Developing a Policy Support System Model for Enhancing Rationality in Water Resource Management Using Structural Equation Modeling
Keywords:
Policy Support System, Rationality Enhancement, Water ManagementAbstract
The policy support system for enhancing rationality in water resource management is an analytical and decision-support tool that, using comprehensive physical, social, economic, and environmental data, assists managers and policymakers in making effective decisions based on facts and accurate information. The primary objective of this research is to design a policy support system to promote rationality in water resource management. A quantitative approach utilizing Structural Equation Modeling (SEM) was employed to design this system, with AMOS software used for analysis. Data collection was carried out using a questionnaire, whose validity was confirmed through construct validity and reliability measured by Cronbach's alpha coefficient at 0.87. The statistical population of this research consisted of managers and experts in the field of water management in Iran. The sample size was determined to be 256 individuals using the Morgan table. The quantitative data analysis led to the identification of 19 key components structured within the proposed model. These components include data collection and analysis, the use of geographic information systems, modeling and simulation, stakeholder analysis, decision support systems (DSS), legal and policy frameworks, performance evaluation and monitoring, training and capacity building, ensuring public participation and transparency, technology transfer and utilization, attention to climate change, inter-organizational alignment, continuous risk assessment and management, and integration with sustainable development, which can be employed in implementing the policy support system for enhancing rationality in water management.
References
M. Davoudi, M. R. Moosavi, and M. H. Sadreddini, "DSS: A hybrid deep model for fake news detection using propagation tree and stance network," Expert Systems with Applications, vol. 198, p. 116635, 2022, doi: 10.1016/j.eswa.2022.116635.
G. Talari, E. Cummins, C. McNamara, and J. O'Brien, "State of the art review of Big Data and web-based Decision Support Systems (DSS) for food safety risk assessment with respect to climate change," Trends in Food Science & Technology, vol. 126, pp. 192-204, 2022, doi: 10.1016/j.tifs.2021.08.032.
I. Ara, L. Turner, M. T. Harrison, M. Monjardino, P. DeVoil, and D. Rodriguez, "Application, adoption and opportunities for improving decision support systems in irrigated agriculture: A review," Agricultural Water Management, vol. 257, p. 107161, 2021, doi: 10.1016/j.agwat.2021.107161.
A. M. Antoniadi et al., "Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review," Applied Sciences, vol. 11, no. 11, p. 5088, 2021, doi: 10.3390/app11115088.
F. C. Uzonwanne, "Rational model of decision-making," in Global encyclopedia of public administration, public policy, and governance, 2023, pp. 11230-11235.
J. D. S. D. Souza et al., "Decision support system for the integrated management of multiple supply systems in the Brazilian Semiarid Region," Water, vol. 15, no. 2, p. 223, 2023, doi: 10.3390/w15020223.
J. J. Mearsheimer and S. Rosato, How states think: the rationality of foreign policy. Yale University Press, 2023.
K. Govindan, H. Mina, and B. Alavi, "A decision support system for demand management in healthcare supply chains considering the epidemic outbreaks: A case study of coronavirus disease 2019 (COVID-19)," Transportation Research Part E: Logistics and Transportation Review, vol. 138, p. 101967, 2020, doi: 10.1016/j.tre.2020.101967.
C. J. Quarton and S. Samsatli, "How to incentivise hydrogen energy technologies for net zero: Whole-system value chain optimisation of policy scenarios," Sustainable Production and Consumption, vol. 27, pp. 1215-1238, 2021, doi: 10.1016/j.spc.2021.02.007.
Z. Szantoi et al., "Addressing the need for improved land cover map products for policy support," Environmental Science & Policy, vol. 112, pp. 28-35, 2020, doi: 10.1016/j.envsci.2020.04.005.
S. Razavi et al., "The future of sensitivity analysis: an essential discipline for systems modeling and policy support," Environmental Modelling & Software, vol. 137, p. 104954, 2021, doi: 10.1016/j.envsoft.2020.104954.
O. Tayirov, I. V. Karimov, A. Toshmamatov, and S. Shomirzayev, "Rational Use of Water Resources in Agriculture (In the Case of Uzbekistan)," International Journal of Business Diplomacy and Economy, vol. 3, no. 5, pp. 142-146, 2024. [Online]. Available: https://www.bing.com/ck/a?!&&p=0cb35793e5b40f428f69dcaf1bd18143266fdcc8187ea2b520ccba249c169e76JmltdHM9MTczMDg1MTIwMA&ptn=3&ver=2&hsh=4&fclid=1948cd75-7b58-6475-0768-dfa57a986544&psq=Tayirov%2c+O.%2c+et+al.+(2024).+%22Rational+Use+of+Water+Resources+in+Agriculture+(In+the+Case+of+Uzbekistan).%22+International+Journal+of+Business+Diplomacy+and+Economy+3(5)%3a+142-146.+%09&u=a1aHR0cHM6Ly93d3cud2Vmb3J1bS5vcmcvZXZlbnRzL3dvcmxkLWVjb25vbWljLWZvcnVtLWFubnVhbC1tZWV0aW5nLTIwMjQv&ntb=1.
Ş. Kılkış, G. Krajačić, N. Duić, and M. A. Rosen, "Advances in integration of energy, water and environment systems towards climate neutrality for sustainable development," Energy Conversion and Management, vol. 225, p. 113410, 2020, doi: 10.1016/j.enconman.2020.113410.
