Hochschule München

HM Business School (FK 10)

Modulbeschreibung

Stand: WiSe 2025

Name Research Study: Challenges in Digital Technology Management
Katalog-Nummer FK 10#PPM#M4.13
Zugehörigkeit zu Curriculum
Master Betriebswirtschaft | M4.13 | 5 Leistungspunkte
Modulverantwortung
Prof. Dr. Eva Anderl
Lehrende
Prof. Dr. Eva Anderl
Marcel Hülsbeck
Prüfung(en)
Prüfungsform: ModA 80 % und Präs 20 %.
Detailangaben: will be provided in class
Hildsmittel: none
Prüfende: Prof. Dr. Eva Anderl , Marcel Hülsbeck
Lehr- und Lernform(en)
| 4 SWS | S - 1 Angebot(e)
Arbeitsaufwand
Präsenzzeit: 0 Stunden
Selbststudium, Vor- und Nachbereitung, Prüfungsvorbereitung: 0 Stunden
Voraussetzungen
Verwendbarkeit
Inhalt / Lernziele


English Version
Name Research Study: Challenges in Digital Technology Management
Katalog-Nummer FK 10#PPM#M4.13
Zugehörigkeit zu Curriculum
Master Betriebswirtschaft | M4.13 | 5 Leistungspunkte
Modulverantwortung
Prof. Dr. Eva Anderl
Lehrende
Prof. Dr. Eva Anderl
Marcel Hülsbeck
Prüfung(en)
Prüfungsform: ModA 80 % und Präs 20 %.
Detailangaben: will be provided in class
Hildsmittel: none
Prüfende: Prof. Dr. Eva Anderl , Marcel Hülsbeck
Lehr- und Lernform(en)
| 4 SWS | S - 1 Angebot(e)
Arbeitsaufwand
Präsenzzeit: 0 Stunden
Selbststudium, Vor- und Nachbereitung, Prüfungsvorbereitung: 0 Stunden
Voraussetzungen
Verwendbarkeit
Inhalt / Lernziele

Intended Learning Outcomes 

The students are enabled to 
  • apply the fundamental principles of research and usage for technology management
  • explain new ways of technology development and business application
  • realize a comprehensive empirical research study, thereby extending their knowledge and competencies of applying scientific research methods
  • present and discuss findings with industry experts and the scientific community, thereby strengthening their ability to engage in critical discourse and reflect on own research findings  

Contents

Artificial Intelligence (AI) is a powerful catalyst for business model innovation (BMI), reshaping how firms create, deliver, and capture value. AI technologies enable new forms of value generation and strategic differentiation, but also demand organizational adaptation and ecosystem collaboration. Firms must balance the development of internal AI capabilities with the external dynamics of BMI, such as ecosystem integration and co-evolution with partners and markets. In this research study seminar, students will investigate how AI drives and transforms BMI, exploring the key enablers, challenges, and success factors of AI-driven business model innovation across industries.

Applied Methods in Economics and Business Administration


Models and methods of analysis (research and analysis methods) 

  • Business and technology forecasting 
  • Business model analysis and evaluation
  • Peer review

Quantitative empirical methods (comparative- statistical, mathematical methods, data analysis) 

  • Surveys
  • Quantitative data analysis (comparative statistics) 

Qualitative interpretative methods (expert interviews, polls, standardised surveys)

  • Interviews
  • Qualitative data analysis

Teaching and learning methods

  • Seminar teaching
  • Presentation
  • Empirical research study

Literature

Heider, A. K., Clauss, T., Hülsbeck, M., Gerken, M., & Rüsen, T. A. (2022). Blood Is Thicker Than Water: The Role Of Family Willingness And Family Ability In Achieving Holistic Digitalisation In Family Businesses. International Journal of Innovation Management, 26(03), 2240009.
Heider, A., Hülsbeck, M., & von Schlenk-Barnsdorf, L. (2022). The role of family firm specific resources in innovation: An integrative literature review and framework. Management Review Quarterly, 72(2), 483-530.
Heider, A., Gerken, M., van Dinther, N., & Hülsbeck, M. (2021). Business model innovation through dynamic capabilities in small and medium enterprises–Evidence from the German Mittelstand. Journal of Business Research, 130, 635-645.
Weimann, V., Gerken, M., & Hülsbeck, M. (2020). Business model innovation in family firms: dynamic capabilities and the moderating role of socioemotional wealth. Journal of Business Economics, 90, 369-399.
Introduction to and identification of further literature as part of the course