The rapid development of e-commerce has made last-mile delivery a critical bottleneck in logistics management, with its efficiency directly impacting operational costs, service quality, and environmental sustainability. To address the multi-criteria decision-making (MCDM) problem of parcel locker location selection, this study constructs an intelligent decision-support framework that integrates the Improved Fuzzy Step-wise Weight Assessment Ratio Analysis (IMF SWARA) and the Weighted Aggregated Sum Product Assessment (WASPAS) methods. Based on real-world data from the Brčko Distribution Center of a regional logistics company (X Express), the research first employs the IMF SWARA method to determine fuzzy weights for six key criteria, including availability, frequency of user requests, and accessibility. The WASPAS method is then applied to comprehensively rank twelve candidate locations. Results indicate that location A2 is the optimal choice, followed by A4 and A3. The robustness of the model is verified through sensitivity analysis, including comparisons with other MCDM methods such as ARAS, EDAS, and MARCOS, as well as systematic variation tests of the $\lambda$ parameter in WASPAS. This framework provides logistics managers with a structured and quantifiable decision-making tool, facilitating data-driven optimization of last-mile delivery networks in complex urban environments and enhancing the sustainability and operational efficiency of logistics systems.
This work provides a complete methodology for adopting well-established AI methods (predictive analytics, LLM agents, forecasting) into Microsoft Dynamics 365 Customer Relationship Management (CRM) for agricultural lending. While not claiming that the algorithms are novel, this work contributes a pragmatic approach to implementing these algorithms that specifically address the regulatory, seasonal, and operational characteristics of agricultural finance, as regulated by the Farm Credit System. It focuses on the real-life constraints and constraints within the regulated financial services industry, and measurable impacts that occurred. The paper provides a domain-oriented application of specific existing AI-CRM integration, with credible statistical testing including an external validation on USDA datasets and benchmarking across peer Farm Credit institutions, as well as cross-institutional analysis. By taking a reasonably conservative duration of 18 months, the Farm Credit institutions noted a statistically significant impact (operational efficiencies of the lending institution to assess member interests) where average case resolution time reduced by 28% (67.2h to 48.4h), and lead conversions improved by 35% (25.9% to 35.0%). Each methodology of implementation also included a series of validations in compliance with regulatory oversight in financial institutions that started to build data governance, model performance compliance through a proactive risk definition, and compliance standards suitable for their institution, and within regulatory standards by regulations. Beyond statistical significance (paired tests, $p <0.001$), practical impact was quantified using absolute and relative changes and bootstrap confidence intervals. The article provides the agricultural lending industry an applied methodology to adopt AI for stakeholder innovation while ensuring they are adept in their enterprise risk management requirement, and still target measurable business outcomes. Given a conservative potential implementation timetable (i.e., 18 months) and validation methodology protocols developed to ensure complete data and model validation, this approach is scalable for agricultural lending implementation and would be a useful instrument across all 72 Farm Credit System institutions.
The digital transformation of commercial banks (DTCB) has altered the way financial institutions collect, process, and use information, with potential implications for firms’ investment behaviour. This study examines whether and how DTCB affects corporate investment efficiency using panel data on Chinese listed companies from 2013 to 2023. The results indicate that a higher level of DTCB is associated with a statistically significant improvement in corporate investment efficiency. Further analysis suggests that this effect operates primarily through two channels: a reduction in financing constraints and a decline in agency costs. The heterogeneity analysis shows that the positive effect of DTCB on investment efficiency is concentrated among privately owned firms, while no significant effect is observed for state-owned enterprises (SOEs). These findings provide evidence that the DTCB reshapes firms’ financing and governance environments in ways that influence investment outcomes. The study contributes to the literature on digital finance and corporate investment by offering firm-level empirical evidence on the economic consequences of banking digitalisation.
Digital finance has increasingly influenced the functioning and stability of industrial systems by reshaping interregional economic linkages. Based on panel data from 31 Chinese provinces spanning the period 2012–2021, this study investigates how the development of digital finance is associated with the spatial structure of industrial chain resilience. A modified gravity model is used to construct interprovincial interaction networks, and social network analysis is applied to examine their structural characteristics and temporal evolution. The empirical results show that the spatial network related to digital finance and industrial chain resilience has become progressively more connected over time, as reflected by a gradual increase in network density. However, substantial regional heterogeneity persists in network position and influence. Provinces with relatively advanced digital finance tend to occupy more central positions and exert stronger structural influence, whereas peripheral provinces remain weakly connected and play limited roles within the network. This asymmetric network configuration constrains the overall stability of the industrial chain system and highlights the importance of coordinated development in digital finance for improving systemic resilience.
The rapid diffusion of artificial intelligence (AI) into decision-making processes has raised critical questions about how AI reshapes human behavior, judgment, and responsibility. While existing studies often emphasize technical performance, less attention has been given to the behavioral dynamics that emerge when humans interact with AI-supported systems. This study addresses this gap by proposing an integrated Strengths, Weaknesses, Opportunities, and Threats–Analytic Hierarchy Process–Technique for Order Preference by Similarity to an Ideal Solution (SWOT–AHP–TOPSIS) framework to systematically evaluate the behavioral impact of AI-assisted decision making. First, key behavioral factors are identified using SWOT analysis, where strengths and weaknesses represent internal human behavioral traits, and opportunities and threats capture external and contextual influences related to human–AI interaction. These factors are then weighted using AHP based on expert judgments, with consistency checks ensuring methodological reliability. Finally, TOPSIS is applied to rank three AI-assisted decision scenarios—human-dominant, shared-control, and AI-dominant decision making—according to their overall behavioral performance. The results indicate that behavioral weaknesses, such as over-reliance on AI and reduced critical thinking, exert the strongest influence on decision quality. Among the evaluated scenarios, human-dominant decision making achieves the highest closeness coefficient, followed by shared-control and AI-dominant scenarios. Sensitivity analysis confirms the robustness of these rankings under reasonable variations in criterion weights. Methodologically, this study demonstrates that the SWOT–AHP–TOPSIS approach, traditionally used in strategic and operational research, can be effectively adapted to behavioral and socio-technical contexts. Substantively, the findings highlight the importance of preserving human cognitive agency in AI-assisted environments. The proposed framework offers a practical and theoretically grounded tool for researchers, designers, and policymakers to assess and guide the behavioral implications of AI-supported decision systems.
This study develops a structured framework for the quantitative assessment of supplier-related risk in organizational supply networks. The proposed methodology is based on the Action Priority (AP) concept from Failure Mode and Effects Analysis (FMEA), which evaluates risk using three factors: Severity (S), Occurrence (O), and Detectability (D). Based on expert assessments and AP decision matrices, individual suppliers are classified into three risk categories: Low (L), Medium (M), and High (H). To enable a more rigorous analytical representation of these qualitative assessments, the risk categories are modeled using triangular fuzzy numbers (TFNs). The fuzzy values associated with individual suppliers are aggregated using the fuzzy arithmetic mean operator and subsequently defuzzified through the centroid method. After normalization, a single quantitative indicator—the Overall Supplier Risk Index—is obtained, providing insight into the company’s overall dependence on its supplier base. The proposed framework is demonstrated through a case study of a furniture manufacturing company in the wood-processing industry involving 39 strategically important suppliers. The results indicate that the analyzed company belongs to the second risk priority level, corresponding to a low overall supply risk exposure. The developed model enables the transformation of qualitative expert evaluations into a single analytical indicator, thereby supporting managerial decision-making in supplier risk monitoring and supply strategy development.
This study addresses the impact of increasing environmental pressures on a global scale on the long-term strategies of businesses, with particular emphasis on the importance of environmental responsibility in port operations. Port activities are directly associated with various environmental issues, including climate change, global warming, air and water pollution, noise pollution, waste management, and energy consumption. Effectively identifying and prioritizing these issues is critical not only for protecting environmental and social well-being but also for enhancing the operational performance and competitiveness of port enterprises. In this context, the aim of the study is to evaluate the priority environmental issues faced by port operations using an analytical approach. To this end, the q-rung orthopair fuzzy step-wise weight assessment ratio analysis (q-ROF-SWARA) method, one of the multi-criteria decision-making techniques, was employed due to its ability to effectively handle uncertainty and subjective expert judgments. The findings indicate that energy consumption is the most significant environmental issue for port operations, while noise is considered the least important relative to other factors. The results provide valuable insights for decision-makers in developing sustainable port management practices and formulating effective environmental strategies.
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