For the summer term 2026, please register here.
Topic: Understanding AI Capabilities and Their Role for Innovation Management
Djerdj Horvat
Contemporary research posits that AI enables organizations to generate new knowledge by synthesizing various data sources to uncover hidden insights and emerging patterns. Additionally, AI applications can exploit existing data to develop novel services, products, process improvements, and business models. The growing interest in AI has sparked enthusiasm regarding its impact on knowledge creation and exploitation within organizations fostering innovation, sustainability and resilience.
This Master Seminar in Innovation Management focuses on AI capabilities, defined as a firm's ability to select, orchestrate, and leverage its AI-specific resources.
The aim of the seminar is (i) to understand the concept of AI capability and its foundation in resource-based view theory, (ii) to explore organizational resources and routines as fundamental building blocks of AI capabilities, and (iii) to examine the role of AI capabilities in fostering organizational learning, innovation, sustainability, and resilience in companies.
Suggested Topics:
1. Theoretical foundations of AI capabilities
2. AI capabilities in manufacturing companies
3. AI capabilities in other industries
4. The influence of AI capabilities on organizational learning
5. Integrating AI capabilities with absorptive capacity
6. Integrating AI capabilities and dynamic capabilities
7. The role of AI capabilities in driving product innovation
8. Transforming service innovation with AI capabilities
9. The impact of AI capabilities on process innovation
10. AI capabilities and business model innovation
11. AI capabilities and digital platforms
12. Building organizational resilience via AI capabilities
13. Emerging trends and future directions in AI capabilities
14. AI capabilities and international strategic decisions (insourcing, outsourcing, offshoring etc.)
15. Alternative suggested topics
Join us in exploring the transformative role of AI capabilities in driving innovation and enhancing organizational performance.
Examination performance:
Your grade will consist of group performance (30%) and of individual performance (70%)
Structure, and methodology:
The course is designed as a classic seminar and is aimed at students in advanced semesters. Students work in groups and individually on well-defined topics in the field of innovation management and innovation economics and show the capability to argue on a theoretically substantiated and empirically verified basis.
The seminar is held in hybrid form. The introductory event, interim presentation and final event will take place in person. The course is structured as follows:
I. Introductory event (on campus)
Students receive an introduction to the content of this year's Master's seminar. A series of subject areas will be presented. Students choose one of these subject areas, in which both their group work and their individual work is to be completed. There will also be an introduction to methodological approaches to working on the topics: (i.) literature analysis, (ii.) qualitative empirical analysis, (iii.) quantitative empirical analysis, (iv.) other methods.
II Group work (+online)
Development of the selected topic area, literature research and processing of the central sources, allocation of individual topics for individual work, including suggestions for methodical processing. Regular online meetings are offered to discuss the progress of the work.
III Interim presentation, results of the group work (on campus)
Each group presents on the following aspects/contents:
- Content specification and delimitation of the subject area
- Presentation, classification and critical appraisal of the literature base
- Presentation of the individual topics (content and methodology)
IV. Processing of the individual topics (+online voting appointments)
Regular online meetings are offered to discuss the progress of the work. Students can obtain processing information on structure, content, methodology, etc.
V. Final presentation, results of individual work (on campus)
Students present and defend the results of their individual work.
Your contact for further questions:
Dr. Djerdj Horvat
E-Mail: djerdj.horvat@uni-hohenheim.de
Suggested literature:
Mikalef, P. and Gupta, M., 2021. Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance. Information & management
Heimberger, H., Horvat, D. and Schultmann, F., 2024. Exploring the factors driving AI adoption in production: a systematic literature review and future research agenda. Information Technology and Management, pp.1-17.
Wamba-Taguimdje, S.L., Wamba, S.F., Kamdjoug, J.R.K. and Wanko, C.E.T., 2020. Impact of artificial intelligence on firm performance: exploring the mediating effect of process-oriented dynamic capabilities. In Digital Business Transformation: Organizing, Managing and Controlling in the Information Age (pp. 3-18). Springer International Publishing.
Abou-Foul, M., Ruiz-Alba, J.L. and López-Tenorio, P.J., 2023. The impact of artificial intelligence capabilities on servitization: The moderating role of absorptive capacity-A dynamic capabilities perspective. Journal of Business Research
Sjödin, D., Parida, V. and Kohtamäki, M., 2023. Artificial intelligence enabling circular business model innovation in digital servitization: Conceptualizing dynamic capabilities, AI capacities, business models and effects. Technological Forecasting and Social Change
Jarrahi, M.H., Kenyon, S., Brown, A., Donahue, C. and Wicher, C., 2023. Artificial intelligence: A strategy to harness its power through organizational learning. Journal of Business Strategy
