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Auswahl der wissenschaftlichen Literatur zum Thema „Crunchbase“
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Zeitschriftenartikel zum Thema "Crunchbase"
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Feldmann, Louise Mort. „TechCrunch and CrunchBase“. Charleston Advisor 17, Nr. 3 (01.01.2016): 34–37. http://dx.doi.org/10.5260/chara.17.3.34.
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Ingham, Alexander, und Leah Kodner. „PrivCo versus Crunchbase“. Journal of Business & Finance Librarianship 22, Nr. 3-4 (02.10.2017): 250–52. http://dx.doi.org/10.1080/08963568.2017.1372019.
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Färber, Michael, Carsten Menne und Andreas Harth. „A Linked Data wrapper for CrunchBase“. Semantic Web 9, Nr. 4 (29.06.2018): 505–15. http://dx.doi.org/10.3233/sw-170278.
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Liang, Yuxian Eugene, und Soe-Tsyr Daphne Yuan. „Predicting investor funding behavior using crunchbase social network features“. Internet Research 26, Nr. 1 (01.02.2016): 74–100. http://dx.doi.org/10.1108/intr-09-2014-0231.
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Purpose – What makes investors tick? Largely counter-intuitive compared to the findings of most past research, this study explores the possibility that funding investors invest in companies based on social relationships, which could be positive or negative, similar or dissimilar. The purpose of this paper is to build a social network graph using data from CrunchBase, the largest public database with profiles about companies. The authors combine social network analysis with the study of investing behavior in order to explore how similarity between investors and companies affects investing behavior through social network analysis. Design/methodology/approach – This study crawls and analyzes data from CrunchBase and builds a social network graph which includes people, companies, social links and funding investment links. The problem is then formalized as a link (or relationship) prediction task in a social network to model and predict (across various machine learning methods and evaluation metrics) whether an investor will create a link to a company in the social network. Various link prediction techniques such as common neighbors, shortest path, Jaccard Coefficient and others are integrated to provide a holistic view of a social network and provide useful insights as to how a pair of nodes may be related (i.e., whether the investor will invest in the particular company at a time) within the social network. Findings – This study finds that funding investors are more likely to invest in a particular company if they have a stronger social relationship in terms of closeness, be it direct or indirect. At the same time, if investors and companies share too many common neighbors, investors are less likely to invest in such companies. Originality/value – The author’s study is among the first to use data from the largest public company profile database of CrunchBase as a social network for research purposes. The author ' s also identify certain social relationship factors that can help prescribe the investor funding behavior. Authors prediction strategy based on these factors and modeling it as a link prediction problem generally works well across the most prominent learning algorithms and perform well in terms of aggregate performance as well as individual industries. In other words, this study would like to encourage companies to focus on social relationship factors in addition to other factors when seeking external funding investments.
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Żbikowski, Kamil, und Piotr Antosiuk. „A machine learning, bias-free approach for predicting business success using Crunchbase data“. Information Processing & Management 58, Nr. 4 (Juli 2021): 102555. http://dx.doi.org/10.1016/j.ipm.2021.102555.
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Chae, Bongsug (Kevin), und Gyuhyeong Goh. „Digital Entrepreneurs in Artificial Intelligence and Data Analytics: Who Are They?“ Journal of Open Innovation: Technology, Market, and Complexity 6, Nr. 3 (29.07.2020): 56. http://dx.doi.org/10.3390/joitmc6030056.
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Digital technologies are key resources for entrepreneurial activities and there is great interest in digital entrepreneurship. While much research has focused on the role of digital technologies in entrepreneurship and how they are shaping the field, there has been relatively little research on those key players of digital entrepreneurship. Using data from Crunchbase and Twitter API and a learning machine, this study attempts to answer the question of “who are digital entrepreneurs?” This study reports that digital entrepreneurs in the artificial intelligence and data analytics (AIDA) industry are more likely to be male and to be active and connected online than non-digital entrepreneurs. In addition, they tend to be more extroverted and less conscientious and agreeable than other, non-digital, entrepreneurs. Our findings help to develop a clearer picture of digital entrepreneurs, which would be of great interest to investors, policy makers, current and future digital entrepreneurs and educators.
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Kézai, Petra Kinga, Szabolcs Fischer und Mihály Lados. „Smart Economy and Startup Enterprises in the Visegrád Countries—A Comparative Analysis Based on the Crunchbase Database“. Smart Cities 3, Nr. 4 (03.12.2020): 1477–94. http://dx.doi.org/10.3390/smartcities3040070.
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The present study seeks to explore the concept of “smart economy” through the definition of the smart city. It also presents smart city subsystems and the smart city model. It focuses on smart and creative startups within the smart city model. The research examines medium-sized cities in the Visegrád countries (Czech Republic, Slovakia, Poland, Hungary) with a population ranging from 100,000 to 1 million inhabitants for startups. The research question is: Where are the medium-sized cities in the Visegrád countries that are both startup centers and smart cities? In the course of the research, the term “smart cities” was based on the definition set by the European Commission and the definition of startup centers was made using data analysis of the American Crunchbase database. As a result of the two studies, it can be concluded that there are no cities in the Visegrád countries with an above average level of both startup presence and smart cities.
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Bishop, Alex, und Juan Mateos-Garcia. „Exploring the Link Between Economic Complexity and Emergent Economic Activities“. National Institute Economic Review 249 (August 2019): R47—R58. http://dx.doi.org/10.1177/002795011924900114.
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Recent studies have shown a strong link between the complexity of economies and their economic development. There remain gaps in our understanding of the mechanisms underpinning these links, in part because they are difficult to analyse with highly aggregated, official data sources that do not capture the emergence of new industrial activities, a potential benefit from complexity. We seek to address some of these gaps by calculating two indices of economic complexity for functional local economies (Travel to Work Areas) in Great Britain, and explore their link with these locations’ economic performance. Seeking to gain a better understanding of the mechanism connecting economic complexity with economic performance, we create a measure of emergent technological activity in a location based on a combination of novel data sources including text from UK business websites and CrunchBase, a technology company directory, and study its link with economic complexity. Our results highlight the potential value of novel, unstructured data sources for the analysis of the links between economic complexity and regional economic development.
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Yang, Song, und Ron Berger. „Relation between start-ups’ online social media presence and fundraising“. Journal of Science and Technology Policy Management 8, Nr. 2 (03.07.2017): 161–80. http://dx.doi.org/10.1108/jstpm-09-2016-0022.
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Purpose The purpose of this study is to examine the emergences of social media such as Facebook, Twitter and Instagram have changed the way human beings communicate and interact. In the past few years, this has become crucial in the context of business, especially in start-up fund raising. Access to venture capital financing is a crucial issue in the entrepreneurial finance literature. To further explore the use of social media for entrepreneurs, the authors have explored how entrepreneurs use social media for fund-raising purposes. The authors have used Application Programming Interfaces (APIs) to collect entrepreneurs’ funding data from Crunchbase and entrepreneurs’ social media data from Facebook and Twitter. The results show that social media is significant for start-ups in their success or failure in fund raising. Investing energy into utilizing online social media and exhausting these platforms consciously contributes to the financial success of start-ups. Therefore, start-ups which are popular among online fans and followers can manage to raise larger amounts of funding in the early stages. Design/methodology/approach This research relies on a wide range of quantitative data, which was obtained from three different online sources which includes Facebook, Twitter and CrunchBase. The use of a variety of internet technologies have been linked to increases in individuals’ social network diversity, which likely increases access to social capital at the individual level (Hampton and Wellman, 2003). The dataset was retrieved by using APIs, which enables the collection of novel metrics, from various sources that provide a well-structured dataset (Priem and Hemminger, 2010). Hypotheses were tested on a longitudinal dataset from 2000 to 2013, comprising general and investment data and social media metrics of start-ups. First, a sample from the database was selected to ensure data availability and reliability. After sampling, all the selected companies’ Twitter and Facebook activities were observed and metrics were analysed. SPSS was used to conduct correlation and regression analyses. Findings This study analysed whether start-ups’ social media convention is able to influence investors’ choices, especially the amount of total funding given. The paper showed that innovative start-up companies were able to benefit from communicating on social media platforms. Start-ups, which were using Facebook and Twitter effectively, focusing on valuable social media metrics, received larger amount of funding in total. Furthermore, it was observed that as their business grew, they intended to put more effort into online social networking. It confirmed the idea that businesses are using social media consciously. Originality/value This is the only paper that the authors could find that examines the relationship between fundraising and activity on social networks.
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Amoroso, Nicola, Loredana Bellantuono, Alfonso Monaco, Francesco De Nicolò, Ernesto Somma und Roberto Bellotti. „Economic Interplay Forecasting Business Success“. Complexity 2021 (19.03.2021): 1–12. http://dx.doi.org/10.1155/2021/8861267.
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A startup ecosystem is a dynamic environment in which several actors, such as investors, venture capitalists, angels, and facilitators, are the protagonists of a complex interplay. Most of these interactions involve the flow of capital whose size and direction help to map the intricate system of relationships. This quantity is also considered a good proxy of economic success. Given the complexity of such systems, it would be more desirable to supplement this information with other informative features, and a natural choice is to adopt mathematical measures. In this work, we will specifically consider network centrality measures, borrowed by network theory. In particular, using the largest publicly available dataset for startups, the Crunchbase dataset, we show how centrality measures highlight the importance of particular players, such as angels and accelerators, whose role could be underestimated by focusing on collected funds only. We also provide a quantitative criterion to establish which firms should be considered strategic and rank them. Finally, as funding is a widespread measure for success in economic settings, we investigate to which extent this measure is in agreement with network metrics; the model accurately forecasts which firms will receive the highest funding in future years.
Dissertationen zum Thema "Crunchbase"
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Lee, Johanna, und Ebba Josefsson. „Covid-19-pandemins påverkan på den svenska Venture Capital-marknaden“. Thesis, Högskolan i Halmstad, Akademin för företagande, innovation och hållbarhet, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-44703.
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Entreprenörskap och företagande är essentiellt för ekonomisk tillväxt i ett land. Det bidrar med innovativa idéer som utvecklar samhället och möjliggör fler arbetstillfällen. För att entreprenörer och företag ska kunna utveckla dessa idéer krävs kapital, vilket emellertid kan vara svårt att erhålla. Nystartade- och tillväxtföretag förknippas med hög risk då de befinner sig i ett stadie där informationsasymmetrin är stor och utfallet av en investering är svår att beräkna. Venture Capital (VC) är en form av riskkapital som finansierar nystartade- och tillväxtföretag med hög risk. De bidrar med finansiering och erfarenhet i utbyte mot en andel av företaget. VC är därmed essentiellt för entreprenörskap, företagande och i längden även samhället. Under våren 2020 utbröt Covid-19-pandemin i Sverige och orsakade en instabilitet i samhället och dess ekonomi. Syftet är därmed att undersöka Covid-19-pandemins eventuella påverkan på den svenska VC-marknaden. För att studera dessa eventuella effekter undersöks förändringar i investeringsantal, investeringsvolym samt vilket stadie dessa investeringar genomförs. Dessutom undersöks förändringar i investeringsmönster, branschförskjutning samt hur den sociala distanseringen kan ha påverkat VC-marknaden. Forskningsfrågan besvaras genom insamling av realdata från Crunchbase samt intervjuer med aktörer på marknaden. Resultatet av studien indikerar att antal VC-investeringar under året har minskat med 18,1%, däremot är investeringsvolymen fortsatt hög och har under året ökat med 155,1%. Det som framförallt kan utläsas är att Seed-investeringar, det vill säga investering i ett tidigt stadie, har drabbats hårdast. Antalet investeringar i detta stadie har minskat med 21% under året. Vidare indikerar resultatet även att den sociala distanseringen till följd av de införda restriktionerna har haft en indirekt inverkan på VC-marknaden. Detta då det inneburit en förändring i VC- processen och arbetssättet. Sammanfattningsvis kan det konstateras att VC-marknaden i Sverige har påverkats av Covid-19-pandemin. Däremot kan ingen större krasch identifieras då investeringsvolymen är fortsatt hög trots en minskning i antal investeringar. Studiens implikationer är en ökad förståelse för hur marknaden fungerar samt hur den har påverkats av Covid-19-pandemin. Resultatet kan vara av intresse för ett flertal intressenter, däribland entreprenörer, investerare, VC-bolag, forskare men framförallt politiska beslutsfattare. Genom en förståelse för marknaden kan politiska beslutsfattare upprätthålla samt stimulera den ekonomiska tillväxten.
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Felgueiras, Marco Filipe Madeira. „Multilabel classification of unstructured data using Crunchbase“. Master's thesis, 2020. http://hdl.handle.net/10071/22188.
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Our work compares different methods and models for multilabel text classification using information collected from Crunchbase, a large database that holds information of more than
600000 companies. Each company is labeled with one more categories, from a subset of 46
possible, and the proposed models predict the categories based solely on the company textual description. A number of natural language processing strategies have been tested for
feature extraction, including stemming, lemmatization, and Part-of-Speech Tagging. This
is a highly unbalanced dataset, where the frequency of each category ranges from 0.7%
to 28%. The first experiment, is a Multiclass classification problem that tries to find the
most probable category using only one model for all categories, with an overall score of
67% using SVM, Naive Bayes and Fuzzy Fingerprints. The second experiment uses makes
use of multiple classifiers, one for each category, and tries to predict the complete set of
categories for each company, with an overal score of 73% precision and 47% recall. The
resulting models may constitute an important asset for automatic classification of texts, not
only consisting of company descriptions, but also other texts, such as web pages, text blogs,
news pages, etc.Este trabalho compara diferentes métodos e modelos para classificação de texto utilizando informação proveniente do Crunchbase, uma grande base de dados que contém dados sobre mais de 600000 empresas. Cada empresa está associada a uma ou mais categorias, de 46 possiveis, e os modelos propostos utilizam apenas a descrição de cada empresa para prever a sua categoria. Foram aplicadas várias técnicas de processamento de linguagem natural para extração de informação incluindo "stemming", lematização e "Part-of-Speech Tagging". Este "dataset" é altamente desiquilibrado, a frequência de cada categoria vai desde 0.7% a 28%. A primeira experiência, é um problema multiclasse que tenta encontrar qual a categoria mais provável para uma empresa utilizando apenas um modelo para todas as categorias, obtendo um resultado global de 67% de "accuracy" utilizando SVM, Naive Bayes e Fuzzy Fingerprints. A segunda experiência utiliza vários classificadores, um por cada categoria, para atribuir todas as categorias de uma determinada empresa obtendo resultados de 73% de precisão e 47% de "recall". Os modelos resultantes do nosso trabalho podem ser um ativo importante para a classificação automática de texto, não só para descrições de empresas mas também para outros textos, como páginas de Internet, blogs, notícias, entre outros.
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LI, YING-SI, und 李穎思. „A Discussion on Crowdfunding-A Study among Crunchbase“. Thesis, 2017. http://ndltd.ncl.edu.tw/handle/zcejqe.
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碩士世新大學
財務金融學研究所(含碩專班)
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Since 2007, Crunchbase and other fund-raising platform has been established, changed the traditional venture capitalist business problems faced. In the case of traditional fund-raising channels, such as bank loans, entrepreneurs can not easily get in touch with angel investors. The process of entrepreneurial application is trivial and complicated, and the variables after funding are likely to cause tension between the sponsor and the entrepreneur The Investors do not know much about the entrepreneur, there will be uncertainty problems, etc., but the emergence of fund-raising platform to improve many problems, including the communication channels, funds more quickly and easily. So that new entrepreneurs in the early start of a good start. The emergence of these fund-raising sites, the venture capitalists, but a big gospel, but also reduce the risk of angel investors. This study focuses on the CrunchBase website before and after the establishment of two times to do research. Analysis of the various industries of the fundraising situation, including the fund-raising areas, the amount raised to fund the company's fund-raising system and so on. For different circumstances, the difference between the two time and how to choose the local or what the reference industry to do the goal of new investment and so on. According to the analysis described in the previous section, this study shows a significant increase in fundraising activities around the establishment of the website. The amount of funds raised in different regions, the United States accounted for the largest, and the length of time to raise funds because of the long time and can raise the funds to follow. The reasons may include the industry itself or other products, etc., most of the industry are re-fund-raising situation. The proportion of the initial public offering fund-raising only about 10% of the proportion, so carefully selected areas or industrial types as the key to venture capital.
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Giaquinto, Luisa Herck. „Angel, seed and founders influence on fintech funding: an emerging market context“. Master's thesis, 2019. http://hdl.handle.net/10362/70654.
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This study examines the difference between FinTechs that received private equity
and venture capital finance with those that did not receive. We test this with a
sample of 2,524 companies across 76 countries over 2008-2018. We find that
country-specific determinants of start-up funding are also relevant to FinTechs.
Furthermore, companies in financing and payments categories are more likely to
receive funding. We show a positive relationship between having received an
angel and a seed round with follow-on finance, and a negative relationship with
having a single founder. The impact of the seed finance and the single founder is
weaker in an emerging market.
Bücher zum Thema "Crunchbase"
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King Cudgel's Challenge (Crunchbone Castle Chronicles). A & C Black Publishers Ltd, 2006.
Den vollen Inhalt der Quelle findenBuchteile zum Thema "Crunchbase"
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Felgueiras, Marco, Fernando Batista und Joao Paulo Carvalho. „Creating Classification Models from Textual Descriptions of Companies Using Crunchbase“. In Information Processing and Management of Uncertainty in Knowledge-Based Systems, 695–707. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50146-4_51.
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Schaffer, Norman, Martin Engert, Guido Sommer, Jasmin Shokoui und Helmut Krcmar. „The Digitized Ecosystem of Tourism in Europe: Current Trends and Implications“. In Information and Communication Technologies in Tourism 2021, 352–64. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-65785-7_34.
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AbstractEmerging digital technologies enable the creation of new services and business models, leading to ecosystems’ continuous change. In the tourism industry, new digital-savvy players like Airbnb have entered and created entirely new market segments, while many existing players are challenged to redefine their business logic. However, the literature does not provide a generic overview of the value network in tourism, including new market players, and their ways of interacting. Therefore, this paper develops a current overview of the value network of the European tourism ecosystem. By analyzing the business models and value streams of 704 European enterprises based on Crunchbase data, we identified 27 distinct roles and their respective interrelations in the domain. To validate the proposed value network, we conducted five expert interviews. Our results highlight the continuously growing importance of intermediaries in tourism. Furthermore, new technology players offer new opportunities for innovative services, creating high dynamism in the industry. Nonetheless, local entities, such as residents and communities, play a central role in European tourism and need to be included in experiences provided to tourists. Scholars and practitioners might use the results to identify disruptive actors and opportunities for innovation and niche creation. Additionally, the results can be used as a basis for further analysis of the ecosystem’s ongoing changes induced through technological advancements or external events such as the COVID-19 pandemic.
Konferenzberichte zum Thema "Crunchbase"
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Batista, Fernando, und Joao Paulo Carvalho. „Text based classification of companies in CrunchBase“. In 2015 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2015. http://dx.doi.org/10.1109/fuzz-ieee.2015.7337892.
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Ferrati, Francesco, und Moreno Muffatto. „Setting Crunchbase for Data Science: Preprocessing, Data Integration and Feature Engineering“. In CARMA 2020 - 3rd International Conference on Advanced Research Methods and Analytics. Valencia: Universitat Politècnica de València, 2020. http://dx.doi.org/10.4995/carma2020.2020.11633.
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In order to support equity investors in their decision-making process, researchers are exploring the potential of machine learning algorithms to predict the financial success of startup ventures. In this context, a key role is played by the significance of the data used, which should reflect most of the variables considered by investors in their screening and evaluation activity. This paper provides a detailed description of the data management process that can be followed to obtain such a dataset. Using Crunchbase as the main data source, other databases have been integrated to enrich the information content and support the feature engineering process. Specifically, the following sources has been considered: USPTO PatentsView, Kauffman Indicators of Entrepreneurship, Academic Ranking of World Universities, CB Insights ranking of top-investors. The final dataset contains the profiles of 138,637 US-based ventures founded between 2000 and 2019. For each company the elements assessed by equity investors have been analyzed. Among others, the following specific areas were considered for each company: location, industry, founding team, intellectual property and funding round history. Data related to each area have been formalized in a series of features ready to be used in a machine learning context.