Deadline is December 31, 2022Papers will be evaluated on a rolling basis.
Dirk Meissner, National Research University Higher School of Economics, Moscow, Russia (firstname.lastname@example.org)
Ilya Kuzminov, National Research University Higher School of Economics, Moscow, Russia (email@example.com)
David Sarpong, Brunel University (David.Sarpong@brunel.ac.uk)
Landscape mapping studies, including trend spotting, new technologies identification, actor mapping and centres of competence benchmarking, have been historically integral element – although not explicitly positioned – of strategic studies aimed at providing information for strategy development at company, regional and national level. However, with increasing information base in number of information and data and its availability humans abilities to process and remember information are challenged calling for support tools to detect, process and interpret these information. That calls for automation and augmentation of strategic analytics by natural language processing technologies (NLP), in particular text-mining (primary processing of large unstructured text arrays) and semantic analysis.
Suggested topics are, but not limited to:
- Expert methods in strategic studies: limitations and potential for renewed role
- Psychophysiological and organisational limitations of humans and human groups in information processing: meaning for strategic studies and big data applications
- Big Data, Data Integration, and Machine Learning technologies for raising completeness, representativeness, and unbiasedness of strategic studies’ results
- Approaches for using Big Data, Open Data, Citizen Data Marketplaces, Small Data, Data APIs for trend spotting, finding insights and weak signals in strategic studies
- Natural language processing technologies, semantics, text mining and big documentary data as a key data tool for strategic studies
- New ways of enhancement and augmentation of data organisation and analysis methods based on NLP and ML
- Applications of language models, advanced NLP and hybrid ML tools in practical tasks of technology strategic studies, future studies, foresight exercises and trend analysis in research of economic sectors and markets
- Applications of digital tools and big data technologies including NLP in sociological studies, digital sociology, and studies of human potential and human capital in modern society
Please prepare the manuscript according to IEEE-TEM’s guidelines and submit the journal’s Manuscript Central site (https://mc.manuscriptcentral.com/tem-ieee). Please clearly state in the cover letter that the submission is for this special issue.