Abstract: This paper demonstrates use of an Excel-based tool called “Electric Vehicle Charging Infrastructure Financial Analysis Spreadsheet Tool” or “EVCI-FAST” developed to analyze public-private partnership approaches to deliver publicly-accessible EV charging infrastructure that would not be commercially viable without a government subsidy. We conduct a high-level screening analysis for a hypothetical bundle of publicly-accessible EV charging stations to assess the financial viability of delivery of electric vehicle charging infrastructure (EVCI) using alternative public-private partnership (P3) structures. The demonstration suggests that EVCI-FAST could assist public agencies in determining whether their budgetary resources are adequate to support a proposed P3 for an EVCI project. The demonstration suggests that EVCI-FAST could also help agencies decide which P3 structuring option would best meet their financial objectives. The results from analysis of the hypothetical project suggest that public agencies could benefit considerably from a P3 structure that uses a minimum revenue guarantee to reduce revenue risk for the private partner.

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Industry 5.0 integrates green technology with sustainability, focusing on supplier-customer interaction and knowledge spillovers. It optimizes resource efficiency, circularity, and social responsibility. Robotics enhance capabilities without replacing human labor, and organic agriculture standards and robotics in the wine industry meet consumer demand. The research explores the adoption of these directional and applicative elements under the Triple Bottom Line (TBL) strategy in Romania and Portugal's wine industry, highlighting its potential for sustainability and environmental protection.The study analyzed data on wine indicators: wine consumption, cultivated vineyard area, grape production, volume of wine produced, and value of exports for Romania and Portugal from the International Organization of Vine and Wine (OIV) and the World Trade Map, providing an overview of the wine industry in Romania and Portugal. The data provides insights into the global market performance of Romanian and Portuguese wines, allowing for a comparative evaluation of production capabilities, domestic consumption dynamics, and export competitiveness. It also identified trends and shifts in the wine industry over time. We designed an ANOVA analysis to test if there are significant differences between Romania and Portugal regarding wine indicators. Through an SEM SmartPLs path analysis, we tested the influence of main factors on the value collected from wine export.

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This study uses time series and machine learning techniques to forecast Saudi Arabia’s refined petroleum output; a significant player in the global energy market. Using data from 1962 to 2022 acquired from the Ministry of Energy, Kingdom of Saudi Arabia, the study evaluates the forecasting performance of different models such as Facebook Prophet, Long Short-Term Memory (LSTM), Gaussian Process (GP), and Auto-Regressive Integrated Moving Average Model(ARIMA) based on metrics including Root mean squared error (RMSE), mean absolute percentage error (MAPE), relative absolute error (RAE), Akaike Information Criterion (AIC), and training time. The study results demonstrate that traditional time series models like ARIMA consistently exhibit superior prediction accuracy, whereas machine learning models like LSTM and GP offer more flexibility but need more data. Conversely, Prophet Model performs poorly as it often overlooks complex patterns within the data. The finding of this research work highlights the need for appropriate methodology use and careful model selection in predictive modeling initiatives to provide decision-makers with relevant information in the energy business. Future research may look into ways to use ensemble modeling techniques and other exogenous factors to increase the accuracy of forecasts for petroleum production.

Abstract: This exploratory study investigates the relationship between cognitive capital skills and performance metrics among call center employees. The research highlights the intricate interplay between cognitive skills and employee performance by analyzing various performance indicators, including call duration, error rates, and customer satisfaction. Findings reveal significant differences in performance components across roles, with registration operators benefiting from longer call durations, while support operators face increased error rates with extended call and wait times. Additionally, the study underscores the importance of cognitive resilience and emotion regulation, demonstrating their positive impact on performance metrics. This research contributes to the literature by emphasizing the role of cognitive capital in enhancing organizational performance and suggests the need for tailored cognitive skill development programs to optimize employee outcomes. The results offer valuable insights for managers seeking to improve performance metrics while fostering a supportive work environment.

Abstract: The European Union’s (EU) Corporate Sustainability Reporting Directive expanded its Non-Financial Reporting Directive requirements to companies with over 250 employees, mandating their sustainability reporting from 2025. This expansion will exceed the number of companies subject to mandatory reporting, presenting new challenges for managers and responsible employees. Companies will have to report according to the European Sustainability Reporting Standards. The Erasmus+ project "Smart Education for Corporate Sustainability Reporting" (SECuRe) addressed gaps in vocational education and training (VET) programs related to sustainability knowledge and reporting. It aimed to establish a unified approach for VET teachers and learners across the EU, preparing them for the evolving job market demands. The project began by developing a knowledge repository and questionnaire, focusing on current reporting practices and job requirements, and continued with the preparation of materials for the training course. The course encompassed six learning units: 1) European legislation and sustainability standards, 2) sustainability management, 3–5) environmental, social, and corporate dimensions, and 6) sustainability reporting. The initiative included multiplier events, pilot applications, and course online tests. To deliver the course effectively, the project utilized an interactive e-learning platform with gamification elements and other engaging activities to enhance learning outcomes.

Abstract: Financial literacy has emerged as a crucial factor in promoting economic stability and resilience, particularly in the context of Montenegro. With the increasing complexity of financial products and the growing need for individuals to make sound financial decisions, the importance of financial literacy cannot be overstated. The paper discusses how financial literacy could help Montenegro's households, which are highly vulnerable to economic volatility and have limited access to financial services. Using a quantitative survey-based approach, research studies in this paper explore the association of levels of financial literacy with measures of economic stability that involve savings rates, active debt management, and access to financial products. The correlation analysis would therefore show that high levels of financial literacy relate to better financial practices of saving and utilizing credit in a responsible manner, hence contributing to economic resilience at the household level. Findings suggest that financial literacy can mitigate the impact of economic shocks, underscoring the need for policies that promote financial education as a tool for sustainable development. This study contributes to the literature on financial literacy in emerging economies and provides actionable insights for policymakers in Montenegro and similar contexts, emphasizing financial education as a pathway to individual and national economic resilience.

Abstract: The influences of a supervisor's perception and behavior on employee output constitute a significant issue that scholars are eager to explore; nevertheless, an effective connection between supervisors' perception and employees' positive behaviors is lacking. Therefore, we investigate the potentiality of such a connection based on social exchange theory and self-determination theory, whereby supervisors can establish an inclusive workplace environment upon receiving organizational support, leading salespeople to perceive a superior team atmosphere that engenders a sense of internal obligation, which eventually gives rise to proactive behavior. However, front-line salespeople demonstrate a considerable extent of autonomous decision-making conduct, and the determination of behavior for job engagement necessitates that each salesperson holds superior intrinsic motivation; accordingly, the moderating impact of the salesperson`s core self-evaluation is also probed in our study. Using Mplus 8.0 to analyze the matched survey of 50 team leaders and 299 employees, we ultimately discovered that the supervisor's perceived organizational support exerts a positive influence on the group-inclusive climate; a group-inclusive climate can enhance the felt obligation and career initiative of front-line salespeople; a supervisor's perceived organizational support can enable salespeople to perceive the inclusive climate of the group, engender their sense of obligation, and ultimately impact their career initiative; and a salesperson's core self-evaluation can significantly moderate the positive effects of the group-inclusive climate.

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This study aims to develop the concept of Self-Social Engineering in the context of tourism, focusing on tourists’ pro-environmental behavior. By integrating psychological theories such as Environmental Psychology Theory, Theory of Planned Behavior, and Norm Activation Theory, the purpose of the investigation was to determine the extent of the direct influence of independent variables of perceived environmental quality, attitude, subjective norm, and perceived behavioral control on self-social engineering and indirect influence of them through intention to engage in environmentally responsible behavior. The structural analysis results from a sample of 191 visitors indicated that the unified model demonstrates a satisfactory predictive capability for self-social engineering. The results show that self-social engineering is critical in encouraging pro-environmental behavior. In addition, this study also highlights the importance of improving individuals’ attitudes and perceptions regarding their ability to carry out pro-environmental actions. The implications of this study are the need to develop effective communication strategies to cultivate environmental conservation values and provide adequate facilities and support to facilitate tourists in carrying out environmentally friendly actions.

Abstract: How can we validate a given Multiple-Criteria Decision-Making (MCDM) approach and compare it to other MCDM methods? This question is not addressed in MCDM literature. This paper presents a novel out-of-sample approach using in-sample (e.g., 70%) data to assess the parameters of a given MCDM model and validate the model using out-of-sample (e.g., 30%) data. MCDM models are ranked based on their accuracies of predicting the out-of-sample data using several randomly selected replicas. We develop a new class of MCDM models based on Geometric Dispersion Theory (GDT) that was recently developed for decision-making under risk. MCDM-GDT is based on a convex combination of maximizing an additive utility function (an arithmetic function) and a multiplicative utility function (a geometric function); this MCDM-GDT function is non-additive and non-linear in criteria and in weights of importance of criteria. A special case of MCDM-GDT is a highly effective concave utility function using k+1 parameters where k is the no. of criteria. We apply MCDM approach for selection of the best wind farm location in Saudi Arabia using 30 expert decision makers (DMs). Then we provide a detailed comparison of several well-known MCDM methods and MCDM-GDT using the out-of-sample approach. The results indicate that most of MCDM methods, except MCDM-GDT, have poor predictive performances. We also develop a new model for group decision-making using a new nonlinear aggregation of the ratings of DMs based on MCDM-GDT; we show that this model has advantages to using the commonly used weighted average rating of DMs.

Abstract: As the logistics industry modernizes, living standards improve, and consumption patterns shift, the demand for fresh food continues to grow, making cold chain logistics for perishable goods a critical component in ensuring food quality and safety. However, the presence of both soft and hard time windows among demand nodes can complicate single-network distribution of perishable goods. In response to these challenges, this paper proposes an optimization model for multi-distribution center perishable goods delivery, considering both one-echelon and two-echelon network joint distribution. The model aims to minimize total costs, including transportation, fixed, refrigeration, goods damage, and penalty costs, all while measuring customer satisfaction by the start time of service at each demand node. A two-stage heuristic algorithm is designed to solve the model. In the first stage, an initial solution is constructed using a greedy approach, based on the principles of the k-medoids clustering algorithm that considers both spatial and temporal distances. In the second stage, the initial routing solution is optimized using a linear programming approach from the Ortools solver combined with an Improved Adaptive Large Neighborhood Search (IALNS) algorithm. The effectiveness of the proposed model and algorithm is validated through a case study analysis. Results indicate that the enhanced algorithm achieves faster convergence and more effective overall cost optimization.