Search Results (1,370)

This paper selects 50 listed commercial banks in China from 2012 to 2023 as research samples, and employs the fixed effects model and Hansen’s threshold regression method to systematically examine the impact mechanism and non-linear characteristics of FinTech development on the profitability of commercial banks. The key findings are summarized as follows: (1) FinTech significantly undermines the overall profitability of commercial banks by reshaping the competitive landscape of the industry and intensifying the technology substitution effect. This is primarily reflected in the reduction in traditional interest income and the erosion of market share in intermediary business. (2) Heterogeneity analysis indicates that large state-owned banks and joint-stock banks experience more pronounced negative impacts compared to small and medium-sized banks. (3) Additional research findings reveal a significant single-threshold effect between FinTech and bank profitability, with a critical value of 4.169. When the development level of FinTech surpasses this threshold, its inhibitory effect diminishes substantially, suggesting that after achieving a certain degree of technological integration, commercial banks may partially alleviate external competitive pressures through synergistic effects. This study offers crucial empirical evidence and theoretical support for commercial banks to develop differentiated technology strategies and for regulatory authorities to design dynamically adaptable policy frameworks. Full article

In an increasingly complex global context, higher education faces the challenge of preparing professionals who are innovative, committed, and socially responsible. Entrepreneurial competence is particularly prominent among the key skills required to meet this goal, given its significant personal and social impact. This study examines how final-year undergraduate students at the University of Deusto (Spain) perceive the importance of entrepreneurial competence—defined as a set of transversal skills, knowledge, and attitudes enabling initiative and opportunity recognition across various contexts—rather than entrepreneurial competence strictly understood as business creation. The sample included 267 students from different faculties. Descriptive, comparative, and ordinal logistic regression analyses (SPSS) were used. The results show that, while entrepreneurial competence was given significant importance, it was ranked comparatively low relative to other competencies. Significant differences by gender were observed, with women rating entrepreneurial competence more highly than men. The faculty variable showed slight disparities, and there were no relevant differences between campuses. These findings highlight the need to reinforce the integration of entrepreneurial competence into educational curricula on a transversal basis, adapting the teaching of this competence to the sociocultural context of students, as well as the need to increase students’ awareness of the importance of entrepreneurial competence. It is proposed that further research should focus on the relationships between intrapreneurship, gender, and academic disciplines, in order to enrich entrepreneurial competence education and its impact on the employability and social commitment of students. Full article

Strengthening climate resilience constitutes a foundational approach through which cities adapt to climate change and mitigate associated environmental risks. However, research on the influence of economic policy environments on climate resilience remains limited. Guided by institutional theory and dynamic capability theory, this study employs a panel dataset comprising 272 Chinese cities at the prefecture level and above, covering the period from 2009 to 2023. It constructs a composite index framework for evaluating the green business environment (GBE) and urban climate resilience (UCR) using the entropy weight method. Employing a two-way fixed-effect regression model, it examined the impact of GBE optimization on UCR empirically and also explored the underlying mechanisms. The results show that improvements in the GBE significantly enhance UCR, with green innovation (GI) in technology functioning as an intermediary mechanism within this relationship. Moreover, climate policy uncertainty (CPU) exerts a moderating effect along this transmission pathway: on the one hand, it amplifies the beneficial effect of the GBE on GI; on the other hand, it hampers the transformation of GI into improved GBEs. The former effect dominates, indicating that optimizing the GBE becomes particularly critical for enhancing UCR under high CPU. To eliminate potential endogenous issues, this paper adopts a two-stage regression model based on the instrumental variable method (2SLS). The above conclusion still holds after undergoing a series of robustness tests. This study reveals the mechanism by which a GBE enhances its growth through GI. By incorporating CPU as a heterogeneous factor, the findings suggest that governments should balance policy incentives with environmental regulations in climate resilience governance. Furthermore, maintaining awareness of the risks stemming from climate policy volatility is of critical importance. By providing a stable and supportive institutional environment, governments can foster steady progress in green innovation and comprehensively improve urban adaptive capacity to climate change. Full article

Adversarial attacks in Natural Language Processing (NLP) present a critical challenge, particularly in sentiment analysis, where subtle input modifications can significantly alter model predictions. In search of more robust defenses against adversarial attacks on sentimental analysis, this research work introduces two novel defense mechanisms: the Lexicon-Based Random Substitute Model (LRSM) and the Word-Variant Voting Model (WVVM). LRSM employs randomized substitutions from a dataset-specific lexicon to generate diverse input variations, disrupting adversarial strategies by introducing unpredictability. Unlike traditional defenses requiring synonym dictionaries or precomputed semantic relationships, LRSM directly substitutes words with random lexicon alternatives, reducing overhead while maintaining robustness. Notably, LRSM not only neutralizes adversarial perturbations but occasionally surpasses the original accuracy by correcting inherent model misclassifications. Building on LRSM, WVVM integrates LRSM, Frequency-Guided Word Substitution (FGWS), and Synonym Random Substitution and Voting (RS&V) in an ensemble framework that adaptively combines their outputs. Logistic Regression (LR) emerged as the optimal ensemble configuration, leveraging its regularization parameters to balance the contributions of individual defenses. WVVM consistently outperformed standalone defenses, demonstrating superior restored accuracy and F1 scores across adversarial scenarios. The proposed defenses were evaluated on two well-known sentiment analysis benchmarks: the IMDB Sentiment Dataset and the Yelp Polarity Dataset. The IMDB dataset, comprising 50,000 labeled movie reviews, and the Yelp Polarity dataset, containing labeled business reviews, provided diverse linguistic challenges for assessing adversarial robustness. Both datasets were tested using 4000 adversarial examples generated by established attacks, including Probability Weighted Word Saliency, TextFooler, and BERT-based Adversarial Examples. WVVM and LRSM demonstrated superior performance in restoring accuracy and F1 scores across both datasets, with WVVM excelling through its ensemble learning framework. LRSM improved restored accuracy from 75.66% to 83.7% when compared to the second-best individual model, RS&V, while the Support Vector Classifier WVVM variation further improved restored accuracy to 93.17%. Logistic Regression WVVM achieved an F1 score of 86.26% compared to 76.80% for RS&V. These findings establish LRSM and WVVM as robust frameworks for defending against adversarial text attacks in sentiment analysis. Full article

The rapid evolution of Industry 4.0 technologies has resulted in a scenario in which collaborative networks are essential to overcome the challenges related to their implementation. However, the frameworks to guide such collaborations remain underexplored. This study addresses this gap by proposing Business Rules and Goals Models to operationalize Industry 4.0 solutions through enterprise collaboration. Using the For Enterprise Modeling (4EM) method, the research integrates qualitative insights from expert opinions, including interviews with 12 professionals (academics, industry professionals, and consultants) from Brazilian manufacturing sectors. The Goals Model identifies five main objectives—competitiveness, efficiency, flexibility, interoperability, and real-time collaboration—while the Business Rules Model outlines 18 actionable recommendations, such as investing in digital infrastructure, upskilling employees, and standardizing information technology systems. The results reveal that cultural resistance, limited resources, and knowledge gaps are critical barriers, while interoperability and stakeholder integration emerge as enablers of digital transformation. The study concludes that successfully adopting Industry 4.0 requires technological investments, organizational alignment, structured governance, and collaborative ecosystems. These models provide a practical roadmap for companies navigating the complexities of Industry 4.0, emphasizing adaptability and cross-functional synergy. The research contributes to the literature on collaborative networks by connecting theoretical frameworks with actionable enterprise-level strategies. Full article

Emerging markets face growing pressures to integrate sustainable English business practices while maintaining economic growth, particularly in addressing environmental challenges and achieving carbon neutrality goals. English Financial information extraction becomes crucial for supporting green finance initiatives, Environmental, Social, and Governance (ESG) compliance, and sustainable investment decisions in these markets. This paper presents FinATG, an AI-driven autoregressive framework for extracting sustainability-related English financial information from English texts, specifically designed to support emerging markets in their transition toward sustainable development. The framework addresses the complex challenges of processing ESG reports, green bond disclosures, carbon footprint assessments, and sustainable investment documentation prevalent in emerging economies. FinATG introduces a domain-adaptive span representation method fine-tuned on sustainability-focused English financial corpora, implements constrained decoding mechanisms based on green finance regulations, and integrates FinBERT with autoregressive generation for end-to-end extraction of environmental and governance information. While achieving competitive performance on standard benchmarks, FinATG’s primary contribution lies in its architecture, which prioritizes correctness and compliance for the high-stakes financial domain. Experimental validation demonstrates FinATG’s effectiveness with entity F1 scores of 88.5 and REL F1 scores of 80.2 on standard English datasets, while achieving superior performance (85.7–86.0 entity F1, 73.1–74.0 REL+ F1) on sustainability-focused financial datasets. The framework particularly excels in extracting carbon emission data, green investment relationships, and ESG compliance indicators, achieving average AUC and RGR scores of 0.93 and 0.89 respectively. By automating the extraction of sustainability metrics from complex English financial documents, FinATG supports emerging markets in meeting international ESG standards, facilitating green finance flows, and enhancing transparency in sustainable business practices, ultimately contributing to their sustainable development goals and climate action commitments. Full article

In an ever-evolving digital environment, organizations must adopt advanced technologies for real-time big data processing to maintain their competitiveness and growth. However, selecting appropriate technologies is a challenge, particularly for small and medium-sized enterprises (SMEs). This study develops a literature review to analyze the methodologies used in the selection of technologies, with a special focus on those associated with the Industry 4.0. Knowledge Development Process-Constructivist (ProKnow-C) method, which was used to build a bibliographic portfolio, examining approximately 3400 articles published between 2005 and 2024, from which 80 were selected for a detailed analysis. The main methodological contributions come from research articles, the ScienceDirect database, the Expert Systems with Applications Journal, studies conducted in Turkey, and publications from the year 2023. The results highlight the predominant use of multi-criteria techniques, emphasizing hybrid approaches that combine various decision-making methodologies. In particular, the analytic hierarchy process (AHP) and TOPSIS methods were employed in 51.25% of the analyzed cases, either individually or in combination. It is concluded that technology selection should be based on flexible and adaptive approaches tailored to the organizational context, aligning long-term strategic objectives to ensure business sustainability and success. Full article

Collaborative business ecosystems (CBEs) are increasingly exposed to disruptive events (e.g., pandemics, supply chain breakdowns, cyberattacks) that challenge organizational adaptability and value creation. Traditional approaches to resilience and robustness often fail to capture the full range of systemic responses. This study introduces a unified mathematical framework to evaluate four crisis response modes—plasticity, resilience, transformative resilience, and antifragility—within complex adaptive networks. Grounded in complex systems and collaborative network theory, our model formalizes both internal organizational capabilities (e.g., adaptability, learning, innovation, structural flexibility) and strategic interventions (e.g., optionality, buffering, information sharing, fault-injection protocols), linking them to pre- and post-crisis performance via dynamic adjustment functions. A composite performance score is defined across four dimensions (Innovation, Contribution, Prestige, and Responsiveness to Business Opportunities), using capability–strategy interaction matrices, weighted performance change functions, and structural transformation modifiers. The sensitivity analysis and scenario simulations enable a comparative evaluation of organizational configurations, strategy impacts, and phase-transition thresholds under crisis. This indicator-based formulation provides a quantitative bridge between resilience theory and practice, facilitating evidence-based crisis management in networked business environments. Full article

Strategic logistics planning is used by management to define action plans that will enable organizations to always make decisions that are in the organization’s best interests. They are based on a knowledge repository of business experiences, which is usually represented by a centralized, organized, and searchable digital system where organizations store and manage critical institutional knowledge. Thus, an institutional knowledge base provides sustainability, making the experiences readily available while keeping them well organized. In this research, the experiences of logistics experts from selected scholarly designs for six-sigma business improvement projects have been collected, classified, and organized to form a logistics knowledge management system. Although originally meant to facilitate current and future decisions in strategic logistics planning of the cooperating companies, it is also used in logistics education to introduce knowledge-based engineering principles to enterprise strategic planning, based on continuous improvement of quality-related product or process performance indicators. The main goal of this article is to highlight the benefits of knowledge-based engineering over the established ontological logistics knowledge base in smart production, based on the predisposition that ontological institutional knowledge base management is more efficient, adaptable, and sustainable. Full article

The COVID-19 pandemic fundamentally disrupted assumptions about entrepreneurship, career planning, and professional development. This study explored how the pandemic influenced the entrepreneurial intentions of undergraduate business students in the United States. Using a qualitative methodology based on in-depth interviews with 31 students at a public Midwestern university, the research interpreted student narratives through the lenses of effectuation theory, resilience theory, and the theory of planned behavior. Findings revealed that many participants reframed entrepreneurship as a strategy for navigating economic uncertainty and enhancing personal agency. Students reported valuing adaptability, resourcefulness, and opportunity recognition, often experimenting with side hustles during the pandemic as a means of resilience. Their entrepreneurial thinking shifted from purely economic motivations toward aspirations for flexibility, self-fulfillment, and purposeful work. The study highlights the formative role of crisis contexts in shaping entrepreneurial identity among emerging professionals. It suggests that entrepreneurship education should move beyond traditional models, fostering skills for navigating complexity and building resilience. In doing so, the findings contribute to broader conversations about youth entrepreneurship, post-pandemic career development, and the evolving demands of the labor market in times of disruption. Full article

Certain industries will bear a disproportionate share of the burden of climate change. Climate change risk perceptions can impact workers’ mental health and well-being; increased climate change risk perceptions are also associated with more favourable adaptive attitudes. It is, therefore, important to understand whether climate risk perceptions differ across workers between industries. We conducted an online survey of British Columbians (16+) in 2021 using social media advertisements. Participants rated how likely they believed their industry (Natural Resources, Science, Art and Recreation, Education/Law/Government, Health, Management/Business, Manufacturing, Sales, Trades) would be affected by climate change (on a scale from “Very Unlikely” to “Very Likely”). Ordinal logistic regression examined the association between occupational category and perceived industry vulnerability, adjusting for socio-demographic factors. Among 877 participants, 66.1% of Natural Resources workers perceived it was very/somewhat likely that climate change would impact their industry; only those in Science (78.3%) and Art and Recreation (71.4%) occupations had higher percentages. In the adjusted model, compared to Natural Resources workers, respondents in other occupations, including those in Art and Recreation, Education/Law/Government, Management/Business, Manufacturing, Sales, and Trades, perceived significantly lower risk of climate change-related industry impacts. Industry-specific interventions are needed to increase awareness of and readiness for climate adaptation. Policymakers and industry leaders should prioritize sectoral differences when designing interventions to support climate resilience in the workforce. Full article

For high-growth firms, designing and implementing strategies to ensure the long-term sustainability of business models is a key priority. Although these strategies are carefully planned to achieve specific outcomes, these firms also encounter contextual factors inherent to entrepreneurship, as well as the potential negative consequences of operating as small- and medium-sized enterprises (SMEs). Consequently, they adapt emergent outcomes to secure positive scaling-up processes. A comprehensive analysis of 69 studies from 1978 to 2023 revealed that 34.8% used sales as the main indicator of high-growth outcomes, 18.8% considered employment to be the most important outcome, and 37.7% incorporated both. The assessment period for these studies spanned three to seven consecutive years. A subsequent review of the existing literature yielded 56 potential new outcomes, emphasising the existence of a diverse array of concepts and metrics with which to assess high-growth performance. The study confirmed sales and positive profits arising during the planning process as strategic outcomes. However, it was also demonstrated that geographical expansion and innovation become emergent outcomes in critical situations. The research also identified that external factors, including an adverse public environment, business context difficulties, and a favourable business environment, may influence the effect of the firm’s high growth. Full article

The aim of this study is to analyze the factors that influence consumer referents or reference points and their interaction during the decision-making process, along with the principles of prospect theory in the metaverse with market and retail examples. We conducted an integrative literature review. Consumers’ preference for reference points is determined and structured during the buying process, which can be affected by potential signals and biased decisions. To guide consumers’ shopping experiences and purchasing behavior in the most effective way, marketers and organizations must investigate the factors that influence consumer reference points beyond physical or tangible attributes. Businesses must be adaptable and adapt their strategies to changing consumer preferences based on reference points. Our findings can advance discussions about how reference points are being used in the market by using consumer decision-making claims in the discursive construction of the metaverse. By comprehending this, developers can create better experiences and assist users in navigating virtual risks. Our research aids us in better comprehending the influence of referents on consumer purchasing decisions in the marketing communications field. Numerous opportunities for academic research into consumer reference points have arisen, in which individuals as digital consumers are influenced by the same biases and heuristics that guide their behavior in reality. Full article

Enterprise architecture (EA) provides a strategic foundation for aligning business processes, IT infrastructure, and organizational strategy, enabling firms to navigate uncertainty and complexity. In developing economies, small and medium-sized enterprises (SMEs) face significant challenges in maintaining financial resilience and sustainable growth amidst frequent disruptions. This study investigates how EA-driven change events affect SME financial performance by activating three key adaptive mechanisms: improvisational capability, flexible IT systems, and organizational culture. A novel classification of EA change triggers is proposed to guide adaptive responses. Using survey data from 291 Pakistani SMEs collected during the COVID-19 crisis, the study employs structural equation modeling (SEM) to validate the conceptual model. The results indicate that improvisational capability and flexible IT systems significantly enhance financial performance, while the mediating role of organizational culture is statistically insignificant. This study contributes to EA and sustainability literature by integrating a typology of EA triggers with adaptive capabilities theory and testing their effects in a real-world crisis context. Full article

This study presents the design and validation of an integrated fire safety system that leverages edge AI, hybrid sensing, and precision suppression to overcome the latency and collateral limitations of conventional smoke detection and sprinkler systems. The proposed platform features a dual-mode sensor array for early fire recognition, motorized ventilation units for rapid smoke extraction, and a 360° directional nozzle for targeted agent discharge using a residue-free clean extinguishing agent. Experimental trials demonstrated an average fire detection time of 5.8 s and complete flame suppression within 13.2 s, with 90% smoke clearance achieved in under 95 s. No false positives were recorded during non-fire simulations, and the system remained fully functional under simulated cloud communication failure, confirming its edge-resilient architecture. A probabilistic risk analysis based on ISO 31000 and NFPA 551 frameworks showed risk reductions of 75.6% in life safety, 58.0% in property damage, and 67.1% in business disruption. The system achieved a composite risk reduction of approximately 73%, shifting the operational risk level into the ALARP region. These findings demonstrate the system’s capacity to provide proactive, energy-efficient, and spatially targeted fire response suitable for high-value infrastructure. The modular design and SmartThings Edge integration further support scalable deployment and real-time system intelligence, establishing a strong foundation for future adaptive fire protection frameworks. Full article