Background
Dr Bone is an active researcher in the field of innovation studies, with a special interest in understanding science and technology diffusion focusing on two sectors, the biomedical sector and more recently Artificial Intelligence.
Dr Bone has a keen interest in the development and use of new methods, using a variety of data and pioneering data science and text mining tools to study innovation, digitisation and the adoption of artificial intelligence. She is also currently convening a interest group within the University of Sussex Business School on the impact Artificial Intelligence to business practices and policies.
Research Interests
- How digital technologies and Artificial Intelligence is developed is adopted by firms
- Trends in the development and use of AI in research landscapes
- Digital transformation in healthcare
- AI and digital policies
Journal articles
Kanger L., Bone F., Rotolo D., Steinmueller W. E., Schot J. (2022) “Deep transitions: A mixed methods study of the historical evolution of mass production”. Technological Forecasting and Social Change, Volume 177, https://doi.org/10.1016/j.techfore.2022.121491.
Coburn J., Bone F., Hopkins M. M., Stirling A. C., Mestre-Ferrandiz J., Arapostathis S., Llewelyn M. (2021) “Appraising research policy instrument mixes: a multicriteria mapping study in six European countries of diagnostic innovation to manage antimicrobial resistance”. Research Policy, Vol.50, No.4, https://doi.org/10.1016/j.respol.2020.104140.
Bone F., Hopkins, M., Rafols, I., Molas-Gallart, J., Tang, P., Davey, G., Carr ,T. (2020) “DARE to be different? A novel approach for analysing diversity in collaborative research projects.” Research Evaluation, Vol.29 No. 3, pp.300-315. https://doi.org/10.1093/reseval/rvaa006.
Grassano N., Rotolo D., Hutton J., Lang F., Hopkins M. (2017) ‘Funding Data from Publication Acknowledgements: Coverage, Uses and Limitations. Journal of the Association for Information Science and Technology.’ Vol. 68 No. 4, pp.999-1017. https://doi.org/10.1002/asi.23737.
Lang F., Chavarro, D. and Liu, Y, (2016) ‘Can Automatic Classification Help to Increase Accuracy in Data Collection?’ Journal of Data and Information Science, Vol.1 No.3, pp. 42-58. https://doi.org/10.20309/jdis.201619.