Participative Cognitive Mapping – a Method to Support the Interdisciplinary Innovative Projects of Megascience
Abstract
The aim of this article is to augment the cognitive mapping procedure and that of the analysis of cognitive diversity to transform them from the research method into the instrument of decision-making support to promote the implementation of the Magacience capacity (the large-scale co-specialized mega-class research facilities for collective use) in regard of large-scale interdisciplinary projects. An important effect of the Megascience is an opportunity for multidisciplinary research and implementation of the research outcome in business sector. In the business intelligence perspective, the Megascience can be described as a distributed network of intangible assets with concentration of the tangible capital. The large cognitive diversity, information overload and the tacit knowledge peculiar to science-business interaction poses the challenges for data retrieval, information filtering and knowledge presentation in Megascience projects. This study objective is to develop decision making framework for the application of the participatory cognitive mapping to drive the decision process in the science-business interaction within the Megascience. The article meets the challenge of the developing an analytical approach for in order to stimulate the decision-making process in application to the complex interdisciplinary and innovative projects, such as, those of Megascience. The article presents an analytical approach for applying the participating cognitive mapping for the support of decision-making involving the interaction of representatives of different disciplines, as well as representatives of the business community, including the algorithm of the new management procedure. According to this approach, the data retrieval, information filtering and knowledge presentation are performed through the cycles of humanmachine interaction which are repeated until achieving the distance ratio target. This framework makes it possible to present shared knowledge/mindset of decision making team. The main advantage of the proposed framework is to offset the subjectivity in building the pool of original constructs by filtering the semi-structured big data. The results allow managers to solve the most complicated task of dealing with the complexity, not only for the complex systems of project type, but also for object type systems, such as a country, region, industry, enterprise.
About the Authors
Alexander Ye. KarlikRussian Federation
Vladimir V. Platonov
Russian Federation
Svetlana A. Krechko
Belarus
References
1. Abramova N.A., Avdeeva Z.K. (2008). Cognitive analysis and the management of situation development: challenges for the methodology, the theory and practice. Problemy Upravleniya [Problems of Management], no. 3, pp. 85–87 (in Russian). URL: http://www.mathnet.ru/links/4442f8280076ffbc4e64c42b437bf4e4/pu163.pdf.
2. Axelrod R. (1976). The structure of decision. Princeton: Princeton University Press.
3. Baggio R., Sheresheva M.Y. (2014). Network approach in economics and management: The interdisciplinary nature. Vestnik MGU. Seria 6. Ekonomika. [MGU Herald: Science, education, economics. A series of economics], no. 2, pp. 3–21 (in Russian). URL: https://www.researchgate.net/publication/281116682_Setevoj_podhod_v_ekonomike_i_upravlenii_mezdisciplinarnyj_harakter.
4. Bergman J.-P., Knutas A., Jantunen A., Luukka P., Karlik A., Platonov V. (2016). Strategic interpretation on sustainability issues: Eliciting cognitive maps of boards of directors. Corporate Governance (Bingley), no. 1 (16), pp. 162–186. URL: h t t p s : / / w w w. e m e r a l d i n s i g h t . c o m / d o i /abs/10.1108/CG-04-2015-0051.
5. Boisot M.H. (1998). Knowledge assets: Securing competitive advantage in the information economy. Oxford: Oxford University Press.
6. Chen H., Chiang R. Storey V. (2012). Business intelligence and analytics: From big data to big impact. MIS Quarterly, no. 4 (36), pp. 1165–1188. URL: https://misq.org/misq/downloads/download/editorial/565.
7. Clore G.L., Huntsinger J.R. (2007). How emotions inform judgment and regulate thought. Trends in Cognitive Science, no. 9 (11), pp. 393–399. URL: https://www.cell.com/trends/cognitivesciences/abstract/S1364-6613(07)00190-8?code=cell-site.
8. Davenport T.H., Pati D.J. (2012). Data scientist: The Sexiest job of the 21st century. Harvard Business Review, no. 10 (90), pp. 70–76. URL: https://hbr.org/2012/10/data-scientist-the-sexiest-job-ofthe-21st-century.
9. De Mauro A., Greco M., Grimaldi M. (2016). A formal definition of Big Data based on its essential features. Library Review, no. 3 (68). pp. 122–135. URL: https://www.emeraldinsight.com/doi/abs/10.1108/LR-06-2015-0061.
10. Drucker P.F. (1999). Management challenges for the 21st century. New York: Harper Business.
11. Dutton J.E., Duncan R.B. (1987). The influence of the strategic planning process on strategic change. Strategic Management Journal, no. 8, pp. 103–116. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/smj.4250080202.
12. Edmondson A.C., Harvey J.F. (2018). Cross-boundary teaming for innovation: Integrating research on teams and knowledge in organizations. Human Resource Management Review, no. 4(28), pp. 347–360. URL: https://www.sciencedirect.com/science/article/pii/S1053482217300219.
13. Eliseeva I.I., Platonov V.V, Bergman Yu.P., Luukka P. (2015). Cognitive diversity and the formation of the dominant logic of innovative companies. Economics of Contemporary Russia, no. 3, pp. 67–80 (in Russian). URL: https://www.ecr-journal.ru/jour/article/view/77.
14. Eliseeva I.I., Platonov V.V., Bergman J.-P., Dyukov I., Ruyuotta P. (2016). The emergence of dominant logic: Looking inside the black box. Economics of Contemporary Russia, no. 4, pp. 53–67 (in Russian). URL: https://www.ecr-journal.ru/jour/article/view/168.
15. Forester J.W. (1971). Counterintuitive behavior of social systems. Technology Review, vol. 73, no. 3, pp. 52–68. URL: https://www.sciencedirect.com/science/article/pii/S004016257180001X.
16. Gao F., Li M., Nakamori Y. (2003). Critical systems thinking as a way to manage knowledge. Systems Research and Behavioral Science, no. 1 (20), pp. 3–19. URL: https://onlinelibrary.wiley.com/doi/abs/10.1002/sres.512.
17. Ignatiev M.B., Karlik A.E., Kukor B.L., Platonov V.V., Yakovleva E.A. (2018). Risk-based information technology in the digital economy: risk management in the electric power industry. Ekonomicheskie nauki [Sciences of Economics], no. 161, pp. 21–29 (in Russian). URL: https://ecsn.ru/files/pdf/201804/201804_21.pdf.
18. Karlik A.E., Platonov V.V. (2015). Organizational and management innovation as a hidden driver of boosting the competitiveness of the Russian industry. Ehkonomicheskoe vozrozhdenie Rossii [Economic revival of Russia], no. 4. pp. 34–44 (in Russian). URL: https://www.ecr-journal.ru/jour/article/view/168.
19. Karlik A.E., Platonov V.V. (2016). Cross-industry spatially localized innovation networks. Ehkonomika Regiona [The Economy of the Region], no. 4, pp. 1218–1232 (in Russian). URL: http://economyofregion.ru/Data/Issues/ER2016/December_2016/ERDecember2016_1218_1232.pdf
20. Kleiner G.B. (2002). The system paradigm and the theory of the firm. Voprosy Ekonomiki, no. 10, pp. 47–49 (in Russian).
21. Kleiner G.B. (2008). The system paradigm and system management. Russkij Zhurnal Menedzhmenta [Russian Journal of Management], no. 3, pp. 27–50 (in Russian). URL: https://rjm.spbu.ru/article/view/475/406.
22. Kleiner G.B. (2015). State – region – industry – enterprise: the framework for the system stability of the Russian economy. P. 2. Ehkonomika Regiona [The Economy of the Region], no. 3, pp. 9–17 (in Russian). URL: http://economyofregion.ru/Data/Issues/ER2015/September_2015/ERSeptember2015_9_17.pdf.
23. Klimenkov G.V., Kukor B.L. (2017). Expert systems and systems of situational management based on logical-linguistic models. Vestnik UGNTU. Nauka, Obrazovanie, Ekonomika. Seria Ekonomika. [UGNTU Herald: Science, education, economics. A series of economics], no. 1, pp. 7–19. URL: https://cyberleninka.ru/article/n/ekspertnye-sistemy-i-sistemy-situatsionnogo-upravleniya-nabaze-logiko-lingvisticheskih-modeley.
24. Langfield-Smith K.M. (1992). Exploring the need for a shared cognitive map. Journal of Management Studies, no. 3 (29), pp. 349–368. URL: https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1467–6486.1992.tb00669.x.
25. Lobanov O.S., Minakov V.F., Minakova T.E., Schugoreva V.A. (2017). NBIC convergence of geoinformation systems in Saint-Petersburg’s information space. Proceedings of 17th International Multidisciplinary Scientific GeoConference, SGEM, no. 21 (17), pp. 471–478. URL: https://sgemworld.at/sgemlib/spip.php?article10230.
26. Mayer-Schonberger V., Cukier K. (2013). Big Data: A revolution that will transform how we live, work and think. London: John Murray. Nooteboom B., Van Haverbeke W., Duysters G., Gilsing V., Van den Oord A. (2007). Optimal cognitive distance and absorptive capacity. Research Policy, no. 36, pp. 1016–1034. URL: https://www.sciencedirect.com/science/article/pii/S0048733307000807.
27. Platonov V.V. (2000). Technological leverage in management of innovation. Preprints of the 11th IFAC International Workshop on Control Applications of Optimization (CAO2000). Saint Petersburg, vol. 1, pp. 283–288. URL: https://www.researchgate.net/publication/317144698_Technological_Leverage_in_Management_of_Innovations.
28. Plotnikov A.S. (2017). Situational approach and methodology of social and humanitarian cognition. Cennosti i Smysly [Values and Meanings], no. 1, pp. 100–49 (in Russian). URL: https://cyberleninka.ru/article/n/situatsionnyy-podhod-i-metodologiya-sotsialno-gumanitarnogo-poznaniya.
29. Rowley J. (2007). The wisdom hierarchy: Representations of the DIKW hierarchy. Journal of Information Science, no. 2 (33), pp. 163–180. URL:http://journals.sagepub.com/doi/abs/10.1177/0165551506070706.
30. Tkachenko E., Rogova E., Bodrunov S. (2016). Intellectual capital assessment and financial indicators for value-based management: The joint application. Proceedings of the 13th International Conference on Intellectual Capital Knowledge Management & Organisational Learning. New York, pp. 250–258. URL: https://publications.hse.ru/mirror/pubs/share/folder/xx6fexd2pa/direct/196871619.
Review
For citations:
Karlik A.Ye., Platonov V.V., Krechko S.A. Participative Cognitive Mapping – a Method to Support the Interdisciplinary Innovative Projects of Megascience. Economics of Contemporary Russia. 2018;(4):65-84. (In Russ.)