Cost-schedule control in construction projects is inherently a continuous decision-making process conducted under conditions of uncertainty, rather than a purely technical or accounting activity. Conventional approaches, which rely on retrospective performance measurement and fragmented indicators, provide limited support for timely managerial intervention and often lead to delayed or suboptimal decisions. This study develops a decision-centric framework that integrates earned value analytics with organizational decision processes to enable proactive and structured cost–schedule control in small and medium-sized construction projects. The proposed framework conceptualizes cost control as a four-stage decision process—situational awareness, diagnostic analysis, predictive assessment, and intervention execution—and establishes explicit linkages between analytical signals and managerial actions. Within this structure, earned value metrics are reinterpreted as decision triggers rather than passive evaluation tools, while organizational roles are reconfigured to support timely interpretation and coordinated response. The framework is examined through an in-depth case study of a gas station construction project exposed to significant environmental and operational uncertainty. The findings indicate that cost overruns are primarily associated with delayed decision responses, fragmented information flows, and misaligned responsibility structures. By embedding real-time performance evaluation within a coherent decision architecture, the proposed approach enables earlier identification of deviations and more targeted managerial interventions. The study contributes to the literature on intelligent management decision-making by demonstrating how analytical tools can be operationalized within organizational contexts to enhance decision quality under uncertainty. It further provides a transferable framework for structuring data-informed decision processes in resource-constrained project environments.
Sustainable logistics hub planning in emerging economies is often challenged by high levels of uncertainty, limited data availability, and the need to balance economic, environmental, and social objectives. Supporting consistent and transparent decision-making under such conditions remains a key issue in infrastructure planning. To address this, the present study develops an intelligent decision-support framework for prioritizing logistics hubs in complex and uncertain environments. The proposed framework combines $q$-rung orthopair fuzzy sets with the ordinal priority approach, enabling the representation of imprecise expert judgments alongside ordinal preference information within a unified multi-criteria structure. The approach is applied to the case of Kenya, where logistics development involves multiple and often conflicting criteria. A comprehensive evaluation system is established, and expert assessments are incorporated to derive priority rankings. The results show that operational efficiency and economic considerations play a dominant role in the decision process, while environmental and social factors receive comparatively lower weights. Sensitivity and comparative analyses confirm the stability and reliability of the findings. The study provides a structured and uncertainty-aware decision-support tool that can assist infrastructure planning and offers practical insights for policy and managerial decision-making in logistics systems.
The rapid expansion of e-commerce has intensified the complexity of last-mile delivery, where increasing parcel volumes and urban constraints continue to challenge traditional distribution models. Among emerging solutions, parcel lockers have gained attention for their potential to improve delivery efficiency while reducing operational and environmental pressures. However, their effectiveness largely depends on appropriate location planning, which requires the simultaneous consideration of multiple and often conflicting criteria. This study develops a multi-criteria decision framework for parcel locker location selection by integrating the Opinion Weight Criteria Method (OWCM) and the Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method. The proposed framework enables the systematic evaluation of alternative locations by combining structured expert judgment with compromise-based ranking. Criteria weights are derived through OWCM to ensure consistency in preference representation, while MARCOS is employed to assess alternatives based on their relative distance from ideal and anti-ideal solutions. The model is applied within a last-mile delivery context to examine its practical applicability. The results identify the most suitable location among a set of feasible alternatives and demonstrate stable performance under varying weighting scenarios. Sensitivity and comparative analyses confirm that the ranking outcomes remain consistent across different conditions and methodological configurations. The findings provide a structured approach to location planning in urban logistics and offer practical support for decision-makers seeking to deploy parcel locker systems under complex operational environments. The proposed framework can be extended to similar decision problems involving infrastructure placement and multi-criteria evaluation.
The banking sector is experiencing a substantial transformation driven by digitalization, evolving customer expectations, and increasing competitive pressure. In hybrid banking environments, where customers interact through both digital and in-branch channels, customer experience and trust have become critical factors shaping managerial and customer decision processes. Although prior research has extensively examined the relationship between customer experience and behavioral intention, trust has predominantly been conceptualized as a mediating mechanism, while its moderating role in hybrid banking contexts remains insufficiently explored. This study investigates the influence of customer experience on trust and purchase intention, with particular emphasis on the moderating effect of trust in hybrid banking decision environments. A quantitative online survey was conducted among 371 bank customers in Germany. The collected data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results showed that customer experience exerted a strong positive effect on trust ($\beta$ = 0.858) and a significant direct effect on purchase intention ($\beta$ = 0.369). Trust also demonstrated a significant positive influence on purchase intention ($\beta$ = 0.370) and significantly strengthened the relationship between customer experience and purchase intention through its moderating effect ($\beta$ = 0.097). The model explained a substantial proportion of variance in trust ($R^2$ = 0.737) and a moderate proportion in purchase intention ($R^2$ = 0.454). The findings indicate that trust functions not only as a direct relational mechanism but also as a contextual condition influencing how customer experience translates into behavioral intention in hybrid banking settings. This study provides a more differentiated understanding of customer decision behavior in digitally integrated banking environments and offers practical implications for customer experience management and trust-oriented decision strategies in the financial services sector.
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