Research Program
Research@IfSTO - Overview
Research at the Institute is focused on three main areas, capturing two interrelated innovation phenomena: the increasing democratization of innovation and the rapid development of artificial intelligence:
The Role of AI for Strategy and Innovation
Open and User Innovation
New Venture Performance and Growth
The empirical research approach at the Institute is predominantly quantitative.
Research Areas
Open and User Innovation
Research Questions
The Institute addresses the following key research questions:
Why do larger crowds yield better results in innovation contests?
How does the introduction of status incentives affect contribution behavior in online communities?
Do and if yes, how do makerspaces facilitate innovation?
How does a departure of their established niche affect social media influencers’ diffusion impact?
What role do (novel) techniques developed by users play in the emergence of innovation?
Relevance and Research Methods
Providing insights to these research questions is important to develop a more nuanced understanding of innovation processes and structures that appear outside the boundaries of firms and that rely on self-selection of participants and voluntary contributions. The angle on technique innovation is deemed relevant as it offers a perspective on the crucial role of human tacit knowledge that cannot be easily reproduced by artificial intelligence as an increasingly powerful technology for innovation.
Research in this area uses a variety of quantitative methods such as experiments, simulations, digital trace data analyses, or surveys.
IfSTO researchers:
Univ.-Prof. Dr. Christopher Lettl
Director of the Institute
The Role of AI for Strategy and Innovation
The Institute's angle on artificial intelligence is how knowledge judgment and recombination that used to require individuals and organizations (e.g., rating an idea or predicting how users will respond to it) shift to machines, and what this shift means for the strategy of firms. The focus is on the division of labor between human and artificial intelligence along the phases of the innovation process, from need recognition and idea generation to evaluation, development, and diffusion, and on the organizations that establish strategies to certify quality of innovative products and processes.
Research Questions
The Institute addresses the following key research questions:
Which phases of the innovation process can AI carry on its own, and in which does it support human judgment?
How does human-AI collaboration affect idea generation, and how do model settings interact with divergent thinking of users?
Can the hallucinations of language models be used as a creative resource in idea generation?
How well can language models simulate users and consumers?
What happens to organizations that are responsible for ensuring the quality of work once AI makes output cheap and allows non-experts to do work that once required professionals?
Relevance and Research Methods
Providing insights to these research questions is important because AI is mostly discussed as a gain in productivity, whereas many of its consequences for innovation are structural: AI changes who generates and selects ideas within a firm. The angle on human-AI collaboration is deemed relevant as it connects AI support in innovation processes to the Institute's research on open and user innovation. Furthermore, we address the broad question of how AI complements human expertise for innovation.
Research in this area uses a variety of quantitative methods such as laboratory and online experiments with human and synthetic participants, simulations, formal modeling, or digital trace data analyses.
Univ.-Prof. Dr. Christopher Lettl
Director of the Institute
New Venture Performance and Growth
This research cluster examines how entrepreneurs and entrepreneurial ventures build, organize, and mobilize talent to grow and innovate. It covers different types of entrepreneurs, ranging from high-tech founders in Silicon Valley to individual entrepreneurs such as user entrepreneurs and gig entrepreneurs working in digital platform contexts.
Research Questions
IfSTO addresses the following key research questions:
How do startups' talent needs and hiring strategies change across stages of venture growth, and how does this affect innovation and the likelihood of an IPO?
How do startups evaluate and select talent across multi-stage hiring processes?
How do entrepreneurs' characteristics (e.g., gender) affect a venture's ability to attract and retain talent and to scale?
How do gig entrepreneurs use their reputations to access new opportunities on digital platforms?
How do startups design governance structures to mobilize user entrepreneurs as contributors to innovation?
Relevance and Research Methods
Answering these questions helps explain why some new ventures grow and innovate while others do not. A central insight is that the value of talent is not fixed but depends on the venture's stage and context, which matters for entrepreneurs, investors, and policymakers concerned with inequality in entrepreneurship.
Research in this area primarily uses quantitative methods such as econometric analyses of large-scale data, including millions of career histories of founders and their employees from LinkedIn, venture investment records, and data from digital platforms such as Upwork and Steam.
This research are is currently represented by our new tenure-track researcher Seongbin (Simon) Yoon. Simon will officially start working at our institute in November 2026.
Institute for Strategy, Technology and Organization