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.

Abstract: The civil aviation sector has played a crucial role in contributing to Mongolian regional development and promoting tourism. The number of inbound passengers served by national air transport to Mongolia has increased significantly due to the construction of the new airport and the introduction of a visa-free regime for specific countries. Forecasting passenger demand is crucial for estimating air passenger potential, expanding airports, and establishing new terminals. The purpose of this study was to examine the predictors of demand for foreign air passengers to Mongolia and to identify the potential for demand. The demand for air passengers arriving from the top 24 foreign countries between 2015 and 2024 was analyzed using the augmented gravity panel data analysis. The results from the regression analysis of the augmented gravity model showed that the coefficients of variables, including Mongolia's real GDP per capita, the number of foreign passengers arriving for travel, work, and visit purposes, the number of foreign passengers arriving within 30 days and from visa-free countries were statistically significant and positive effects on the demand with expected signs. In contrast, the number of foreign passengers arriving for study purposes and the Covid effect was insignificant.

Abstract: Lifestyle entrepreneurship has emerged as a meaningful form of entrepreneurial activity in rural and low-density territories, where personal values, community attachment, and place identity intertwine with business creation. This study explores how lifestyle entrepreneurs embed themselves within their local contexts and contribute to the revitalisation of rural communities through tourism. Drawing on a qualitative multiple case study of three tourism-based ventures in the Planalto Mirandês (Portugal), the research examines entrepreneurs’ motivations, forms of community involvement, business models, and perceived challenges. The findings reveal that lifestyle entrepreneurs are driven by intrinsic motivations rooted in personal fulfilment, territorial attachment, and social purpose rather than profit maximisation. Their enterprises demonstrate varying degrees of community engagement—from transactional collaborations to transformational partnerships—and generate social, cultural, and environmental value despite structural barriers such as bureaucracy, informality, and seasonality. The study contributes to the literature on lifestyle entrepreneurship and community-based tourism by proposing a conceptual model that integrates individual, relational, and territorial dimensions of embedded entrepreneurship. Practical implications are discussed for policy frameworks aiming to foster sustainable development and resilience in low-density regions.

Abstract: Accurate pharmaceutical demand forecasting is essential to ensure timely drug availabil-ity, minimize waste, and enhance the sustainability of healthcare supply chains. However, existing statistical, machine learning, and deep learning approaches often struggle to capture the nonlinear and dynamic demand patterns arising from drug substitutions, comorbidity treatments, and seasonal disease fluctuations. To address this challenge, we propose KG-GCN-LSTM, a novel hybrid model that integrates a pharmaceutical knowledge graph (KG) with deep learning techniques. The knowledge graph encodes se-mantic relationships among drugs and symptoms, thereby enriching the contextual in-formation for a Graph Convolutional Network (GCN). The outputs of the GCN are subse-quently processed by a Long Short-Term Memory (LSTM) network to capture temporal dynamics in drug demand. Experiments on real-world pharmacy sales data demonstrate that KG-GCN-LSTM consistently outperforms established benchmarks—including ARI-MA, SVR, XGBoost, RNN, CNN-LSTM, and NBEATS, achieving a 3.62% reduction in Symmetric Mean Absolute Percentage Error (SMAPE) relative to NBEATS. These findings underscore the potential of knowledge graph–enhanced deep learning in fostering resili-ent and sustainable pharmaceutical supply chains, thereby improving resource allocation, mitigating shortages, and ultimately enhancing public health outcomes.

Abstract: This article presents the scientific validation of a Management Model for International Cooperation aimed at the development of renewable energy sources in the Cuban electricity sector. The objective of this research is to validate the content of the developed questionnaire, thus obtaining an instrument with 22 activities and five subprocesses for application in the management of international collaboration projects. The methodology used was quantitative, descriptive, and psychometric, with content validity based on expert judgment, employing statistical treatment using the Statistical Software for the Social Sciences (SSPS V.20). It is concluded that the results show high internal consistency of the construct, where Cronbach's alpha values greater than 0.90 are considered optimal in all subprocesses of the model, confirming its relevance and reliability for the strategic management of international renewable energy projects.

Abstract: With worsening energy and environmental issues, new energy vehicles (NEVs) have emerged as the automotive industry’s future that aims to address high energy use and carbon emissions of traditional fuel vehicles. However, NEV research mostly focuses on theoretical analysis due to short industry history, limited data, and incomplete systems, hindering accurate sales prediction. Online reviews now offer a new perspective for forecasting by influencing consumer decisions. Based on consumer behavior and neural network theories, this study reviews relevant literature, selects NEV sales-influencing factors (economy, technology, policy, consumers, with preprocessed crawled online reviews), constructs an index system screened via grey correlation analysis, and establishes four models (GRU, Seq2Seq, Attention-GRU, Attention-Seq2Seq) for training and testing. Results show online reviews, battery output, and public charging piles effectively support NEV sales prediction. The Attention-Seq2Seq model outperforms the other three across all metrics.

Abstract: As global efforts to achieve the Sustainable Development Goals (SDGs) enter a critical phase, there is a growing need for analytical tools that reflect the complexity and heterogeneity of development pathways. This study introduces a probabilistic classification framework designed to uncover latent typologies of national performance across the seventeen Sustainable Development Goals. Unlike traditional ranking systems or composite indices, the proposed method uses raw, standardised goal-level indicators and accounts for both structural variation and classification uncertainty. The model integrates a Bayesian decision tree with penalised spline regressions and includes regional covariates to capture context-sensitive dynamics. Based on publicly available global datasets covering more than 150 countries, the analysis identifies three distinct development profiles: structurally vulnerable systems, transitional configurations, and consolidated performers. Posterior probabilities enable soft classification, highlighting ambiguous or hybrid country profiles that do not fit neatly into a single category. Results reveal both monotonic and non-monotonic indicator behaviours, including saturation effects in infrastructure-related goals and paradoxical patterns in climate performance. This typology-sensitive approach provides a transparent and interpretable alternative to aggregated indices, supporting more differentiated and evidence-based sustainability assessments. The findings provide a practical basis for tailoring national strategies in alignment with structural conditions and the multidimensional nature of sustainable development.

Abstract: Background. African startups operate amid acute capital scarcity and fragmented institutions, which complicates credible quality revelation to investors. Purpose. Focusing on the venture as the unit of analysis, this study investigates how milestones pursued inside university-affiliated accelerators function as signals of venture quality under scarcity, and how entrepreneurial behaviors shape the clarity and credibility of those signals. Design/methodology/approach. We track 17 technology ventures across East- and West-African university accelerators over 185 venture-quarters, combining venture-level panel regressions, event-study analysis, and fuzzy-set QCA. Two composite measures—the Governance-Readiness Index and the Signal-Portfolio Index—capture internal capability building and externally legible signals. Findings. Ventures graduating from university-affiliated accelerators secured roughly three times more equity than matched non-accelerated peers, with heterogeneity explained by milestone attainment and the breadth/strength of signal portfolios. We articulate a signal–noise paradox: effectuation/bricolage behaviors enable survival and progress under constraint yet can appear ambiguous to investors, attenuating signal clarity unless paired with governance readiness and externally validated milestones. Originality/value. The paper elevates signaling theory as the primary lens for early-stage ventures in emerging-market contexts, treats effectuation/bricolage as behavioral mechanisms, and situates staged financing as process logic and triple-helix/institutional-voids as contextual moderators. In doing so, it refines entrepreneurial signaling theory for scarcity contexts and offers actionable diagnostics for accelerators and policymakers designing inclusive, quality-assuring programs.

Abstract: Public concern about environmental issues has led to growing interest in sustainability across various sectors, including entrepreneurship. However, beyond the concern for environmental protection and the preservation of natural resources for future generations, additional conditions are necessary to foster the development of sustainable entrepreneurship. While developed countries provide examples and evidence of the successful implementation of this concept, its application in developing countries presents challenges due to a range of limiting factors. In addition to essential financial support, the literature often highlights the lack and/or complexity of sustainability reporting, the absence of standards and clearly defined sustainability metrics, insufficient regulation, and the lack of support from higher education institutions as barriers to the transition toward sustainable entrepreneurship. This paper aims to examine the feasibility of applying the concept of sustainable entrepreneurship in Western Balkan countries, taking into account the aforementioned constraints. For the purpose of the empirical research, potential limitations were evaluated by managers and business owners in Albania, Bosnia and Herzegovina, North Macedonia, and Serbia. The results of the study answer the question of whether developing countries have the potential to foster sustainable entrepreneurship, given the analyzed constraints, or whether the implementation of this concept is reserved solely for large enterprises and economically advanced countries.

Abstract: We conducted a cross-sectional study using secondary data from 506 neonates with suspected sepsis to assess improvements in turnaround time, bacterial yield, prescription appropriateness, and death rates during 2022 to 2024 at a tertiary military hospital in Accra, Ghana following operational research for the period 2017- 2020. During, 2022-2024, the median turnaround time, was 48 hours for culture-positive cases and five days for culture-negative cases. 28.9% (n=146) of neonates were discharged before culture results were available, including 27 (5.3%) who were later confirmed culture positive. Overall, 15.4% (n=78) were confirmed culture positive, with late-onset sepsis making up 11.7% (n=59). Treatment was changed in 25.4% (n=128) of post-culture cases, with 4.8% (n=24) were culture positive. Gram-positive organisms were predominant 56.5% (n=39), with coagulase-negative Staphylococcus 37.7% (n=26) and Staphylococcus aureus 13.0% (n=9) being the most common; half of these were multidrug-resistant. Gram-negative also made up 43% (n=30), with Klebsiella pneumoniae as the predominant 20.3% (n=14), followed by E coli 10.1% (n=7). High resistance to penicillin were seen among most isolates with 100% of Enterococcus faecalis, 87.5% of S. aureus, and 66.7% of coagulase-negative Staphylococcus. Turnaround time improved in the present study compared to the previous study, culture positivity decreased to 13.6% in 2022-2024 from 29% during 2017-2020, and mortality rates remained about 7% across both periods. Most frequent empirically prescribed antibiotic combinations include gentamicin (4mg/kg/24-36 hourly) and penicillin (50,000units/kg/12 hourly) 63.6% (n=318), penicillin (50,000units/kg/12 hourly) and amikacin (15mg/kg/24 hourly) 8% (n=40), ciprofloxacin (10mg/kg/12 hourly) and piperacillin-tazobactam ( 90mg/kg/8-12 hourly) 7.8% (n=39), and amikacin (15mg/kg/24 hourly) and piperacillin-tazobactam ( 90mg/kg/8-12 hourly) 7.6% (n=38). 4.8% (n=24) out of the culture-positive reports 15.3% (n=77) treatments were changed, while no readmission was recorded after two weeks post-discharge for those discharges without their report. There was a notable decrease in culture positivity rates and a stable mortality rate from 2017 to 2024; significant challenges remain in the management of neonatal sepsis at this military hospital. Reinforcing infection control measures, strengthening antimicrobial stewardship, and maintaining ongoing resistance surveillance are critical to guiding effective empirical therapy and improving neonatal outcomes in this setting.