Universität zu Köln

Lehmann, Matthias ORCID: 0000-0001-5137-6137 (2026). On the Effects of Complementary and Substitutive Use of Generative Artificial Intelligence in Innovation Processes. PhD thesis, Universität zu Köln.

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Abstract

This dissertation investigates how substitutive (automation) and complementary (augmentation) modes of generative artificial intelligence (AI) integration shape human behavior and performance within the two fundamental building blocks of innovation: learning and ideation. Through field studies and laboratory experiments in educational settings, our research demonstrates that while complementary use - utilizing Large Language Models (LLMs) as personal tutors - improves deep comprehension, substitutive use via automated solution generation increases task volume at the cost of actual understanding, ultimately widening inequality by harming learners with less prior knowledge. In the domain of design ideation, our work reveals that frontloading manual sketching induces design fixation and reduces idea variance, yet paradoxically results in significantly higher design quality by anchoring focus and mitigating AI-induced off-task distractions. Synthesizing these findings, this dissertation reveals that the specific mode of integration is the primary determinant of innovation outcomes, showing that misplaced substitution erodes essential mental effort while strategic complementarity reinforces the user engagement necessary for sustained performance. These insights yield actionable design principles for innovation managers, advocating for a new philosophy of human-AI collaboration that prioritizes safeguarding human cognitive involvement to realize the full potential of generative technology.

Item Type:	Thesis (PhD thesis)
Creators:	Creators Email ORCID ORCID Put Code Lehmann, Matthias lehmann.mattspam@gmail.com https://orcid.org/0000-0001-5137-6137 UNSPECIFIED
URN:	urn:nbn:de:hbz:38-798064
Date:	2026
Language:	English
Faculty:	Faculty of Management, Economy and Social Sciences
Divisions:	Faculty of Management, Economics and Social Sciences > Business Administration > Supply Chain Management > Professur für Supply Chain Innovation
Subjects:	Management and auxiliary services
Uncontrolled Keywords:	Keywords Language generative artificial intelligence English innovation management English large language models English learning English design ideation English
Date of oral exam:	29 January 2026
Referee:	Name Academic Title Sting, Fabian J. Prof. Dr. Fügener, Andreas Prof. Dr. Fugger, Nicolas Prof. Dr.
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/79806