Search Results (224)

In the context of both domestic and international economic landscapes, regional policy has emerged as an increasingly influential factor shaping the developmental trajectories of Chinese enterprises. Despite its growing significance, the extant literature lacks a comprehensive and systematically visualized synthesis that encapsulates the scope and trends of research in this domain. This study addresses this critical gap by conducting an integrative bibliometric and qualitative review of the academic output related to regional policy and Chinese firm growth. Drawing on a final dataset comprising 3428 validated academic publications—selected from an initial pool of 3604 records retrieved from the Web of Science Core Collection between 1991 and 2022, the research employs a two-stage methodological framework. In the first phase, advanced bibliometric tools, and software applications, including RStudio, Bibliometrix, VOSviewer, and CitNetExplorer, are utilized to implement techniques such as keyword co-occurrence analysis, thematic clustering, and the tracing of thematic evolution over time. These methods facilitate rigorous data cleansing, breakpoint identification, and the visualization of intellectual structures and emerging research patterns. In the second phase, a targeted qualitative review is conducted to evaluate the influence of regional policies on Chinese firms across three critical stages of business development: start-up, expansion, and maturity. The findings reveal that regional policy interventions generally exert a positive influence on firm performance throughout all stages of development. Notably, a significant concentration of citation activity occurred prior to 2017; however, post-2017, the volume of scholarly publications, journal-level impact (as measured by h-index), and author-level influence experienced a marked increase. Among the 3428 analyzed publications, a substantial portion—2259 articles—originated from Chinese academic institutions, highlighting the strong domestic research interest in the subject. Furthermore, since 2015, there has been a discernible shift in keyword co-occurrence trends, with increasing scholarly attention directed towards sustainable development issues, particularly those related to carbon dioxide emissions and green innovation, reflecting evolving policy priorities and environmental imperatives. Full article

The European Union’s Artificial Intelligence Act (EU AI Act) is expected to be a major legal breakthrough in an attempt to tame AI’s negative aspects by setting common rules and obligations for companies active in the EU Single Market. Globally, there is a surge in investments to encourage research, development and innovation in AI that originates both from governments and private firms. The EU recognizes that the new Regulation (EU) 2024/1689 is difficult for start-ups and SMEs to cope with and it announced the release of tools, in the near future, to ease that difficulty. To facilitate the active participation of SMEs in the AI arena, we propose a framework that could assist them to better comply with the challenging EU AI Act during the development life cycle of an AI system. We use the spiral SDLC model and we map its phases and development tasks to the legal provisions of Regulation (EU) 2024/1689. Furthermore, the framework can be used to promote innovation, improve their personnel’s expertise, reduce costs and help the companies avoid the proposed substantial fines described in the Act. Full article

In the context of increasing consumer demand for high-quality, residue-free fruits and the growing emphasis on sustainable postharvest technologies, identifying effective, eco-friendly treatments to maintain grape quality during storage has become a critical focus in modern viticulture. Over the course of this study, we examined the influence of seaweed extract (derived from Ascophyllum nodosum) and ethanol-based postharvest treatments on the postharvest quality of the ‘Tarsus Beyazı’ grape. The seaweed extract was applied at six specific phenological stages according to the BBCH scale: BBCH 13 (3rd–4th leaf stage, 0.40%), BBCH 60 (first flower sheath opening, 0.50%), BBCH 71 (fruit set, 0.50%), BBCH 75 (chickpea-sized berries, 0.50%), BBCH 81 (start of ripening, 0.60%), and BBCH 89 (harvest maturity, 0.60%). After harvest, grape clusters were subjected to four different postharvest treatments: untreated control, control + ethanol (20% ethanol immersion for 10 s), seaweed extract alone (preharvest applications only), and seaweed extract + ethanol (combining both preharvest and postharvest treatments). Grapes were stored at 0–1 °C and 90–95% RH for three weeks, followed by a shelf-life evaluation period of three days at 20 °C and 60–65% RH. The findings revealed that seaweed treatments, especially when applied during cluster formation and berry development, effectively mitigated physiological deterioration, preserving stem turgidity and enhancing berry firmness. In contrast, ethanol showed variable responses, occasionally exerting negative effects, with only marginal benefits observed when applied at optimal developmental stages. Both the type and timing of application emerged as critical determinants of key quality attributes such as weight loss, decay incidence, and must properties (TSS, pH, TA). Correlation and heat map analyses indicated the interrelationships among these parameters and the differential impacts of treatments. These results suggest that phenological-stage-specific seaweed applications hold significant potential as a sustainable strategy to extend the storage life and maintain the market quality of ‘Tarsus Beyazı’ grapes. Full article

Historical monuments enrich lives by connecting people to their community and heritage. They are key to understanding population movements and local development. This study analyzes how the presence of castles classified as National Monuments or of Public Interest can attract tourists, extend their stay, and motivate local authorities to invest in cultural activities, in particular in low-density areas. Heritage and tourism connect through economic, cultural, social, and territorial goals. They boost local economies, preserve cultural traditions, empower communities, and protect landscapes. Thoughtful planning ensures that regional character is maintained and a balance is struck between growth, preservation, equity, and safeguarding for the future. To evaluate the economic and touristic impact of classified castles in Portuguese municipalities, we started by analyzing the ‘Existence of Castle’ variable against three key factors: tourism flows (number of guests and average stay) and municipal expenditure on cultural activities Additionally, we considered other important explanatory variables from alternative studies, including the following: the number of parishes, maximum and minimum altitude, cultural heritage and sports activity expenses, resident population, density, monthly earnings, commercial firms, Tourism Carrying Capacity, tourism revenues, and overnight stays. Our empirical results reveal that medieval castles generate diverse effects on cultural activities managed by municipalities. Quantile regressions demonstrate that municipalities with fewer resources but with a castle invest more in cultural activities, regardless of how expenditure is measured. Additionally, the presence of a castle attracts more tourists and extends their stay, indicating significant potential for economic and tourist development which is not yet fully realized. Full article

Background: This research aims to comprehensively evaluate the influence of firm capabilities, resources, and Artificial Intelligence (AI) on supply chain financing within the Jordanian context. It also analyzes the mediating role of blockchain technology and trust in these relationships. Methods: A conceptual model was utilized to empirically examine the suggested relationships. Data were gathered from a sample of 349 Jordanian start-ups focusing on AI and blockchain technologies via a five-point Likert scale questionnaire. Partial Least Square regression-based Structural Equation Modeling (PLS-SEM) facilitated by SmartPLS4 was used to perform the analysis. Results: The findings reveal that firm capabilities/resources and AI positively impact supply chain financing. Blockchain technology and trust serve as mediators, enhancing the effects of these factors on supply chain financing. Conclusions: The study highlights the role of innovative technologies in improving financial information security and collaboration among supply chain partners. It emphasizes how firm capabilities, resources, and emerging technologies such as AI and blockchain influence supply chain financing as they foster trust and security in financial transactions, offering valuable insights for decision-makers in the supply chain domain. Full article

The successful introduction of new cultivars depends on the evaluation of complex parameters essential for the consumers, market, and fruit producers. A new scab-resistant apple cultivar, ‘Wuranda’ (SQ159/Natyra^®/Magic Star^® × Honeycrisp), recently introduced in Norway and managed under the name Fryd^©, is prone to biennial bearing. Therefore, one of the first tasks, investigated in Southwestern Norway by the Norwegian Institute of Bioeconomy Research, NIBIO-Ullensvang in 2021–2024, was the establishment of optimal crop load level based on the combination of productivity, fruit quality, and return bloom. The apple cultivar Fryd (‘Wuranda’) was propagated on ‘M.9’ rootstock and planted in 2019. The trial was performed in the same orchard for four consecutive years, starting three years after planting. Crop load level affected average fruit mass but had no impact on cv. Fryd fruit quality parameters at harvest such as blush, ground color, firmness, soluble solid content, or starch degradation. Fruit size variation was diminished by crop load regulation, and most fruits fell into 2–3 grading classes. Crop load, not the yield per tree, was the determining factor for the return bloom. The optimal crop load level depended on the orchard age. To guarantee a regular bearing mode of cv. Fryd planted on M.9 rootstock at a 3.5 × 1 m distance and trained as slender spindle, crop load of 5.5–6 fruits cm⁻² TCSA (trunk cross-sectional area) in the 3rd year, 7.5–8 fruits cm⁻² TCSA in the 4th year, and 6.5–7 fruits cm⁻² TCSA in the 5th year should be maintained. Full article

The impact of intellectual capital on green innovation has been extensively studied at the firm level. However, the influence of moderating factors on this dynamic at the national level remains underexplored in previous studies. This study examines the role of institutional quality in moderating the relationship between national intellectual capital and green innovation across seventeen Asia–Pacific economies over the last twenty years, starting from 2000. Various techniques are employed to account for cross-sectional dependence and slope homogeneity in panel data analysis, enabling the examination of this relationship over the long and short term. The study also considers the marginal effects of national intellectual capital on green innovation at different degrees of institutional quality. Overall findings indicate that increasing national intellectual capital and institutional quality increases green innovation. Interestingly, the effects of national intellectual capital on green innovation intensify with a greater degree of institutional quality. We also find that enhancing economic growth and the efficient exploitation of natural resources appear to stimulate green innovation in Asia–Pacific economies. Findings imply that policies to improve green innovation should align with traditional economic growth strategies and effectively leverage intangible resources, particularly national intellectual capital. This unique empirical study examines the moderating role of institutional quality in the national intellectual capital–green innovation nexus in Asia–Pacific economies. Full article

Despite physical infrastructure being known as a critical enabler of entrepreneurship, cycling infrastructure and its role in entrepreneurship remain largely unexplored. However, a well-established cycling infrastructure can support green start-up activity by facilitating connectivity and the exchange of knowledge and ideas without the reliance on carbon-intensive transport, which aligns with their environmental goals. This article studies the relationship between cycling infrastructure and green start-up activity at the regional (NUTS-3) level in Germany and whether this relationship is amplified by the wider entrepreneurial ecosystem (EE). This study is virtually the first to examine how a well-established cycling infrastructure is conducive to start-up activity. With firm-level data from the IAB/ZEW Start-up Panel, multilevel regression models are used to account for differences in green start-up activity across urban, intermediate, and rural regions. The findings show a strong significant and positive relationship between cycling infrastructure and green start-up activity at the regional level, even after including various controls. However, this relationship is not amplified by the wider ecosystem. In the transition towards a Green Economy, policymakers should invest in cycling infrastructure because of its supportive role towards green start-ups. Full article

The research concentrates on determining the degree of internationalization of born global SMEs, believing that some push factors determine internationalization, pull factors, and internal firm-specific factors. Three important factors were found in looking into the causes of internationalization in born global firms: push, pull, and internal firm-specific factors. The study used a survey instrument with a sample of 280 manufacturing-related SMEs chosen from manufacturing clusters in India. A metric called the “index of internationalization” is used to gauge how internationalization in SMEs takes shape. The results demonstrated that internal firm-specific factors influence the internationalization of firms relatively highly compared to push and pull factors. The results unequivocally demonstrate that developing economies have distinct factors that cause internationalization, opening up new avenues for further study. The research aids in the identification of the elements that will enhance early internationalization and tries to draw the attention of young entrepreneurs. This research also helps prioritize the factors responsible for early internationalization. These findings are pertinent for the practitioners and researchers working in this area. This research is helpful for start-ups looking for global opportunities; this research categorizes factors significant in the global journey of the born global firms. Full article

We explore the question of whether entrepreneurial education matters for medium-sized venture performance. We do so to better understand the conflicting evidence indicating that, while such education appears to have significant positive micro-level effects, it has no significant macro-level effects. The growing investment in entrepreneurial education has increased intentions and start-up rates in treatment effects studies but has yet to make an impact on national start-up or survival rates. To address the contradiction, we apply a different empirical approach—one based on the capabilities view—where we survey venture entrepreneurs about their firm’s performance and their education in order to determine whether their skills-enhancing entrepreneurial training is or is not a key driver of success. We find that while that training significantly increases their confidence, when taken as a whole—as a multidimensional measure—that entrepreneurial education does not significantly influence venture performance. We discuss the implications for research, policy, and education. Full article

Businesses are influenced by the cyclical nature of economic development and distinct stages in the corporate life cycle. Accurate early-warning mechanisms are crucial to mitigating bankruptcy risk, enabling timely rescue measures. This article analyses the reliability of various bankruptcy prediction models, including those by Kliestik et al., Poznanski, the modified Zmijewski, Jakubik–Teply, and Virag–Hajdu, across corporate life cycle stages. Reliability was assessed using five metrics: accuracy, balanced accuracy, F1 and F2 scores, and the Matthews correlation coefficient (MCC). The sample included over 5000 SMEs from Central Europe, with financial data from 2022. The findings reveal a U-shaped trend in financial distress risk, with start-ups and declining enterprises facing the highest risks. The results indicate that the Kliestik et al. model shows consistent reliability across all life cycle stages, while the Poznanski model shows more variability. Conversely, the Virag–Hajdu model exhibits significant variability in reliability, with its best performance observed during the Decline stage. The modified Zmijewski and Jakubik–Teply models show lower MCC values overall, with the modified Zmijewski model performing better at predicting the financial distress of mature shake-out firms compared to other stages. Full article

In this study, we introduce a detailed framework for predicting market conditions and selecting stocks by integrating machine learning techniques with ontological financial analysis. The process starts with ontology-based stock selection, categorizing companies using fundamental financial indicators such as liquidity, profitability, debt ratios, and growth metrics. For instance, firms showcasing favorable debt-to-equity ratios along with robust revenue growth are identified as high-performing entities. This classification facilitates targeted analyses of market dynamics. To predict market states—categorizing them into bull, bear, or neutral phases—the framework utilizes a Non-Stationary Markov Chain (NMC), BERT, to assess sentiment in financial news articles and Long Short-Term Memory (LSTM) networks to identify temporal patterns. Key inputs like the Sentiment Index (SI) and Illiquidity Index (ILLIQ) play essential roles in dynamically influencing regime predictions within the NMC model; these inputs are supplemented by variables including GARCH volatility and VIX to enhance predictive precision further still. Empirical findings demonstrate that our approach achieves an impressive 97.20% accuracy rate for classifying market states, significantly surpassing traditional methods like Naive Bayes, Logistic Regression, KNN, Decision Tree, ANN, Random Forest, and XGBoost. The state-predicted strategy leverages this framework to dynamically adjust portfolio positions based on projected market conditions. It prioritizes growth-oriented assets during bull markets, defensive assets in bear markets, and maintains balanced portfolios in neutral states. Comparative testing showed that this approach achieved an average cumulative return of 13.67%, outperforming the Buy and Hold method’s return of 8.62%. Specifically, for the S&P 500 index, returns were recorded at 6.36% compared with just a 1.08% gain from Buy and Hold strategies alone. These results underscore the robustness of our framework and its potential advantages for improving decision-making within quantitative trading environments as well as asset selection processes. Full article

Apples are considered one of the most extensively consumed fruits worldwide, prized for their superior nutritional profile, bioactive compounds, and sensory characteristics. The primary aim of this study was to evaluate the physical and sensory characteristics of two apple varieties, Braeburn and Golden Delicious, based on different harvest dates and during a 15-day shelf life period, using multiple quality assessment perspectives. The first part focused on key textural attributes and their changes over time. Golden Delicious fruit exhibited fewer alterations in texture, with only firmness showing significant changes during shelf life. In contrast, Braeburn fruit experienced alterations in multiple textural parameters, including fracturability, springiness, average peak load, and firmness, indicating a higher sensitivity to shelf life effects. Both apple varieties were significantly influenced by the harvest date. From a Kano model perspective, the quality attributes of apples were categorized as one-dimensional and attractive features, while oral processing requirements fell into three distinct categories: attractive, reverse, and indifferent. For the Braeburn variety, firmness was the dominant sensory attribute at the start of the shelf life at each harvest date, decreasing gradually over time. In contrast, the Golden Delicious apple maintained dominance in both firmness and juiciness across all harvest dates and shelf life testing days. Emotional responses during mastication were similarly distributed for all three harvest dates for the Golden Delicious variety, highlighting the stability of sensory experiences for this variety. These findings provide valuable insights into the complex interplay of textural, sensory, and emotional responses to apple varieties, offering practical implications for apple breeding and post-harvest management. Full article

Climate risk refers to the risks associated with climate change and has already started to impact various sectors of the economy. In this work, we focus on the impact of physical risk on the probability of default for a firm in the agribusiness sector. The probability of default is estimated based on the Merton model, where the firm defaults when its asset value falls below the threshold defined by its liabilities. We study the relationship between the stock value of the firm and global surface temperature anomalies, observing that an increase in temperature negatively affects the stock value and, consequently, the asset value of the firm. A decrease in the asset value of the firm translates into an increase in its probability of default. We also propose a model to assess the exposure of the firm to transition risk. Full article

The fusion of livestreaming e-commerce and AI technology is booming, and many firms have started to replace human streamers with AI streamers. Despite their popularity, the acceptance of AI streamers by consumers varies widely and the signaling effects of AI streamers still remain unclear. We build an analytical model and compare scenarios where the acceptance level is either exogenously given or endogenously determined, highlighting the implications for firms’ optimal separation strategy. Our findings suggest that in markets with moderate information asymmetry, using both price and acceptance level as joint signals can be more profitable for high-quality firms. Conversely, in highly asymmetric markets, firms must incur additional costs to distinguish their high-quality products, regardless of the signaling strategy employed. Our paper provides strategic insights for firms aiming to leverage AI streamers in diverse market conditions. Full article