Special Issue: Application of Big Data Analysis for Trend Spotting Related to the Development and Use of Human Capital

Guest Editors

Dirk Meissner, National Research University Higher School of Economics, Moscow, Russia,
Ilya Kuzminov, National Research University Higher School of Economics, Moscow, Russia,
David Sarpong, Brunel University (


Landscape mapping studies, including trend spotting, new technologies identification, actor mapping and centres of competence benchmarking, historically had been an integral part, not explicitly positioned, of various strategic studies aimed at stating the current conditions and prospects of development of countries and regions, sectors of economy and domains of knowledge. With the progress of strategic analytical studies as a specific activity meeting needs of high-level – and increasingly often distributed multilevel – decision-makers, foresight studies, including Future oriented technology analysis (Cagnin et al. 2008) and Science and Technology (S&T) Foresight (ForSTI) (Miles et al. 2016), emerged as a special framework of such research, with various mapping activities being formally named and methodologically scoped within. This task is especially relevant for the purpose of identifying the prospects for human capital development. In this regard, the identification of promising areas of S&T that can make a significant contribution to the advancement of human potential requires constant scanning, monitoring and analysis of the processes taking place in this area.

However, the growing gap between the needs and capacities of strategic analytics is becoming increasingly apparent. Strategic management systems at the state and corporate levels experience a growing need of timely and objective information (evidence-based policy), summarised to the state where it could be transformed into specific management decisions (actionable intelligence). At the same time, the fact that traditional institutional and organisational mechanisms, such as hierarchical systems of specialised centres of competence (research institutes, analytical centres, consulting companies), are unable to meet this need, becomes more and more transparent. If we look at the scale, generality, and negative consequences of this trend, we may see that the management system of social, economic and S&T spheres suffers a growing data intelligence crisis. 

This trend is caused by the unprecedented increase in volumes of significant and relevant information (Reinsel et al., 2018) on the backdrop of digitalisation of the global economy. The increase in the volume of information that is valuable and relevant for decision-making is associated not only with the growth in the ability of information systems to record and reflect a greater number of phenomena in economics, technology and other spheres. It is also conditioned by the structural complexity of these areas, diversification, emergence of a greater number of parallel development processes, where different teams, organisations and other centres of competence are participating in parallel innovation activities. 

Consequently, basic natural limitations of the abilities of the human brain to process and remember information require automation and augmentation of strategic analytics by natural language processing technologies, in particular text-mining (primary processing of large unstructured text arrays) and semantic analysis (semantic structuring of pre-processed text data). The most significant potential in this area is associated with processing of large text-data and their metadata (natural language processing, NLP). In recent years, NLP has experienced a technological revolution (Saravia, 2018) due to the development of deep learning and the emergence of embedding models that assign each stable letter combination, a word, a language term or even a document a high dimension numerical vector, thereby allowing to predict an element by its context or a context by its element, calculate the semantic proximity of terms and documents, perform “semantic arithmetic operations”. At the same time, new generations of multilayer embedding models appear – still very large in volume and computationally complex, but they radically expand the NLP capabilities. We are talking about such models and approaches as BERT, ULMfit, GPT-2, ELMo, which encode the meaning of words and add new, generative capabilities (filling in the gaps in text, writing augmented text on the initial fragment, answers to questions with explanation of the choice) to the capabilities of traditional embedding models (such as word2vec, GloVe, fastText). The development of NLP and automatic machine learning (AutoML) applications in NLP leads to a significant decrease in the dependence on wide-scale expert labour. Meanwhile, even embedding models and neural networks in NLP tasks do not yet allow to automatically generate accurate subject taxonomies and ontologies; they do not produce the results (especially in expert tasks) that are achievable on the basis of expensive ontologically-controlled methods (in a manner of speaking, “automatically generated on-demand expert systems” are not yet possible). Nevertheless, every day in global scientific and business communities, steps are being taken in this direction, which it is important to track in order not to miss new opportunities for application in strategic analytics.

Special Issue’s scope, including potential themes to be addressed in the Special Issue

Driven by the growing interest in new instruments for fact-based decision-making, we propose to organize a special issue aimed to place a scholarly call for academic endeavour to provide an elective and comprehensive  knowledge that elaborates on application of big data analysis for trend spotting. We solicit for papers from scholars who are actively working in this broad area of enquiry to recommend approaches that strengthen the existing analytical practice and provide fresh insights that would set the roadmap for further enquiries. Contributions may address, but are not limited to, the topics listed in the following:• 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

Notes for Prospective Authors

Submitted papers should not have been previously published nor be currently under consideration for publication elsewhere. Conference papers may only be submitted if the paper has been completely re-written and if appropriate written permissions have been obtained from any copyright holders of the original paper.Manuscripts should be submitted through the publisher’s online system. Submissions will be reviewed according to the journal’s rigorous standards and procedures through a double-blind peer review by at least two qualified reviewers. 

Submission Process

Please prepare the manuscript according to IEEE-TEM’s guidelines ( and submit to the journal’s Manuscript Central site ( Please upload the paper on the IEEE TEM Editorial Manager clearly indicating in the cover letter that the submission is for the IEEE TEM Special Issue on Application of Big Data Analysis for Trend Spotting related to the Development and Use of Human Capital.


Papers submitted by December 31, 2022

References and key literature related to the motivation and focus of the proposed issue

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Miles, I., Saritas, O., & Sokolov, A. (2016) Foresight for science, technology and innovation. Springer International Publishing

Reinsel D., Gantz J., Rydning J. (2018) The Digitization of the World. From Edge to Core. IDC White Paper I Doc# US44413318 I November 2018.

Saravia E. (2018) NLP 2018 Highlights.

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Turian, J., Ratinov, L., & Bengio, Y. (2010, July). Word representations: a simple and general method for semi-supervised learning. In Proceedings of the 48th annual meeting of the association for computational linguistics (pp. 384-394).

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Günther, W. A., Mehrizi, M. H. R., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. The Journal of Strategic Information Systems, 26(3), 191-209.

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IEEE Transactions on Engineering Management is journal of the Technology and Engineering Management Society of IEEE, published quarterly since 1954. It is dedicated to the publication of peer-reviewed original contributions, by researchers and practitioners, regarding the theory and practice of engineering, technology, and innovation management.

Editor in Chief

Tugrul U Daim, PhD PICMET Fellow
Professor and Director
Technology Management Doctoral Program
Department of Engineering and Technology Management
Maseeh College of Engineering and Computer Science
Portland State University, Portland OR
United States

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