presenting a modified, full-gradient version of the backpropagation learning algorithm that preserves (or rather crystallizes) selected neural weights while leaving other weights adaptable (or rather fluid). In a design-science-oriented manner, a prototype of a feedforward ANN is demonstrated and refined using the new learning method. The results show that the so-called crystallizing backpropagation increases the control possibilities of neural structures and interpretation chances, while learning can be carried out as usual. Since neural hierarchies are established because of the algorithm, ANN compartments start to function in terms of cognitive levels. This study shows the importance of dealing with ANN in hierarchies through backpropagation and brings in learning methods as novel ways of interacting with ANN. Practitioners will benefit from this interactive process because they can restrict neural learning to specific architectural components of ANN and can focus further development on specific areas of higher cognitive levels without the risk of destroying valuable ANN structures.
Faced with the triad of time-cost-quality, the realization of production tasks under economic conditions is not trivial. Since the number of Artificial-Intelligence-(AI)-based applications in business processes is increasing more and more nowadays, the efficient design of AI cases for production processes as well as their target-oriented improvement is essential, so that production outcomes satisfy high quality criteria and economic requirements. Both challenge production management and data scientists, aiming to assign ideal manifestations of artificial neural networks (ANNs) to a certain task. Faced with new attempts of ANN-based production process improvements [8], this paper continues research about the optimal creation, provision and utilization of ANNs. Moreover, it presents a mechanism for AI case-based reasoning for ANNs. Experiments clarify continuously improving ANN knowledge bases by this mechanism empirically. Its proof-of-concept is demonstrated by the example of four production simulation scenarios, which cover the most relevant use cases and will be the basis for examining AI cases on a quantitative level.