The Effectiveness of Technology Based Collaborative Training in Optimizing Organizational Decision Making: A Systematic Literature Review

Authors

  • Nurni Arrina Lestari Muhammadiyah Sukabumi University Author
  • Musoli Aisiyah University Yogyakarta Author

Keywords:

Learning, Decision Making, Collaboration, Technology.

Abstract

This study aims to provide a comprehensive understanding of the context and theoretical foundations applied in organizational decision-making, specifically in optimizing technology-based collaborative training. Using the PRISMA systematic review method, this study analyzed 10 empirical articles on learning and decision-making published from 2020 to 2024 from various countries. Each theory applied in these studies is outlined with a brief description and recommendations for a future research agenda. Although various previous studies have produced extensive findings, this review found that most studies focused on the effectiveness of technology-based collaborative training in improving learning and decision-making processes. The reviewed theories, such as Constructivist Learning Theory, Flow Theory, and Markov Decision Process Theory, highlight the importance of collaborative learning, incremental decision-making, and high engagement and motivation in organizational contexts. The results of this study have significant implications for the field of learning and decision-making, which practitioners should seriously consider. These findings are expected to form the basis for policy recommendations that encourage the adoption of innovative and evidence-based learning methods within organizations.

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References

Abdulla, A., & Baryannis, G. (2024). A hybrid multi-criteria decision-making and machine learning approach for explainable supplier selection. Supply Chain Analytics, 7(May). https://doi.org/10.1016/j.sca.2024.100074

Behavioral Decision Making - 2022 - Frechen - Wait did I do that Effects of previous decisions on moral decisions-making.pdf. (nd).

Brink, S. M. (2023). Exploring accounting students' experiences during the COVID-19 pandemic to inform teaching and learning decision-making post pandemic. Journal of Applied Research in Higher Education. https://doi.org/10.1108/JARHE-08-2023-0324

Brnabic, A., & Hess, L.M. (2021). Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making. BMC Medical Informatics and Decision Making, 21(1). https://doi.org/10.1186/s12911-021-01403-2

Chan, CWH, Tang, FWK, Cheng, HY, Chow, KM, Kwok, ZCM, Li, C., … Chair, SY (2024). Effect of simulation-based zoom learning on clinical decision-making among undergraduate nursing students and experiences of students and instructors: A mixed methods study. Heliyon, 10(9), e30039. https://doi.org/10.1016/j.heliyon.2024.e30039

Chen, I. C. (2024). Predicting regional sustainable development to enhance decision-making in brownfield redevelopment using machine learning algorithms. Ecological Indicators, 163(May), 112117. https://doi.org/10.1016/j.ecolind.2024.112117

Cui, T., Yang, X., Jia, F., Jin, J., Ye, Y., & Bai, R. (2024). Mobile robot sequential decision making using a deep reinforcement learning hyper-heuristic approach. Expert Systems with Applications, 257(November 2022), 124959. https://doi.org/10.1016/j.eswa.2024.124959

Eskiyurt, R., & Özkan, B. (2024). Exploring the impact of collaborative learning on the development of critical thinking and clinical decision-making skills in nursing students: A quantitative descriptive design. Heliyon, 10(17). https://doi.org/10.1016/j.heliyon.2024.e37198

Giovanniello, J., Bravo-Rivera, C., Rosenkranz, A., & Matthew Lattal, K. (2023). Stress, associative learning, and decision-making. Neurobiology of Learning and Memory, 204(August), 107812. https://doi.org/10.1016/j.nlm.2023.107812

Heid, S., Hanselle, J., Fürnkranz, J., & Hüllermeier, E. (2024). Learning decision catalogs for situated decision making: The case of scoring systems. International Journal of Approximate Reasoning, 171(April), 109190. https://doi.org/10.1016/j.ijar.2024.109190

Hu, WCY, Dillon, HCB, & Wilkinson, T. J. (2023). Educators as Judges: Applying Judicial Decision-Making Principles to High-Stakes Education Assessment Decisions. Teaching and Learning in Medicine, Vol. 35, p. 168–179. https://doi.org/10.1080/10401334.2022.2038176

Jȩdrzejewski, A., & Hernandez, L. (2024). Symmetric conformity functions make decision-making processes independent of the distribution of learning strategies. Physical Review Research, 6(3). https://doi.org/10.1103/PhysRevResearch.6.033093

Kotorov, I., Krasylnykova, Y., Pérez-Sanagustín, M., Mansilla, F., & Broisin, J. (2024). Supporting Decision-Making for Promoting Teaching and Learning Innovation: A Multiple Case Study. Journal of Learning Analytics, 11(1), 21–36. https://doi.org/10.18608/jla.2024.8131

Landwehr, J. P., Kühl, N., Walk, J., & Gnädig, M. (2022). Design Knowledge for Deep-Learning-Enabled Image-Based Decision Support Systems: Evidence From Power Line Maintenance Decision-Making. Business and Information Systems Engineering, 64(6), 707–728. https://doi.org/10.1007/s12599-022-00745-z

Liu, Q., Li, X., Tang, Y., Gao, X., Yang, F., & Li, Z. (2023). Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends. Sensors, 23(19). https://doi.org/10.3390/s23198229

Loftus, T.J., Filiberto, A.C., Li, Y., Balch, J., Cook, A.C., Tighe, P.J., … Bihorac, A. (2020). Decision analysis and reinforcement learning in surgical decision-making. Surgery (United States), 168(2), 253–266. https://doi.org/10.1016/j.surg.2020.04.049

Medel, D., Bonet, A., Jimenez Herrera, M., Sevilla, F., Vilaplana, J., Cemeli, T., & Roca, J. (2024). Interactive Virtual Simulation Case: A Learning Environment for the Development of Decision-Making in Nursing Students. Teaching and Learning in Nursing, 000. https://doi.org/10.1016/j.teln.2024.08.002

Pei, Z., Rojas-Arevalo, A.M., de Haan, F.J., Lipovetzky, N., & Moallemi, E.A. (2024). Reinforcement learning for decision-making under deep uncertainty. Journal of Environmental Management, 359(April), 120968. https://doi.org/10.1016/j.jenvman.2024.120968

Rafie Papkiadeh, S., Taheri-Ezbarami, Z., Mirzaie Taklimi, M., Kazemnejad Leili, E., & Razaghpoor, A. (2024). Comparing the effects of problem- and task-based learning on knowledge and clinical decision-making of nursing students concerning the use of transfusion medicine in pediatric nursing: An educational quasi-experimental study in Iran. Heliyon, 10(14), e34521. https://doi.org/10.1016/j.heliyon.2024.e34521

Resch, K. (2023). Diversity skills for future teachers: how transformative learning prepares pre-service teachers for diversity in Austrian schools. Higher Education, Skills and Work-based Learning, 13(1), 66–79. https://doi.org/10.1108/HESWBL-05-2022-0096

Rosenbäck, R., & Svensson, A. (2024). Management learning in public healthcare during pandemics. Learning Organization, 31(3), 394–410. https://doi.org/10.1108/TLO-01-2023-0002

Speekenbrink, M. (2022). Chasing Unknown Bandits: Uncertainty Guidance in Learning and Decision Making. Current Directions in Psychological Science, 31(5), 419–427. https://doi.org/10.1177/09637214221105051

Sun, D., Ding, Y., Wen, H., Zhang, F., Zhang, J., Gu, Q., & Zhang, J. (2024). SHAP-PDP hybrid interpretation of decision-making mechanism of machine learning-based landslide susceptibility mapping: A case study at Wushan District, China. Egyptian Journal of Remote Sensing and Space Science, 27(3), 508–523. https://doi.org/10.1016/j.ejrs.2024.06.005

Tweed, M., & Wilkinson, T. (2019). Student progress decision-making in programmatic assessment: Can we extrapolate from clinical decision-making and jury decision-making? BMC Medical Education, 19(1). https://doi.org/10.1186/s12909-019-1583-1

Vázquez-Calatayud, M., García-García, R., Regaira-Martínez, E., & Gómez-Urquiza, J. (2024). Real-world and game-based learning to enhance decision-making. Nurse Education Today, 140(May). https://doi.org/10.1016/j.nedt.2024.106276

Yoon, H., Scopelliti, I., & Morewedge, C. K. (2021). Decision making can be improved through observational learning. Organizational Behavior and Human Decision Processes, 162(September 2020), 155–188. https://doi.org/10.1016/j.obhdp.2020.10.011

Zhang, S., Lu, G., Wang, W., Li, Q., Wang, R., Zhang, Z., … Zhang, F. (2024). A predictive machine-learning model for clinical decision-making in washed microbiota transplantation on ulcerative colitis. Computational and Structural Biotechnology Journal, 24(April), 583–592. https://doi.org/10.1016/j.csbj.2024.08.021

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Published

2026-02-03

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How to Cite

The Effectiveness of Technology Based Collaborative Training in Optimizing Organizational Decision Making: A Systematic Literature Review. (2026). DANESIA: International Journal of Social Sciences and Economics, 1(1), 1-13. https://journal.ods-publications.com/index.php/danesia/article/view/1