Search Results (117)

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

The digital economy has amplified the role of technological innovation in transforming financial services and business models. Patent data offer valuable insights into these dynamics, especially within the growing FinTech ecosystem. This study conducts a bibliometric analysis of academic research that utilizes PATSTAT, a global database managed by the European Patent Office, focusing on its application in studies related to digital innovation, finance, and economic transformation. A systematic mapping of publications indexed in Scopus, Web of Science, Wiley, Emerald, and Springer Nature is carried out using Biblioshiny and Bibliometrix in RStudio 2025.05.0, complemented by graph-based visualizations via VOSviewer 1.6.20. The findings reveal a growing body of research that leverages PATSTAT to explore technological trajectories, intellectual property strategies, and innovation systems, particularly in areas such as blockchain technologies, AI-driven finance, digital payments, and smart contracts. This study contributes to the literature by highlighting the strategic value of patent analytics in the FinTech landscape and offers a reference point for researchers and decision-makers aiming to understand emerging trends in financial technologies and the digital economy. Full article

The electric vehicle (EV) business model integrates advanced battery technology, dynamic power train architectures, and intelligent energy management systems with ecosystem strategies and digital services. It incorporates environmental sustainability through lifecycle analysis and renewable energy integration. China, with 9.49 million EV sales in 2023 (33% market share), faces infrastructure gaps constraining further growth. China is strategically mitigating CO₂ emissions while fostering economic expansion, notwithstanding constraints such as suboptimal battery technology advancements, elevated production expenditure, and enduring ecological impacts. This Political, Economic, Social, Technological, Legal, Environmental (PESTLE) assessment, operationalized through a survey of 800 stakeholders and Statistical Package for the Social Sciences IBM SPSS SPSS (Version 28) quantitative analysis (factor loading = 0.73 for Technology; eigenvalue = 4.12), identifies infrastructure gaps as the dominant barrier (72% of stakeholders). Political factors (β = 0.82) emerged as the strongest adoption predictor, outweighing economic subsidies in significance. The adoption of EVs in China presents a significant prospect for reducing CO₂ emissions and advancing technology. However, economic barriers, market dynamics, inadequate infrastructure, regulatory uncertainty, and social acceptance issues are addressed in the assessment. The study recommends prioritizing infrastructure investment (e.g., 500 K fast-charging stations by 2027) and policy stability to overcome adoption barriers. This study provides three key advances: (1) quantification of PESTLE factor weights via factor analysis, revealing technological (infrastructure) and political factors as dominant; (2) identification of infrastructure gaps, not subsidies, as the primary adoption barrier; and (3) demonstration of infrastructure’s persistence post-subsidy cuts. These insights redefine EV adoption priorities in China. Full article

Prior research has focused on the technical and institutional challenges of blockchain adoption. However, little is known about how blockchain ventures claim categorical space in the market and how such domain positioning influences their visibility and evaluation. This study investigates the relationship between strategic domain positioning and market recognition among blockchain-based ventures, with a particular focus on applications relevant to e-commerce, such as non-fungible tokens (NFTs) and decentralized finance (DeFi). Drawing on research on categorization, legitimacy, and the technology lifecycle, we propose a domain lifecycle perspective that accounts for the evolving expectations and legitimacy criteria across blockchain domains. Using BERTopic, a transformer-based topic modeling method, we classify 9665 blockchain ventures based on their textual business descriptions. We then test the impact of domain positioning on market recognition—proxied by Crunchbase rank—while examining the moderating effects of external validation signals such as funding events, media attention, and organizational age. Our findings reveal that clear domain positioning significantly enhances market recognition, but the strength and direction of this effect vary by domain. Specifically, NFT ventures experience stronger recognition when young and less institutionally validated, suggesting a novelty premium, while DeFi ventures benefit more from conventional legitimacy signals. These results advance our understanding of how categorical dynamics operate in emerging digital ecosystems and offer practical insights for e-commerce platforms, investors, and entrepreneurs navigating blockchain-enabled innovation. Full article

Enterprise digital transformation has emerged as a key strategy for enhancing innovation capacity in the age of the digital economy. This article aims to analyze the influence mechanism of digital transformation on corporate innovation and evaluate the mediating function of corporate innovation and cooperative cultures between digital transformation and corporate innovation capability. This work builds a panel data model based on data from Chinese A-share listed businesses from 2012 to 2021, empirically analyzes it using the Tobit model and the fixed effects model with instrumental variables technique, and uses the mediation effect test to uncover the course of action. According to the report, digital transformation significantly enhances creativity capability; second, corporate collaborative and innovation cultures mediate the relationship between digital transformation and innovation outcomes, and cultural capital becomes a crucial link; and third, the influence of digital transformation on corporate innovation capability is greater in state-owned enterprises, non-monopoly industries, and high-tech industries. According to the study, businesses should work to realize the dual-wheel drive of “technological investment + cultural cultivation” and establish an open and collaborative innovation ecosystem, while the government should intensify the development of digital infrastructure, enhance the supporting system, encourage cultural construction and talent supply, and create an environment that supports the synergistic development of digitization and innovation. Full article

The emergence of InsurTech has significantly transformed the traditional insurance industry, leading to the development of a new ecosystem characterized by digital intermediation, strategic partnerships, and increasing interdependence among actors. This paper investigates the structural configuration of the InsurTech ecosystem, emphasizing its role in reshaping how value is created, delivered, and captured across the industry. Based on a sample of 364 active InsurTech firms from 2020 to 2023, the research employs network analysis to map the interactions and co-occurrences among seven defined archetypes: Enablers, Innovators, Connectors, Integrators, Protectors, Transformers, and Disruptors. The findings reveal a trend toward higher density and functional complementarity among archetypes by providing a framework for understanding the dynamics of the InsurTech ecosystem and the strategic implications. Building on these findings, this paper introduces a novel five-phase framework for understanding the ecosystem’s evolution: (1) digitalization and technologies, (2) customer-centric approach, (3) data and analytics, (4) platform-based business models, and (5) ecosystem partnerships. This research advances the theoretical understanding of InsurTech as a networked system of role-based interdependencies and provides a methodological approach to analyzing this scenario through network theory. Furthermore, it contributes to academic discourse and industry practice, offering practical guidance for insurers, startups, and policymakers by enabling actionable insights into the strategic positioning of InsurTech archetypes within the evolving insurance industry landscape. Full article

As consumer behavior increasingly shifts toward hyperlocal, digitally mediated retail journeys, community unmanned stores have emerged as a transformative model that integrates smart technologies with community proximity services. These fully automated stores offer convenient, contactless shopping and hybrid digital–physical interactions, playing an increasingly important role within broader omnichannel digital retail ecosystems. However, there remains a lack of validated instruments to assess customer experience in such autonomous and locally embedded retail formats. This study develops and validates an ECUS-scale (an experience in community unmanned store scale), a multidimensional measurement tool grounded in qualitative research and refined through exploratory and confirmatory factor analysis. The scale identifies nine key dimensions—convenient service, smooth transaction, preferential price, good quality, safe environment, secure payment, comfortable space, comfortable interaction, and friendly image—across 36 items. These dimensions reflect the technological, spatial, and emotional–social aspects of customer experience in unmanned retail settings. The findings demonstrate that the ECUS-scale offers a robust framework for evaluating consumer experience in low-staffed, tech-enabled community stores, with strong relevance to omnichannel digital retail strategies. Theoretically, it advances the literature on smart retail experience by capturing underexplored dimensions such as emotional engagement with technology and perceptions of safety in staff-free environments. Practically, it serves as a diagnostic tool for businesses to enhance experience design and optimize customer engagement across digital and physical touchpoints. Full article

The convergence of blockchain and metaverse technologies is poised to redefine how Global Value Chains (GVCs) create, capture, and distribute value, yet scholarly insight into their joint impact remains scattered. Addressing this gap, the present study aims to clarify where, how, and under what conditions blockchain-enabled transparency and metaverse-enabled immersion enhance GVC performance. A systematic literature review (SLR), conducted according to PRISMA 2020 guidelines, screened 300 articles from ABI Global, Business Source Premier, and Web of Science records, yielding 65 peer-reviewed articles for in-depth analysis. The corpus was coded thematically and mapped against three theoretical lenses: transaction cost theory, resource-based view, and network/ecosystem perspectives. Key findings reveal the following: 1. digital twins anchored in immersive platforms reduce planning cycles by up to 30% and enable real-time, cross-border supply chain reconfiguration; 2. tokenized assets, micro-transactions, and decentralized finance (DeFi) are spawning new revenue models but simultaneously shift tax triggers and compliance burdens; 3. cross-chain protocols are critical for scalable trust, yet regulatory fragmentation—exemplified by divergent EU, U.S., and APAC rules—creates non-trivial coordination costs; and 4. traditional IB theories require extension to account for digital-capability orchestration, emerging cost centers (licensing, reserve backing, data audits), and metaverse-driven network effects. Based on these insights, this study recommends that managers adopt phased licensing and geo-aware tax engines, embed region-specific compliance flags in smart-contract metadata, and pilot digital-twin initiatives in sandbox-friendly jurisdictions. Policymakers are urged to accelerate work on interoperability and reporting standards to prevent systemic bottlenecks. Finally, researchers should pursue multi-case and longitudinal studies measuring the financial and ESG outcomes of integrated blockchain–metaverse deployments. By synthesizing disparate streams and articulating a forward agenda, this review provides a conceptual bridge for international business scholarship and a practical roadmap for firms navigating the next wave of digital GVC transformation. Full article

The integration of artificial intelligence (AI) technologies is reshaping diverse domains of human activity, including natural resource management, urban and rural planning, agri-food systems, industry, energy, education, and healthcare. However, the impact of AI on the sustainability of local ecosystems remains insufficiently systematized. This highlights the need for a comprehensive review that considers spatial, sectoral, and socio-economic characteristics of regions, as well as interdisciplinary approaches to sustainable development. This study presents a scoping review of 198 peer-reviewed publications published between 2010 and March 2025, focusing on applied cases of AI deployment in local contexts. Special attention is given to the role of AI in monitoring water, forest, and agricultural ecosystems, facilitating the digital transformation of businesses and territories, assessing ecosystem services, managing energy systems, and supporting educational and social sustainability. The review includes case studies from Africa, Asia, Europe, and Latin America, covering a wide range of technologies—from machine learning and digital twins to IoT and large language models. Findings indicate that AI holds significant potential for enhancing the efficiency and adaptability of local systems. Nevertheless, its implementation is accompanied by notable risks, including socio-economic disparities, technological inequality, and institutional limitations. The review concludes by outlining research priorities for the sustainable integration of AI into local ecosystems, emphasizing the importance of cross-sectoral collaboration and scientific support for regional digital transformations. Full article

This study focuses on the intrinsic mechanisms and sustainable value of artificial intelligence (AI)-driven green collaborative innovation in enterprises amid the global green low-carbon transition, revealing new pathways for digital technology-enabled green development. Based on the data of China’s A-share listed companies jointly applying for green patents with other entities from 2010 to 2023, this study used a high-dimensional fixed effect model to empirically find that artificial intelligence significantly promotes green collaborative innovation. This promoting effect proved more pronounced in the case of high macroeconomic uncertainty, large enterprises and SOEs. A mechanism test revealed that artificial intelligence drives green collaborative innovation primarily by reducing transaction costs and optimizing the labor structure. A moderating effect analysis showed that green investor entry and CEO openness can strengthen the facilitating effect of artificial intelligence on green collaborative innovation. In addition, the facilitating effect of artificial intelligence on green collaborative innovation helps companies reduce carbon emissions and improve ESG performance, driving the transformation of business ecosystems toward environmental sustainability. From a technology–organization–environment co-evolution perspective, this research clarifies the micro-level operational chain of AI-enabled green innovation, providing theoretical support for developing countries to achieve leapfrog low-carbon transitions through digital technologies. Practically, it offers actionable insights for advancing AI-enabled green industries, constructing collaborative green innovation ecosystems, and supporting the realization of the United Nations Sustainable Development Goals (SDGs). Full article

In contemporary research, the digital transformation of industries and societies has increased the importance of interdisciplinary exploration, particularly when addressing the complex challenges faced by modern organizations and social systems. From the perspective of digitalization, this literature review examines the intricate interactions between three key research domains: behavior dynamics, environmental impact, and fairness. By reviewing a wide range of studies and methodologies, it reveals new insights, challenges, and opportunities that arise at the intersection and through the interdependencies of these areas within digital ecosystems. Through a structured approach covering preliminary background, state-of-the-art methods, and comprehensive analysis, this document seeks to reveal the synergies and divergences among these domains. Special emphasis is placed on their implications in the digitalization of modern circular economy, business models, and supply chain management contexts where these domains converge in meaningful ways. Additionally, through an extensive review of the existing literature, this document highlights the current state of research and identifies notable gaps. These include issues such as ensuring fairness in digitalized sustainable strategies, understanding the role of digital behavior dynamics in promoting environmental management, and managing environmental impacts in new digitally driven business models. By weaving together these diverse elements, this work offers a novel perspective, emphasizing the importance of collaborative and integrative research in shaping a sustainable and equitable digital future. Full article

This article presents a methodology of the decision-making process using mathematical tools, which can become a practical tool for decision-makers on the functioning and strategic development of business ecosystems and their components depending on changes in the internal, micro- and macroenvironment. An algorithm for finding linearly independent elements of digital business ecosystems and a new option for interpreting their results are developed. It is proposed that this impact be determined by calculating the eigenvectors of the matrix and identifying the dependence through the contribution of the eigenvectors to the overall mechanism of the system. A system analysis of the properties of business ecosystems is carried out with a subsequent description of the methodology for studying the impact of financial indicators on it. As an example of data for calculation, the article uses Yandex data for 2023, as the most indicative business ecosystem with all the necessary properties. The results of the study allow formulating recommendations for top management on making management decisions and points of application of efforts. Combining the developed methodology with promising technologies for automated calculation of big data and AI will provide a flexible management tool with the possibility of its further development. Full article

Optimizing customer journeys is a critical challenge in e-commerce and financial services, attracting attention from marketing, operations research, and business analytics. Traditional customer analytics models, such as rule-based segmentation and regression models, rely heavily on structured transactional data, limiting their ability to capture latent behavioral patterns and adapt to multi-channel dynamics. These models often struggle to integrate unstructured data sources, failing to provide adaptive, personalized insights. To address these limitations, this study proposes a multi-stage data-driven framework integrating latent Dirichlet allocation (LDA) for behavioral insights, deep learning for predictive modeling, and heuristic algorithms for adaptive decision-making. Empirical validation using Taiwanese financial institution data shows a 15% improvement in predictive accuracy compared to traditional machine-learning models, significantly enhancing customer lifetime value (CLV) predictions and multi-channel resource allocation. This research highlights the practical value of integrating structured and unstructured data for improving customer analytics. Our framework leverages LDA to extract behavioral patterns from customer interactions, enriching predictive models and enhancing real-time decision-making in financial services. Robustness checks confirm the scalability and adaptability of this approach, offering a data-driven strategy for long-term value optimization in dynamic digital ecosystems. Full article

The rapid advancement of Industry 4.0 and Industry 5.0 technologies presents unprecedented opportunities to align start-up incubators with smart cities’ sustainability goals, fostering innovation and addressing complex urban challenges. This study introduces the Smart City-Incubator Sustainability Framework (SCISF)—a structured conceptual model that integrates sustainable business model innovation, digital transformation, and circular economy principles into incubator practices. Through an integrative literature review, conceptual framework development, and empirical application, the research identifies six key components essential for aligning incubators with smart city objectives: strategic vision alignment, technological integration, circular economy practices, public engagement, scalability, and impact monitoring. The framework’s empirical application to the Gdańsk Entrepreneurship Foundation (GEF) incubator demonstrates its effectiveness in assessing incubator contributions to urban sustainability. The findings highlight strengths in public engagement and strategic vision, alongside opportunities to enhance Industry 5.0 integration, cross-sector partnerships, and ESG-driven impact reporting. By bridging the gap between city objectives and start-up ecosystems, the SCISF provides actionable insights for policymakers, urban planners, and incubator managers to foster smart, circular, and resilient urban environments. Full article

Lack of digitization in data sharing between enterprises and inspection solutions suppliers negatively affects cash flows between parties, which results in late payments that negatively affect the adoption of automatic inspection equipment. This paper contributes to improving the implementation of a new Inspection as a Service Business Model for deploying automatic inspection solutions using non-destructive inspection solutions, and to enhance workflows by integrating Blockchain and Smart Contracts. The Inspection as a Service offers flexible, cloud-based, or on-premise inspection solutions through the Marketplace, reducing upfront costs with a recurring service fee and automated payments. The marketplace platform supports automatic payment processes and facilitates industry adoption of IaaS solutions. The digital ecosystem offers improved capital expenditure and payback periods. It enhances communication, collaboration, data sharing, and payment processes through a subscription model. The case study demonstrates that the IaaS Business Model (on-premise or cloud) improves the economic feasibility of automatic non-destructive inspection solutions by lowering initial investments and enhancing return on investment and payback periods, even with higher operating costs. The analysis confirms the profitability and sustainability of IaaS Business Model over traditional one-fee selling by emphasizing its potential to improve operational performance and sustainability in manufacturing. The current proposal of automatic non-destructive solutions implements a new revenue model based on pay-per-use or volume, which makes it more financially viable to adopt this technology in industry. Full article