Search Results (200)

Innovative small and medium-sized enterprises (SMEs) in European Widening Countries face persistent financing gaps, yet empirical evidence on how modern financing instruments shape post-investment performance trajectories remains scarce. This study applies a decision tree classification approach to examine post-investment revenue growth of 57 innovative SMEs across twelve European Widening Countries that received alternative financing grants, venture capital, business angel investment, or crowdfunding—between 2020 and 2022. Two research questions are addressed: which pre-investment firm characteristics predict revenue growth following modern financing, and how does the initial revenue level shape post-investment performance trajectories over a three-year observation window. Variable importance analysis indicates that pre-investment revenue level accounts for 83.9% of the predictive importance in the CART model, with development stage as the only secondary predictor (16.1%). Financing type was not identified as a discriminative predictor within the present sample. Low-revenue firms benefit most consistently from alternative financing, advancing an average of 1.51 revenue categories over three years, with 82.9% of firms exhibiting a growth trajectory. Medium-revenue firms exhibit a delayed growth pattern; and higher-revenue firms show persistent stagnation, suggesting a possible ceiling effect in post-investment revenue growth. The RQ1 model achieved cross-validated accuracy of 65.0%; the RQ2 model achieved 38.3%, reflecting the complexity of predicting four trajectory categories from a limited sample. These findings suggest that pre-investment firm characteristics may warrant greater attention alongside financing type when interpreting post-investment SME performance in Widening Country ecosystems. Complementary Logistic Regression and Random Forest analyses yielded broadly consistent results, providing additional support for the robustness of the reported findings. Full article

This paper investigates two marketing strategies a reward-based crowdfunding platform employs to align its preferences with an entrepreneur’s choice of pledge and target levels. These are (a) how to promote campaigns to potential backers, and (b) how to share campaign revenues with the entrepreneur. Kickstarter, for instance, promotes a set of campaigns by compiling a list of “recommended” projects. This research shows that the platform’s choice of the promotion rule may expose entrepreneurs to the risk of not generating sufficient funds to start production, which can damage their and the platform’s reputation. When the platform’s reputational risk is not very high, it reduces the risk of non-delivery by increasing the revenue share of the entrepreneur. The platform’s strategies are likely to ensure production when backers derive warm glow from pledging, when the entrepreneur’s development cost is low, or when the entrepreneur has minimal reputational cost if production fails. However, low reputational costs motivate the entrepreneur to lower the target, thus increasing the likelihood of insufficient funds to start production. We propose strategies the platform can use, including customizing the revenue share based on the campaign characteristics, to rectify such misalignments. Full article

Drawing on signaling theory, this study investigates the role of lead founders’ human capital signals in the success outcomes of equity crowdfunding (ECF) campaigns. While prior research emphasizes entrepreneurial teams or broadly defined founder characteristics, the role of dominant entrepreneurial actors remains underexplored. We focus on the lead founder, defined as the individual combining founder status, CEO authority, and ownership concentration, as the primary signal carrier in ECF contexts. Using a multi-platform dataset of 1067 campaigns from Republic Europe, Crowdcube, Mamacrowd, and Invesdor (2012–2024), we examine how lead founders’ education and experience shape investor decisions. Our results indicate that industry-related education is the strongest predictor of the number of investors. Furthermore, while industry experience alone can positively predict investor engagement, its role disappears once education is accounted for, suggesting that education in industry-related fields can outweigh industry experience in shaping investor perceptions. Additionally, our findings suggest that entrepreneurial experience and attendance at a top-ranked university do not contribute meaningfully to explaining investor participation. Accordingly, the study contributes to the human capital signaling literature by showing that investors evaluate the incremental informational value of human capital signals rather than assessing each signal independently, and highlights the centrality of the lead founder in decision-making under highly uncertain crowdfunding environments. Full article

The energy sector’s transition to a circular economy (CE) is critical for achieving global decarbonization and resource security. The primary objective of this systematic literature review is to examine the co-evolution of circular business models (CBMs) and green financing mechanisms across the energy value chain. To achieve this, we synthesized 93 high-impact studies published between 2015 and 2024, which were retrieved from the Web of Science and Scopus databases. Using the 10R hierarchy as an analytical framework, this study identifies a strategic shift from low-order recycling to high-value circularity, such as rethink, repurpose, and remanufacture. We analyze the role of the EU Taxonomy, green bonds, and equity crowdfunding in de-risking circular investments, while highlighting the “transparency paradox” in second-life markets and the “efficiency-waste trade-off” in rapid technological turnovers. Our findings reveal that while digital catalysts like blockchain and AI optimize resource flows, their scaling is hindered by a lack of empirical validation and fragmented regulations. The review concludes by proposing a “regulatory-technical nexus” for future research, emphasizing the need for circular digital twins and standardized decommissioning protocols to bridge the gap between theoretical optimization and operational reality in the renewable energy sector. Full article

Artificial intelligence is increasingly embedded in consumer-facing FinTech, but trust in AI-enabled finance depends not only on performance, but also on whether users can understand and appropriately evaluate algorithmic outputs. This review synthesizes research on AI disclosure, explainability, and related transparency cues in consumer-facing FinTech, with particular attention to whether these cues support trust calibration rather than merely increasing trust or adoption. Searches in Scopus and Web of Science identified nine formally included studies and six adjacent contextual studies. The available evidence base is concentrated in robo-advisory and adjacent AI-enabled investment advising, with only limited evidence on automated credit decisions and crowdfunding recommendation platforms. The most studied cues are explanation/explainable AI and broader advisory or platform transparency, whereas disclosure, responsibility attribution, user control, and information-quality cues remain underexamined. Across the formal corpus, transparency cues are generally associated with more positive trust-related outcomes, especially trust and adoption-oriented responses. However, only a small subset of studies addresses trust calibration through outcomes such as reliance, fairness, accountability, and contestability. Overall, the current literature supports transparency more strongly as an acceptance mechanism than as a basis for appropriately bounded trust. 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

The rapid evolution of fintech has accelerated the integration of blockchain technology and cryptocurrencies into crowdfunding platforms, reshaping entrepreneurial finance and challenging traditional conceptions of money, intermediation, and financial risk. This study empirically examines the socio-cultural, demographic, and behavioural factors influencing funders’ perceptions and investment decisions in crypto-crowdfunding, an emerging model situated at the intersection of digital currencies, financial inclusion, and decentralised capital formation. Using primary survey data from a focus group of 50 respondents measuring perceptions through a structured five-point Likert questionnaire, the analysis investigates how risk perception, trust and security, investor awareness, and perceived benefits shape participation in crypto-crowdfunded projects. The findings indicate that blockchain-based features such as transparency and decentralisation are associated with variations in perceived trust and risk assessment, rather than uniformly enhancing investor confidence. Socio-demographic characteristics emerge as significant determinants of investor awareness, perceived risks, and expected benefits, confirming pronounced behavioural heterogeneity in digital-finance participation. Regression results reveal strong interdependencies between trust, risk perception, and awareness, underscoring the importance of informational quality and risk-governance mechanisms in supporting sustainable adoption. By providing empirical evidence on individual-level determinants of participation in crypto-crowdfunding, the study contributes to the literature on the future of money by clarifying how crypto-crowdfunding operates as a behavioural-financial phenomenon embedded in decentralised governance structures. Full article

The COVID-19 pandemic disrupted traditional financing channels, accelerating the adoption of crowdfunding as an alternative capital-raising mechanism across Africa. Limited access to finance for women entrepreneurs remains a pivotal issue globally, particularly in Africa, where most women are significantly underrepresented. This study investigates the role of gender differences in campaign design, communication, and signalling in crowdfunding success during the COVID-19 pandemic, rather than focusing on gender as a direct factor. Guided by signalling, Congruity, Feminist Economics, and Social Network theories, the research investigates whether gender-related differences in campaign presentation and communication strategies contributed to variations in funding outcomes. A quantitative research approach was adopted to analyse reward-based crowdfunding campaigns launched between March 2020 and December 2020. The study measures success through funding attainment ratios and goal achievement rates. The findings reveal nuanced gender effects, with male-led campaigns exhibiting strengths in social engagement and narrative appeal, while female-led campaigns demonstrated advantages in higher average contribution sizes. This awareness contributes to the literature on gender and entrepreneurial finance in crisis contexts, offering practical implications for platform design, policy interventions, and inclusive funding strategies in Africa’s evolving crowdfunding ecosystem. In line with the empirical results, the impact of gender is mostly indirect and mediated by campaign variables such as updates, videos, and engagement intensity. Full article

Energy Communities (ECs) have emerged as central legal instruments for decentralized renewable energy deployment across Europe; however, their long-term viability depends critically on financial sustainability mechanisms that remain inadequately understood. This study examines the economic foundations of ECs through a narrative literature review of revenue generation, cost allocation, and the capital mobilization pathways in three representative European markets (Germany, Spain, and Italy). A structured Scopus database search identified 280 peer-reviewed studies published between 2019 and 2025. Following systematic screening, 89 articles were selected for analysis through bibliometric mapping in R (Biblioshiny) and qualitative synthesis in NVivo. The analysis reveals that stable feed-in tariffs, tax incentives, and self-consumption remuneration schemes form the primary revenue mechanisms, while cost management effectiveness varies substantially across countries due to differing grid-charge structures and administrative frameworks. Capital access remains constrained for smaller communities despite hybrid financing innovations combining public grants, cooperative equity, and emerging crowdfunding mechanisms. Regulatory heterogeneity, high upfront investment requirements, and limited institutional credit availability continue to impede scalability. The findings emphasize that achieving widespread EC adoption requires harmonized policy frameworks, transparent cost-sharing arrangements, and diversified investment instruments that align local participation with national decarbonization objectives while ensuring equitable access across diverse socio-economic contexts. Full article

Crowdfunding has emerged as an alternative funding source among entrepreneurs, businesses, and industries. In recent years, research on machine learning-based project classification models has been conducted with the aim of predicting the success of crowdfunding campaigns, both for entrepreneurs and investors. However, most of the research has focused on classification approaches using non-content information such as project metadata, creators’ behavior, and social history, but there have been few attempts to use text content data per se, particularly in order to provide explanations and evidence for how the prediction decisions were made. To address this point, we propose to use a deep learning-based approach called Hierarchical Attention Network (HAN) to predict the success of crowdfunding campaigns and provide explanation and justification of the prediction decisions using attention weights. We collect publicly available data of crowdfunding campaigns and build our success prediction model with an accuracy of 86.38% and 87.29%, using an Updates section and backers’ comments in a Comments section, respectively. We also explore the feasibility of early success prediction during the funding period (up to 2 months), with as much as 80.99% accuracy in 1 to 2 months. Finally, we examine word and sentence attention weight scores to clarify key factors in predicting crowdfunding success. Full article

In the context of tightening sustainability regulations and rising demands for transparent and responsible capital allocation, understanding how digital financial innovations influence market efficiency has become increasingly important. This study examines the impact of Financial Technology (FinTech) solutions and crowdfunding platforms on sustainable market efficiency, volatility dynamics, and risk structures in the United Kingdom. Using weekly data for the Financial Times Stock Exchange 100 (FTSE 100) index from January 2010 to June 2025, the analysis applies the Lo–MacKinlay variance ratio test to assess compliance with the Random Walk Hypothesis as a proxy for informational efficiency. Firm-level proxies for FinTech and crowdfunding activity are constructed using the Nomenclature of Economic Activities (NACE) and Standard Industrial Classification (SIC) systems. The empirical results indicate substantial deviations from random-walk behavior in crowdfunding-related market segments, where persistent positive autocorrelation and elevated volatility reflect liquidity constraints and informational frictions. By contrast, FinTech-dominated segments display milder inefficiencies and faster information absorption, pointing to more stable price-adjustment mechanisms. After controlling for structural distortions through heteroskedasticity-consistent corrections and volatility adjustments, variance ratios converge toward unity, suggesting a restoration of informational efficiency. The results provide relevant insights for investors, regulators, and policymakers seeking to align financial innovation with the objectives of sustainable financial systems. Full article

When are emotional appeals effective for entrepreneurial ventures seeking funding? Questioning the assumption that all positive emotions are equally effective, we propose and empirically validate the idea that only emotions well-aligned with an organization’s purpose retain their signaling value. We use EmoBERTa, a transformer-based emotion detector, to analyze 275,197 Kickstarter campaigns (2009–2020) and find that emotional expression generally reduces campaign success, indicating increased professionalism in the crowdfunding marketplace. However, campaigns with an explicit prosocial mission are more successful when they express caring emotions compared to other positive emotions (admiration, desire, excitement, joy, love, and optimism). We also found that this effect is more pronounced when campaigns receive no institutional endorsement, such as Kickstarter staff picks. Together, this suggests that emotional appeals matched to an organization’s mission are most effective, especially when the projects are not institutionally recognized. Together, this study enhances our understanding of when and why certain positive emotions are more persuasive than others, challenging conventional wisdom that all positive emotions are equally effective. Full article

Crowdfunding platforms like Kickstarter have reshaped early-stage financing by allowing entrepreneurs to connect directly with potential supporters. As a fast-expanding part of digital commerce, crowdfunding offers significant opportunities but also substantial risks for both entrepreneurs and platform operators, making predictive analytics an essential capability. Although crowdfunding shares some operational features with traditional e-commerce, its mix of financial uncertainty, emotionally charged storytelling, and fast-evolving social interactions makes it a distinct and more challenging forecasting problem. Accurately predicting campaign outcomes is especially difficult because of the high-dimensionality and diversity of the underlying textual and behavioral data. These factors highlight the need for scalable, intelligent data science methods that can jointly exploit structured and unstructured information. To address these issues, this study proposes a novel AI-based predictive framework that integrates a Convolutional Block Attention Module (CBAM)-enhanced symmetric autoencoder for compressing high-dimensional Generative AI (GenAI) BERT embeddings with meta-heuristic feature selection and advanced classification models. The framework systematically couples attention-driven feature compression with optimization techniques—Genetic Algorithm (GA), Jaya, and Artificial Rabbit Optimization (ARO)—and then applies Long Short-Term Memory (LSTM) and Gradient Boosting Machine (GBM) classifiers. Experiments on a large-scale Kickstarter dataset demonstrate that the proposed approach attains 77.8% accuracy while reducing feature dimensionality by more than 95%, surpassing standard baseline methods. In addition to its technical merits, the study yields practical insights for platform managers and campaign creators, enabling more informed choices in campaign design, promotional tactics, and backer targeting. Overall, this work illustrates how advanced AI methodologies can strengthen predictive analytics in digital commerce, thereby enhancing the strategic impact and long-term sustainability of crowdfunding ecosystems. Full article

Sustainable transport innovation often faces funding gaps, as traditional public and private sources rarely support early-stage or high-risk initiatives. Crowdfunding, enabled by digital transformation, is emerging as a complementary financing mechanism for this sector. This study presents a literature review combined with bibliometric mapping to examine the evolving research landscape on crowdfunding in transport. Three research questions guide the analysis: RQ1—What are the dominant research areas at the intersection of crowdfunding and transport? RQ2—What types of transport projects are financed via crowdfunding? RQ3—What research gaps and future directions emerge for transport innovation financing? Findings reveal three core research areas: (1) Sustainability and finance, (2) Fintech and blockchain, and (3) Management and consumer behavior. We propose a typology of crowdfunded transport projects comprising five categories: (1) Large-scale transport infrastructure, (2) Sustainable local mobility, (3) Innovative start-ups, (4) New business models, and (5) Advanced systems and technologies. This demonstrates crowdfunding’s versatility beyond traditional infrastructure, supporting high-risk innovations critical for decarbonization and technological transformation. The study highlights domain-specific challenges—such as integrating PPP models with digital finance and ensuring investor protection—and emphasizes crowdfunding’s role as an enabler of low-carbon transition aligned with global climate strategies (EU Green Deal, SDGs). Despite its potential, investor safety remains a major concern. Policy implications include sandbox regulation, standardized risk assessment, and operationalizing PPP–crowdfunding hybrids to unlock large-scale and innovative transport projects. Full article

This article examines the growing relevance of photovoltaic (PV) energy amid rising electricity demand, sustainability goals, and the need for flexible energy management in households and enterprises. It analyzes six PV business models, ownership, leasing, Power Purchase Agreement (PPA), energy communities/peer-to-peer (P2P), crowdfunding, and subscription-based Solar-as-a-Service, using the Business Model Canvas (BMC) framework. A systematic literature review was combined with a unified BMC for each model, enabling structured comparison of value propositions, customer segments, cost structures, revenue streams, and risk allocation. The results show that no single universal model exists; each addresses different financial capacities, risk preferences, and strategic needs of households, SMEs, large enterprises, and energy communities. Significant differences were found in investment requirements, operational involvement, scalability, and potential for energy independence. The study’s novelty lies in providing a coherent, cross-model comparison using a standardized BMC approach, offering insights not systematically explored in previous research. These findings support informed decision-making for organizations considering PV adoption and provide a basis for further research on innovative energy management strategies. The topic is highly relevant in the context of the accelerating global energy transition, technological advances, regulatory changes, and increasingly diverse customer profiles, highlighting the need for comprehensive comparative analyses to guide flexible photovoltaic deployment. Full article