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Discovering technological opportunities of cutting-edge technologies: A methodology based on literature analysis and artificial neural network

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  • Cammarano, Antonello
  • Varriale, Vincenzo
  • Michelino, Francesca
  • Caputo, Mauro

Abstract

This paper presents a methodology for discovering technological opportunities of cutting-edge technologies by capturing information from literature. Technological opportunities are expressed in the form of business applications of technologies in untested contexts, i.e. unexplored industries and business processes. The paper underscores the advantages of discovering opportunities starting from existing emerging practices presented in scientific papers, in contrast with the current state of the art that prefers other datasets. The business cases presented in journals are converted into the triad technology-industry-process and the impact on the business performance is reported. From the analysis of 33,285 papers, 14,739 existing triads have been captured. An artificial neural network has been trained using this dataset, enabling accurate forecasting of the potential impact of vacant combinations technology-industry-process. The methodology has been tested on 11 cutting-edge technologies: 3D printing, artificial intelligence, blockchain, computing, digital applications, geo-spatial technologies, immersive environments, internet of things, open & crowd-based platforms, proximity technologies, robotics. For each technology, a technological opportunity map is provided to show the best vacant areas for future implementation. The methodology distinguishes between combinations with uncertain and confident expected impact, so that companies can focus on the most promising areas. Implications for both practice and academia are discussed.

Suggested Citation

  • Cammarano, Antonello & Varriale, Vincenzo & Michelino, Francesca & Caputo, Mauro, 2024. "Discovering technological opportunities of cutting-edge technologies: A methodology based on literature analysis and artificial neural network," Technological Forecasting and Social Change, Elsevier, vol. 209(C).
  • Handle: RePEc:eee:tefoso:v:209:y:2024:i:c:s0040162524006097
    DOI: 10.1016/j.techfore.2024.123811
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    as
    1. Robinson, Douglas K.R. & Huang, Lu & Guo, Ying & Porter, Alan L., 2013. "Forecasting Innovation Pathways (FIP) for new and emerging science and technologies," Technological Forecasting and Social Change, Elsevier, vol. 80(2), pages 267-285.
    2. Nazarenko, Anastasia & Vishnevskiy, Konstantin & Meissner, Dirk & Daim, Tugrul, 2022. "Applying digital technologies in technology roadmapping to overcome individual biased assessments," Technovation, Elsevier, vol. 110(C).
    3. Delgosha, Mohammad Soltani & Hajiheydari, Nastaran & Talafidaryani, Mojtaba, 2022. "Discovering IoT implications in business and management: A computational thematic analysis," Technovation, Elsevier, vol. 118(C).
    4. Massaro, Maurizio, 2023. "Digital transformation in the healthcare sector through blockchain technology. Insights from academic research and business developments," Technovation, Elsevier, vol. 120(C).
    5. Mariani, Marcello M. & Machado, Isa & Magrelli, Vittoria & Dwivedi, Yogesh K., 2023. "Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions," Technovation, Elsevier, vol. 122(C).
    6. Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
    7. Wang, Chang & Geng, Hongjun & Sun, Rui & Song, Huiling, 2022. "Technological potential analysis and vacant technology forecasting in the graphene field based on the patent data mining," Resources Policy, Elsevier, vol. 77(C).
    8. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
    9. Yoon, Byungun & Magee, Christopher L., 2018. "Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 105-117.
    10. Lee, Hyunmin, 2023. "Converging technology to improve firm innovation competencies and business performance: Evidence from smart manufacturing technologies," Technovation, Elsevier, vol. 123(C).
    11. Cannavacciuolo, Lorella & Ferraro, Giovanna & Ponsiglione, Cristina & Primario, Simonetta & Quinto, Ivana, 2023. "Technological innovation-enabling industry 4.0 paradigm: A systematic literature review," Technovation, Elsevier, vol. 124(C).
    12. Seo, Wonchul & Yoon, Janghyeok & Park, Hyunseok & Coh, Byoung-youl & Lee, Jae-Min & Kwon, Oh-Jin, 2016. "Product opportunity identification based on internal capabilities using text mining and association rule mining," Technological Forecasting and Social Change, Elsevier, vol. 105(C), pages 94-104.
    13. Kim, Gabjo & Bae, Jinwoo, 2017. "A novel approach to forecast promising technology through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 228-237.
    14. Yoon, Byungun & Park, Inchae & Coh, Byoung-youl, 2014. "Exploring technological opportunities by linking technology and products: Application of morphology analysis and text mining," Technological Forecasting and Social Change, Elsevier, vol. 86(C), pages 287-303.
    15. Zhao, Shengchao & Zeng, Deming & Li, Jian & Feng, Ke & Wang, Yao, 2023. "Quantity or quality: The roles of technology and science convergence on firm innovation performance," Technovation, Elsevier, vol. 126(C).
    16. Tsouri, Maria & Hansen, Teis & Hanson, Jens & Steen, Markus, 2022. "Knowledge recombination for emerging technological innovations: The case of green shipping," Technovation, Elsevier, vol. 114(C).
    17. Heller, Jonas & Chylinski, Mathew & de Ruyter, Ko & Mahr, Dominik & Keeling, Debbie I., 2019. "Let Me Imagine That for You: Transforming the Retail Frontline Through Augmenting Customer Mental Imagery Ability," Journal of Retailing, Elsevier, vol. 95(2), pages 94-114.
    18. Hong, Suckwon & Kim, Juram & Woo, Han-Gyun & Kim, Young-Choon & Lee, Changyong, 2022. "Screening ideas in the early stages of technology development: A word2vec and convolutional neural network approach," Technovation, Elsevier, vol. 112(C).
    19. Bamel, Umesh & Talwar, Shalini & Pereira, Vijay & Corazza, Laura & Dhir, Amandeep, 2023. "Disruptive digital innovations in healthcare: Knowing the past and anticipating the future," Technovation, Elsevier, vol. 125(C).
    20. Sophie Boutillier & Blandine Laperche & Didier Lebert & Sana Elouaer-Mrizak, 2023. "A systemic analysis of the technological trajectory at company level based on patent data: The case of Sanofi's vaccine technology," Post-Print halshs-04028255, HAL.
    21. Wang, Zhinan & Porter, Alan L. & Wang, Xuefeng & Carley, Stephen, 2019. "An approach to identify emergent topics of technological convergence: A case study for 3D printing," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 723-732.
    22. Wang, Ming-Yeu & Fang, Shih-Chieh & Chang, Yu-Hsuan, 2015. "Exploring technological opportunities by mining the gaps between science and technology: Microalgal biofuels," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 182-195.
    23. Cho, Han Pil & Lim, Hyunsu & Lee, Dongmin & Cho, Hunhee & Kang, Kyung-In, 2018. "Patent analysis for forecasting promising technology in high-rise building construction," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 144-153.
    24. Noh, Heeyong & Song, Young-Keun & Lee, Sungjoo, 2016. "Identifying emerging core technologies for the future: Case study of patents published by leading telecommunication organizations," Telecommunications Policy, Elsevier, vol. 40(10), pages 956-970.
    25. Parteka, Aleksandra & Kordalska, Aleksandra, 2023. "Artificial intelligence and productivity: global evidence from AI patent and bibliometric data," Technovation, Elsevier, vol. 125(C).
    26. Carmona-Lavado, Antonio & Gimenez-Fernandez, Elena M. & Vlaisavljevic, Vesna & Cabello-Medina, Carmen, 2023. "Cross-industry innovation: A systematic literature review," Technovation, Elsevier, vol. 124(C).
    27. Boutillier, Sophie & Laperche, Blandine & Lebert, Didier & Elouaer-Mrizak, Sana, 2023. "A systemic analysis of the technological trajectory at company level based on patent data: The case of Sanofi's vaccine technology," Technovation, Elsevier, vol. 124(C).
    28. Acciarini, Chiara & Cappa, Francesco & Boccardelli, Paolo & Oriani, Raffaele, 2023. "How can organizations leverage big data to innovate their business models? A systematic literature review," Technovation, Elsevier, vol. 123(C).
    29. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.
    30. Haessler, Philipp & Giones, Ferran & Brem, Alexander, 2023. "The who and how of commercializing emerging technologies: A technology-focused review," Technovation, Elsevier, vol. 121(C).
    31. Song, Kisik & Kim, Karp Soo & Lee, Sungjoo, 2017. "Discovering new technology opportunities based on patents: Text-mining and F-term analysis," Technovation, Elsevier, vol. 60, pages 1-14.
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