Search Results (29)

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

The rapid rise in the popularity of cryptocurrencies has drawn increasing attention from investors, entrepreneurs, and the public in recent years. However, this rapid growth comes with risk: many coins fail early and become what are known as “dead coins”, defined by a lack of recorded activity for more than a year. This study applies deep learning techniques to estimate the short-term risk of a cryptocurrency’s death. Specifically, three Recurrent Neural Network architectures, Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), and Gated Recurrent Unit (GRU), were trained on 18-month time series of daily closing prices and trading volumes using a stratified five-fold cross-validation framework. The models’ predictive performances were compared across input windows ranging from 10 to 180 days. Using the previous 180 days of data as input, GRU achieved the highest point accuracy of 0.7134, whereas BiLSTM exhibited the best performance when evaluated across input sequence lengths varying from 10 to 180 days, reaching an average accuracy of 0.676. These findings show the ability of recurrent architectures to anticipate short-term failure risks in cryptocurrency markets. Theoretically, the study contributes to financial risk modeling by extending time series classification methods to cryptocurrency failure prediction. Practically, it provides investors and analysts with a data-driven early-warning tool to manage portfolio risk and reduce potential losses. Full article

This research examines the drivers of lean implementation in sustainable energy enterprises (SEEs) to balance efficiency, sustainability, and competitiveness. This research investigates the interdependence among lean drivers and classifies them by driving power and dependence. This study followed a novel mixed-method approach combining a systematic literature review for driver identification, interviews with entrepreneurs for expert consensus, and analysis using total interpretive structural modelling (TISM), cross-impact matrix multiplication applied to classification (MICMAC), and a graph-theoretic approach (GTA). The result indicated that leadership commitment, teamwork and collaboration, and time management are high drivers; cost reduction, resource optimization, and continuous improvement are linkage drivers; and customer focus and flexibility are found as dependent drivers, revealing the sustainable outcome. This provides a structured pathway for the SEEs for the lean implementation drivers, where prioritization is required. The exploration adds to the Resource-Based View, dynamic capability theory, system theory, etc. The study calls for policymakers’ interventions in designing capacity-building programmes, leadership training, and collaborations. This research incorporated the antecedents–decisions–outcomes (ADO) framework for highlighting the antecedents, leading to decisions, and the outcomes of the choices, with future research questions connecting with multiple sustainable development goals (SDGs), such as SDG7, SDG9, SDG12, and SDG13. Full article

Sustainable energy entrepreneurs promote sustainable development by focusing more on energy efficiency. This study examines the interdependence and driving–dependent relationships among the socioeconomic factors (SEFs) influencing sustainable energy enterprises (SEEs). A mixed-methods approach is used, beginning with a literature review and expert consensus, followed by total interpretive structural modeling (TISM) and cross-impact matrix multiplication applied to classification (MICMAC) analysis. Seven key SEFs are finalized through interviews with 12 experts. Data are then collected from 11 SEEs. The study reveals that the regulatory and institutional framework emerges as the primary driving factor influencing other SEFs, including financial accessibility, market demand, technological innovation, and infrastructure readiness. Social and cultural acceptance is identified as the most dependent factor. The study proposes future research directions by identifying the United Nations sustainable development goals (SDGs) related to the antecedents, decisions, and outcomes with theoretical linkages through the Antecedents–Decisions–Outcomes (ADO) framework. The major SDGs identified are SDG 4 (education), SDG 7 (energy), SDG 9 (industry), SDG 11 (communities), and SDG 13 (climate). The study highlights that regulatory support, funding access, skill development, and technology transfer are required areas for strategic focus. Understanding the hierarchy of SEs supports business model innovation, investment planning, and risk management. Full article

In urban environments, eating and drinking out (EDO) is a widespread activity among residents and visitors, generating a wealth of digital footprints that reflect consumer experiences. These digital traces provide businesses with opportunities to enhance their services and guide entrepreneurs in selecting optimal locations for new establishments. This study investigates the relationship among urban spatial features, pedestrians and digital consumer interactions at EDO venues. It highlights the utility of integrating urban mobility and spatial data to model digital consumer behavior, offering potential urban planning and business strategies. By analyzing Melbourne’s city center, we evaluate how factors, such as pedestrian count by sensors on the streets, residential density, the centralities and geometric properties of streets, and place-specific characteristics, influence consumer reviews and ratings on Google Maps. The study employs a random forest machine learning model to predict review volumes and ratings, categorized into high and low classes. The results indicate that pedestrian counts and residential density are key predictors for both metrics, while centrality measures improve the prediction of visitor scores but negatively impact review volume predictions. The geometric features of streets play varying roles across different prediction tasks. The model achieved a 65% F1-score for review volume classifications and a 62% for visitor score. These findings not only provide actionable understanding for urban planners and business stakeholders but also contribute to a deeper understanding of how spatial dynamics affect digital consumer behavior, paving the way for more sustainable urban development and data-driven decision-making. Full article

One of the main challenges faced by many rural areas is the presence of natural constraints, such as climatic conditions, steep slopes, and poor soil quality, which make agricultural production and activities more difficult and costly. In these areas, there is a significant risk of agricultural land abandonment, leading to potential losses in biodiversity, degradation of rural landscapes, desertification, and increased forest fire risk. The Common Agricultural Policy (CAP) aims to mitigate these risks through specific payment schemes provided to areas facing natural and other specific constraints. In this context, mapping and measuring territorial differentiation is essential for informing policy responses. At the end of the previous CAP programming period, the EU updated its classification of Less Favored Areas (LFAs), experimenting with a flexible approach based on common biophysical criteria (definitions and thresholds) and methodological guidelines to delineate territorial differentiations that are both locally relevant and comparable across member states. This contribution presents a review of the current state of data and spatial inference systems used in Italy to delineate biophysical limitations and assess the presence of factors that may help offset the impact of natural constraints. This process has supported the analysis of territorial differentiation and highlighted the related implications for agricultural entrepreneurs operating in diverse contexts. Full article

With the growing awareness among entrepreneurs and consumers within the tourism industry regarding the field of sustainable development, it seems necessary to base hotel classification systems on an integrated approach to management quality, including the fulfillment of ecological and environmental criteria. However, this topic has yet to be recognized in the literature, especially concerning the European market. Therefore, this article aims to fill this research gap and indicate how environmental standards have been implemented in the European system of hotel classification—Hotelstars Union (HSU)—and that in force in Poland. With regard to these systems, the differences in the classification criteria taken into account by the certifying organizations in the European Union (EU) and the administrative hotel categorization system in Poland are pointed out. Particular emphasis is placed on the criteria related to environmental management in the hotel industry. The criteria related to environmental management were identified through an analysis of the criteria of eight environmental management systems that are applicable to the contemporary hotel industry. The results of this research prove that, even though the HSU system takes the principles of sustainable development into account to a much greater extent than the Polish system, its primary weakness is that most of its criteria related to the environment are optional. In addition, the optional criteria have higher weights than the obligatory ones. In turn, the Polish classification system makes very little reference to environmental standards. Moreover, it is not detailed, and the assessment criteria are not up to date. Therefore, it is recommended that a greater number of environmental criteria be included in the next update of the HSU system, which should be obligatory for hotels to meet. In addition, the sustainability criteria included in the HSU system should be adapted to the policy system aimed at the hotel industry in Poland and other EU countries. Full article

Environmental entrepreneurship is considered a critical avenue for sustainable development. Despite increasing studies on its benefits and impacts, we lack insights about its corresponding process. There is an increasing need for policymakers to know how to develop this process and what is required for its definition. This research aims to build an innovative theoretical model to explore the trigger factors for the environmental entrepreneurship process in Saudi Arabia, which is the ultimate focus point of this research. To achieve this objective, a deep literature review and a survey of expert entrepreneurs’ opinions were performed. Experts and professionals in entrepreneurship validated the opportunity of readiness factors identified. Data were gathered using a combined approach based on closed-ended questionnaires and arranged interviews according to a schedule. Employing the mixed influence matrix of cross-impact multiplications applied to classification (MICMAC) and the Interpretive Structural Modeling (ISM) approach, the research identifies a hierarchical model of these factors, addressing why and how they interact according to a specific order and priorities. The investigation reveals 15 factors that are structured into three main levels of influence: (1) factors driving the adoption of environmental entrepreneurship orientation, (2) factors related to environmental entrepreneurship intention, and (3) factors for materializing environmental entrepreneurship behavior. The findings emphasize the complementarity between environmental entrepreneurship intention and orientation as a key factor in generating entrepreneurial behavior. This research pioneers the empirical exploration of environmental entrepreneurship as a process. It contributes significantly to theoretical and practical domains by offering a pragmatic framework and better understanding for policymakers and stakeholders to focus on key factors that facilitate this process. This paper is innovative because it uses the integrative ISM–MICMAC approach, supported by a primary and brief bibliometric analysis of entrepreneurship. Full article

This paper aims to describe a highly pervasive and under-reported context-specific phenomenon of entrepreneurship, specifically in light of the rise in different types of entrepreneurial activities in Africa. Explicably, this paper accepts that entrepreneurship typologies are different across the globe. Utilising the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA), a total of 1230 articles were discovered from Scopus, Web of Science, and ProQuest databases. Subsequently, a screening for suitability was conducted, resulting in the exclusion of 1200 articles based on criteria such as publication year, jurisdiction, and abstract review. Ultimately, 30 journal articles were imported into Zotero Reference Manager and Atlas for further examination. This study found that a state of entrepreneurship mishap exists in Africa. The utilisation of a uniform approach in supporting entrepreneurs is identified as another challenge. Lastly, this study introduces nine novel entrepreneur typologies: Lifepreneurs, Part-timers, Hobbypreneurs, Entremployees, Empreneurs, Techpreneurs, Carte-blanche, Profeneurs, and Smartpreneurs. These typologies serve as a conceptual framework for categorising various entrepreneurial initiatives based on a true reflection of the African entrepreneurial ecosystem, attributes, and key success factors. Theoretically, our paper fills a gap in the academic literature regarding the study of entrepreneurship in developing countries and its classifications. Practically, this paper could offer African policymakers a blueprint for supporting startups at a local level. Significantly, this work stands out for providing a classification system that captures the authentic nature of entrepreneurial undertakings within African economies. Full article

With the rapid development of information technology, digital platforms (DPs) have emerged as an indispensable aspect of the entrepreneurial landscape, which has a profound impact on entrepreneurial innovation and practice. However, there remains a gap in understanding the specific ways in which DPs influence the entrepreneurial process. Therefore, within the framework of information management theory, this study aims to deepen the understanding of how DPs foster entrepreneurial innovation and enhance entrepreneurial effectiveness, thereby enhancing the overall efficiency of DPs. Firstly, the concept of information management in contemporary social enterprise management is thoroughly examined and reviewed to establish a robust theoretical foundation for further research. Secondly, the conceptual framework and core elements of DPs are extensively discussed, elucidating their role and function in the entrepreneurial process. On this basis, a theoretical model illustrating the impact of DPs on entrepreneurial innovation and activities is formulated, along with six specific research hypotheses. These hypotheses encompass the effects of DPs on entrepreneur behavior, enterprise development, and the platform–entrepreneur relationship. To verify the validity of these hypotheses, a questionnaire survey involving 398 entrepreneurs is conducted to facilitate in-depth communication. Simultaneously, the various attributes of entrepreneurs are categorized, and the resulting classifications are thoroughly analyzed and evaluated. The results show that the DP plays a positive role in promoting the development of platform enterprises, improving intrapreneurship activities, and enhancing the ability of intrapreneurship. In addition, DPs can effectively regulate the relationship between platform enterprises and entrepreneurs and promote the self-development of entrepreneurs. Furthermore, the entrepreneurial growth process has positively influenced platform enterprises. This finding strongly supports the practical application of DPs and provides valuable guidance for entrepreneurs to leverage them effectively in fostering innovation and entrepreneurial activities in the digital era. Meanwhile, this offers crucial insights for platform enterprises on enhancing their service offerings to entrepreneurs and fostering the sustainable development of the entrepreneurial ecosystem. In summary, through rigorous theoretical discourse and empirical analysis, this study emphasizes the significant role of DPs in the entrepreneurial journey, providing robust backing for their continued advancement and utilization in the entrepreneurial realm. Full article

Entrepreneurship can provide a creative, disruptive, problem-solving-oriented approach to the current economic, environmental, and social challenges of the world. This article aims to provide an analysis about the way universities can have an impact on developing entrepreneurial competence in students through extracurricular activities. The research relies on a questionnaire survey of students at the University of Petrosani, who participated in a range of entrepreneurial activities both online during the COVID-19 pandemic and face-to-face afterwards. The methodology consisted of applying principal component analysis to reduce the dimensionality of the indicators, followed by classification of the respondents through cluster analysis and training of a feedforward neural network. After finishing the network-training process, the error was minimized, resulting in three classes of respondents. Furthermore, based on the three classes, follow-up conclusions, policies, and decisions can be issued regarding the perception of entrepreneurship at the societal level, which is beneficial for academia and entrepreneurs, as well as for future research undertaken in this field. The key conclusion of our research is that entrepreneurship education is a real facilitator of the transition to sustainable entrepreneurship. Students perceived meeting successful entrepreneurs as being among the most effective extracurricular activities, assessing online activities as useful, and the field of study proved to be an important factor in their entrepreneurial intention. Full article

The seasonal nature of fruits and vegetables has an immense impact on the process of seeking methods that allow extending the shelf life in this category of food. It is observed that through continuous technological changes, it is also possible to notice changes in the methods used to examine and study food and its microbiological aspects. It should be added that a new trend of bioactive ingredient consumption is also on the increase, which translates into numerous attempts that are made to keep the high quality of those products for a longer time. New and modern methods are being sought in this area, where the main aim is to support drying processes and quality control during food processing. This review provides deep insight into the application of artificial intelligence (AI) using a multi-layer perceptron network (MLPN) and other machine learning algorithms to evaluate the effective prediction and classification of the obtained vegetables and fruits during convection as well as spray drying. AI in food drying, especially for entrepreneurs and researchers, can be a huge chance to speed up development, lower production costs, effective quality control and higher production efficiency. Current scientific findings confirm that the selection of appropriate parameters, among others, such as color, shape, texture, sound, initial volume, drying time, air temperature, airflow velocity, area difference, moisture content and final thickness, have an influence on the yield as well as the quality of the obtained dried vegetables and fruits. Moreover, scientific discoveries prove that the technology of drying fruits and vegetables supported by artificial intelligence offers an alternative in process optimization and quality control and, even in an indirect way, can prolong the freshness of food rich in various nutrients. In the future, the main challenge will be the application of artificial intelligence in most production lines in real time in order to control the parameters of the process or control the quality of raw materials obtained in the process of drying. Full article

This paper maps the cultural ecosystem services (CES) of a well-known giant-wave hotspot located in Nazaré, Portugal. The paper adopts a qualitative approach combining an auto-ethnographic direct observation of a journey and the content analysis of photos and videos posted on the YouTube and Facebook pages of tourists and operators. A total of 44 geotagged photos from a sample of 6914 photos retrieved from Flickr allowed the classification and spatial distribution of several CES: (1) recreational—surf activities; (2) aesthetic—photography; (3) spiritual—dark tourism and risk recreation; (4) intangible heritage—maritime knowledge; (5) scientific—wave height forecast; (6) sense of place; and (7) social relations. The paper also proposes a theoretical framework that highlights the pull drivers (risk recreation, storm chasing, or spectacular death voyeurism) and the push drivers (e.g., marketing campaigns and wave forecasts alerts) that explain the behaviors of the big-wave spectators/chasers during the experience journey. Public decision-makers, destination marketing organizations, tourism operators, and business entrepreneurs must acknowledge the relevance of journey mapping in order to identify the moments of stress and the touchpoints associated with peak/positive experiences generated by these CES. This study confirms some push and pull factors assessed by previous studies. Full article

Different types of entrepreneurial activities are more or less conducive to socio-economic development. Among others, opportunity entrepreneurs are found to have a greater impact on economic growth, innovation, and employment compared to necessity entrepreneurs (pushed by the risk of being unemployed). In this context, the main goal of the study is to find the answer to the following research question: Which business sectors in European countries are sensitive to the unemployment push effect and thus more prone to necessity entrepreneurship? The paper provides further insight into the unemployment push hypothesis by investigating this issue in the group of 20 European countries across 11 business sectors (NACE Rev. 2 classification): manufacturing, construction, and services of business economies (nine sectors). The issue is examined by analyzing Eurostat and World Bank data for 2004–2020 using the panel vector autoregression (p-VAR) approach. The results confirm the unemployment push effect only in wholesaling, retailing, and repair services. The effect, however, seems to be triggered by entry behaviors in the Central Eastern European countries, which are more active in creating new businesses and more prone to necessity entrepreneurship. In addition, the interplay between unemployment and new enterprise dynamics in wholesaling, retailing, and repair services seems to be relatively more robust to existing heterogeneity within entrepreneurs, countries, and estimation techniques. The implications of the results could assist policymakers responsible for active labor market instruments promoting entrepreneurial attitudes. Full article

The study analyzes the performance levels achieved by Romanian wholesaler SMEs from Hunedoara County and to advance some practical courses of action designed to support local entrepreneurs in improving efficiency and in embracing circular economy (CE) practices. We employed cluster and discriminant analyses with a focus on providing an accurate classification of trade SMEs, according to their performance. Three distinct classifications of SMEs resulted in: (i) a class of enterprises displaying high levels of the rates of return, whose business owners exhibited an increased risk appetite and applied investment policies focusing on future development by means of incorporating digital instruments; (ii) a class of SMEs displaying average levels of rates of return, which manage to survive in situations of crisis through maintaining the position previously gained on the market; and (iii) a class of SMEs exhibiting a precarious financial position and employing defensive strategies, with managers who are reluctant to take risks in the innovation processes. Furthermore, we present a cloud computing solution which was tailored for one of the companies from our sample that appeared to be the most likely to carry out the transition from the average SME performance class to the higher class. The main advantages that are brought about by the IT solution in terms of environmental sustainability consist of: improvements in energy efficiency, the decrease in carbon footprints, the lowering of operational costs and the diminishing of the amount of e-waste. Full article