The transformation of public services into electronic formats (e-services) has gained significant momentum with the advancement of information and communication technologies, particularly due to the widespread use of the Internet and increasing citizen expectations.

This systematic review seeks to synthesize the existing literature on the integration of blockchain technology into sustainable finance, with a particular focus on its role in enhancing transparency and accountability. A bibliometric analysis was conducted using the PRISMA methodology, incorporating a meta-analysis of scholarly articles published between 2018 and 2023. The analysis was based on data extracted from databases such as Springer Link, Dimensions, and Google Scholar, using the search terms "blockchain," "sustainable," "finance," "transparency," and "accountability." Open-access articles from reputable, peer-reviewed journals were selected to ensure the reliability of the data. Research questions were framed following the PICo method, addressing the specific impacts of blockchain technology on sustainable finance systems. The review highlights that blockchain has the potential to significantly enhance transparency and accountability in sustainable finance by providing robust mechanisms for transaction traceability and verification. Notably, blockchain technology has been applied to improve carbon market management, facilitate green bond issuance, and support the disclosure of Environmental, Social, and Governance (ESG) data. Despite these promising applications, several challenges remain, including regulatory uncertainties, technological limitations, and integration complexities, which could hinder its widespread adoption. To facilitate the global integration of blockchain in sustainable finance, it is recommended that financial institutions invest in technological infrastructure and training. Furthermore, policymakers should work towards harmonizing regulatory frameworks, while researchers are urged to pursue interdisciplinary, empirical studies to address the potential and limitations of blockchain technology. A shift in academic curricula to include blockchain’s implications in finance and sustainability is also recommended to better prepare future professionals. In conclusion, while blockchain holds significant promise for improving transparency and accountability, its broader adoption will require addressing technological, regulatory, and socio-economic barriers.

The strategic positioning of distribution, sales, and service facilities plays a critical role in ensuring the efficiency, reliability, and cost-effectiveness of supply chains. In particular, the location of such facilities within the transshipment network significantly influences both operational costs and consumer satisfaction by affecting delivery times and service quality. This study introduces a mixed-integer linear programming (MILP) model designed to optimize the layout of a postal supply chain network. The model aims to minimize the key cost components, including transportation, facility location, and holding costs, within a four-echelon supply chain consisting of suppliers, warehouses, retailers, and recipients. Parcels are initially collected by suppliers and delivered to regional warehouses, which then allocate them to selected retail locations. The selection of optimal retail locations is based on a cost minimization criterion, after which parcels are transported to the final delivery points—post offices situated in various cities. A distinctive feature of the proposed model is the assumption that demand at the recipient level is determined at the supplier level, thereby facilitating more centralized demand management and reducing uncertainties in the planning process. The model incorporates several constraints, such as flow balance, capacity limitations, and retailer selection. The optimization problem is solved using LINGO 16 software, and a comprehensive analysis is conducted to identify the optimal configuration of retailer locations and parcel flow distribution. A numerical example is provided to demonstrate the practical application of the model, and sensitivity analysis is performed to assess the impact of key parameters—such as retailer capacity and initial inventory levels—on the overall cost. The results indicate that increasing retailer capacity leads to a reduction in total supply chain costs, highlighting the benefits of economies of scale and parcel consolidation. However, an increase in the initial quantity of parcels results in higher costs due to elevated transportation and holding expenses. These findings offer valuable insights for decision-makers seeking to optimize postal supply chains, balancing the need for cost efficiency with the provision of high-quality service.

The retail sector is increasingly confronted with challenges arising from digital disruption and shifts in consumer behaviour. Amidst this transformation, the integration of augmented reality (AR) has been identified as a promising avenue to revitalise the in-store shopping experience, offering a means to engage customers more effectively and enhance competitiveness. This study investigates the extent to which AR applications can improve the shopping experience in physical retail settings, with particular emphasis on their capacity to foster customer flow states. A survey of 239 participants, comprising both general consumers and retail professionals, was conducted to explore the impact of AR on the shopping process. The findings suggest that AR significantly enhances the shopping experience, contributing to heightened customer engagement and immersion. However, while AR is found to influence flow states, the flow experience itself does not mediate the relationship between AR use and the shopping experience. These results offer important insights into the application of AR in brick-and-mortar retail environments, providing a management-oriented perspective on how its strategic implementation can generate sustainable competitive advantages. Moreover, the study contributes to existing AR literature by extending the understanding of its role in traditional retail, highlighting practical considerations for retailers aiming to adopt such technologies. The evidence also underscores the potential of AR in fostering behaviours and experiences that are essential for maintaining the competitiveness of physical stores in the digital age. Therefore, the adoption of AR technologies is not only recommended for enhancing the customer experience but also for driving innovation within the retail industry.

The optimization of railway train selection in Pakistan has become increasingly critical due to rapid population growth and rising travel demands. Despite efforts by the Railway Transport (RT) Department to enhance efficiency, productivity, and safety through policy reforms and infrastructure advancements, persistent challenges such as outdated technology, infrastructure bottlenecks, frequent delays, and inadequate maintenance continue to hinder progress. Addressing these issues is imperative to ensuring sustainable, efficient, and resilient railway operations. Given the multifaceted and uncertain nature of railway system modeling and management, decision-making (DM) processes necessitate robust methodologies capable of handling imprecise and ambiguous data. In this study, an innovative DM framework is introduced, leveraging intuitionistic fuzzy sets (IFSs) as an advanced extension of fuzzy sets (FSs) to manage uncertainty and hesitation in complex scenarios. By employing Einstein t-norm and t-conorm-based operators, novel operational laws for intuitionistic fuzzy credibility numbers (IFCNs) are proposed. Three key aggregation techniques—Confidence Intuitionistic Fuzzy Credibility Einstein Weighted Averaging (CIFCEWA), Confidence Intuitionistic Fuzzy Credibility Einstein Ordered Weighted Averaging (CIFCEOWA), and Confidence Intuitionistic Fuzzy Credibility Einstein Hybrid Weighted Averaging (CIFCEHWA) operators—are developed to provide a structured approach for processing and analyzing intuitionistic fuzzy data. To evaluate the practical applicability and reliability of the proposed methodology, a structured DM algorithm is formulated and validated using a real-world railway train selection case study. The incorporation of confidence levels within the IFCN framework enhances DM precision by quantifying the degree of certainty, thereby reducing risk and improving reliability. The findings demonstrate that the proposed approach effectively addresses the inherent uncertainties in railway selection processes, leading to more informed and strategic planning. Furthermore, the applicability of IFCNs extends beyond railway systems, offering valuable insights for domains such as artificial intelligence, financial DM, management science, and engineering, where uncertainty plays a pivotal role.