Technology convergence can trigger technological innovation and change. Therefore, it is required to develop an approach to predict the convergence between technology fields that did not exist in the past. It will allow a frim to preoccupy a completely new competitive advantage that is different from that of its competitors. The timely anticipation of converging technology fields allows the innovating firms to recognize the changing business developments associated with the technology convergence. A variety of researchers have presented supervised learning-based approaches to predict potential technology fields where technology convergence is taking place using patents. They have developed machine learning models which capture the associations between the past and future connections between technology classes. Although their contributions are absolutely significant, they have a limitation in that they do not consider in depth the technological properties that are outputs of technological activities performed in each technology field. To ensure that the predicted future connections between technology fields are reasonable, technological properties that can specifically imply technology convergence should be clearly reflected in the process of the supervised learning. Motivated to remedy this problem, this study proposes a supervised learning-based approach to anticipating potential technology convergence by using the link prediction results, the technological influence relationships, and the technological relevance between technology classes. Using these as input features, several classification models that predict new technology convergence are trained and a voting classifier is developed to ensemble all the models. This study is expected to contribute to identifying new technology opportunities that can be realized through technology convergence. Furthermore, this study will assist firms to reflect the identified opportunities on their technology roadmap and make business decisions to penetrate the relevant market in a timely manner.

This paper analyzes the efficiency of the input and output of R&D activity and the status of its development and management in universities in each region of China and proposes suggestions for improvement. The DEA–Malmquist model was used to analyze the static and dynamic science and technology statistics for universities in each region during 2006–2019 to reveal changes in the input and output of R&D activity. The overall efficiency of the R&D activity of universities in all regions of China was low. Among the 27 regions studied, DEA revealed effective efficiency in 20 regions in 2006, accounting for 74.07%, and in 19 regions in 2019, accounting for 70.37%. The Malmquist index was greater than 1 in 17 regions in 2006–2019, with an average value of 1.023 during 2006–2019. The technological progress of R&D activity in universities in each region plays a major role in the improvement of the overall efficiency. Conclusions: The efficiency of the R&D activity of Chinese universities in all regions is low in general, with large disparities between regions. The R&D activity of Chinese universities lacks scientific management. It is necessary to optimize the allocation of research resources, construct an evaluation system for the efficiency of R&D activity, and offer incentives for research to improve the output and promote the transformation of results in Chinese universities.

This study proposes a novel approach to anticipating technology convergence in the bio-healthcare sector by integrating text mining based on transformer models and supervised learning methodologies. The overarching goal is to develop a robust method for predicting technology convergence, leveraging the interrelationships between technology topics extracted from patents and research articles. Through the application of advanced techniques and by leveraging the strengths of transformer-based models such as BERTopic with KeyBERT and OpenAI integration to generate technology topics, we identified potential convergence opportunities and explored emerging trends within the dataset. The proposed method seeks to predict technology convergence effectively by employing various machine learning and deep learning techniques to train prediction models by integrating technological similarity, link prediction measures, and causal relationships between technology topics as input features, offering a more accurate and comprehensive understanding of the intricate relationships within the technological landscape. This study contributes to the literature on technology convergence by offering a novel methodology for anticipating future trends and identifying opportunities for interdisciplinary collaboration in the bio-healthcare sector. Overall, the outcomes of this study hold significant implications for businesses seeking to capitalize on emerging convergence opportunities for sustainable growth.