Autonomous Technology Systems for Future Competitiveness

Depending on the background of the student, literature/tutorials/videos are likely to be recommended to read and/or watch before commencing the course.
The course is technology management oriented and will not require any direct autonomous technology skills. 

The rise of autonomous solutions exemplifies the ongoing digitalisation and societal transformation based on sets of technology systems. While the technologies are still developing and the potential commercial opportunities are plenty, the autonomous capabilities develop in many directions. The purpose of the course is to provide students with knowledge about the basic terminologies to understand and analyse the potential of autonomous technology systems across domains for future competitiveness.

The course develops an understanding of autonomous capabilities as a subset of digital capabilities and how these may be used to obtain future competitiveness. This knowledge creates a foundation for researching, analysing, and making recommendations across various technology applications and domains: maritime use, overland transportation, electric vehicles, and unmanned aircrafts like drones. The aim is therefore to enable students to understand the wider opportunities and consequences of applying autonomous technology systems across domains, value chains, and industries. 

The main themes of the course will be: 

• The basic terminology of technology systems, including emerging technologies

• A theoretical introduction to dynamic capabilities

• Digital, systemic, and autonomous capabilities

• Delimitation of autonomous systems, including the role of Artificial Intelligence and software

• Barriers to growth and increased autonomy in the domains of overland transportation, electric vehicles, drones, and shipping

• Application and business development based on autonomous systems

Upon completion of the course, students can:

• Explain theories and methods pertaining to the state-of-the-art within autonomous technology systems and digital capabilities

• Explain the main technology components within the autonomous technology systems and compare their maturity across specific domains

• Explain the relevance of capability-based theories for autonomous technology systems

• Reflect on overarching categories of drivers and barriers and formulate their relevance for the competitiveness of autonomy technology systems

• Explain and reflect on ecosystem challenges in developing and commercialising autonomous technology systems

Upon completion of the course, students will demonstrate skills in such a way that they are able to:

• Apply models to assess challenges related to increased autonomy in specific use cases

• Apply models to conduct a maturity assessment within various domains of autonomous technology systems adoption

• Outline and exemplify opportunities and barriers for increased autonomy in specific use cases by applying a systems perspective

Upon completion of the course, students will demonstrate competence in such a way that they are able to:

• Compare knowledge of autonomous systems with needs and barriers within specific business domains

• Craft an overview of needs and requirements to inform a company’s approach to develop or adopt autonomous systems 

• Develop plans for value creation and value capture for companies and ecosystems based on autonomous technology systems

• Combine insights on barriers and opportunities to inform strategic decisions pertaining to grow businesses based on autonomous technology systems

These are examples of literature. The final literature will be announced on Itslearning during August:

• Kane, G.C., Palmer, D., Phillips, A.N., Kiron, D., & Buckley, N. (2017). Achieving digital maturity, MIT Sloan Management Review and Deloitte University Press, July 2017

• Rotolo, D., Hicks, D., & Martin, B.R. (2015). What is an emerging technology? Research policy, 44(10), 1827-1843.

• Schilke, O., Hu, S., & Helfat, C.E. (2018). Quo vadis, dynamic capabilities? A content-analytic review of the current state of knowledge and recommendations for future research, Academy of Management Annals, 12(1), 390–439.

• Thomson, L. (2022). A maturity framework for autonomous solutions in manufacturing firms: the interplay of technology, ecosystem, and business model, International Entrepreneurship and Management Journal, 18, 125-152.

• Tsvetkova, A., Hellström, M., & Ringbom, H. (2021). Creating value through product-service-software systems in institutionalized ecosystems – The case of autonomous ships, Industrial Marketing Management, 99, 16-27.

A combination of lectures, case studies, and group discussions. Students are expected to contribute actively to discussion and case presentations in class.

The expected workload is based on a guideline of 27 hours per ECTS credit point. For this course, this results in 67.5 hours. These hours are distributed across preparation and class attendance, preparation for the exam, and the exam itself.

The 67.5 work hours are distributed as follows:

Lectures: 15 hours.

Preparation for lectures: 42.5 hours.

Preparation for and the exam itself: 10 hours.

Total: 67.5 hours.