Table of Links Abstract and 1. Introduction 2 Related Work and 2.1 Technology Convergence Approaches 2.2 Technology Convergence Measurements 2.3 Technology Convergence Models 3 Data 4 Method and 4.1 Proximity Indices 4.2 Interpolation and Fitting Data 4.3 Clustering 4.4 Forecasting 5 Results and Discussion and 5.1 Overall Results 5.2 Case Study 5.3 Limitations and Future Works 6 Conclusion and References Appendix 2 Related Work This section provides an overview of the current state of the art in technology convergence approaches. Additionally, we explore various measurements and models that researchers have used to identify technological convergence, particularly in the context of emerging technologies. 2.1 Technology Convergence Approaches The approaches used to assess technological convergence are diverse, with many researchers relying on proximity indices between technologies. While patent-based approaches are common in the literature (Song et al., 2017; Curran, 2011; Lee, 2018; Kim, 2017), our focus shifts towards a bibliometric-based approach. These approaches often utilize metrics like citation counts, publication frequencies, and keyword connections (Chand & Bhatt, 2021). In addition, most existing research focuses on convergence after academic discovery but before market implementation (Curran, 2011). However, these studies often lack comprehensive early-stage scientific analysis, a gap we aim to address in our approach. Emerging technologies have a significant presence in the literature, with diverse definitions (Halaweh, 2013; Liu, 2020; Garner, 2017; Carley, 2018). We compare two main factors: one looks at socioeconomic factors like uncertainty and cost, while the other emphasizes scientific features such as novelty, growth, and community integration. In this study, we choose the latter, as it aligns with our focus on early-stage scientific convergence. In existing works, technology convergence is typically studied separately from emerging technologies, often utilizing completely different sets of approaches. However, in our study, we aim to identify technology convergence in emerging technologies by combining and utilizing methods and indicators that are often employed separately. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland. Table of Links Abstract and 1. Introduction Abstract and 1. Introduction 2 Related Work and 2.1 Technology Convergence Approaches 2 Related Work and 2.1 Technology Convergence Approaches 2.2 Technology Convergence Measurements 2.2 Technology Convergence Measurements 2.3 Technology Convergence Models 2.3 Technology Convergence Models 3 Data 3 Data 4 Method and 4.1 Proximity Indices 4 Method and 4.1 Proximity Indices 4.2 Interpolation and Fitting Data 4.2 Interpolation and Fitting Data 4.3 Clustering 4.3 Clustering 4.4 Forecasting 4.4 Forecasting 5 Results and Discussion and 5.1 Overall Results 5 Results and Discussion and 5.1 Overall Results 5.2 Case Study 5.2 Case Study 5.3 Limitations and Future Works 5.3 Limitations and Future Works 6 Conclusion and References 6 Conclusion and References Appendix Appendix 2 Related Work This section provides an overview of the current state of the art in technology convergence approaches. Additionally, we explore various measurements and models that researchers have used to identify technological convergence, particularly in the context of emerging technologies. 2.1 Technology Convergence Approaches The approaches used to assess technological convergence are diverse, with many researchers relying on proximity indices between technologies. While patent-based approaches are common in the literature (Song et al., 2017; Curran, 2011; Lee, 2018; Kim, 2017), our focus shifts towards a bibliometric-based approach. These approaches often utilize metrics like citation counts, publication frequencies, and keyword connections (Chand & Bhatt, 2021). In addition, most existing research focuses on convergence after academic discovery but before market implementation (Curran, 2011). However, these studies often lack comprehensive early-stage scientific analysis, a gap we aim to address in our approach. Emerging technologies have a significant presence in the literature, with diverse definitions (Halaweh, 2013; Liu, 2020; Garner, 2017; Carley, 2018). We compare two main factors: one looks at socioeconomic factors like uncertainty and cost, while the other emphasizes scientific features such as novelty, growth, and community integration. In this study, we choose the latter, as it aligns with our focus on early-stage scientific convergence. In existing works, technology convergence is typically studied separately from emerging technologies, often utilizing completely different sets of approaches. However, in our study, we aim to identify technology convergence in emerging technologies by combining and utilizing methods and indicators that are often employed separately. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland. Authors: Authors: (1) Alessandro Tavazz, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland, Institute of Mathematics, EPFL, 1015, Lausanne, Switzerland and a Corresponding author (tavazale@gmail.com); (2) Dimitri Percia David, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland and Institute of Entrepreneurship & Management, University of Applied Sciences of Western Switzerland (HES-SO Valais-Wallis), Techno-Pole 1, Le Foyer, 3960, Sierre, Switzerland; (3) Julian Jang-Jaccard, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland; (4) Alain Mermoud, Cyber-Defence Campus, armasuisse Science and Technology, Building I, EPFL Innovation Park, 1015, Lausanne, Switzerland.
