Abstract: Shipping is urgently exploring alternative vessel energy sources across a wide range of options - from other fossil fuels to renewables - with a view to more sustainable ship propulsion. Based on processing of publicly available data, the authors discuss the prospects of the supply chains for 16 vessel power sources alternative to oil, comparing descriptive statistics across respective fuel supply chain Key Performance Indicators (KPIs) to evaluate potentiality along with hidden vulnerabilities. While finding marked dif-ferences across calculated mean, standard deviation and coefficient of variation values, the authors do not preclude the development of parallel ship fuel supply chains, unlike the case of previous fuel transitions in shipping. To support this scenario, already forming in practice, they emphasize the enabling attributes of today’s world fleet in terms of total capacity and of size of each of the main shipping sectors which could eventually sustain nowadays multiple fuel supply chains. Concluding on limitations and challenges that such an energy-source multitude can create, the authors underline the need to consider in the Life-Cycle Assessment (LCA) of shipping fuels their total impact, including necessary ship hardware changes for a more thorough assessment of fuels’ impact across the entire shipping services’ supply chain.

Abstract: Evaluating national climate policy performance requires frameworks that integrate multiple dimensions while accommodating diverse development pathways. This study develops a Multi-Attribute Utility Theory (MAUT) framework to construct a Climate Policy Performance Index (CPPI) for 187 countries. The index integrates four dimensions—mitigation, adaptation, economic capacity, and governance—using explicit utility functions and policy-aligned weights derived from climate policy priorities. Data are drawn from the Global Carbon Project, ND-GAIN Country Index, and World Bank indicators. Results reveal substantial cross-national heterogeneity, with CPPI scores ranging from 33.67 (Turkmenistan) to 78.46 (Norway). Nordic countries lead with balanced excellence across dimensions, while alternative high-performance pathways emerge through mitigation leadership (Uruguay, Costa Rica) or governance-economy strength (Singapore). Regional analysis identifies Europe as the top-performing region (mean = 59.92), whereas Sub-Saharan Africa achieves unexpectedly high rankings despite low emissions, owing to weak institutional capacity. The relationship between income and climate performance is non-monotonic: lower-middle-income countries achieve comparable aggregate scores to high-income nations, with near-perfect mitigation performance compensating for weaker governance. Sensitivity analysis shows that ranking robustness is comparable across equal, adaptation-focused, and multiplicative weighting schemes (Spearman's ρ > 0.83), whereas mitigation-focused weights yield substantially different orderings (ρ = 0.47). The CPPI correlates moderately with ND-GAIN (r = 0.40) and weakly negatively with CO₂ per capita (r = −0.28), indicating the framework captures distinct aspects of climate policy performance. The proposed methodology advances beyond existing indices by providing axiomatic foundations, transparent utility specifications, and comprehensive sensitivity analysis, offering a theoretically grounded tool for cross-national climate policy evaluation.

Abstract: This study examines how artificial intelligence (AI) contributes to contemporary processes of authenticity evaluation by functioning as a multimodal diagnostic cue in consumer decision-making. Drawing on survey data collected from 468 visitors at Terra Madre Salone del Gusto in Turin, Italy, the study tests a structural model comprising five latent constructs: Authenticity Trust, Perceived AI Usefulness and Diagnosticity, Multimodal Value, User Engagement, and Behavioural Intentions. The findings indicate that heritage-based and institutional authenticity cues remain foundational in consumers’ evaluations, but are increasingly interpreted and conditionally reinforced through interaction with AI-mediated information perceived as credible and diagnostically informative. Multimodal inputs—particularly the integration of textual, visual, and auditory narratives—are associated with richer authenticity perceptions and higher levels of user engagement. Experiential enjoyment during interaction with the AI system is positively related to intentions to adopt AI-supported evaluation tools, while behavioural intentions also encompass a willingness to pay a premium for products confirmed as authentic. Although the use of a convenience sample limits generalisability, the results highlight the broader potential of multimodal AI systems to reduce evaluative uncertainty and support trust formation in complex cultural and consumer environments. Conceptually, the study advances the notion of augmented authenticity, defined as a hybrid evaluative process in which tradition-based trust mechanisms are dynamically interpreted and reinforced through perceived AI diagnosticity and multimodal coherence. By situating AI within culturally embedded processes of meaning-making rather than purely instrumental evaluation, the findings contribute to interdisciplinary debates on technology-mediated trust, consumer judgement, and the societal implications of AI-assisted decision-making.

Abstract: Distributed projects spanning time zones and cultures strain communication, coordination, and control, demanding project management practices that explicitly govern stakeholder alignment, information flow, and decision cadence. This paper synthesizes evidence on how iterative delivery rituals can be embedded within PM governance—linking standups, sprint reviews, and retrospectives to communication plans, risk registers, change control, and visibility dashboards—to raise predictability in global initiatives. A practical framework maps collaboration tooling (e.g., video, messaging, shared wikis) to specific PM objectives, while outlining mitigations for language barriers, cultural divergence, and trust deficits common to dispersed teams. Reported benefits include clearer requirements, faster feedback cycles, improved knowledge sharing, and higher transparency, counterbalanced by recurring risks such as time-zone friction, uneven tool access, and coordination overheads, with checklists provided for PMOs to operationalize at scale. The contribution equips project planners and delivery leads with actionable playbooks to achieve scope, schedule, and quality targets under high uncertainty—without relying on co-location.

Abstract: This paper proposes and empirically validates the Procurement Literacy Capability Theory (PLCT), advancing a capability-based explanation of how ethical behavioural intention is formed in public procurement. Departing from dominant compli-ance-orientated perspectives, PLCT conceptualises procurement literacy as an inter-connected set of competencies that develop sequentially and translate into ethical readiness rather than direct rule following. Using survey data from 776 undergraduate procurement students in Ghana, the study applies structural equation modelling to test the core propositions of the theory with a fully validated multidimensional procure-ment literacy instrument. The results provide strong empirical support for PLCT. The procurement literacy di-mensions operate as a structured capability system, not as isolated skills. Digital pro-curement literacy significantly predicts planning and decision-making capability, which in turn predicts supplier and contract management literacy. Supplier management ca-pability exerts a significant effect on ethical procurement practice literacy, which emerges as the strongest direct predictor of ethical behavioural intention. Legal and policy knowledge literacy shows no significant direct effect on ethical intention but op-erates indirectly through ethical procurement practice capability, confirming full medi-ation. The bootstrapped indirect effects were statistically significant, supporting the sequential and interdependent assumptions of the theory. By proposing and validating PLCT, this study contributes a new theoretical lens to un-derstanding the formation of ethical behaviour in procurement. The findings suggest that ethics in procurement is shaped by capability development and practical compe-tence rather than rule awareness alone, with important implications for procurement education and professional training.

Abstract:

Since the start of the Conservation Reserve Program (CRP) in 1985, the US farmers has participated in the program with offering a portion of their environmentally sensitive lands to the program in exchange of annual rental payments. However, recent declining enrollments in the programs have raised concerns regarding its spatial relevance to environmental needs and economic status and incentives in participating regions. Therefore, this study explores the CRP participation and its drivers across regions to understand any spatial patterns that may exist. To do so, this research employs a combination of spatial analyses, named as exploratory spatial data analysis (ESDA). Incorporating CRP participation rates and three contributing factors to the program, including CRP rental rate, soil erosion on cultivated farmlands, and farm income per acre, the approach applies Global Moran’s I, Univariate Local Indicators of Spatial Association (LISA), and Bivariate LISA (BiLISA) to answer the research questions. To validate the methodological framework, the study applies it to the Midwestern US counties which are one of the main contributors to the program. The results revealed significant spatial clustering for the variables and regional heterogeneity in CRP participation, implying that a uniform, nationwide policy design may not adequately address local environmental and economic conditions. Additionally, spatial mismatches for counties with high soil erosion risk and offered with strong rental incentives may not consistently achieve higher participation, implying inefficiencies in current CRP targeting and offer-selection mechanisms. Overall, the results support a shift toward a more data-driven, spatially informed decision-making process when it comes to strategizing CRP implementation.

Abstract: This study investigates the sustainability, resilience, and institutional performance of urban governance systems by operationalizing key thermodynamic principles energy, exergy, entropy, equilibrium, open systems, and irreversibility within a political and behavioral systems framework. Urban political systems are conceptualized as open, non-equilibrium systems, characterized by continuous flows of resources, information, and institutional feedback across metropolitan governance structures. Within this model, energy represents systemic inputs to urban governance, exergy denotes usable governing capacity at the city and metropolitan scale, and entropy reflects levels of institutional disorder, inefficiency, and systemic degradation affecting urban sustainability. The study first formulates a conceptual analytical model defining urban political entropy and systemic exergy as measurable variables associated with institutional stability, crisis-management capability, adaptability, and reform potential in urban and metropolitan governance. It then conducts a comparative empirical analysis of Germany, Türkiye, China, and South Africa using normalized indicators derived from international datasets for 2023, with particular attention to their implications for urban governance capacity and city-level institutional performance. These indicators are employed to construct proxy measures for the Exergy Efficiency Ratio, Societal and Institutional Entropy, and overall urban governance capacity. The comparative results reveal that open and decentralized governance systems tend to maintain higher exergy efficiency and lower entropy levels at the urban scale, whereas highly centralized systems, although effective in resource mobilization, tend to accumulate greater systemic entropy over time. Transitional governance systems exhibit hybrid and fluctuating thermodynamic characteristics in their urban institutional structures. The findings empirically support the Thermodynamic Model of Political Systems and demonstrate its utility as a predictive and diagnostic framework for evaluating urban institutional efficiency, resilience, and sustainability. By quantifying political energy flows and entropy dynamics within urban governance systems, this study contributes to the development of an integrated systems thermodynamics of cities and provides a robust analytical foundation for sustainable urban governance, institutional reform, and long-term strategic policy design.

Abstract: Despite substantial growth in eco-innovation (EI) research, most studies rely on cross-sectional data, limiting understanding of the temporal dynamics of EI and its determinants under varying macroeconomic conditions. This study addresses this gap by analysing panel data from Spanish manufacturing firms across three phases of the business cycle: pre-crisis expansion (2004–2007), the global financial crisis (2008–2013), and recovery (2014–2016). We investigate the drivers of two distinct types of eco-innovation: efficiency EI (energy and material savings) and environmental EI (reducing environmental harm), focusing on the role of regulation, institutional interventions, and firm-level innovation capacities. Using a random-effects panel probit model that accounts for unobserved firm heterogeneity, we examine how these drivers operate across different macroeconomic contexts. Our findings reveal that regulation consistently fosters EI, while the influence of subsidies, R&D capacity, and collaborative networks is more context-dependent, particularly during economic downturns. The results highlight the cumulative, path-dependent, and cyclical nature of eco-innovation, providing novel insights into the conditions that enable firms to sustain green innovation over time.

Abstract: The sustainability of the beef and dairy industry requires a systems approach that in-tegrates environmental stewardship, economic viability, and social responsibility. Over the past two decades, global genetic consortia have advanced data-driven germplasm programs to enhance sustainability across cattle systems. These initiatives employ multi-trait selection indices aligned with consumer demands and supply chain trends, targeting production, longevity, health, and reproduction which include greenhouse gas mitigation, improved resource efficiency, operational safety, and optimized animal welfare as outcomes. This study analyzes strategic initiatives, germplasm portfolios, and data platforms from leading genetics companies in the USA, Europe, and Brazil. U.S. programs combine genomic selection with reproductive technologies such as in-vitro fertilization and sexed semen to accelerate genetic progress. European efforts emphasize resource efficiency, welfare, and environmental impact, while Brazilian strategies focus on adaptability to tropical conditions, heat tolerance, and disease re-sistance. Furthermore, mathematical models and decision-support tools are increas-ingly used to balance profitability with environmental goals, reducing sustainability trade-offs through data-driven resource allocation. Industry wide collaboration among stakeholders and regulatory bodies underscores a rapid shift toward sustainabil-ity-oriented cattle management strategies, positioning genetics and technology as key drivers of long-term resilience.

Abstract:

The research focused on Sustainable Development Goal 8, which promotes decent work and economic growth, by studying theories related to determining the profile of online shoppers. The overall objective was to determine the characteristics of the digital consumer profile and the segments to which digital customers of optical stores in Chimbote belong in 2025. The research was applied, with a quantitative approach, a non-experimental design, and a descriptive-correlational level. The population consisted of 1,800 customers from 2024, with a sample of 317 customers. Simple random sampling was used to obtain data through a survey. The results showed the existence of five segments based on the profiles found: exclusive aesthetics, natural aesthetics, whimsical aesthetics, practical naturals, and traditional naturals. This was corroborated by the hypothesis test, where the resulting p-value of 0.018 was less than 0.05, confirming the existence of digital consumer profile characteristics according to the segments to which the digital customer belongs. In conclusion, the data obtained made it possible to determine the main characteristics that define the profiles of digital consumers in the optical sector of Chimbote.

Abstract: Although AI is widely believed to have transformative potential in organizations, recent reports reveal that many organizations are grappling with value derivation therefrom and the ability to take ownership of due ethical and regulatory demands, among other responsible uses of technology. Our goal is to examine these challenges with a view to proposing an approach to effective AI adoption by organizations and pave the way for further impact studies. As a first step, we reviewed and clarified these challenges, categorizing them into Weak or Non-Existent Strategy, Poor Data Readiness and Privacy Concerns, Inadequate Integration with Existing Technology Stack, Inadequate Human Knowledge Skills and Attitudes/Abilities, Scalable and Secure Infrastructure Challenges, Ethical Governance Concerns, Regulatory Framework Lag, Responsibility and Accountability Concerns as well as Reliability Concerns. Next, we carried out a thematic review of constituent AI technology innovation concepts and tools that have adoption potential in organizations vis-à-vis Enterprise Resource Planning (ERP). In the light of these reviews, we used inductive reasoning to propose an approach to AI adoption and create a tool (OAAD) that exemplifies our recommendations, and which could facilitate well-informed adoption and real-life impact research. To set a compass for our effective adoption approach proposal, we expanded on Yang et al. (2024) and defined organizational AI readiness as the organization’s capacity and disposition to deploy and use AI technology tools in ethical, responsible and accountable ways that add value to the organization. Finally, we make some recommendations for progressive impact studies in line with our proposed adoption experimentation.

Abstract: This study examines the working capital management efficiency of Türkiye’s forestry and logging sector over the 2009–2024 period using the index method developed by Bhattacharya (1997). The analysis utilizes sector balance sheet and income statement data published by the Central Bank of the Republic of Türkiye. Performance, utilization, and efficiency indexes were calculated to assess both the effectiveness of investments in current asset subcomponents and the degree to which these assets support sales growth. Results indicate that, despite fluctuations observed during certain years, the average values of all three indices for micro-scale, small-scale, and overall sectoral groups exceeded 1 over the study period, suggesting that enterprises in this sector generally managed their working capital efficiently. The highest index levels were reached in 2022, largely driven by a sharp increase in net sales relative to current assets. A comparison with existing research on wood products, paper, and furniture manufacturing sectors demonstrates that the forestry and logging sector exhibits relatively higher working capital efficiency, suggesting a stronger capability to maintain liquidity and support operational performance under changing economic conditions. Given the scarcity of prior research applying the index method to this sector, the study contributes new empirical evidence and demonstrates the suitability of index-based efficiency measurement for sector-level financial data. The results also suggest several implications: enterprises should reinforce cash-flow forecasting, improve monitoring of current asset subcomponents, and adopt scale-appropriate working capital policies. Policymakers may consider supporting micro- and small-scale companies through financial training initiatives and improved access to short-term credit instruments. Future research could incorporate firm-level datasets, expand coverage to medium and large enterprises, and apply alternative efficiency techniques to further validate and extend the findings.

Abstract: : Marine ecotourism and SME digitalization are increasingly seen as key drivers for coastal community welfare, yet their combined impact, particularly through local economic empowerment, remains underexplored. This study addresses this gap by proposing an integrative model to examine how marine ecotourism and SME digitalization influence local community welfare, mediated by SME empowerment, and moderated by government support. A quantitative, cross-sectional survey was conducted with 312 marine tourism entrepreneurs in North Minahasa, Indonesia, and data were analyzed using Partial Least Squares Structural Equation Modeling. The results show that ME and SD have a significant positive effect on SE and LCW. However, ME and SD were found to be insignificant on LCW. Crucially, SE fully mediates the relationship between both ME and SD on LCW, indicating that empowerment is the primary mechanism for welfare improvement. Furthermore, GS was found to significantly strengthen the positive relationship between SE and LCW. This study concludes that empowering local SMEs is the critical bridge for transforming ecotourism and digitalization into tangible community welfare, and this process is significantly amplified by a supportive institutional environment provided by the government.

Abstract:

This study utilized Scopus to examine patterns in scientific publications, co-authorship, institutional contributions, and shifts in the discipline's primary themes. Analytical techniques such as VOSviewer and Bibliometric have been employed to identify indicators of success and examine the structure of collaborative networks and word matching. They demonstrate that scientific research continues to improve. Contemporary research topics focus on utilizing renewable energy and ensuring environmental protection. Significant efforts are directed towards environmental policies, hybrid designs incorporating green energy, solar energy systems, and eco-friendly business practices. The collaboration network of co-authors highlights the partnership between leading Algerian institutions and experts. Additionally, international researchers are being brought together to collaborate in innovative ways. Although there has been considerable progress, much of the research in this area remains technology-centric. Taxation policies, green finance, closed-loop economies, and governmental procedures have yet to be fully integrated. The study tells us more about what scientists in Algeria know about sustainability and diversity. It also finds areas where scientists from different fields can work together better and suggests future research and policy initiatives that will help the country move towards a more sustainable and diverse economy.

Abstract: Smart farming has established itself as a strategic field in the digital and sustainable transformation of the agri-food sector. The rise of technologies such as the Internet of Things (IoT), artificial intelligence (AI), machine learning, big data, and blockchain has revolutionized production systems, improving efficiency, sustainability, and adaptability to climate change. In this context, scientific research on smart farming has grown exponentially, becoming a key axis for the fulfillment of the Sustainable Development Goals (SDGs). The objective of this study was to analyze the evolution, structure, and impact of scientific production in smart farming, identifying its main trends, authors, journals, and contributions to the SDGs. To this end, a bibliometric analysis was applied to 1,580 articles indexed in the Web of Science (WoS) database, using productivity, citation, and impact indicators based on Price's, Lotka's, Bradford's, and Zipf's laws, as well as the Hirsch index. The results reveal important growth in scientific production between 2014 and 2024, with a strong concentration in high-impact journals and international collaboration networks. In conclusion, smart farming represents an engine of innovation and sustainability, integrating science, technology, and digital management to address the global challenges of food security, climate change, and sustainable development.

Abstract: This study explores what drives housing prices in Ames, Iowa by looking at both the usual structural and spatial characteristics of homes and a set of new variables engineered from the original dataset.Three newly created variables including the percentage of finishing living area, the proportion of basement area to total living space, and years since last remodel are used to enhance interpretability and predictive power. In this paper, I investigate the effectiveness of engineered variables with traditional predictors over five supervised learning models including Linear Regression, Ridge Regression, Lasso Regression, Random Forest, and XGBoost. Model performance was assessed quantitatively using RMSE, R², correlation coefficients, and SHAP-based interpretability analyses. All results show that engineered features consistently improved predictive accuracy across all models as extra values but not in dominant effects. SHAP analysis further reveals that while traditional predictors remain highly influential, engineered features offer additional explanatory depth by capturing some obvious structural and temporal patterns.

Abstract: This study presents a data-driven framework for segmenting customers in the highly competitive Korean credit card market using a large-scale, anonymized dataset from a leading issuer. We applied a systematic feature reduction process, reducing an initial set of 565 variables to 138 informative attributes. Principal Component Analysis was then employed to transform these features into three interpretable dimensions: Spending Volume, Credit & Loan Dependency, and Membership & Credit. We evaluated multiple clustering algorithms, including K-means, Hierarchical Clustering, and Self-Organizing Maps, finding that K-means clustering with three segments provided the highest internal validity and clearest interpretability along the value-risk axes. The analysis identified three distinct customer segments: (1) High-Value, Low-Risk Customers characterized by high spending and stable repayment; (2) Low-Value, Low-Risk Customers, representing the largest, most conservative segment; and (3) High-Risk Customers, who exhibit active spending but a high dependency on loans and installments, coupled with a higher delinquency rate yet long membership tenure. Our findings provide actionable managerial implications for differentiated strategies in value creation, customer activation, and risk-aware relationship management. To the best of our knowledge, this is the first empirical study to segment customers using actual behavioral data from the Korean credit card industry, offering a practical model for precision marketing and risk management in the digital finance era.

Abstract:

Women's entrepreneurship drives inclusive economic development and creates positive ripple effects throughout society. This study investigates the effects of entrepreneurship education in sustainability on female students, with particular emphasis on determining whether changes in entrepreneurial intentions were driven by the changes of the factors of the Theory of Planned Behavior. We employ a comparative framework with male students to contextualize our findings. The survey employed a pre-test/post-test group design (before and after the entrepreneurship course). The sample consisted of 271 business students from a Greek university (157 female students, 114 male students). After the course, women indicated positive changes in attitude, perceived behavioral control and entrepreneurial intention. MEMORE macro revealed that both the positive changes in attitude and perceived behavioral control affected the positive change in entrepreneurial intention. Conversely, men indicated only positive effect in perceived behavioral control. Notably, the levels of the attitude, perceived behavioral control and entrepreneurial intention in women before the course were much lower than men. These findings underscore the importance of entrepreneurship education in sustainability, as a tool with a transformative force in the positive impacts in women's entrepreneurship and gender equity that leads to sustainable growth.

Abstract: This research investigates the impact of the digital economy on high-quality agricultural development, specifically its impact on Agricultural Green Total Factor Productivity (AGTFP). The research combines Dynamic Qualitative Comparative Analysis (QCA) and Necessary Condition Analysis (NCA) methods on data from 31 provinces in China between 2011 and 2023. The study produces the following results, (1) A single element of the digital economy is not a necessary condition for improving AGTFP. The driving effect relies on the interactions of multiple elements. Configuration analysis indicates viable driving paths to AGTFP, resulting in four effective driving paths, including financial-government dual-driver model path, infrastructure-government dual-driver model path，financial-resource dual-driver model path and industry-led driver model path. (2) The paths to achieving high-quality agricultural development vary among different regions. The eastern region places more emphasis on the integration of finance and policy, while the central and western regions stress the synergy between infrastructure and the government. (3) The pathways are stable with respect to time. The most notable realization is that digital finance pathways show higher overall constancy for the years of study and have a temporal stability of greater than 0.85 in most years. This study combines the TOE framework with configuration analysis, expanding the theoretical framework of agricultural digitalization, revealing the key paths for the digital economy to promote the development of green agriculture, and providing empirical evidence for formulating differentiated digital agriculture policies.

Abstract: European policy promotes a "triple transition”, integrating digital innovation, ecological sustainability (green policy goals), and social inclusion in development initiatives. Cultural and creative industries (‘CCIs’) can be pivotal in this process, given their societal role beyond the production of products and services and their ability to shape responses to ubiquitous challenges. The objective of this study is examining how institutional mandates interact with organic innovation dynamics in the CCIs regarding the simultaneous integration of all three policy pillars in creative projects. We use data on 5,601 initiatives from the EU's Creative Europe program (2013-2024) as a natural experiment. As of 2021, Creative Europe’s calls for proposals have begun suggesting the inclusion of all three pillars of the triple transition in funded creative projects. This policy shift enables the comparison of pre- and post-mandate trends. Results reveal an intrinsic upward trajectory in projects with simultaneous digital, green and social goals (i.e. ‘triple-pillar’ projects), even before the shift. This pattern persisted after 2021 as well. However, the mandate substituted for other catalysts like international collaboration. Pre-2021, multi-country partnerships significantly predicted triple-focus within projects. Post-2021 however, this link vanished as even local projects complied with Creative Europe’s suggestions. Instead, larger project budgets and grants emerged as key enablers, indicating a trade-off in cost efficiency. Mandated comprehensiveness required greater resources for implementation. Our findings therefore underscore that policy can reinforce bottom-up creativity. However, it reshapes processes, potentially burdening smaller actors. To maximise policy impact, mandates should pair with funding support and flexibility.