A Fuzzy Optimization Framework for Sustainable and Behavior-Aware Marketing Decisions

Context: The increasing adoption of Internet of Things (IoT) technologies has transformed digital marketing into a software-intensive, data-driven ecosystem requiring continuous optimization under uncertainty. Existing decision-support approaches primarily optimize engagement or cost independently and rarely integrate behavioral dynamics, sustainability constraints, and managerial preferences within a unified information systems framework. Objectives: This study develops and evaluates a fuzzy multi-objective optimization framework that supports intelligent software-based marketing decision making by simultaneously maximizing customer engagement, minimizing digital resource consumption, and reducing behavioral saturation in IoT-enabled environments. Methods: A multi-objective mathematical model was developed in which customer responsiveness is represented through probabilistic engagement parameters, while fuzzy membership functions and a Max–Min satisfaction criterion represent imprecise managerial aspiration levels across the conflicting objectives. The small-scale experiment was solved exactly in GAMS to obtain reference Pareto-optimal solutions, whereas the large-scale experiment was conducted as a simulation study using NSGA-II and MOPSO to evaluate scalability and algorithmic performance. Both experimental settings relied exclusively on synthetically generated datasets; no real-world enterprise, customer-level, or campaign-level marketing data were used. Performance was assessed through Pareto-front analysis, key performance indicators, sensitivity analysis, and scenario-based managerial evaluation. Results: The proposed framework successfully generated high-quality Pareto-optimal solutions across multiple optimization objectives. NSGA-II consistently achieved superior customer engagement, personalization efficiency, and behavioral balance, whereas MOPSO demonstrated faster execution and lower sustainability costs. Sensitivity analysis confirmed the robustness of the framework under varying behavioral parameters, while scenario analysis showed that different optimization strategies can be selected according to organizational priorities. The principal limitation is that the framework has been evaluated only in controlled synthetic environments, which limits direct empirical generalization to operational enterprise marketing settings. Future research should validate the framework using longitudinal enterprise marketing data, real-time IoT interaction streams, and field-based deployment studies. Conclusions: The proposed framework contributes to information systems research by integrating fuzzy decision support, multi-objective optimization, and behavioral modeling into a scalable software architecture for IoT-enabled marketing. The approach enables adaptive, explainable, and sustainable decision making, providing organizations with a practical decision-support system capable of balancing customer experience, operational efficiency, and digital sustainability in intelligent marketing ecosystems.