In an era characterized by intense labor market competition for skilled and motivated personnel, the adoption of innovative strategies, such as gamification, has emerged as a critical factor for cultivating an engaging workplace environment. This investigation explores the impact of gameful experiences on employee behavior within the context of credit institutions, focusing on three primary behaviors: knowledge sharing, team identity development, and affective commitment to the organization. An empirical analysis, conducted through the collection of 382 employee responses, reveals that gameful experiences exert a significant positive influence on these behaviors. Specifically, it is demonstrated that such experiences enhance the propensity for knowledge sharing among colleagues, foster the development of a stronger team identity, and increase affective commitment towards the company. These findings contribute to the expansion of the nomological network of gameful experience in the professional setting, highlighting the individual team behaviors that are pivotal for organizational success. Furthermore, the results advocate for the integration of gamification strategies within workplace design, underscoring the potential of gameful experiences to promote behaviors that are beneficial to organizational objectives. By delving into the relatively unexplored domain of gamification within workplace design, this research not only enriches the academic discourse on gamification but also provides practical insights for the application of gameful experiences to enhance employee engagement and behavior. In doing so, it underscores the transformative potential of gamification in shaping workplace dynamics and fostering an environment conducive to collaborative and committed work practices.

In geographically isolated regions, where infrastructure limitations and remote locations pose significant challenges, a mobile application has been developed to facilitate an efficient emergency response system. This system, designed to bridge the gap in emergency support, employs a multi-faceted strategy that combines human expertise with advanced machine learning (ML) technologies. Upon activation through the application, a coordinated mechanism is triggered, dispatching local mechanics equipped with the necessary tools, resources, vehicles, and spare parts to the site of the emergency. This immediate on-site assistance is essential for addressing mechanical failures and ensuring timely support for individuals in remote areas.At the heart of the application lies a sophisticated ML model, trained on an extensive dataset comprising a wide array of emergencies likely to occur in rural settings. This model, characterized by its convolutional neural network (CNN) architecture and optimized for mobile deployment through TensorFlow Lite (TFLite), demonstrates an impressive diagnostic accuracy rate of 98%. Such precision significantly enhances the application’s capacity to diagnose issues accurately, prioritize response efforts, and optimize resource allocation.Moreover, the application leverages data-driven insights not only to streamline the emergency response process but also to facilitate predictive maintenance. By continuously learning from incoming data, the ML model can predict potential problems and suggest preventative measures to users, thereby minimizing the likelihood of future breakdowns. This predictive capability underscores the application’s role in promoting resilience within rural communities.Community engagement is further encouraged through the inclusion of local mechanics in the emergency response network. This initiative not only expands the pool of available skilled professionals but also fosters a sense of community solidarity, crucial for enhancing the system’s overall effectiveness.In summary, the development of this mobile application represents a significant advancement in emergency assistance for rural communities. By integrating real-time response capabilities with sophisticated ML models, the system not only addresses the immediate challenges of emergency support in remote areas but also contributes to the creation of a more resilient and interconnected community fabric.

The paradigm shift towards sustainable transportation underscores the burgeoning focus on electric vehicles (EVs) as a viable alternative to combustion-powered counterparts. Concurrently, the corpus of scholarly publications exploring this domain has expanded, endeavoring to address multifaceted challenges across various disciplines. Among the methodologies enlisted, Multi-criteria Decision Analysis (MCDM) emerges as a pivotal tool for decision-makers, facilitating the resolution of complex problems characterized by multiple criteria and alternatives. This research employs a modified Systematic Literature Review (SLR) methodology, integrating the Analytical Hierarchical Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for article ranking. This novel approach not only enhances the precision of the ranking process but also earmarks the articles exerting substantial influence on the scholarly landscape. The exhaustive review culminates in a curated portfolio of 73 seminal articles, with a pronounced emphasis on Charging Stations applications, accounting for approximately 42.46% of the collective focus. This study's findings illuminate the prevailing trends within the nexus of EV research and MCDM, delineating a trajectory for future inquiries and applications. In doing so, it underscores the indispensable role of MCDM in navigating the complexities inherent in the transition to electrified mobility solutions. The meticulous application of the SLR methodology, augmented by AHP and TOPSIS, not only refines the academic discourse but also paves the way for a more structured and impactful exploration of EVs within the realm of sustainable transportation.

This study aims to elucidate the role of established public utility logistics in facilitating the integration of transition economies into the modern, developed world, with a particular focus on comprehensive waste management and the implementation of green logistics schemes. The research highlights the “green islands” waste collection system—networks of collection points that serve as separators for secondary raw materials from generated waste. It is demonstrated that such systems not only contribute to the optimization of public utility company costs and the selection of optimal transport routes but also play a crucial role in elevating public awareness regarding the importance of the 3R principle (Reduce, Reuse, Recycle). A significant contribution of this study lies in its demonstration of how academic knowledge can be transferred to the business sector through spin-offs, evidencing a theoretical model of green logistics schemes that can increase the total amount of secondary raw materials recovered from waste by 20% by 2030 in the City of Doboj. The research underscores the role of citizens, students, and businesses as primary waste producers in transition economies, emphasizing the effectiveness of a rewards system for conscientious waste selection at the source. Moreover, the establishment of an optimal transport route designed to support these green islands is shown to enhance the collection and recycling of valuable secondary raw materials, thereby preventing their disposal in landfills without value recovery. This innovative approach not only raises public awareness towards a more sustainable environment but also establishes a foundation for long-term environmental health. Through the lens of green logistics, this study presents a compelling model for comprehensive waste management in transition economies, advocating for practices that ensure the sustainable management of resources and contribute to environmental protection and public health.

In multi-criteria decision-making (MCDM), accurately quantifying qualitative data and simulating real-world scenarios remains a significant challenge, particularly in the presence of inherent imprecision and incompleteness of information. Fuzzy logic, recognized for its capacity to model uncertainty and ambiguity, emerges as a pivotal theory in decision-making processes. This study introduces an enhancement to the Defining Interrelationships Between Ranked Criteria II (DIBR II) method, employing triangular fuzzy numbers with variable confidence intervals for the determination of criteria weight coefficients-essential for assessing their significance and impact on final decisions. The enhanced method, hereafter referred to as the Fuzzy-DIBR II (F-DIBR II), is elaborated upon through a comprehensive description of its algorithmic steps, underscored by a numerical example that highlights its potential. Validation of F-DIBR II is undertaken via a comparative analysis against the traditional DIBR II approach, placing particular emphasis on its application within the Fuzzy Complex Proportional Assessment (COPRAS) framework, geared towards evaluating sustainable mobility measures. This focal point not only reaffirms the necessity of integrating fuzzy logic into the DIBR II methodology but also validates its practical applicability in addressing real-world issues. Contributions of this research extend beyond the theoretical enhancements of fuzzy theory within the MCDM landscape, offering tangible implications for the application of F-DIBR II in sustainable mobility analyses. The consistency in professional terminology throughout the study ensures clarity and coherence, aligning with the stringent standards of top-tier academic journals.