Innovation impact of big science organizations – conditions, interactions, and knowledge spillover

Big Science Organizations (BSOs) - known as research infrastructures in Europe - are increasingly important as influencers in innovation ecosystems, as they are capable of driving technological advancement for the relevant industries. Studies on BSOs have shown their importance in technological breakthrough and their impact on national and international economies and society.

The relationship between BSOs and industrial firms is an area of interest in terms of innovation in recent research. BSO-industry collaborations both create new knowledge and engage in knowledge spillover, generating impact on both scientific and innovative performance for those involved. The theories on knowledge spillover, open innovation, supply-driven innovation and proximity economics will be used as the foundation for this project, and the areas to which this project will make theoretical contributions.

Dealing with uncertainty and complexity in business model innovation for sustainability

Business model innovation is a type of a forward-looking exercise where organizations intend to create or transform their business logic and assess its future outcomes in order to create and capture value. To achieve business model resilience and manage through unforeseeable circumstances optimal decision-making is crucial. Yet, when innovating business models, innovators are often faced with complexity and uncertainty. Such circumstances may hinder optimal and rational outcomes of the innovation process. This is especially relevant in the context of business model innovation for sustainability (BMIfS), where the forecasted impact is crucial for the entire system.

Business model innovation, as one of “entry points” to business at large, has a great potential of contributing to sustainable development goals. Therefore, a deeper understanding of the BMIfS process is needed to promote sustainability in practice and stimulate further research on the topic. This study adopts the cognitive perspective to explore in detail the process and phases of BMIfS.

The overall aim is to understand the cognitive patterns that innovators follow to cope with uncertainty and complexity within the process of BMIfS.

New functions and effects of intermediaries for innovation and entrepreneurship, equipped with artificial Intelligence

The goal of this research project is to find how artificial intelligence/machine learning tools can support innovation intermediaries (hubs, incubators, science parks, clusters, networks, RTOs, etc.) when providing innovation and/or entrepreneurial services to industry. Furthermore, we study how the implementation of these technologies fits in the digitalisation strategy of these organisations.

The outcome of the project aims to provide a framework for public and private organisms designing, implementing and/or running Innovation intermediary’s organizations and implementing AI/ML technologies.

Monetizing carbon savings for cleantech startups

A persistent cleantech startup financing gap inhibits the innovation’s potential to curb climate change, and the designated financial instruments for carbon savings could be better utilized to minimize the gap. Notably, the rise in corporate voluntary climate commitments is finally gaining momentum and both academics and practitioners recognize the need for diverse projects and financing solutions for reaching their goals. At the same time, there is a lack of research connecting cleantech financing options with current carbon trading practices.
The intended outcome of this project is to generate research findings leading toward frameworks and methods to guide and support financing mechanisms for cleantech ventures relating to voluntary carbon offsets.

Farnaz is investigating how gender bias affects the gender gap in entrepreneurship. She is interested in examining how the social construction of gender and entrepreneurship influence individual cognitions and perceptions, and inform biases, which effect the entrepreneurial processes in various ways. She is passionate about finding a solution by exploring how machine learning and big data can help expose implicit gender biases to reduce the gender gap in entrepreneurship.