1. Proceedings
  2. I3M
  3. 2021
  4. DHSS
  5. 10.46354/i3m.2021.dhss.004

Modelling Team Cohesion during Military Conscription: a Multidimensional Model for Task Cohesion

  • a Svajone Bekesiene ,
  • b Rasa Smaliukiene,
  • c Ramute Vaicaitiene
  • abcThe General Jonas Žemaitis Military Academy of Lithuania, Silo 5a, Vilnius, 10322, Lithuania
Cite as
Bekesiene S., Smaliukiene R., Vaicaitiene R. (2021). Modelling Team Cohesion during Military Conscription: a Multidimensional Model for Task Cohesion. Proceedings of the 11th International Defence and Homeland Security Simulation Worskhop (DHSS 2021), pp. 25-34. DOI: https://doi.org/10.46354/i3m.2021.dhss.004
 Download PDF

Abstract

This research aims to predict conscripts’ task cohesion in groups using artificial neural network modelling (NNM). The prediction of task cohesion during military conscription lies on two domains of research. The first is related to team cohesion, its deconstruction, and its measurement, while the second is allied to nonlinear modelling in group behaviour research. To predict this multidimensional and complex phenomenon, the multilayer perceptron (MLP) and the radial basis function (RBF) neural networks are used. As a result, the team cohesion in conscript groups, which is a key variable in conscription service effectiveness, was predicted with high accuracy (MPL MOD2= 88% and RBF MOD8=90%) by the models created. The performed modeling shows that according to MPL MOD2 norm cohesion has 100% of normalized importance, while according to RBF MOD8, interpersonal cohesion is the best predictor (normalized importance=100%) for task cohesion in groups during conscription service. 

References

  1. Abubakar, A. M., Namin, B. H., Harazneh, I., Arasli, H., & Tunç, T. (2017). Does gender moderates the relationship between favoritism/nepotism, supervisor incivility, cynicism and workplace withdrawal: A neural network and SEM approach. https://doi.org/10.1016/j.tmp.2017.06.001
  2. Al-Yaaribi, A., & Kavussanu, M. (2017). Teammate prosocial and antisocial behaviors predict task cohesion and burnout: The mediating role of affect. Journal of Sport and Exercise Psychology, 39(3), 199–208. https://doi.org/10.1123/jsep.2016-0336
  3. Balducci, C., Fraccaroli, F., & Schaufeli, W. B. (2010). Psychometric Properties of the Italian Version of the Utrecht Work Engagement Scale (UWES-9). European Journal of Psychological Assessment, 26(2). https://doi.org/10.1027/1015-5759/a000020
  4. Baughman, D. R., & Liu, Y. A. (1995). Fundamental and Practical Aspects of Neural Computing. In Neural Networks in Bioprocessing and Chemical Engineering. Elsevier. https://doi.org/10.1016/B978-0-12-083030-5.50008-4
  5. Bekesiene, S., Smaliukiene, R., & Vaicaitiene, R. (2021). Using artificial neural networks in predicting the level of stress among military conscripts. Mathematics, 9(6), 626. https://doi.org/10.3390/math9060626
  6. Bonebright, D. A. (2010a). 40 years of storming: A historical review of tuckman’s model of small group development. Human Resource Development International, 13(1), 111–120. https://doi.org/10.1080/13678861003589099
  7. Bonebright, D. A. (2010b). 40 years of storming: a historical review of Tuckman’s model of small group development. Human Resource Development International, 13(1). https://doi.org/10.1080/13678861003589099
  8. Caruana, R., Lawrence, S., & Giles, L. (2001). Overfitting in neural nets: Backpropagation, conjugate gradient, and early stopping — Penn State. Advances in Neural Information Processing Systems, 402–408. https://pennstate.pure.elsevier.com/en/publications/overfitting-in-neural-nets-backpropagation-conjugate-gradient-and
  9. Ennouri, K., ben Ayed, R., Triki, M. A., Ottaviani, E., Mazzarello, M., Hertelli, F., & Zouari, N. (2017). Multiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis. 3 Biotech, 7(3). https://doi.org/10.1007/s13205-017-0799-1
  10. Hedlund, E., Börjesson, M., & Österberg, J. (2015). Team Learning in a Multinational Military Staff Exercise. Small Group Research, 46(2). https://doi.org/10.1177/1046496414568462
  11. Hochreiter, S., & Schmidhuber, J. (1999). Feature extraction through LOCOCODE. Neural Computation, 11(3), 679–714. https://doi.org/10.1162/089976699300016629
  12. Jennings, P. L., & Hannah, S. T. (2011). The moralities of obligation and aspiration: Towards a concept of exemplary military ethics and leadership. Military Psychology, 23(5), 550–571. https://doi.org/10.1080/08995605.2011.600158
  13. Jue, J., Ha, J. H., & Jang, Y. (2020). The Person-in-the-Rain Drawing as a Predictor of Conscript Soldiers’ Unit Cohesion. Psychology, 11(04), 594–605. https://doi.org/10.4236/psych.2020.114040
  14. Käihkö, I. (2018). Broadening the Perspective on Military Cohesion. Armed Forces & Society, 44(4). https://doi.org/10.1177/0095327X18759541
  15. Lambić, D., Lazović, B., Djenić, A., & Marić, M. (2018). A novel metaheuristic approach for collaborative learning group formation. Journal of Computer Assisted Learning, 34(6), 907–916. https://doi.org/10.1111/jcal.12299
  16. Lei, P. W., & Wu, Q. (2007). An NCME instructional module on: Introduction to structural equation modeling: Issues and practical considerations. Educational Measurement: Issues and Practice, 26(3), 33–43. https://doi.org/10.1111/j.1745-3992.2007.00099.x
  17. Losada, M., & Heaphy, E. (2004). The Role of Positivity and Connectivity in the Performance of Business Teams: A Nonlinear Dynamics Model. American Behavioral Scientist, 47(6), 740–765. https://doi.org/10.1177/0002764203260208
  18. Ludermir, T. B., Yamazaki, A., & Zanchettin, C. (2006). An optimization methodology for neural network weights and architectures. IEEE Transactions on Neural Networks, 17(6), 1452–1459. https://doi.org/10.1109/TNN.2006.881047
  19. Menke, J. E., & Martinez, T. R. (2008). A Bradley-Terry artificial neural network model for individual ratings in group competitions. Neural Computing and Applications, 17(2), 175–186. https://doi.org/10.1007/s00521-006-0080-8
  20. Neruda, R., & Kudová, P. (2005). Learning methods for radial basis function networks. Future Generation Computer Systems, 21(7), 1131–1142. https://doi.org/10.1016/j.future.2004.03.013
  21. Norgaard, M., Ravn, O., Poulsen, N. K., & Hansen, L. K. (2000). Neural Networks for Modelling and Control of Dynamic Systems - A Practitioner’s Handbook | M. Norgaard | Springer. Springer-Verlag.
  22. Petersen, B., Eys, M., Watson, K., & Eva, M. B. (2019). Taking stock of youth sport group dynamics research: A scoping review. Kinesiology Review, 8(3), 260–268. https://doi.org/10.1123/kr.2019-0027
  23. Reed D., Thomas T., Reynolds S., Hurter J., Eifert L. (2019). Deep Learning of Virtual-Based Aerial Images: Increasing the Fidelity of Serious Games for Live Training. The 9th International Defence and Homeland Security Simulation Workshop. 1-9 (13-21). http://www.msc-les.org/proceedings/dhss/2019/DHSS2019.pdf
  24. Rielly, R. J. (2000). Confronting the Tiger - Small Unit Cohesion in Battle. Military Review, 80(6), 61–65. https://www.scribd.com/document/126229307/Confronting-the-Tiger-Small-Unit-Cohesion-in-Battle-Robert-J-Reilly-91637655-Small-Unit-Leadership#download
  25. Salo, M. (2008). Determinants of Military Adjustment and Attrition During Finnish Conscript Service. www.mpkk.fi
  26. Sledge, M. (2007). Soldier Dead: How We Recover, Identify, Bury, & Honor Our Military Fallen. Columbia University Press. http://cup.columbia.edu/book/soldier-dead/9780231135153
  27. Song, R.-G., Zhang, Q.-Z., Tseng, M.-K., & Zhang, B.-J. (1995). The application of artificial neural networks to the investigation of aging dynamics in 7175 aluminium alloys. Materials Science and Engineering: C, 3(1). https://doi.org/10.1016/0928-4931(95)00068-2
  28. Şuşnea, E. (2010). Supervised Learning Techniques for Virtual Military Training. www.presidency.ro
  29. Sykora, P., Kamencay, P., Hudec, R., Benco, M., & Sinko, M. (2020). Comparison of Neural Networks with Feature Extraction Methods for Depth Map Classification. AiMT Advances in Military Technology, 15(1), 2533–4123. https://doi.org/10.3849/aimt.01326
  30. Thomas, M. S. C., Forrester, N. A., & Ronald, A. (2016). Multiscale Modeling of Gene-Behavior Associations in an Artificial Neural Network Model of Cognitive Development. Cognitive Science, 40(1), 51–99. https://doi.org/10.1111/cogs.12230
  31. Tuckman, B. W., & Jensen, M. A. C. (1977). Stages of Small-Group Development Revisited. Group & Organization Management, 2(4), 419–427. https://doi.org/10.1177/105960117700200404
  32. van Vianen, A. E. M., & de Dreu, C. K. W. (2001). Personality in teams: Its relationship to social cohesion, task cohesion, and team performance. European Journal of Work and Organizational Psychology, 10(2), 97–120. https://doi.org/10.1080/13594320143000573
  33. Yang, Y. K., Sun, T. Y., Huo, C. L., Yu, Y. H., Liu, C. C., & Tsai, C. H. (2013). A novel self-constructing Radial Basis Function Neural-Fuzzy System. Applied Soft Computing Journal, 13(5), 2390–2404. https://doi.org/10.1016/j.asoc.2013.01.023
  34. Zorlu, K., & Bastemur, C. (2014). A Mediator Role of Perceived Organizational Support in Workplace Deviance Behaviors, Organizational Citizenship and Job Satisfaction Relations. International Journal of Research in Business and Social Science (2147-4478), 3(3), 18–36. https://doi.org/10.20525/ijrbs.v3i3.106