Search Results (201)

Given the urgent need for corporate green transformation in the context of global climate governance, the sustainable development goals, and China’s dual carbon goals, this study examines the spillover effects of venture capital networks formed through common shareholder ties on green technology innovation from a complex network perspective. Based on regression analysis of panel data from Chinese A-share STAR and ChiNext Market listed companies between 2015 and 2023, we find the following: (1) Within venture capital networks, enterprises with higher centrality and structural hole positions exhibit more significant green technology innovation performance. (2) This facilitation effect varies across firm types. Private enterprises, foreign-invested enterprises and enterprises with weaker ESG performance rely more heavily on network advantage for innovation. (3) The mechanism analysis shows that occupying advantageous positions in venture capital networks enables firms to increase R&D personnel and R&D expenditure, thereby strengthening their ability to absorb external knowledge and transform innovation resources, which further enhances green technology innovation output. Full article

The phenomenon of followership is widely observed in the e-commerce industry. Crowdfunding, as a model of e-commerce entrepreneurship, has attracted many investors. Principal investors function as “leaders” who exert influence on follow-on (subsequent) investors. Accurately identifying principal investors in online entrepreneurial ventures and analyzing their preferences could enhance the success rate of fundraising. Grounded in the BTS (Behavior–Text–Social) framework, this study constructs a multi-dimensional model comprising 15 sub-indicators across three domains: user behavior, textual data, and social connections. A neural network is employed for training and prediction. By integrating the central and peripheral routes elicited from the Elaboration Likelihood Model (ELM), which ranks influence, principal investors are identified. The experiment results indicate that ELM-derived ranking demonstrates the highest consistency (error = 0.15), followed by user behavior (error = 0.30), social metrics (error = 0.71), and textual features (error = 0.95). Weight analysis using SHAP highlights the relative importance of structural holes, out-degree centrality, investment times, and investment moments. Furthermore, principal investors exhibit a preference for local projects and occupy dual roles. This study provides a theoretical foundation and practical guidance for identifying principal investors, thereby improving financing performance and mitigating investment risks for follow-on investors. Full article

In recent years, cross-border e-commerce and digital trade activities in transition economy countries and regions have continued to grow. Based on resource orchestration theory and empowerment theory, this paper examines the influence mechanism of digital leadership on international entrepreneurial performance and investigates the moderating effect of platform support. Analyzing survey data from 227 Chinese cross-border e-commerce enterprises using structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA), the study finds that: (1) Digital leadership positively influences the international entrepreneurial performance of cross-border e-commerce enterprises through the mediating roles of brand management capability and product innovation capability; (2) Platform support plays a positive moderating role in the relationship between brand management capability and international entrepreneurial performance in cross-border e-commerce; (3) Platform support moderates the mediating effect of brand management capability in the relationship between digital leadership and international entrepreneurial performance of cross-border e-commerce enterprises; (4) Based on fsQCA analysis, two antecedent configurations for achieving high international entrepreneurial performance in cross-border e-commerce are identified. These findings hold significant theoretical implications for research on cross-border digital platforms and international new ventures, while also providing robust empirical support for enterprises seeking to achieve international entrepreneurial success through the implementation of digital strategies. Full article

Many real-world screening tasks in venture capital must rank large start-up candidate pools under conditions of tight review capacity, time-varying information, and rare investment success outcomes. When datasets are constructed retrospectively, post-decision updates can leak into features and inflate performance, especially with random splits. This study proposes a leakage-aware, time-based evaluation framework for capacity-constrained screening formulated as a top-K ranking problem. Using a dataset of 117,141 early-stage firms as an empirical testbed, features were constructed strictly as of a reference time $t_0$, a 180-day temporal embargo was enforced around the train–test boundary, and generalization was assessed with time-ordered splits. Because venture capital decisions are made on a shortlist, evaluation emphasizes ranking quality using PR-AUC, Lift@K, Precision@K/Recall@K, and NDCG@K, reported with bootstrap confidence intervals. Under this leakage-aware protocol and with strong class imbalance, maturity-related signals achieve the strongest PR-AUC (0.0144), while team and combined signals yield the best top-50 shortlist concentration. Finally, probability calibration substantially improves reliability for threshold planning (Brier score reduced from 0.0972 to 0.0161 with sigmoid calibration) while leaving ranking essentially unchanged. Overall, the study provides a leakage-aware evaluation template and an interpretable baseline for time-dependent venture capital screening tasks involving start-up selection, investment success prediction, leakage risk, and limited review capacity. Full article

A growing body of literature argues in favor of the influence of organizational culture (OC) on firm performance (FP). Yet this consensus often emanates from studies that over-emphasize the direct culture–performance relationship, with methodologies that are deficient in revealing causal mechanisms and prone to giving ambiguous results. To address these gaps, this study proposes and tests an integrated theoretical framework, synthesizing the Schema Theory, Resource-Based View/Capability theory, and Contingency Theory of Firm Performance. This framework establishes a foundational influence mechanism of OC on performance, moving from cognitive schemas to actualized capabilities and environmental fit. Using data from 249 construction firms in Ghana, we employed a three-stage analytical process; using cluster analysis, we identified five cultural clusters, dominated by Clan and Adhocracy culture types (Organic cultures). Cross-tabulation revealed that large and resource-rich firms (D1K1 and D2K2) were more likely to exhibit balanced cultural profiles. Initial analysis using Kruskal–Wallis H Test showed no significant performance difference between balanced and organic clusters. However, when multiple regression was employed to control for firm classification and adverse industry conditions, the Balanced Culture profile emerged as a statistically significant predictor of superior performance. Consequently, we argue that while an Appropriate Culture, one dominated by organic traits and values, provides survival in a challenged environment, the Balanced Culture profile serves as a critical enabler of superior firm performance, once resource constraints and industry stressors are neutralized. Our findings hold particular importance for international–local joint ventures, where cultural alignment is a critical success factor. Additionally, the proposed framework establishes a robust theoretical foundation for future studies, especially those conceptualizing organizational culture as a foundational, independent variable. Full article

The paper aims to provide spatial evidence on where to prioritize place-based support for women’s entrepreneurship by linking the geography of female founders to the proximity of formal support infrastructure in Germany. To pursue this objective, we assemble nationwide venture microdata covering founding cohorts 2015–2021 and geocoded locations of support infrastructure. Using GIS-based (Geographic Information System) spatial analysis, in particular kernel density and hot/cold-spot mapping and bivariate correlations, we quantify proximity-entrepreneurship associations and identify policy-relevant hot- and cold-spots. Our findings reveal female entrepreneurship clusters in major urban corridors. Proximity to support infrastructure is positively associated with women founders. At 10 km buffers, correlations reach r = 0.26 for women and r = 0.31 for men. Effects attenuate at 20 km (r = 0.15 and r = 0.14). We map actionable cold-spots, i.e., places with sparse infrastructure and low female-founder presence, alongside high-performing hot-spots. As a practical implication, we propose a spatial targeting logic: resources should be concentrated in identified cold-spots via women-focused hubs, mobile advisory, and improved last-mile accessibility. Progress through a compact KPI set should be monitored. Targeted spatial support can advance gender equity in entrepreneurship while strengthening regional cohesion and efficient public spending. Full article

The entrepreneurial performance of new ventures operating within the sustainable open innovation paradigm remains underexplored, particularly in terms of how specific sustainability-oriented practices translate into measurable performance outcomes. Prior research has largely examined sustainability, entrepreneurship, and open innovation in isolation, offering limited empirical evidence on their combined effects at the early venture stage. To address this gap, this study analyzes panel data from 407 new ventures incubated in science and technology parks, employing regression-based panel data analysis to examine the relationships between sustainable practices, open innovation engagement, and entrepreneurial performance. The findings suggest that new ventures widely adopt sustainable materials and energy as key strategies, which significantly influence entrepreneurial performance. In contrast, support from local communities does not have a statistically significant impact. Among the sociodemographic factors tested, only the number of years participating in open innovation networks shows a significant effect on entrepreneurial performance. Theoretically, this study advances sustainable open innovation literature by empirically integrating sustainability practices into entrepreneurship performance models. From a managerial perspective, the findings offer actionable insights for entrepreneurs and incubator managers, highlighting which sustainability strategies and network engagements are most likely to yield performance benefits in new ventures. Full article

Exploitation alliances struggle to maintain long-term stability, adapt to changing conditions, and deliver lasting value. Although contractual governance is widely recognized as crucial to exploitation alliance performance, the role of specific clauses remains unclear, especially in turbulent environments. This study examined how contractual clauses interact and affect exploitation alliance performance. Clause-level data collected from 110 senior managers involved in Chinese residential joint ventures (JVs), which are typical examples of exploitation alliances, were analyzed using partial least squares structural equation modeling (PLS-SEM). Results indicate that constitutional clauses, such as decision-making mechanisms and the alliance manager, promote the implementation of input–output controls, which in turn boost operational efficiency, the core of sustainable performance in exploitation alliance. Input–output controls both mediate and moderate the effect of constitutional clauses on risk controls. Conversely, clauses on division of labor and risk control have no direct effect on operational efficiency. This study identifies a three-tier contractual hierarchy and shows that operational efficiency arises mainly from performance-oriented clauses, rather than protective safeguards. These insights offer a micro-foundational view on designing contracts to foster resilient, value-creating collaborations in repeated, resource-intensive ventures. Full article

Antonopoulou and Dare’s ongoing collaborative projects (Phi Books 2008: ongoing; Digital Dreamhacker 2013: ongoing) enact an open-ended, experimental set of slow ‘Fictioning’ practices and actions that involve performing, diagramming, or assembling to create or anticipate new modes of existence. In this paper, the authors use the visual essay form to evidence how their daily practices of drawing, writing, and exchanging, position art and the artist. These practices unfold without, in this case, the utilitarian, economic, and epistemic priorities and systems of reductive representation which underpin the extractive models of Generative AI and other ‘innovative’ intermediaries, systems which expedite content and regulate consumption in the cultural and creative industries and in ‘arts and humanities’ education. Focusing on their creative practices, Antonopoulou and Dare reposition commodified notions of productivity, creativity, and innovation, seeking what Haraway describes as a way ‘of making, thinking and worlding’ beyond the neoliberal imperatives of extracting profit from labour. Positioned within an era of escalating precarity combined with ecological and political instability driven by extractive colonialism, the temporality of collaboration and drawing over decades is proposed as an act of material resistance to art’s subsumption into the venture capitalist hype cycles. Such cycles are associated with an accelerating array of crises, discussed here. Full article

This study presents a hybrid analytical framework that enhances project selection by achieving reasonable predictive accuracy through the integration of expert judgment and modern artificial intelligence (AI) techniques. Using an enterprise-level dataset of 10,000 completed software projects with verified real-world statistical characteristics, we develop a three-step architecture for intelligent decision support. First, we introduce an extended Analytic Hierarchy Process (AHP) that incorporates organizational learning patterns to compute expert-validated criteria weights with a consistent level of reliability ($CR=0.04$), and Linear Programming is used for portfolio optimization. Second, we propose a machine learning architecture that integrates expert knowledge derived from AHP into models such as Transformers, TabNet, and Neural Oblivious Decision Ensembles through mechanisms including attention modulation, split criterion weighting, and differentiable tree regularization. Third, the hybrid AHP-Stacking classifier generates a meta-ensemble that adaptively balances expert-derived information with data-driven patterns. The analysis shows that the model achieves 97.5% accuracy, a 96.9% F1-score, and a 0.989 AUC-ROC, representing a 25% improvement compared to baseline methods. The framework also indicates a projected 68.2% improvement in portfolio value (estimated incremental value of USD 83.5 M) based on post factum financial results from the enterprise’s ventures.This study is evaluated retrospectively using data from a single enterprise, and while the results demonstrate strong robustness, generalizability to other organizational contexts requires further validation. This research contributes a structured approach to hybrid intelligent systems and demonstrates that combining expert knowledge with machine learning can provide reliable, transparent, and high-performing decision-support capabilities for project portfolio management. Full article

New ventures operating in volatile and unpredictable environments must rely on rapid adaptation and decisive action, making improvisation a critical entrepreneurial capability. This study examines how improvisation enhances new venture performance by uncovering the psychological and learning-based mechanisms through which its effects unfold. Drawing on the Knowledge-Based View (KBV) and Social Learning Theory (SLT), the model proposes that improvisation strengthens entrepreneurial self-efficacy, enabling entrepreneurs to approach uncertainty with greater confidence and adaptive judgment. Using a two-wave survey of 322 startup founders in Turkey and analyses conducted through PROCESS and complementary SEM estimation, the findings show that improvisation significantly boosts both entrepreneurial self-efficacy and new venture performance. Entrepreneurial self-efficacy emerges as a key mediating mechanism, indicating that improvisational experiences help entrepreneurs develop mastery, reinforce capability beliefs, and translate spontaneous action into improved outcomes. The results further suggest that improvisational episodes provide immediate learning cues that enhance situational awareness and decision-making agility, deepening the psychological pathway that links spontaneous behavior to venture performance. Additionally, relative explorative learning significantly moderates the relationship between improvisation and entrepreneurial self-efficacy, demonstrating that entrepreneurs benefit more from improvisation when they actively pursue new knowledge, experiment with unfamiliar approaches, and challenge routine assumptions. This moderating role clarifies when improvisation produces its strongest effects, while the mediating mechanism explains how performance improvements materialize through confidence-building processes. By integrating these mechanisms into a unified explanation, the study advances understanding of the improvisation–performance relationship and highlights the importance of learning-oriented behavior in converting spontaneous action into sustained entrepreneurial advantage. The findings offer theoretical contributions and actionable insights for entrepreneurs seeking to strengthen adaptability, resilience, and competitiveness in fast-changing environments. Full article

The escalating complexity, density, and agility of the modern electromagnetic environment (CME) pose unprecedented challenges to radar signal deinterleaving, a cornerstone of electronic intelligence. While traditional methods face significant performance bottlenecks, the advent of artificial intelligence, particularly deep learning, has catalyzed a paradigm shift. This review provides a systematic, comprehensive, and forward-looking analysis of the radar signal deinterleaving landscape, critically bridging foundational techniques with the cognitive frontiers. Previous reviews often focused on specific technical branches or predated the deep learning revolution. In contrast, our work offers a holistic synthesis. It explicitly links the evolution of algorithms to the persistent challenges of the CME. We first establish a unified mathematical framework and systematically evaluate classical approaches, such as PRI-based search and clustering algorithms, elucidating their contributions and inherent limitations. The core of our review then pivots to the deep learning-driven era, meticulously dissecting the application paradigms, innovations, and performance of mainstream architectures, including Recurrent Neural Networks (RNNs), Transformers, Convolutional Neural Networks (CNNs), and Graph Neural Networks (GNNs). Furthermore, we venture into emerging frontiers, exploring the transformative potential of self-supervised learning, meta-learning, multi-station fusion, and the integration of Large Language Models (LLMs) for enhanced semantic reasoning. A critical assessment of the current dataset landscape is also provided, highlighting the crucial need for standardized benchmarks. Finally, this paper culminates in a comprehensive comparative analysis, identifying key open challenges such as open-set recognition, model interpretability, and real-time deployment. We conclude by offering in-depth insights and a roadmap for future research, aimed at steering the field towards end-to-end intelligent and autonomous deinterleaving systems. This review is intended to serve as a definitive reference and insightful guide for researchers, catalyzing future innovation in intelligent radar signal processing. Full article

The importation of prefabricated buildings into colonies was a prevalent practice during the British colonial expansionist venture. However, in post-colonial Nigeria today, many of these prefabricated houses have either been largely modified or have vanished without architectural or written records. This undocumented disappearance poses a challenge to the development of architectural restoration proposals for the remaining few, especially with the authenticity of materials, as well as their morphology, configuration, use, and function being heavily contested. Among the remaining few that have undergone layers of modifications and are on the verge of total collapse is the Egbo Egbo Bassey House, imported and built in Old Calabar between 1883 and 1886 and declared a National Monument of Nigeria in 1959. Given the dearth of architectural and historical data, this paper aims to reconstruct its architectural morphology, chronological modification, and historical uses and functions, with the view of developing an evidence-based architectural restoration proposal for its adaptive reuse. The data was collected through semi-structured interviews (n = 16), archival research at the National Museum (archival file ID: TF128/C.25/A and TF120/C.20/A), and a measured architectural survey, which was performed using laser tapes and laser rangefinders. Annotated building images were captured using a Canon 5D Mark III and a DJI Marvic 3 drone. Comparative analysis with two other exemplars of prefabricated houses in the region was also conducted to consolidate oral, archival, and field data. Three architectural modification stages, namely 1886, 1959, and 2012, were determined for the analytical framework. Architectural outputs include measured 2-dimensional drawings (scale 1:50) and 3-dimensional models for the three historical stages. The accuracy of each model was ensured through methodical triangulation and confidence rubric ratings. The result of this paper provides a replicable inquiry methodology, which can be used to develop an evidence-based workflow for developing a restoration proposal for architectural heritage in contexts where architectural and historical data are not available or contested. As a limitation, this research does not include an analysis of wood typology, structural testing, and statistical analysis of material. Full article

In the context of rapidly developing emerging industries, shifting investment hotspots, and a turbulent external environment, investment institutions continuously adjust and manage their ego network strategies to ensure survival and promote sustainable development. The long-term development and competitiveness of venture capital (VC) firms largely depend on their ability to generate excess returns and achieve successful exits, such as IPOs and mergers and acquisitions. This study focuses on venture capital ego networks from a dynamic perspective. From both the node and tie dimensions, it systematically examines the effects of ego network dynamics—growth and diversity—on investment performance. It further explores the underlying mechanisms through network stability and information diffusion. Based on empirical analysis using Wind database data from 2013 to 2022, we find that the growth of VC ego networks has a significant negative effect on investment performance, and this effect works through reduced network stability. In contrast, ego network diversity shows a significant positive effect on investment performance, with project information diffusion playing a mediating role. Based on the above findings, we suggest that venture capital firms should shift their ego network management strategy from blind and simple “rapid expansion” to quality-focused “careful cultivation”. While maintaining the stability of their ego networks, firms should also pay attention to the diversity of relationship configurations, so as to better transform network resources into investment performance and promote the growth and sustainable development of venture capital firms. Full article

While entrepreneurship is recognized as a key driver of economic development, barriers such as financing constraints, regulation, and inefficient capital allocation continue to limit its potential, especially in Widening countries. This study examines how different financing mechanisms contribute to improving business performance in innovative companies and whether such instruments trigger business model adaptation. A quantitative survey was conducted among 81 companies, and the collected data was analyzed using correlation analyses to test predefined hypotheses. The findings indicate that financing mechanisms significantly influence business improvement, with grants and venture capital showing the strongest effects. Financing also often induces adaptations in business models, partially confirming the hypothesis that such changes enhance financial outcomes. The analysis of the Global Entrepreneurship Index (GEI) further reveals that, although higher-ranked countries tend to perform better overall, no strong correlation exists between GEI ranking and access to financing. The study contributes to the literature by emphasizing the differentiated effects of financing instruments on firm growth and by offering theoretical insights and practical implications for policymakers seeking to strengthen entrepreneurial ecosystems in emerging regions. Full article