Search Results (10,257)

In today’s rapidly evolving global landscape, the need to cultivate innovation-ready, globally competent talent has become a strategic imperative. This study critically investigates how sustainable open innovation strategies—particularly within non-profit organizations and university alliances—can serve as a catalyst for global talent development. Responding to the growing demand for interdisciplinary, cross-sectoral collaboration, the research employs a robust mixed-methods approach, integrating the Analytic Hierarchy Process (AHP) and Fuzzy Analytic Hierarchy Process (FAHP) to evaluate and prioritize key strategic factors. The findings reveal that initiatives such as international internship programs, operational funding mechanisms, joint research ventures, and technology transfer are essential drivers in creating environments that nurture and scale global talent. Building on these insights, this study introduces a structured, sustainable innovation model that categorizes strategies into three tiers—collaborative, interactive, and foundational service-oriented actions—providing a practical roadmap for resource optimization and strategic planning. More than a theoretical exercise, this research offers actionable guidance for non-profit leaders, academic administrators, and corporate partners. It highlights the reciprocal value of multi-sector collaboration and contributes to a broader understanding of how mission-driven innovation ecosystems can foster resilient, future-ready workforces. By positioning non-profit–academic partnerships at the center of global talent strategies, the study sets a foundation for rethinking how institutions can co-create value in addressing pressing global challenges. Full article

This study examines the impact that fat-tailed distributions of the spread residuals have on the optimal orders for pairs trading of stocks and cryptocurrencies. Using daily data from selected pairs, the spread dynamics has been modeled through a mean-reverting Ornstein–Uhlenbeck process and investigates how deviations from normality affect strategy design and profitability. Specifically, we compared four fat-tailed distributions—Lévy stable, generalized hyperbolic, Johnson’s S_U, and non-centered Student’s t—and showed how they modify optimal entry and exit thresholds, trading frequency, and performance metrics. The main findings reveal that the proposed pairs trading strategy correctly captures some key stylized facts of residual spreads such as large jumps, skewness, and excess Kurtosis. Interestingly, we considered regime-switching behaviors to account for structural changes in market dynamics, providing empirical evidence that optimal trading rules are regime-dependent and significantly influenced by the residual distribution’s tail behavior. Unlike conventional approaches, we optimized the entry signal and link heavy tails not only to volatility clustering but also to the nonlinearity in switching regimes. These findings suggest the need to account for distributional properties and dynamic regimes when designing robust pairs trading strategies, providing a more realistic and effective framework of these strategies in highly volatile and non-normal markets. Full article

The restaurant industry, especially fine dining, is characterized by intense competition and an increasing number of closures. Wine is a profit lever, but the exploitation of sales potential can present a challenge. As consumers encounter more extensive wine lists, they often find themselves overwhelmed. A restaurant experiment in Stuttgart, Germany, examined strategies to simplify decision-making for customers and their impact on wine purchases and the dining experience. This experiment, conducted at a small fine-dining establishment, aimed to understand how wine descriptions and pairing recommendations influence customer choices and behavior, revealing key insights that wineries and restaurants can leverage to boost wine sales. The findings underscore the power of wine descriptions and strategic pairing recommendations in enhancing customer engagement. They suggest that restaurants can increase their wine sales by integrating well-crafted wine descriptions into menus, while wineries can benefit by providing comprehensive tasting notes and pairing suggestions that align with restaurant offerings. For both sectors, understanding the sensory and psychological factors that shape wine appreciation can offer a competitive edge. Full article

A deep reinforcement learning framework is presented for strategy generation and profit forecasting based on large-scale economic behavior data. By integrating perturbation-based augmentation, backward return estimation, and policy-stabilization mechanisms, the framework facilitates robust modeling and optimization of complex, dynamic behavior sequences. Experimental evaluations on four distinct behavior data subsets indicate that the proposed method achieved consistent performance improvements over representative baseline models across key metrics, including total profit gain, average reward, policy stability, and profit–price correlation. On the sales feedback dataset, the framework achieved a total profit gain of 0.37, an average reward of 4.85, a low-action standard deviation of 0.37, and a correlation score of $R^2=0.91$. In the overall benchmark comparison, the model attained a precision of 0.92 and a recall of 0.89, reflecting reliable strategy response and predictive consistency. These results suggest that the proposed method is capable of effectively handling decision-making scenarios involving sparse feedback, heterogeneous behavior, and temporal volatility, with demonstrable generalization potential and practical relevance. Full article

Dilemmas related to the development of demand for renewable energy encourage continuous evaluation of such investments in various locations, taking into account market and environmental conditions. The conducted study concerns the analysis of the profitability of investment in a 1.65 MW wind turbine with a hub height of 70 m in various zones in Poland. The analysis was performed using the clustering method (cluster analysis and the Czekanowski diagram). Computer simulation was also used using the Hybrid Optimization of Multiple Energy Resources (HOMER), ver. x64 3.18.4 software. As a result, three zones were distinguished that ensure differentiation in the rates of return on investment in wind energy. The authors positively verified the hypothesis about the spatial differentiation of profitability in relation to the examined factors. The justification for investments in wind farms was demonstrated and factors determining their profitability were indicated. It was emphasized that, in the case of wind farms, energy production is relatively predictable, which shapes the benefits for investors, and facilitates financial planning and long-term return on investment. Full article

The increasing adoption of Renewable Energy Sources (RESs), due to international energy policies mainly related to the decarbonization of electricity production, raises several operating issues for power systems, which need “flexibility” in order to guarantee reliable and secure operation. RESs can be considered examples of Distributed Energy Resources (DERs), which are typically electric power generators connected to distribution networks, including photovoltaic and wind systems, fuel cells, micro-turbines, etc., as well as energy storage systems. In this case, improved operation of power systems can be achieved through coordinated control of groups of DERs by “aggregators”, who also offer a “flexibility service” to power systems that need to be appropriately remunerated according to market rules. The implementation of the aggregator function requires the development of tools to optimally operate, control, and dispatch the DERs to define their overall flexibility as a “market product” in the form of bids. The contribution of the present paper in this field is to propose a new optimization strategy for flexibility bidding to maximize the profit of the aggregator in flexibility markets. The proposed optimal scheduling procedure accounts for important practical and technical aspects related to the DERs’ operation and their flexibility estimation. A case study is also presented and discussed to demonstrate the validity of the method; the results clearly highlight the efficacy of the proposed approach, showing a profit increase of 10% in comparison with the base case without the use of the proposed methodology. It is evident that quantitatively more significant results can be obtained when larger aggregations (more participants) are considered. Full article

The study constructed a supply chain inventory model for sellers and buyers that integrates payment-time-dependent demand, product defects, misclassification risks, and carbon emission tax considerations. The model was designed to optimize payment time, replenishment time, and order quantities to maximize the seller’s profit per unit time. Theoretical analysis showed that profit exhibited joint concavity with respect to both payment time and replenishment time. An algorithm was also formulated to derive optimal solutions. Finally, numerical experiments and sensitivity analyses validated the model and offered practical insights for managing inventories involving imperfect products. Results indicated that higher responsiveness of demand to payment timing, greater demand coefficients, better product prices, and higher scrap values led to increased seller profits, while greater misclassification, credit default risks, and carbon tax rate reduced it. These insights help decision-makers select suitable parameter values for efficient operations. Full article

Stock returns are a critical aspect of investment decisions, and understanding whether climate change commitment influences stock returns is essential for informed decision-making. This paper investigates the impact of climate change commitment on the stock returns in the GCC countries for non-financial companies during the period of 2010–2023. The sample consisted of a total of 285 companies collected using the Refinitiv Eikon platform. The developed model was estimated using panel GMM regression. The results suggested that when companies reported their climate change commitment, it was appreciated by high demand for their stock and in turn caused the stock return to be higher. In addition, profitability and growth affect stock returns significantly positively, and this implies that investors concentrate on whether the company has higher profits and better growth prospects to demand more shares, and this affects the share prices positively. In addition, the size of a company has been found to affect the stock return positively, and this suggests that investors in the GCC feel confident in demanding the shares of large companies. Moreover, the results showed that leverage significantly negatively affects stock return, and this implies that investors interpret the position of highly leveraged companies to be bad due to worries that companies will not be able to service their loans. Such results might help investors to formulate their investment strategies and select their shares based on significant determinants. Finally, our results hold based on the reported robustness of results. Full article

This study investigates the impact of financial technology (Fintech) on bank stability in the Middle East and North Africa (MENA). Utilizing panel data from 94 banks in 10 countries over a 13-year period from 2011 to 2023, this research employs panel GMM regression to examine the relationship between the level of Fintech adoption, as measured by the Fintech index, and a bank’s stability. This paper controls for bank characteristics (efficiency, profitability, size, liquidity risk, and dividend payout ratio) and macroeconomic variables (GDP growth and inflation). The Fintech index is calculated using data text mining from the banks’ annual reports. This research contributes to the existing literature by providing empirical evidence of the positive effects of Fintech adoption in the MENA banking sector. The positive findings underscore the transformative impact of Fintech on banking stability, highlighting the importance of technological integration in MENA’s financial institutions for growth, stability, and effective strategies. The robustness of the results regression confirmed that our findings hold. Full article

Today, businesses operate to maintain their assets sustainably and profitably, but they face resource scarcity and environmental problems. In order to overcome this problem, businesses should focus on environmental, social, and economic development while considering the environment. In this context, green human resource practices and sustainable leadership, which have emerged from green management philosophy, increase the environmental awareness of businesses and support them in protecting resources while also positively affecting many human resource metrics, such as performance and commitment at the business level. In recent years, definitions have been established, and scales have been developed within the scope of green human resource and sustainability leadership research, which has been a subject of interest in the relevant literature. Because studies measuring the effects of green human resource management (HRM) and sustainable leadership at the business level are rare, this work is important as it provides resources for further research. In this context, this research aimed to analyze the effects of green human resource management practices and sustainable leadership on employee commitment. In order to reach a general judgment about a system consisting of a large number of people and find answers to the research questions, the general screening model, which allows for single or relational screenings over the entire system or a group of samples to be taken from it, was selected. The correlational model, a type of quantitative research model, was used to examine the relationships between the variables within the scope of this research. According to the results, the effects of green HRM practices on organizational commitment change significantly through the mediation of sustainable leadership. The findings reveal that, for organizations seeking to foster a sustainable business culture, merely implementing green policies is insufficient; leaders must also embody these practices and motivate their workforce. Such an integrated strategy enhances both ecological sustainability and employee commitment, thereby securing a lasting competitive edge. Full article

In order to evaluate the impact of k-means clustering on portfolio optimization, this study groups enterprises based on profitability, liquidity, and solvency indicators. The study confirms the positive correlation between risk, return, and risk-adjusted performance through an analysis of historical financial records. After the companies were divided into two groups, equal-weighted portfolios were created using these groupings. Although they produced higher returns, cluster 1 portfolios, which included more risky companies, also showed more volatility. Cluster 0 portfolios, on the other hand, offered less risk and more consistent results. Portfolios clustered by ROA, OCFM, and GPM outperformed the market benchmark and produced the highest returns adjusted for risk, according to Sharpe Ratio analysis. Furthermore, the study emphasizes that although solvency and liquidity metrics play a role in portfolio selection, increased liquidity does not always translate into improved risk-adjusted performance. In terms of methodology, Silhouette Analysis outperformed the Elbow technique in determining the optimal number of clusters. All things considered, the results show how data-driven clustering techniques may be used to align portfolio strategies to investors’ risk tolerances. Full article

The global energy transition is accelerating, bringing new challenges to power systems. A high penetration of renewable energy increases grid volatility. Virtual power plants (VPPs) address this by dynamically responding to market signals. They integrate renewables, energy storage, and flexible loads. Additionally, they participate in multi-tier markets, including energy, ancillary services, and capacity trading. This study proposes a load factor-based VPP pre-dispatch model for optimal resource allocation. It incorporates the coupling effects of electricity–carbon markets. A Nash negotiation strategy is developed for multi-VPP cooperation. The model uses an accelerated adaptive alternating-direction multiplier method (AA-ADMM) for efficient demand response. The approach balances computational efficiency with privacy protection. Revenue is allocated fairly based on individual contributions. The study uses data from a VPP dispatch center in Shanxi Province. Shanxi has abundant wind and solar resources, necessitating advanced scheduling methods. Cooperative operation boosts profits for three VPPs by CNY 1101, 260, and 823, respectively. The alliance’s total profit rises by CNY 2184. Carbon emissions drop by 31.3% to 8.113 tons, with a CNY 926 gain over independent operation. Post-cooperation, VPP1 and VPP2 see slight emission increases, while VPP3 achieves major reductions. This leads to significant low-carbon benefits. This method proves effective in cutting costs and emissions. It also balances economic and environmental gains while ensuring fair profit distribution. Full article

This study investigates how Environmental, Social, and Governance (ESG) scores and the Corporate Governance Index (CGI) jointly influence firm value in Türkiye. To address the contextual limitations of global ESG metrics, this study incorporates the CGI, a country-specific governance measure developed by Capital Markets Board of Türkiye, as a complementary indicator. Using panel data from 44 non-financial firms listed on the Istanbul Stock Exchange between 2019 and 2023, the study applies a random effects regression model with robust standard errors. The findings indicate that both ESG and CGI scores are positively and significantly associated with firm value, along with profitability (ROA), while financial leverage and liquidity (CR) show negative effects. The results underscore the strategic value of aligning sustainability performance with governance quality, particularly in emerging market contexts. This study contributes to the literature by providing empirical evidence for an integrated ESG–CGI framework and offers practical insights for corporate managers, investors, and policymakers. Full article

This paper examines the electoral cycle and the conduct of the central government’s fiscal policy. It uses a panel database with disaggregated spending and revenue series for 34 middle-income countries over 2000–2022. A dynamic panel approach was used to look at overall government spending and income, and their parts, to find budget patterns during election seasons. The analytical methodology employs the two-step system generalized method of moments to address endogeneity concerns. The dynamic effect captured by the first lag of budgetary indicators suggests that the widening of that indicator is persistent. There is evidence that the current government is opportunistic, which suggests that the electoral cycle affects fiscal performance, especially when it comes to spending on economic matters and taxes on income, profits, and capital gains. Policymakers should be more aware of the government’s opportunistic impact during the electoral period. To keep the budget stable, regulating corruption and having a democratic attitude might lessen the effects of the electoral cycle. Full article

The dynamics of carbon transfer and shifting consumer preferences toward low-carbon products significantly influence firms’ strategic choices and accelerate their transition to greener practices. This study models a secondary supply chain involving a supplier, a high-carbon manufacturer, and a low-carbon manufacturer, analyzing equilibrium outcomes for pricing and profit under two power structures: one where the high-carbon manufacturer holds greater influence, and another where both manufacturers have equal power. Numerical simulations are used to examine how carbon transfer and consumer preferences shape pricing, profitability, and strategic responses across the supply chain. The results show that high-carbon manufacturers with greater market power raise prices to offset the cost of carbon, while those with equal power are more constrained by competition and have to track market dynamics in pricing. Low-carbon manufacturers, more sensitive to consumer preferences, benefit from rising demand, gaining pricing power and sales, while high-carbon manufacturers need to raise prices initially and then gradually reduce them. Although increased carbon transfers offer high-carbon manufacturers greater strategic flexibility, they raise supplier costs and prices for high-carbon products, with limited effect on low-carbon manufacturers. Full article