mail Viktoria Pammer-Schindler Research Area Manager
mail Hermann Stern Business Area Manager
Understand and design for data-driven business from the perspectives of business models, technology-enhanced (organizational) learning and knowledge management.
Our goal is to understand practice together with technological tools in data-driven business, to design innovative, interactive and intelligent systems grounded in this understanding, and to evaluate the impact of novel technology on work practice, business processes, and overall business models.
Challenges in data-driven business model innovation
We have interviewed companies who are in the process of implementing data-driven business model innovation processes. Challenges arise on the organisational, legal, and technological level. The crux in data-driven business business model innovation is that it requires interdisciplinary knowledge and collaboration.
Semi-Automatic Digital Workspace
Many processes are defined by, or start with, a non-structured textual description. Examples are product specifications, or project contracts. We have co-designed with stakeholders a digital workspace that is initialised with a single document; and automatically pre-filled using natural language processing and textmining methods with applicable norms, regulations, templates, and results from similar processes. This speeds up the set-up time of knowledge workers, and supports compliance with complex, global, and fast-changing norms. The digital workspace also allows documentation of the ongoing process, thus filling an organisation’s knowledge base, and facilitates handover between different departments in a single organisational process.
In global environments, both in-house training and training of customers often requires extensive travel times, and inefficient face-2-face times. We are exploring and designing different virtual training and blended learning set-ups, and evaluating the impact on work and learning efficiency. Face-2-face training is more immersive, more interactive, and less prone to disruption than virtual training.
Learning from Experience
We have explored data as basis for learning from experience via adaptive learning support. We have developed theory-inspired reflection interventions which have been integrated in multiple use cases, ranging from IT consulting to b2b call center work, and expert professional training in the medical domain. In the case of b2b call centers for instance, we could show that interactive, collaborative mood tracking facilitated peer and management support within the team, and could observe an increased customer satisfaction – a key performance indicator in call centers.
Challenges for learning in Industry 4.0
We have explored the challenges for learning and knowledge management in Industry 4.0. There is a shared understanding that work in Industry 4.0 will increasingly be knowledge work; and industrial workforce will need in-situ live decision making and engaging, flexible learning support.