Big Data Analytics for Venture Capital Application：Towards Innovation Performance Improvement

Sun, Wenqi; Zhao, Yuanjun; Sun, Lu

doi:10.1016/j.ijinfomgt.2018.11.017

Cited by 69 publications

(28 citation statements)

References 39 publications

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“…e higher the richness of social resources, the lower the cost of obtaining loans [8]. ere is a significant negative correlation between the two [9].…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Niu

Cai

2020

Complexity

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With the continuous development of big data technology, the data of online lending platform witness explosive development. How to give full play to the advantages of data, establish a credit risk assessment model, and realize the effective control of platform credit risk have become the focus of online lending platform. In view of the fact that the network loan data are mainly unbalanced data, the smote algorithm is helpful to optimize the model and improve the evaluation performance of the model. Relevant research shows that stochastic forest model has higher applicability in credit risk assessment, and cart, ANN, C4.5, and other algorithms are also widely used. In the influencing factors of credit evaluation, the weight of the applicant’s enterprise scale, working years, historical records, credit score, and other indicators is relatively high, while the index weight of marriage and housing/car production (loan) is relatively low.

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“…e higher the richness of social resources, the lower the cost of obtaining loans [8]. ere is a significant negative correlation between the two [9].…”

Section: Introductionmentioning

confidence: 99%

Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Niu

Cai

2020

Complexity

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“…For example, IB researchers may use Big Data to expand their examinations of questions related to how business processes relate to organizational performance across different countries. These include, for example, marketing strategies and the use of consumer-behavior data (e.g., Hofacker, Malthouse & Sultan, 2016;Matz & Netzer, 2017), managerial metrics (e.g., Mintz, Currim, Steenkamp & de Jong, 2020), knowledge sharing (e.g., Haas, Criscuolo & George, 2015), and capital-allocation decisions (e.g., Sun, Zhao & Sun, 2020).…”

Section: Solutions For Challenge #2: Idiosyncratic Samples or Contextsmentioning

confidence: 99%

Methodological practices in international business research: An after-action review of challenges and solutions

2020

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We combine after-action review and needs-assessment frameworks to describe the four most pervasive contemporary methodological challenges faced by international business (IB) researchers, as identified by authors of Journal of International Business Studies articles: Psychometrically deficient measures (mentioned in 73% of articles), idiosyncratic samples or contexts (mentioned in 62.2% of articles), less-than-ideal research designs (mentioned in 62.2% of articles), and insufficient evidence about causal relations (mentioned in 8.1% of articles). Then, we offer solutions to address these challenges: demonstrating why and how the conceptualization of a construct is accurate given a particular context, specifying whether constructs are reflective or formative, taking advantage of the existence of multiple indicators to measure multi-dimensional constructs, using particular samples and contexts as vehicles for theorizing and further theory development, seeking out particular samples or contexts where hypotheses are more or less likely to be supported empirically, using Big Data techniques to take advantage of untapped sources of information and to reanalyze currently available data, implementing quasi-experiments, and conducting necessary-condition analysis. Our article aims to advance IB theory by tackling the most typical methodological challenges and is intended for researchers, reviewers and editors, research consumers, and instructors who are training the next generation of scholars.

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“…Fintech itself is based on "science and technology," its purpose is to promote the transformation and upgrading of finance, and it is an important force to promote the development of green finance [19].…”

Section: Related Workmentioning

confidence: 99%

Research on the Impact of Green Finance and Fintech in Smart City

Liu

et al. 2020

Complexity

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The green development level reflected in the green finance index and the evaluation of the degree of green development in smart cities have important practical effects on economic transformation. For promoting the transformation and upgrading of finance, we select 2013–2019 data and construct a distributed lag model to analyse the important role played by green finance and financial technology in the construction of smart cities. In the paper, we find green finance promotes the construction of smart cities, and only it will not appear until nine months late. Financial technology has an opposite impact on the construction of smart cities, this is mainly because the research and marketization of financial technology are higher, and the cost is also higher. This influence will appear in the next 14 months.

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Big Data Analytics for Venture Capital Application：Towards Innovation Performance Improvement

Cited by 69 publications

References 39 publications

Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Big Data Analytics for Complex Credit Risk Assessment of Network Lending Based on SMOTE Algorithm

Methodological practices in international business research: An after-action review of challenges and solutions

Research on the Impact of Green Finance and Fintech in Smart City

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