Devising an individually oriented method for selection of scientific activity subjects for implementing scientific projects based on scientometric analysis
DOI:
https://doi.org/10.15587/1729-4061.2021.248040Keywords:
subject of scientific activity, scientometric analysis, scientific project, multicriterial problem of choiceAbstract
The main factors influencing the choice of individual subjects of the scientific activity or potential partners and executors for scientific and educational projects were analyzed. The specific features of choosing project executors of different categories were indicated. The functional responsibilities of project participants in accordance with the project structure were described.
The individually oriented method for choosing subjects of scientific activity as executors of scientific and educational projects was developed, taking into account the productivity of their scientific activities in the past and considering the structure of projects. To determine the merits of the subjects of scientific activity, which are included in the relevant scientific subject spaces, it is necessary to apply the procedure of their productivity assessment. In addition, it is necessary to predict a change in productivity in the future based on retrospective data for this subject. Next, it is required to solve the multi-criteria problem of the choice among the subjects of scientific activity who are quite productive in the opinion of the project manager. The use of the developed method reduces the subjective impact on making a decision regarding the choice of project executors. This is due to the fact that they are chosen by automated calculation of scientometric indicators of subjects, guided only by open sources of information.
The individually oriented method for the selection of subjects of scientific activity was verified on the example of the formation of three applications of research projects. As a result, the average percentage of scientists who meet the requirements of project managers for each scientific subject space was about 46.55 %. The percentage of those involved in the project from those who were selected is about 24.07 %. The probability of cooperation is higher among those who have an average H-index.
References
- Bogers, M., Chesbrough, H., Moedas, C. (2018). Open Innovation: Research, Practices, and Policies. California Management Review, 60 (2), 5–16. doi: http://doi.org/10.1177/0008125617745086
- Beck, S., Bergenholtz, C., Bogers, M., Brasseur, T.-M., Conradsen, M. L., Di Marco, D. et. al. (2020). The Open Innovation in Science research field: a collaborative conceptualisation approach. Industry and Innovation, 1–50. doi: http://doi.org/10.1080/13662716.2020.1792274
- Kazakovtsev, V., Oreshin, S., Serdyukov, A., Krasheninnikov, E., Muravyov, S., Bezvinnyi, A. et. al. (2020). Recommender system for an academic supervisor with a matrix normalization approach. 2020 International Conference on Control, Robotics and Intelligent System. doi: http://doi.org/10.1145/3437802.3437817
- Bushuyev, D., Bushuieva, V., Kozyr, B., Ugay, A. (2020). Erosion of competencies of innovative digitalization projects. Scientific Journal of Astana IT University, 1, 70–83. doi: http://doi.org/10.37943/aitu.2020.1.63658
- Sihombing, D. I., Sitompul, O. S., Sutarman, Nababan, E. (2018). Combining the use of analytical hierarchy process and lexicographic goal programming in selecting project executor. IOP Conference Series: Materials Science and Engineering, 420. doi: http://doi.org/10.1088/1757-899x/420/1/012113
- Chu, X. N., Tso, S. K., Zhang, W. J., Li, Q. (2000). Partners Selection for Virtual Enterprises. Proceedings of the 3th World Congress on Intelligent Control and Automation, 164–168. doi: http://doi.org/10.1109/wcica.2000.859940
- Al‐Khalifa, A. K., Eggert Peterson, S. (1999). The partner selection process in international joint ventures. European Journal of Marketing, 33 (11/12), 1064–1081. doi: http://doi.org/10.1108/03090569910292276
- Feng, W. D., Chen, J., Zhao, C. J. (2000). Partners Selection Process and Optimization Model for Virtual corporations Based on Genetic Algorithms. Journal of Tsinghua University (Science and Technology), 40, 120–124.
- Zhong, Y., Jian, L., Zijun, W. (2009). An integrated optimization algorithm of GA and ACA-based approaches for modeling virtual enterprise partner selection. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 40 (2), 37–56. doi: http://doi.org/10.1145/1531817.1531824
- Schall, D. (2014). A multi-criteria ranking framework for partner selection in scientific collaboration environments. Decision Support Systems, 59, 1–14. doi: http://doi.org/10.1016/j.dss.2013.10.001
- Wagner, C. S., Leydesdorff, L. (2005). Network structure, self-organization, and the growth of international collaboration in science. Research Policy, 34 (10), 1608–1618. doi: http://doi.org/10.1016/j.respol.2005.08.002
- Fu, F., Hauert, C., Nowak, M. A., Wang, L. (2008). Reputation-based partner choice promotes cooperation in social networks. Physical Review E, 78 (2). doi: http://doi.org/10.1103/physreve.78.026117
- Kleinberg, J. M. (1999). Authoritative sources in a hyperlinked environment. Journal of the ACM, 46 (5), 604–632. doi: http://doi.org/10.1145/324133.324140
- Page, L., Brin, S., Motwani, R., Winograd, T. (1999). The PageRank Citation Ranking: Bringing Order to the Web. Available at: http://ilpubs.stanford.edu:8090/422/
- Haveliwala, T. H. (2002). Topic-sensitive PageRank. Proceedings of the 11th International Conference on World Wide Web – WWW '02. New York, 517–526. doi: http://doi.org/10.1145/511446.511513
- Xu, H., Kuchansky, A., Biloshchytska, S., Tsiutsiura, M. (2021). A Conceptual Research Model for the Partner Selection Problem. 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST). doi: http://doi.org/10.1109/sist50301.2021.9465931
- Yershov, S. V., Ponomarenko, R. M. (2018). Parallel Fuzzy Inference Method for Higher Order Takagi–Sugeno Systems. Cybernetics and Systems Analysis, 54 (6), 1003–1012. doi: http://doi.org/10.1007/s10559-018-0103-3
- Wang, D., Yang, X.C., Wang, G.R. (2002). Implementation of Partner Selection in Virtual Enterprise Based on Fuzzy-AHP. Journal of Northeastern University, 21 (6), 606–609.
- Lizunov, P., Biloshchytskyi, A., Kuchansky, A., Andrashko, Y., Biloshchytska, S. (2019). Improvement of the method for scientific publications clustering based on n-gram analysis and fuzzy method for selecting research partners. Eastern-European Journal of Enterprise Technologies, 4 (4 (100)), 6–14. doi: http://doi.org/10.15587/1729-4061.2019.175139
- Li, B., Zhang, J. (2021). A Cooperative Partner Selection Study of Military-Civilian Scientific and Technological Collaborative Innovation Based on Interval-Valued Intuitionistic Fuzzy Set. Symmetry, 13 (4), 553. doi: http://doi.org/10.3390/sym13040553
- Gladka, M., Kravchenko, O., Hladkyi, Y., Borashova, S. (2021). Qualification and Appointment of Staff for Project Work in Implementing IT Systems Under Conditions of Uncertainty. 2021 IEEE International Conference on Smart Information Systems and Technologies (SIST). doi: http://doi.org/10.1109/sist50301.2021.9465897
- Kolomiiets, A., Morozov, V. (2020). Investigation of Optimization Models in Decisions Making on Integration of Innovative Projects. Lecture Notes in Computational Intelligence and Decision Making, 51–64. doi: http://doi.org/10.1007/978-3-030-54215-3_4
- Boyko, R., Shumyhai, D., Gladka, M. (2016). Сoncept, Definition and Use of an Agent in the Multi-agent Information Management Systems at the Objects of Various Nature. Advances in Intelligent Systems and Computing, 59–63. doi: http://doi.org/10.1007/978-3-319-48923-0_8
- Biloshchytskyi, A., Kuchansky, A., Andrashko, Y., Omirbayev, S., Mukhatayev, A., Faizullin, A., Toxanov, S. (2021). Development of the set models and a method to form information spaces of scientific activity subjects for the steady development of higher education establishments. Eastern-European Journal of Enterprise Technologies, 3 (2 (111)), 6–14. doi: http://doi.org/10.15587/1729-4061.2021.233655
- Kuchansky, A., Biloshchytskyi, A., Andrashko, Y., Biloshchytska, S., Honcharenko, T., Nikolenko, V. (2019). Fractal time series analysis in non-stationary environment. 2019 IEEE International Scientific-Practical Conference: Problems of Infocommunications Science and Technology, 236–240. doi: http://doi.org/10.1109/picst47496.2019.9061554
- Mulesa, O., Geche, F., Batyuk, A., Myronyuk, I. (2018). Using a system approach in the process of the assessment problem analysis of the staff capacity within the health care institution. IEEE Conference: Computer science and information technologies (CSIT 2018), 177–180. doi: http://doi.org/10.1109/stc-csit.2018.8526749
- Mulesa, O., Geche, F., Voloshchuk, V., Buchok, V., Batyuk, A. (2017). Information Technology for time series forecasting with considering fuzzy expert evaluations. IEEE Conference: Computer Science and Information Technologies, 105–108. doi: http://doi.org/10.1109/stc-csit.2017.8098747
- Mulesa, O., Geche, F. (2016). Designing fuzzy expert methods of numeric evaluation of an object for the problems of forecasting. Eastern-European Journal of Enterprise Technologies, 3 (4 (81)), 37–43. doi: http://doi.org/10.15587/1729-4061.2016.70515
- Chen, L., Jagota, V., Kumar, A. (2021). Research on optimization of scientific research performance management based on BP neural network. International Journal of System Assurance Engineering and Management. doi: http://doi.org/10.1007/s13198-021-01263-z
- Liu, L., Ran, W. (2019). Research on supply chain partner selection method based on BP neural network. Neural Computing and Applications, 32 (6), 1543–1553. doi: http://doi.org/10.1007/s00521-019-04136-6
- Han, J., Teng, X., Cai, X. (2019). A novel network optimization partner selection method based on collaborative and knowledge networks. Information Sciences, 484, 269–285. doi: http://doi.org/10.1016/j.ins.2019.01.072
- Wi, H., Oh, S., Mun, J., Jung, M. (2009). A team formation model based on knowledge and collaboration. Expert Systems with Applications, 36 (5), 9121–9134. doi: http://doi.org/10.1016/j.eswa.2008.12.031
- Lungeanu, A., Huang, Y., Contractor, N. S. (2014). Understanding the assembly of interdisciplinary teams and its impact on performance. Journal of Informetrics, 8 (1), 59–70. doi: http://doi.org/10.1016/j.joi.2013.10.006
- Lungeanu, A., Sullivan, S., Wilensky, U., Contractor, N. S. (2015). A computational model of team assembly in emerging scientific fields. 2015 Winter Simulation Conference (WSC). doi: http://doi.org/10.1109/wsc.2015.7408559
- Lungeanu, A., Carter, D. R., DeChurch, L. A., Contractor, N. S. (2018). How Team Interlock Ecosystems Shape the Assembly of Scientific Teams: A Hypergraph Approach. Communication Methods and Measures, 12 (2-3), 174–198. doi: http://doi.org/10.1080/19312458.2018.1430756
- Wang, Q., Ma, J., Liao, X., Du, W. (2017). A context-aware researcher recommendation system for university-industry collaboration on R&D projects. Decision Support Systems, 103, 46–57. doi: http://doi.org/10.1016/j.dss.2017.09.001
- Lizunov, P., Biloshchytskyi, A., Kuchansky, A., Andrashko, Y., Biloshchytska, S. (2020). The use of probabilistic latent semantic analysis to identify scientific subject spaces and to evaluate the completeness of covering the results of dissertation studies. Eastern-European Journal of Enterprise Technologies, 4 (4 (106)), 21–28. doi: http://doi.org/10.15587/1729-4061.2020.209886
- Biloshchytskyi, A., Kuchansky, A., Andrashko, Y., Mukhatayev, A., Toxanov, S., Faizullin, A. (2020). Methods of Assessing the Scientific Activity of Scientists and Higher Education Institutions. 2020 IEEE 2nd International Conference on Advanced Trends in Information Theory (ATIT), 162–167. doi: http://doi.org/10.1109/atit50783.2020.9349348
- Biloshchytskyi, A., Kuchansky, A., Paliy, S., Biloshchytska, S., Bronin, S., Andrashko, Y. et. al. (2018). Development of technical component of the methodology for projectvector management of educational environments. Eastern-European Journal of Enterprise Technologies, 2 (2 (92)), 4–13. doi: http://doi.org/10.15587/1729-4061.2018.126301
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Huilin Xu, Alexander Kuchansky, Myroslava Gladka
This work is licensed under a Creative Commons Attribution 4.0 International License.
The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.
A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.