Projects per year
Organisation profile
Organisation profile
The Quantitative Science and Technology team explores and applies data-driven methodologies to understand complex societal, technological, and economic challenges. We specialize in science, technology, and innovation (STI) studies, leveraging AI-driven foresight, large-scale data analytics, and algorithmic evaluation frameworks to assess the evolving landscape of innovation.
Our work revolves around developing and validating novel methodologies, including AI agents for large-scale information extraction from unstructured data sources, Large Language Models (LLM)-based evaluation tools, and text-based indicators. By integrating structured and unstructured data sources, we translate complex data into actionable insights, helping organizations make informed decisions in an increasingly uncertain world.
While our team works across diverse domains—economics, corporate strategy, sustainability, and policy evaluation—we are united by our commitment to methodological rigor, innovation-driven decision-making, and interdisciplinary problem-solving. We collaborate closely with ministries, businesses, the European Commission, and research institutions to provide customized AI solutions, scalable analytics, and transparent evaluation frameworks for decision-making and policy design.
- We are particularly interested in understanding:
How can AI-driven analytics and novel big data approaches enhance our understanding of scientific and technological evolution? - How can we measure, validate, and enhance the reliability of AI and LLM-based methodologies in innovation studies?
- What are the emerging AI-driven evaluation and foresight tools that can help organizations navigate technological change?
- How do we design robust, transparent, and explainable frameworks for AI applications in policy and decision-making?
- What is the role of advanced analytics in understanding the data economy, corporate foresight, and the changing nature of work and skills?
- How can algorithmic solutions, data storytelling, and interactive visualizations improve the communication of complex research findings?
- How can we expand the capabilities of AI-driven research methodologies while ensuring scalability, efficiency, and ethical considerations?
Through continuous methodological advancement, proactive customer collaboration, and a commitment to trust and transparency, we aim to pioneer the next generation of innovation analytics and contribute to AI’s responsible and effective integration into research and policy.
We have worked on knowledge graphs in Sfinno project.
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Collaborations and top research areas from the last five years
Profiles
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Sajad Ashouri
- Quantitative science and technology studies - Research Scientist
Person: Research Scientists
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Carolyn Cole
- Quantitative science and technology studies - Research Scientist
Person: Research Scientists
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Matthias Deschryvere
- Quantitative science and technology studies - Senior Scientist
Person: Senior Scientists
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Future of Software Engineering
Wallin, A. (Manager), Nuutinen, M. (Participant), Komssi, M. (Participant), Huuhanmäki, J. (Participant), Heinisuo, E. (Participant), Leikas, J. (Participant), Hajikhani, A. (Participant) & Ailisto, H. (Participant)
1/06/25 → 31/05/27
Project: Business Finland project
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Supporting the study on the next data frontier: generative AI, regulatory compliance and international dimensions
Hajikhani, A. (Manager) & Ashouri, S. (Participant)
1/02/25 → 31/10/25
Project: Consultancy
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NEMO: Language Modules development
Hajikhani, A. (Manager), Schmidt, R. (Participant) & Toivonen, S. (Participant)
1/01/25 → 1/01/30
Project: EU project
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Anticipatory governance of emerging technologies: White paper
Lehenkari, J., Lähteenmäki-Smith, K., Leväsluoto, J., Hämäläinen, H. & Cole, C., 3 Mar 2025, VTT Technical Research Centre of Finland. 24 p. (VTT White Paper, Vol. 2025).Research output: Book/Report › Report
Open AccessFile -
Mapping the discourse of Sustainable Development Goals: a mixed-method bibliometric and thematic exploration
Mortazavi, S., Hajikhani, A., Laine, I. & Salloum, C., 2025, (E-pub ahead of print) In: Management Decision.Research output: Contribution to journal › Review Article › peer-review
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Mid-term evaluation of the leading company initiative (LCI) partnerships - Final report
Lähteenmäki-Smith, K., Hajikhani, A., Naumanen, M., Nyman, J. & Laasonen, V., 31 Mar 2025, Helsinki: Business Finland. 77 p.Research output: Book/Report › Report
Open Access
Datasets
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Yhteiskunnalliset yritykset ja innovaattorit
Naumanen, M. (Creator), VTT Technical Research Centre of Finland, 2023
Dataset
Equipment
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SFINNO 2.0 – Advancing the Database of Finnish Innovations
Hajikhani, A. (Manager), Deschryvere, M. (Contact), Cole, C. (Administrator), Rytky, M. (Administrator), Vainikainen, S. (Administrator) & Schmidt, R. (Administrator)
Quantitative science and technology studiesFacility/equipment: Service
Activities
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𝐀𝐈 𝐢𝐧 𝐒𝐜𝐢𝐞𝐧𝐜𝐞, 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲, 𝐚𝐧𝐝 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐏𝐨𝐥𝐢𝐜𝐲 𝐒𝐭𝐮𝐝𝐢𝐞𝐬 – 𝐏𝐚𝐫𝐚𝐝𝐢𝐠𝐦 𝐒𝐡𝐢𝐟𝐭 𝐨𝐫 𝐍𝐞𝐜𝐞𝐬𝐬𝐚𝐫𝐲 𝐂𝐨𝐦𝐩𝐫𝐨𝐦𝐢𝐬𝐞
Hajikhani, A. (Speaker)
14 May 2025Activity: Talk or presentation types › Keynote or plenary presentation
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𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐧𝐠 𝐀𝐈 𝐢𝐧 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐚𝐧𝐝 𝐈𝐧𝐧𝐨𝐯𝐚𝐭𝐢𝐨𝐧 𝐏𝐨𝐥𝐢𝐜𝐲: 𝐎𝐩𝐩𝐨𝐫𝐭𝐮𝐧𝐢𝐭𝐢𝐞𝐬 𝐚𝐧𝐝 𝐑𝐢𝐬𝐤𝐬
Hajikhani, A. (Speaker)
11 Jun 2025 → 13 Jun 2025Activity: Talk or presentation types › Keynote or plenary presentation
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What to learn about partnerships from the mid-term Veturi evaluation?
Hajikhani, A. (Speaker)
21 May 2025Activity: Talk or presentation types › Public or invited talk
Press/Media
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Policy Brief: Experience Industry bounces back from the COVID-19 pandemic and reaches a new record-high revenue of 26.5 billion euros in 2023
29/04/24
1 Media contribution
Press/Media: Public Engagement Activities
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Tutkimus: Palkkatyöyhteiskunta ei kannusta yrittäjyyteen eikä kasvuun
Heinonen, J. & Naumanen, M.
19/04/23
1 Media contribution
Press/Media: Other
Impacts
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Tiedenurkkaus: Luovista aloista elämystalouteen – tilannekuva luovien alojen ja tapahtuma-alan liiketoiminnasta
Naumanen, M. (Participant)
Impact: Societal Impact