2.Measuring Innovation and Technological Emergence

This theme emphasizes enhancing methods for tracking the emergence of science and technology and their alignment with broader societal goals. It includes creating models and tools that identify patterns and trends in innovation, offering actionable insights for policymakers and researchers.

3.Technology Role in Business Transformation

This theme explores the transformative impact of artificial intelligence on business productivity and innovation. It examines AI adoption strategies, their effects on organizational performance, and the integration of AI-driven tools to optimize processes and promote sustainable growth.

1.Mapping Scientific Progress

This theme focuses on developing quantitative frameworks to systematically analyze and map the evolution of scientific concepts and technologies. By utilizing advanced bibliometric techniques and natural language processing, it provides valuable insights into the trajectory of scientific research and innovation.

1.Mapping Scientific Progress

Cloud-based bibliometric analysis service for systematic mapping studies

This paper addresses the challenge of tracking the growing volume of scientific publications by introducing an open, cloud-based solution for bibliographic analysis. The tool automates time-consuming steps in systematic mapping studies, providing a more accessible and efficient method for researchers to analyze the state of the art in various scientific fields.

Research themes in big data analytics for policymaking: Insights from a mixed‐methods systematic literature review

This paper investigates the emerging role of big data and data analytics in public policy by conducting a literature review using bibliometric and qualitative analyses. It identifies nine key thematic clusters, with a focus on big data's impact on the policy cycle, data-driven decision-making, and productivity, and proposes a research agenda to guide future studies in this field.

Mapping the Sustainable Development Goals (SDGs) in Science, Technology and Innovation: Application of Machine Learning in SDG-Oriented Artefact Detection

This paper explores the alignment of science and technology with Sustainable Development Goals (SDGs) by developing a machine learning model to classify SDG relevance in patent documents. The findings highlight the extent to which patents address SDGs, offering insights to improve the alignment of R&D efforts, patenting strategies, and contributions toward achieving global sustainability goals.

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2. Measuring Innovation and Technological Emergence

Mapping inclusive innovation: A bibliometric study and literature review

The paper explores the evolving concept of inclusive innovation, which focuses on affordability and localization to address poverty in underdeveloped regions. Using bibliometric methods, the study identifies five key thematic clusters and proposes a conceptual model to guide future research on inclusive innovation and its role in fostering social empowerment and systemic inclusion.

Measuring digitalization at scale using web scraped data

This paper investigates the productivity effects of AI adoption using job advertisement data and firm-level productivity metrics. The findings reveal that AI adoption increases productivity but primarily in large firms, with delayed benefits due to technological immaturity and a three-year investment lag, shedding light on the "AI-related return of the Solow paradox."

Innovation for Sustainability: Literature Review and Bibliometric Analysis

This book chapter presents a literature review and bibliometric analysis on innovation for sustainability, focusing on five goal-oriented discussions: strategic, operational, organizational, collaborative, and systemic. The study highlights the evolution of the subject across disciplines since 2002 and offers insights for researchers and practitioners to bridge scientific knowledge with practical applications in organizations and businesses.

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3.Technology Role in Business Transformation

A critical review of large language models: Sensitivity, bias, and the path toward specialized AI

This article critically reviews large language models (LLMs), highlighting their sensitivity, bias, and limitations in analyzing text data. It emphasizes the importance of specialized models for precise and unbiased analysis, demonstrating their advantages over general-purpose models in tasks requiring domain-specific accuracy and relevance.

Return of the Solow-paradox in AI? AI-adoption and firm productivity

This paper investigates the productivity effects of AI adoption using job advertisement data and firm-level productivity metrics. The findings reveal that AI adoption increases productivity but primarily in large firms, with delayed benefits due to technological immaturity and a three-year investment lag, shedding light on the "AI-related return of the Solow paradox."

Connecting firm's web scraped textual content to body of science: Utilizing Microsoft Academic Graph hierarchical topic modeling

This paper presents a method to link textual data from company websites to scientific knowledge using Natural Language Processing (NLP). By connecting economic classification systems like NACE with Microsoft Academic Graph topic modeling, the study demonstrates how web scraping, NLP, and data transformation can bridge traditional classifications with insights from new data sources.

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