اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدRecommending Investors for Crowdfunding Projects
139
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
To bring their innovative ideas to market, those embarking in new ventures have to raise money, and, to do so, they have often resorted to banks and venture capitalists. Nowadays, they have an additional option: that of crowdfunding. The name refers to the idea that funds come from a network of people on the Internet who are passionate about supporting others projects. One of the most popular crowdfunding sites is Kickstarter. In it, creators post descriptions of their projects and advertise them on social media sites (mainly Twitter), while investors look for projects to support. The most common reason for project failure is the inability of founders to connect with a sufficient number of investors, and that is mainly because hitherto there has not been any automatic way of matching creators and investors. We thus set out to propose different ways of recommending investors found on Twitter for specific Kickstarter projects. We do so by conducting hypothesis-driven analyses of pledging behavior and translate the corresponding findings into different recommendation strategies. The best strategy achieves, on average, 84% of accuracy in predicting a list of potential investors Twitter accounts for any given project. Our findings also produced key insights about the whys and wherefores of investors deciding to support innovative efforts.
قيم البحث
اقرأ أيضاً
So far little effort has been put into researching the importance of internal ERP project stakeholders mutual interactions,realizing the projects complexity,influence on the whole organization, and high risk for a useful final outcome. This research
analyzes the stakeholders interactions and positions in the project network, their criticality, potential bottlenecks and conflicts. The main methods used are Social Network Analysis, and the elicitation of drivers for the individual players. Information was collected from several stakeholders from three large ERP projects all in global companies headquartered in Finland, together with representatives from two different ERP vendors, and with two experienced ERP consultants. The analysis gives quantitative as well as qualitative characterization of stakeholder criticality (mostly the Project Manager(s), the Business Owner(s) and the Process Owner(s)), degree of centrality, closeness, mediating or bottleneck roles, relational ties and conflicts (individual, besides those between business and project organizations), and clique formations. A generic internal stakeholder network model is established as well as the criticality of the project phases. The results are summarized in the form of a list of recommendations for future ERP projects to address the internal stakeholder impacts .Project management should utilize the latest technology to provide tools to increase the interaction between the stakeholders and to monitor the strength of these relations. Social network analysis tools could be used in the projects to visualize the stakeholder relations in order to better understand the possible risks related to the relations (or lack of them).
Over the past decade, many startups have sprung up, which create a huge demand for financial support from venture investors. However, due to the information asymmetry between investors and companies, the financing process is usually challenging and t
ime-consuming, especially for the startups that have not yet obtained any investment. Because of this, effective data-driven techniques to automatically match startups with potentially relevant investors would be highly desirable. Here, we analyze 34,469 valid investment events collected from www.itjuzi.com and consider the cold-start problem of recommending investors for new startups. We address this problem by constructing different tripartite network representations of the data where nodes represent investors, companies, and companies domains. First, we find that investors have strong domain preferences when investing, which motivates us to introduce virtual links between investors and investment domains in the tripartite network construction. Our analysis of the recommendation performance of diffusion-based algorithms applied to various network representations indicates that prospective investors for new startups are effectively revealed by integrating network diffusion processes with investors domain preference.
The increasing relevance of Internet-based markets requires a sustained investigation into the relationship between design and user behavior. This research begins within the sociology of quantification and markets to investigate the impacts of basic
design decisions on user behavior and individual success on a widely used crowdfunding website. This study looks at one common design feature, publishing recipients sex, on the probability of receiving funding. Following research in the sociology of gender, these effects are defined along individual, behavioral, and structural dimensions. The results reveal that before teachers sex was published, gender discrimination was weak and inconsistent. However, afterward gender discrimination increases by an order of magnitude and becomes systematized. Contrary to expectation, donors did not discriminate by sex category, but by teachers structural position and the kinds of language they used. Implications for research on gender discrimination, priming, and online behavior are discussed.
Gift giving is a ubiquitous social phenomenon, and red packets have been used as monetary gifts in Asian countries for thousands of years. In recent years, online red packets have become widespread in China through the WeChat platform. Exploiting a u
nique dataset consisting of 61 million group red packets and seven million users, we conduct a large-scale, data-driven study to understand the spread of red packets and the effect of red packets on group activity. We find that the cash flows between provinces are largely consistent with provincial GDP rankings, e.g., red packets are sent from users in the south to those in the north. By distinguishing spontaneous from reciprocal red packets, we reveal the behavioral patterns in sending red packets: males, seniors, and people with more in-group friends are more inclined to spontaneously send red packets, while red packets from females, youths, and people with less in-group friends are more reciprocal. Furthermore, we use propensity score matching to study the external effects of red packets on group dynamics. We show that red packets increase group participation and strengthen in-group relationships, which partly explain the benefits and motivations for sending red packets.
The 2020 coronavirus pandemic was a historic social disruption with significant consequences felt around the globe. Wikipedia is a freely-available, peer-produced encyclopedia with a remarkable ability to create and revise content following current e
vents. Using 973,940 revisions from 134,337 editors to 4,238 articles, this study examines the dynamics of the English Wikipedias response to the coronavirus pandemic through the first five months of 2020 as a quantitative portrait describing the emergent collaborative behavior at three levels of analysis: article revision, editor contributions, and network dynamics. Across multiple data sources, quantitative methods, and levels of analysis, we find four consistent themes characterizing Wikipedias unique large-scale, high-tempo, and temporary online collaborations: external events as drivers of activity, spillovers of activity, complex patterns of editor engagement, and the shadows of the future. In light of increasing concerns about online social platforms abilities to govern the conduct and content of their users, we identify implications from Wikipedias coronavirus collaborations for improving the resilience of socio-technical systems during a crisis.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
