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.
The reliability and interpretability of the Complex Proportional Assessment (COPRAS) method in multi-criteria decision-making (MCDM) have been critically re-evaluated. Although COPRAS has frequently been promoted as a method capable of separately assessing the influence of benefit and cost criteria without requiring explicit inversion of cost attributes, it is demonstrated that these claims are not fully supported by the mathematical structure of the method. A theoretical analysis reveals that COPRAS inherently relies on Sum normalization, through which hidden attribute prioritization and rating distortion may be introduced. Furthermore, it is shown that, in the presence of a single cost criterion, COPRAS becomes mathematically equivalent to the Weighted Sum Model (WSM) implemented with Sum normalization and a nonlinear inverse-sum transformation of cost criteria. Consequently, the purported methodological distinction between COPRAS and conventional additive aggregation approaches is substantially reduced. Particular attention is drawn to the nonlinear inversion embedded in the COPRAS formulation for cost criteria aggregation. Because the inverse transformation is applied to the total contribution of cost criteria rather than to individual criterion values, the resulting influence of cost attributes on the final utility score is shown to be only indirectly represented. Under certain conditions, significant discrepancies are produced between the nominal and actual contribution of cost criteria, thereby affecting both rating stability and ranking consistency. Through numerical demonstrations and comparative analyses, distortions in alternative ratings and rank reversals are identified when COPRAS results are compared with those obtained from the conventional WSM framework. The analysis further indicates that the observed inconsistencies are primarily associated with the combined effects of Sum normalization and nonlinear cost treatment. To address these limitations, the WSM integrated with a linear cost-transformation procedure based on the Reverse Sorting (ReS) algorithm is proposed as a more transparent and mathematically consistent alternative. The findings suggest that the application of COPRAS in practical MCDM problems should be approached with caution, particularly in decision environments where ranking sensitivity and interpretability are of critical importance.
Evaluating technological innovation performance in regional public hospitals requires balancing multiple policy objectives, including operational efficiency, distributive equity, and innovation value creation. Conventional evaluation methods often rely on fixed indicator weights, which inadequately capture trade-offs among competing objectives and limit their usefulness for strategic resource allocation. To address this limitation, this study develops a multi-objective decision optimization framework that reformulates innovation performance evaluation as a constrained decision-making problem under fiscal, institutional, and policy conditions. A multi-objective linear programming model is constructed to jointly optimize efficiency, fairness, and innovation value. Using three-year panel data from regional public hospitals, the framework is validated through comparative evaluation, sensitivity analysis, and statistical testing. The results show that the optimized weighting structure improves institutional performance balance, reduces inter-regional disparities in innovation capacity, and strengthens the contribution of research investment to technological output and knowledge transformation. Human capital composition, research funding intensity, and technology commercialization capability are identified as key variables shaping the innovation performance frontier. Scenario analysis further shows that institutional performance varies under different policy preferences, highlighting the need for adaptive weighting mechanisms. The findings provide a practical and interpretable framework for evidence-based innovation performance evaluation and public hospital governance.
The rapid integration of artificial intelligence (AI) into mobile banking applications has considerably transformed digital financial services, shifting the primary challenge from user adoption to sustaining long-term usage. In emerging digital banking markets such as Indonesia, continuance intention has become critical to the development of mobile banking. The purpose of this study is to examine, from a post-adoption perspective, the effects of artificial intelligence capabilities and trust on continuance intention in mobile banking. A quantitative research design was employed to conduct a cross-sectional survey of 150 mobile banking users in Indonesia. The results obtained from Partial Least Squares Structural Equation Modeling (PLS-SEM) showed that both artificial intelligence capabilities and trust had significant positive effects on continuance intention in mobile banking. More specifically, users’ perceptions of artificial intelligence capabilities, such as personalization, responsiveness, automation, and learning ability, all played a crucial role in reinforcing continued usage. In addition, trust, as a core psychological determinant, directly affected users’ willingness to rely on AI-enabled mobile banking and to be loyal to such services. Simply put, technological competence alone was not sufficient to sustain long-term usage without corresponding levels of user trust. Therefore, the development of advanced AI functionality and trust-building strategies should be aligned. This study contributes to the literature on mobile banking and information systems by conducting post-adoption research through the integration of artificial intelligence capabilities and trust within a parsimonious research model. With a focus on continuance intention rather than initial adoption, the study provided a more relevant explanation for user behavior in a competitive digital banking environment. The findings offered convincing and practical insights for banks and fintech providers to ensure long-term sustainability of mobile banking services.
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