AI and Innovation Management, 3 credits
AI och innovationshantering, 3 hp
Course code: IN8045
School of Business, Innovation and Sustainability
Level: Second cycle
Select course syllabus
Finalized by: Forsknings- och utbildningsnämnden, 2024-09-18 and is valid for students admitted for spring semester 2025.
Main field of study with advanced study
Industrial Management, Second cycle, has only first-cycle course/s as entry requirements. (A1N)Entry requirements
Degree of Bachelor of Science in Engineering including an independent project 15 credits or Degree of Bachelor of Science including an independent project 15 credits or the equivalent. The degree must be equivalent to a Swedish högskoleingenjörsexamen or Swedish kandidatexamen and must have been awarded from an internationally recognised university. Applicants must have written and verbal command of the English language equivalent to English course 6 in Swedish Upper-Secondary School. Exemption of the requirement in Swedish is granted.
Placement in the Academic System
The course is given as a single subject course.
Objectives
This course aims to reflect on the influence of Artificial Intelligence (AI) in Innovation Management. Following successful completion of the course, the student should be able to:
Following successful completion of the course the student should be able to:
Knowledge and understanding
- describe how AI can support the innovation process
- summarize the benefits and risks of AI in the innovation process
Skills and ability
- apply methods and frameworks to adopt AI in products, services, and technology developments
- manage AI in innovation processes in different organization contexts
Judgement and approach
- reflect on environmental aspects (e.g., regulations) around AI for its use in innovation management
Content
The course introduces the concept of innovation management and its types. Aspects related to the TOE framework (technology, organization, and environmental) are discussed. The course examines how AI can benefit products, services and technology developments. The course is divided into three modules that cover product innovation (e.g., NPD), service innovation, and technology development.
Students achieve the course objectives by participating in practical exercises through web-based learning interactions, podcasts, and examinations and reflections on the introduction of AI in the innovation process
Language of Instruction
Teaching Formats
The course is based on blended learning through lectures, seminars, and online learning. The course includes practical exercises based on reflective essays, quizzes, and podcasts. All course information and material are uploaded to the Halmstad University learning platform.
Grading scale
Examination formats
The course is examined through individual assignments and written take-home examinations.
2303: Assignments, 2 credits
Two-grade scale (UG): Fail (U), Pass (G)
2304: Written Take- home Examination, 1 credits
Two-grade scale (UG): Fail (U), Pass (G)
Exceptions from the specified examination format
If there are special reasons, the examiner may make exceptions from the specified examination format and allow a student to be examined in another way. Special reasons can e.g. be study support for students with disabilities.
Course evaluation
Course evaluation is part of the course. This evaluation offers guidance in the future development and planning of the course. Course evaluation is documented and made available to the students.
Course literature and other materials
Literature list 2025-01-20 – Until further notice
Brock, J. K. U., & Von Wangenheim, F. Demystifying AI: What digital transformation leaders can teach you about realistic artificial intelligence. California Management Review. (2019). 61(4), s. 110-134.
Garbuio, M., & Lin, N. Artificial intelligence as a growth engine for health care startups: Emerging business models. California Management Review. (2019). 61(2), sida 59-83.
Grewal, D., Guha, A., Satornino, C. B., & Schweiger, E. B. Artificial intelligence: The light and the darkness. Journal of Business Research. (2021). , 136, s. 229-236.
Huang, M. H., & Rust, R. T. Artificial intelligence in service. Journal of Service Research. (2018). 21(2), s.155-172.
Kinkel, S., Baumgartner, M., & Cherubini, E. Prerequisites for the adoption of AI technologies in manufacturing–Evidence from a worldwide sample of manufacturing companies. Technovation. (2022). 110, 102375.
Lebovitz, S., Levina, N., & Lifshitz-Assaf, H. Is AI Ground Truth Really ‘True’? The Dangers of Training and Evaluating AI Tools Based on Experts’ Know-What.(2021) MIS Quarterly
Zhang, H., Zhang, X., & Song, M. Deploying AI for New Product Development Success: By embracing and incorporating AI in all stages of NPD, companies can increase their success rate of NPD projects. Research-Technology Management. (2021). , 64(5), s. 50-57.