Cao, RuoxingWang, JianningBasem, AliHussein, Rasha AbedSalahshour, SoheilBaghaei, S.2025-02-172025-02-1720250952-19761873-676910.1016/j.engappai.2025.1100292-s2.0-85215431170https://doi.org/10.1016/j.engappai.2025.110029https://hdl.handle.net/20.500.14517/7656This study employed artificial intelligence with machine learning method and used an artificial neural network to estimate waste management, travel expenses, and paper consumption by manipulating varying water and energy costs, as well as the number of employees. The results show that increased costs and employee numbers have an impact on waste management and travel expenses, while paper consumption remained consistent. Research and analysis show that viral advertising outperforms traditional advertising in terms of brand awareness, attitudes, engagement, and purchase intent. Linear regression analysis confirmed the accuracy of the network's predictions. Recognizing that every organization is part of a larger system, cooperation within this system occurs when subsystems interact with environmental issues. Hence, it becomes possible not only to make organizations environmentally conscious but also to foster environmental consciousness in each employee, underlining the significance of individual and collective contributions to environmental sustainability. Within the context of social care, this study aims to explore the role of strategic management and artificial intelligence in optimizing human resources management to address environmental concerns. By examining the intersection of these fields, this study endeavors to provide insights into how organizations can leverage technology and strategic planning to cultivate sustainable human resource management practices in the realm of social care.eninfo:eu-repo/semantics/closedAccessEnvironmental SustainabilityHuman Resources ManagementGreen Human ResourceManagementArtificial Neural NetworkWaste ManagementFuzzy Logic And Computer ScienceArticleQ1Q1143WOS:0014072524000010