Unveiling the Landscape of High-Tech Transfer in Industry 5.0: A Text Mining Exploration
Publish Year: 1403
Type: Journal paper
Language: English
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Document National Code:
JR_JADM-12-3_005
Index date: 31 December 2024
Unveiling the Landscape of High-Tech Transfer in Industry 5.0: A Text Mining Exploration abstract
The international transfer of high technologies plays a pivotal role in the transformation of industries and the transition to Industry 5.0 - a paradigm emphasizing human-centric, sustainable, and resilient industrial development. However, this process faces numerous challenges and complexities, necessitating a profound understanding of its key variables and concepts. The present research aimed to identify and analyze these variables in the realm of high technology transfer in Industry 5.0. Following a systematic literature review protocol, 84 relevant articles published between 2017 and 2024 were selected based on predefined criteria including relevance to the research topic, publication quality, and citation impact. These articles were analyzed using a comprehensive text mining approach incorporating keyword extraction, sentiment analysis, topic modeling, and concept clustering techniques implemented through Python libraries including NLTK, SpaCy, TextBlob, and Scikit-learn. The results categorize the key variables and concepts into five main clusters: high technologies (including AI, IoT, and robotics), technology transfer mechanisms, Industry 5.0 characteristics, implementation challenges (such as cybersecurity risks and high adoption costs) and opportunities (including increased productivity and innovation potential), and regulatory frameworks. These findings unveil various aspects of the technology transfer process, providing insights for stakeholders while highlighting the critical role of human-technology collaboration in Industry 5.0. The study's limitations include potential bias from focusing primarily on English-language literature and the inherent constraints of computational text analysis in capturing context-dependent nuances. This research contributes to a deeper understanding of technology transfer dynamics in Industry 5.0, offering practical implications for policymaking and implementation strategies.
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Unveiling the Landscape of High-Tech Transfer in Industry 5.0: A Text Mining Exploration authors
Arezoo Zamany
Department of Technology Management, Faculty of Management and Economics, Science and Research Branch, Islamic Azad University, Tehran, Iran.
Abbas Khamseh
Department of Industrial Management, Karaj Branch, Islamic Azad University, Karaj, Iran.
Sayedjavad Iranbanfard
Department of Management, Shiraz Branch, Islamic Azad University, Shiraz, Iran.
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