Search Results (394)

This study explores the determinants of startup intention within the context of e-commerce platform-based startups in South Korea. We employ an extended technology acceptance model (TAM) that integrates individual, social, and entrepreneurial characteristics. A two-step analytical approach is applied, combining variable extraction through data mining and hypothesis testing using structural equation modeling. The results indicate that personal and social factors—such as entrepreneurial mindset and social influence—positively affect perceived usefulness, while job relevance and exposure to successful startup models enhance perceived ease of use. In contrast, security concerns and technological barriers negatively impact these relationships, posing critical obstacles to platform-based startups. This study extends the TAM framework to the platform-based startup context, offering theoretical contributions and proposing policy implications, including promoting digital literacy, developing entrepreneurial networks, and addressing security and regulatory issues. These insights offer a deeper understanding of how platform environments shape entrepreneurial behavior, providing practical guidance for startup founders, developers, and policymakers. Full article

An FR4-based X-band low phase noise oscillator loading an additional high-Q cavity resonator was designed in this study using substrate-integrated suspended line (SISL) technology. The additional resonator was coupled to an oscillator by the transmission line (coupling TL). The impact of the additional resonator on startup conditions, Q factor enhancement, and phase noise reduction was thoroughly investigated. Three oscillators loading an additional high-Q cavity resonator, loading an additional high-Q cavity resonator and performing partial dielectric extraction, and loading an original parallel feedback oscillator for comparison were presented. The experimental results showed that the proposed oscillator had a low phase noise of −131.79 dBc/Hz at 1 MHz offset from the carrier frequency of 10.088 GHz, and the FOM was −197.79 dBc/Hz. The phase noise was reduced by 1.66 dB through loading the additional resonator and further reduced by 1.87 dB through partially excising the substrate. To the best of our knowledge, the proposed oscillator showed the lowest phase noise and FOM compared with other all-FR4-based oscillators. The cost of fabrication was markedly reduced. The proposed oscillator also has the advantages of compact size and self-packaging properties. Full article

This research examines the shifting geography of small firm innovation in the U.S. by tracking the location of small business innovation research (SBIR) and small business technology transfer (STTR) awardees between 2010 and 2024. The SBIR and STTR are “seed fund” awards coordinated by the Small Business Administration (SBA) and funded through 11 U.S. federal agencies. Of particular interest is whether the number of individual SBA awards, awarded firms, and/or funding amounts are (1) becoming increasingly concentrated within regional innovation hubs and (2) exhibiting a shift toward or away from urban centers and other walkable, transit-accessible urban neighborhoods, particularly since 2020 and the COVID-19 pandemic. While the rise of remote work and pandemic-related fears may have reduced the desirability of urban spaces for both living and working, there remain significant benefits to spatial agglomeration that may be especially crucial for startups and other small firms in the knowledge- or information-intensive industries. The results suggest that innovative activity of smaller firms has indeed trended toward more centralized, denser, and walkable urban areas in recent years while also remaining fairly concentrated within major metropolitan innovation hubs. The pandemic appears to have resulted in a measurable, though potentially short-lived, cessation of these trends. Full article

Altitude Test Facility (ATF) compressor systems are widely used in aero-engine tests. These systems achieve the control of gas pressure and transport through complex operation processes. With advancements in the aviation industry, there is a growing demand for higher performance, greater safety, and more energy efficiency in digital ATF systems. Hybrid modeling is a technology that combines many methods and can meet these requirements. The Modular Dynamic System Greitzer (MDSG) compressor model, including mechanistic and data-driven modeling approaches, is combined with a neural network to obtain a BPNN-MDSG hybrid modeling method for the digital turbine system. The digital simulation is linked with the physical sensors of the ATF system to realize real-time simulation and monitoring. The steady and dynamic conditions of the actual system are simulated in virtual space. Compared with the actual results, the average error of steady mass flow is less than 3%, and the error of pressure is less than 1%. The average error of dynamic mass flow is less than 5%, and the error of pressure is less than 3%. The simulation and characteristic predictions are carried out in BPNN-MDSG virtual space. The anti-surge characteristics of the ATF system under start-up conditions are obtained. The full-condition anti-surge operation map of the system is obtained, which provides guidance for the actual operation of the ATF system. Full article

This paper explores the effectiveness of an innovative FinTech risk-scoring model to predict the risk-appropriate return for short-term credit sales. The risk score serves to mitigate the information asymmetry between the seller of receivables (“Seller”) and the purchaser (“Funder”), at the same time providing an opportunity for the Funder to earn returns as well as to diversify its portfolio on a risk-appropriate basis. Selling receivables/credit to potential Funders at a risk-appropriate discount also helps Sellers to maintain their short-term financial liquidity and provide the necessary cash flow for operations and other immediate financial needs. We use 18,304 short-term credit-sale transactions between 23 April 2020 and 30 September 2022 from the private FinTech startup Crowdz and its Sustainability, Underwriting, Risk & Financial (SURF) risk-scoring system to analyze the risk/return relationship. The data includes risk scores for both Sellers of receivables (e.g., invoices) along with the Obligors (firms purchasing goods and services from the Seller) on those receivables and provides, as outputs, the mutual gains by the Sellers and the financial institutions or other investors funding the receivables (i.e., the Funders). Our analysis shows that the SURF Score is instrumental in mitigating the information asymmetry between the Sellers and the Funders and provides risk-appropriate periodic returns to the Funders across industries. A comparative analysis shows that the use of SURF technology generates higher risk-appropriate annualized internal rates of return (IRR) as compared to nonuse of the SURF Score risk-scoring system in these transactions. While Sellers and Funders enter into a win-win relationship (in the absence of a default), Sellers of credit instruments are not often scored based on the potential diversification by industry classification. Crowdz’s SURF technology does so and provides Funders with diversification opportunities through numerous invoices of differing amounts and SURF Scores in a wide range of industries. The analysis also shows that Sellers generally have lower financing stability as compared to the Obligors (payers on receivables), a fact captured in the SURF Scores. Full article

This study proposes an improved super-twisting sliding mode (STSM) control method for a brushless doubly fed induction generator (BDFIG) used in standalone ship shaft power generation systems. Focusing on the problem of the low tracking accuracy of the power winding (PW) voltages caused by the parameter perturbation of BDFIG systems, a mismatched uncertain model of the BDFIG is constructed. Additionally, an improved STSM control method is proposed to address the power load variation and compensate for the mismatched uncertainty through virtual control technology. Based on the direct vector control of the control winding (CW), the proposed method ensured that the voltage amplitude error of the power winding could converge to the equilibrium point rather than the neighborhood. Finally, in the experimental investigation of the BDFIG-based ship shaft independent power system, the dynamic performance in the startup and power load changing conditions were analyzed. The experimental results show that the proposed improved STSM controller has a faster dynamic response and higher steady-state accuracy than the proportional integral control and the linear sliding mode control, with strong robustness to the mismatched uncertainties caused by parameter perturbations. Full article

In today’s globalized and rapidly evolving technological landscape, innovation serves as a critical driver of economic growth and competitive advantage. The concept of an innovation ecosystem has emerged to elucidate the complex interactions among various stakeholders—including public sectors, startups, academia, businesses, NGOs, and venture capitalists—who collaborate and compete to foster technological advancements and economic growth. Open innovation emphasizes leveraging external ideas alongside internal efforts to enhance innovation capabilities, fostering more dynamic and resilient systems. Additionally, learning from innovation failures plays a crucial role in shaping effective strategies for growth, as startups often translate these learnings into robust innovation frameworks. Given the increasing complexity and interconnectedness of innovation ecosystems, traditional metrics often fail to capture their dynamic and collaborative nature. The European Innovation Scoreboard (EIS) provides a comprehensive framework for assessing the innovation performance of EU countries, offering insights into the overall health and performance of innovation ecosystems. This review article addresses the need to identify metrics and methods for measuring open innovation’s impact on innovation ecosystems. Building upon foundational theories and empirical findings, this study proposes a framework for evaluating the impact of open innovation on innovation ecosystems. It integrates insights from the academic literature with EIS metrics to develop robust methods for assessing open innovation’s multifaceted influence. This review article is particularly relevant as firms and policymakers strive to understand which metrics are most affected by open innovation and how these can be leveraged to enhance the performance and sustainability of innovation ecosystems. Full article

This paper proposes a conceptual framework for studying the KPIs of digital accelerators. Therefore, a critical review was developed and we derived clear recommendations in terms of KPIs that researchers must consider when evaluating digital accelerators applicable to startups. Digital startup businesses must focus on product, external factors, process, capability, organizational factors, and market to achieve sustainability. The proposed framework asserts that profitability, growth, productivity, and size are key categories that should be taken into consideration while grouping KPIs. These categories should be grouped into three different dimensions, economic, technological, and organization/client. The proposed KPIs can help the accelerator program evaluate its own performance and make the necessary adjustments to improve the program and eventually measure the startup’s success. Full article

This research evaluates four hydrogen (H₂) production technologies via water electrolysis (WE): alkaline water electrolysis (AWE), proton exchange membrane electrolysis (PEME), anion exchange membrane electrolysis (AEME), and solid oxide electrolysis (SOE). Two scoring and ranking methods, the MACBETH method and the Pugh decision matrix, are utilized for this evaluation. The scoring process employs nine decision criteria: capital expenditure (CAPEX), operating expenditure (OPEX), operating efficiency (SOE), startup time (SuT), environmental impact (EI), technology readiness level (TRL), maintenance requirements (MRs), supply chain challenges (SCCs), and levelized cost of H₂ (LCOH). The MACBETH method involves pairwise technology comparisons for each decision criterion using seven qualitative judgment categories, which are converted into quantitative scores via M-MACBETH software (Version 3.2.0). The Pugh decision matrix benchmarks WE technologies using a baseline technology—SMR with CCS—and a three-point scoring scale (0 for the baseline, +1 for better, −1 for worse). Results from both methods indicate AWE as the leading H₂ production technology, which is followed by AEME, PEME, and SOE. AWE excels due to its lowest CAPEX and OPEX, highest TRL, and optimal operational efficiency (at ≈7 bars of pressure), which minimizes LCOH. AEME demonstrates balanced performance across the criteria. While PEME shows advantages in some areas, it requires improvements in others. SOE has the most areas needing enhancement. These insights can direct future R&D efforts toward the most promising H₂ production technologies to achieve the net-zero goal. 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

A diode-based rectifier (DR) is an attractive transmission technology for offshore wind farms, which reduces the volume of large bulk platforms. A novel parallel–series DC wind farm based on a distributed DR is proposed, which meets the requirements of high voltage and high power with an isolation capability from other units. The coupling mechanism between a modular multilevel converter (MMC) and a DR has been built, and the coordinate control strategy for the whole system has been proposed based on the MMC triple control targets with intermediate variables. Under the proposed control strategy, the system automatically operates at maximum power point tracking (MPPT). The feasibility of topology and the effectiveness of the control strategy are verified under start-up, power fluctuation, onshore alternating current (AC) fault, and direct current (DC) fault based on the power systems computer-aided design (PSCAD)/electromagnetic transients including direct current (EMTDC) simulation. 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

CO₂ flooding technology has been established as a key technique that is both economically viable and environmentally sustainable, achieving enhanced oil recovery (EOR) while advancing CCUS objectives. This study addresses the challenge of optimizing CO₂ flooding well patterns in tight oil reservoirs through a geological–engineering integrated approach. A semi-analytical model incorporating startup pressure gradients and miscible/immiscible two-phase flow was developed to dynamically adjust injection intensity. An effective driving coefficient model considering reservoir heterogeneity and fracture orientation was proposed to determine well pattern boundaries. Field data from Blocks A and B were used to validate the models, with the results indicating optimal injection intensities of 0.39 t/d/m and 0.63 t/d/m, respectively. Numerical simulations confirmed that inverted five-spot patterns with well spacings of 240 m (Block A) and 260 m (Block B) achieved the highest incremental oil production (3621.6 t/well and 4213.1 t/well) while reducing the gas channeling risk by 35–47%. The proposed methodology provides a robust framework for enhancing recovery efficiency in low-permeability reservoirs under varying geological conditions. Full article

Over the past years, the biopharmaceutical industry has been marked by substantial growth, with key players dominating market sales. A major change in research and development is the onset of digital twin (DT) technology in manufacturing. This work gives an overview of the market and major stakeholders, such as contract development and manufacturing organizations (CDMOs), regulatory bodies, and academia, their functions, and challenges. Fundamental concepts and definitions are reviewed and serve as an overview of the challenges ahead of the full adaptation of DTs in manufacturing. Using established market analysis tools, the environment is analyzed, and a business case is developed. Opportunities and threats for small startups and larger pharmaceutical companies to gain a competitive edge are analyzed and evaluated. Even small groups of 4–16 employees enable significant margins at a return on investment of less than 1 year. Full article

Startups in the clean energy and environmental technology (CEET) sector can develop sustainable innovations, but mobilizing private finance has been difficult. As the venture capital (VC) investment model was found to be not well-suited for the CEET startups, diverse types of investors have received more attention. However, since previous studies have been dominated by a VC-centric perspective in the US and have overlooked collaborative relationships, the roles of various CEET investors have not been systematically analyzed. This study aims to analyze the diverse investors in the CEET investor network formed through co-investment syndication, using Japan as an underexplored regional context. Based on Japan’s comprehensive data from 2008 to 2022, this study examines the evolution, structure, and communities of the network. The analysis identified the development stages of the investor network: the formation stage (2008–2012), the expansion and diversification stage (2013–2017), and the stable growth stage (2018–2022). The results confirmed the strong influence of VCs, while a community analysis suggested the bridging role of governmental venture capital. The findings based on the CEET investor network contribute to expanding both the theoretical understanding and practical implications for overcoming the financing difficulties of CEET startups to address the climate change crisis. Full article