Afraid of Niche, Tired of Mass: Atypical Idea Combination on Crowdfunding Platform

A new idea usually follows a stream of similar ideas yet simultaneously combines atypical elements from ideas outside this stream. A successful business idea usually balances well between familiarity and atypicality. To investigate the relationship between atypicality innovation and crowdfunding project performance, we collected data from one of the largest crowdfunding platforms in China. We build a similarity network of crowdfunding projects to measure the degree of atypicality innovation for these projects. Using a double machine learning model, we find that the atypical combination of mainstream and niche ideas has a significant positive effect on the individual project\u27s funding, i.e., five times more successful than other projects. We also find the potential reasons that cause the poor performance of niche and mainstream projects. Donors are more conservative due to the high risk of niche projects and driven away by the monotonous repetition of mainstream projects

Fintech and the financial services industry in South Africa

A preliminary literature review indicated that little to no research exists on the Fintech phenomena in the South African context. The purpose of this study is to gain the perceptions of South African bankers on the Fintech phenomena and to develop a deeper understanding of the Fintech phenomena in the South African context. An empirical exploratory qualitative approach was employed and an interpretivism research paradigm was utilised. A detailed literature review was conducted into the Fintech phenomena as well as the financial services sector in South Africa. The use of purposive sampling was initiated, and the sample of the study consisted of five individuals who work for the largest South African banks. The data was gathered through in-depth structured interviews which consisted of ten predetermined research questions. Rich data was obtained which was then analysed through the use of content analysis and coding. This enabled the transcription of data and the extraction of codes which assisted in obtaining findings that answered the research question. Authenticity, rigor, trustworthiness and credibility criteria was applied from the onset and throughout the research study. The themes that emerged from the data analysis process were navigated so that findings could be reached by comparing the themes to previous literature. In conclusion, six themes emerged from the data analysis, namely; collaboration can create new markets and create market share, investing in Fintech to overcome legacy infrastructure by going digital, Fintech companies are more customer focused, balance regulation between protecting the industry and creating an innovative environment competition in future will be fiercer, competition is good for the performance of the financial services industry and the Fintech phenomena is positive for the banking industry. Certain findings and conclusions were drawn; regulations in South Africa do allow for innovation, regulation is not biased towards banks, banks innovate and collaborate through Fintech, Fintech is positive for the banking industry, South African banks are investing in preparation for Fintech, South African banks have limited budgets, there is no significant loss of market share for banks due to Fintech companies, market share can be gained by banks partnering with Fintech companies and the future of the financial services industry in South Africa

Hyperscale Data Processing With Network-Centric Designs

Today’s largest data processing workloads are hosted in cloud data centers. Due to unprecedented data growth and the end of Moore’s Law, these workloads have ballooned to the hyperscale level, encompassing billions to trillions of data items and hundreds to thousands of machines per query. Enabling and expanding with these workloads are highly scalable data center networks that connect up to hundreds of thousands of networked servers. These massive scales fundamentally challenge the designs of both data processing systems and data center networks, and the classic layered designs are no longer sustainable. Rather than optimize these massive layers in silos, we build systems across them with principled network-centric designs. In current networks, we redesign data processing systems with network-awareness to minimize the cost of moving data in the network. In future networks, we propose new interfaces and services that the cloud infrastructure offers to applications and codesign data processing systems to achieve optimal query processing performance. To transform the network to future designs, we facilitate network innovation at scale. This dissertation presents a line of systems work that covers all three directions. It first discusses GraphRex, a network-aware system that combines classic database and systems techniques to push the performance of massive graph queries in current data centers. It then introduces data processing in disaggregated data centers, a promising new cloud proposal. It details TELEPORT, a compute pushdown feature that eliminates data processing performance bottlenecks in disaggregated data centers, and Redy, which provides high-performance caches using remote disaggregated memory. Finally, it presents MimicNet, a fine-grained simulation framework that evaluates network proposals at datacenter scale with machine learning approximation. These systems demonstrate that our ideas in network-centric designs achieve orders of magnitude higher efficiency compared to the state of the art at hyperscale