This study explores how financial literacy affects students’ financial well-being by looking at the psychological factors that play a role—specifically self-control, mental budgeting, and mental accounting—as mediating variables. The research aims to better understand how cognitive and psychological resources help young people achieve financial well-being in a university setting. Using a quantitative, survey-based approach, the study gathered data from 781 students across four Indonesian cities. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine both the direct and indirect relationships between the variables. The results show that financial literacy has a significant impact on financial well-being, both directly and indirectly, through the influence of self-control, mental budgeting, and mental accounting. Based on the Conservation of Resources Theory, financial literacy serves as a cognitive resource that helps students protect and manage their financial assets more effectively. The findings suggest that universities and policymakers should develop financial literacy programs that also address psychological aspects such as self-control and budgeting habits. Integrating these elements can help students feel more financially secure and maintain better life balance. Overall, this study offers both theoretical and practical insights by connecting financial literacy with the management of psychological resources, and it points to new directions for developing psychology-based financial education to enhance young people’s financial well-being.

The purpose of this paper is to empirically investigate the effect of sustainability knowledge dimensions which are recycling knowledge, reuse knowledge and use efficiently knowledge on sustainability intention, attitude and behavior toward water consumption. This study is important while it is the first empirical study that has examined the links between a set of sustainable water consumption concepts within an “water-poor,” Arab developing country context. A conceptual model of the connections between the above-mentioned factors was developed and the posited hypotheses were tested using a survey data set of 512 questionnaires collected from consumers in Jordan. The findings show that use efficiently knowledge has the strongest effect on sustainable intention, followed by recycling knowledge and reuse knowledge. Moreover, use efficiently knowledge has the strongest effect on a sustainable attitude followed by reuse knowledge and recycling knowledge. Additionally, sustainable intention has a stronger effect on sustainable behavior than sustainable attitude. Such results would provide useful insights for policymakers, nonprofit organizations, and businesses to integrate themes of sustainable behavior into their policies and programs, thereby facilitating better progress toward the sustainability of water resources in developing countries.

In this paper, we extend the multi-skilled resource-constrained multi-project scheduling problem (MSRCMPSP) by introducing the concept of multiple project types (MSRCMPMTSP). The study considers two categories of projects: investment projects, which generate positive net present values (NPVs), and mandatory projects, which result in negative NPVs but are required to be executed. To solve this problem, we employ a priority rule-based heuristic approach. Specifically, forward scheduling is applied to projects expected to yield positive NPVs, whether they are optional or mandatory. In contrast, backward scheduling is used for projects with negative NPVs, as this strategy minimizes the impact of excessive negative NPVs. The dataset for this study is constructed using the design of experiments (DoE) methodology, enabling a comprehensive evaluation of the proposed heuristic. We compare the performance of our approach against randomly generated schedules through extensive simulations. The results indicate that the heuristic is effective in addressing the MSRCMPMTSP.

A majority of companies are involved in the planning and execution of projects. The number of projects that companies need to evaluate has significantly increased in recent years. This trend has various causes, such as the digitalization of corporate processes, diversification, or strategic positioning in the face of ever-changing market conditions. The characterization of projects into mandatory and optional, as well as the evaluation of these projects, can be conducted based on various mixed criteria, which may include both cost and benefit criteria. The limited resources of companies necessitate a critical assessment of projects. They must be ranked based on realistic and plausible criteria regarding their benefits and objectives of the company. In the literature, various approaches to project evaluation exist. Examples include financial assessment, the utilization of evaluation models considering risks, or even multi-criteria models that incorporate different aspects of projects into the evaluation process. We propose a robust, scalable, and easily calibrated multi-criteria evaluation model for project evaluation and ranking, encompassing evaluation criteria such as financial criteria measured by Net Present Value (NPV), Risk, Classification, Priority, Strategy, and Sustainability. To achieve this goal, the Technique for Order of Preference by Similarity to Ideal Solution with multi type projects multi mixed cost and benefit criteria (TOPSIS-MTPMMCBC) is employed. The model is adapted to evaluate and rank optional and mandatory projects. An important feature of the projects in this study is that the criteria values of the projects can have negative or positive values. Particularly noteworthy is the increasing significance of sustainability as a key criterion for businesses, driven by political mandates. Consequently, a decision based on the criterion of sustainability will be important in the future and is implemented in the proposed model. The proposed research can be adapted to use a variety of Key Performance Indicators (KPIs) as multiple decision criteria. An objective calculation of the criteria weights for sample dataset was carried out using the CRITIC method, followed by a sensitivity analysis. Subsequently, the optimization of the project portfolio was carried out by combining an integer programming model with the proposed TOPSIS-MTPMMCBC method. An initial solution of project evaluation and ranking is conducted to demonstrate the applications.

The article highlights the consequences and transformational changes in labor markets and labor resources due to the spread of artificial intelligence. The purpose of the article is to empirically study the processes of interaction between artificial intelligence and national labor markets, as well as to develop methodological tools for quantifying the impact of AI technologies on labor market parameters. The research methodology is based on the envelope model (“input” orientation) to assess the level of transformation of labor resources and labor markets as a result of the spread of artificial intelligence for a sample of European Union countries. The study found a significant transformation of the employment structure under the influence of artificial intelligence, where the most vulnerable occupational groups are office workers, data entry operators, call center workers, accountants, and administrative staff with routine analytical and administrative tasks. The study identified countries that can optimally transform AI diffusion (the Netherlands, France, Portugal, Italy, and Malta) for the domestic labor market into results when the trend of economic development and the realization of human capital potential is formed. The obtained results confirmed the existence of reserves for optimizing the interaction of artificial intelligence with the labor market, with an emphasis on the need to adapt AI to the specifics of economic models of national economies.

Digital transformation is a challenge that also impacts the selection of tools for implementing projects. Which tools are suitable for handling complex digital twins? Project management must respond to this with suitable approaches. The challenge for decision-makers is to choose the right one. Based on literature research and a case study, influencing factors are derived and practice-relevant project management approaches are collected. Furthermore, a decision model is developed that, on the one hand, supports the decision-maker in selecting tools before and during the project, and on the other hand makes empirical values from past projects usable for future decisions. The results show that the number of influencing factors is large, and the approaches are di-verse. In complex projects, this can lead to complex decision-making situations that require appropriate decision models. The developed “Supervised Decision Model – L5” is based on five levels (L): (L1) Building a database; (L2) Derivation of algorithms; (L3) Initial approach selection; (L4) Review of the initial selection; (L5) Using experiences for future decisions. In practice it turns out that complex projects – like Digital Twins - often fail. Modified decision models for selecting suitable approaches should therefore take the following as-pects into account: (a) decision-makers are actively supported in the initial decision phase; (b) initial decisions once made are checked in the early phase of the project and corrected if necessary; (c) the lessons learned are recorded in the database as empirical value and used for future decisions.

In recent years, the adoption of advanced machine learning techniques has revolutionized approaches to solving complex problems, such as identifying occurrences of forest fires. Among these techniques, the use of Convolutional Neural Networks (CNNs) combined with ensemble methods is particularly promising. To investigate the feasibility of detecting fires using video streams from Unmanned Aerial Vehicles (UAVs), the lightweight CNN architecture MobileNetV2 was utilized for real-time detection. Several experiments were conducted on the DeepFire dataset, which comprises an equal number of images with and without fire, to evaluate MobileNetV2's performance. Notably, the architecture's linear bottlenecks and the efficient use of inverted residuals ensure high accuracy without compromising on feature extraction capabilities. For a comprehensive assessment, MobileNetV2 was benchmarked against other models, including DenseNet121, EfficientNetV2S, and VGG16. Accuracy was enhanced by averaging predictions through methods such as voting or summing results. As documented in the literature, MobileNetV2 consistently outperforms other architectures in computational efficiency and provides an excellent balance between efficiency and the quality of learned features over multiple epochs. This study underscores the suitability of MobileNetV2 for real-time applications on drones, particularly for the detection of forest fires in resource-constrained environments. The results show that MobileNetV2 achieves the highest accuracy (0.994), sensitivity (0.994), and specificity (0.998) among the tested models, with low standard deviations across all metrics. In contrast, EfficientNetV2S exhibited the lowest accuracy and sensitivity, both at 0.779, with a specificity of 0.829. The ensemble (Sum) method achieved an average accuracy of 0.989, sensitivity of 0.989, and specificity of approximately 0.988. Therefore, MobileNetV2 not only delivers the highest accuracy and stability but also demonstrates that the choice of ensemble method significantly affects the results.

Management practitioners often assume that raises in the contractual absolute maximum bandwidth of Internet connections increase the attitudinal bonds of customers with their access provider. The present study challenges this perspective: In a sample of up to 752 German-speaking Internet users with a wireline Internet access at home, it explores two relative speed characteristics of broadband connections (customer quality experiences in terms of delivered transmission speeds (1) relative to the maximum down- and upload bandwidths as per customer contract, and (2) relative to speeds emphasized in advertisements of a consumer’s access provider) as potential determinants of customer affinity with an access provider. Furthermore, correlations are analyzed between the frequency with which customers use speed tests to check the quality of their wireline Internet connection and their attitudinal bonds with their access provider. Multivariate regression analysis reveals that the impression that advertised wireline access bandwidths are exaggerated compared to the speeds actually delivered and – to a lesser extent – the perception that the contractual maximum speed is reached only in a low share of a customer’s Internet sessions significantly decrease customer satis-faction and significantly increase customer inclination to churn. The effects of the two speed assessments are not independent of one another: Perceived speed exaggerations in a provider’s ads impair customer bonds less (more) strongly if the experienced share of Internet sessions with speeds below the contractual maximum bandwidth is high (low). More frequent speed testing reduces customer bonding, especially in case that advertised speeds of a customer’s provider are experienced as overblown.

The firms and the industries still using the ways and means of promotion and publicizing the products have still not achieved the level of success as the modern means of social media is providing in creating a huge demand for their products. This study involves the modern means of communication agility promises success, development, and significance of the product in an efficient manner. Social media plays a vital role in creating a demand for the products by explaining the need of the product and a very effective source of creating a link among industries, firms, management, employees and especially the individual ones who come to know the skills and expertise. The impact of social media in strengthening the relationship with the customer has been very keenly observed and the findings show that such research has detailed that the use of social media has influenced the internal and the external capabilities. The data gathered in this study shows that the firms and the industries using the approach of social media have a revolutionary impact on building and strengthening the relationship between the customer and the management, employees. This study shows a strong bond between a firm and the customer using social media.

Fabric Formworks which are made using textile sheets such as Polyolefin, Polyesters/Polyethylene Terephthalate, nylon and Polypropylene are being used instead of conventional formworks in the construction industry. This article summarises significant studies and provides an updated review of references on the structural performance of fabric formworks over the last decade. The survey showed that they could be categorised into seven themes; namely, “Feasibility studies and manufacturing methods of complex structural elements”; “Finite element modelling, structural optimizing and form-finding themes”; “Pneumatic / Vacuumatic formwork theme”; “Durability, sustainability, efficiency and quality improvement”; “Cable-net fabric formwork theme”; “Stay-in-place structural formworks theme”; and “Review papers”. These categories comprise about 25%, 17%, 15%, 15%, 10%, 6% and 6% of related studies respectively.