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Motivation In Work Organizations

By asking users of career-oriented social networking sites I investigated their job search behavior. For further IS-theorizing I integrated the number of a user's contacts as an own construct into Venkatesh's et al. UTAUT2 model, which substantially rose its predictive quality from 19.0 percent to 80.5 percent concerning the variance of job search success. Besides other interesting results I found a substantial negative relationship between the number of contacts and job search success, which supports the experience of practitioners but contradicts scholarly findings. The results are useful for scholars and practitioners.

Getting a Job via Career-Oriented Social Networking Sites: The Weakness of Ties

Many countries know financial consumer credit ratings, and recent years have also seen a proliferation of rating systems in relation to online platforms and in the ‘sharing economy’, such as eBay, Uber and Airbnb. In the view of many Western observers, however, the emerging Chinese Social Credit System indicates a paradigm shift compared to these former rating systems as it aims for a comprehensive and uniform social rating based on penalty and award mechanisms. By contrast, this article suggests that the evolving forms of the Chinese system should be seen as a specific instance of a wider phenomenon. Thus, it develops a framework that compares different rating systems by reference to their drafters, users, aims, scoring systems, application, use of algorithms, enforcement and accountability; it identifies shortcomings of both low and high interventionist rating systems; and it discusses a range of regulatory approaches and emerging issues that law makers should consider.

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The Chinese Social Credit System: A Model for Other Countries?

While career-oriented social networking markets (CSNM) such as LinkedIn or XING are increasingly appreciated by a large number of internet users, our understanding of CSNM benefits and factors influencing the intention to use CSNM for a job search is very limited. For theoretical work on CSNM I extend Venkatesh’s et al. UTAUT2 model by presenting a new concept considering a user’s ties based on Granovetter’s social network theory. The evaluation of the extended model, which asks users of CSNM about their job search behavior and their search success, shows a predictive quality increase from 19.0 percent to 80.5 percent. Post hoc analyses reveal a substantial negative relationship between the number of a user’s ties and its job search success, which supports the experience of practitioners but contradicts scholarly findings. A level of about 150 contacts is most effective in terms of getting job offers, which confirms Dunbar’s number. The results are useful for scholars and practitioners.

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Getting a job via career-oriented social networking markets

Career development theory explains that although graduates can make their own career decisions, such decisions can also be determined by a variety of influences, such as family, school, community, ...

Who Influences College Students’ Career Choices? An Empirical Study of Hospitality Management Students

While emerging economies have seen an explosive growth of Internet population, these countries lack sophisticated credit scoring system or credit bureaus to predict creditworthiness of individuals. Leveraging the widespread adoption of social media and social network sites in emerging markets, microfinance institutions innovate on new credit scoring methods using novel data sources. In this paper, we propose a Bayesian method for social network-based credit scoring that helps to address network sparsity and data scarcity, common with ego-centric networks. Our empirical results suggest that by incorporating social network information, we can improve the creditworthiness prediction in microfinance. We believe that although lending to the poor without incurring high default rates is challenging, social network-based methods can be an effective approach used for developing countries that face the financial exclusion problem.

Social Media-Driven Credit Scoring: The Predictive Value of Social Structures

Recent year have witnessed the emergence of Financial Technology (FinTech) innovations as a promising resolution to financial exclusion, via mechanisms that empower the financially underprivileged to gain access rights into the traditional financial industry. However, academic research has provided little guidance on how to strategize the IT-enabled empowerment mechanisms for FinTech innovations to realize both business success and financial inclusion. In this study, we conduct an in-depth revelatory case study on a novel Chinese FinTech innovation, Yu’E Bao, the bellwether in revolutionizing the country’s financial industry dramatically through the successful empowerment of a large population of financially underprivileged (i.e., “grassroots consumers”, translated from “cao gen” in Chinese). Through our systematic qualitative analyses of news articles collected since the product launch, we derive a two-stage theoretical model, examined through the lens of “empowerment”, and unravel the mechanisms underlying FinTech innovation’s empowerment process on grassroots consumers. Most importantly, we elucidate a “duality of empowerment”, that is, the catalyst for Yu’E Bao’s success soon becomes the impediment to Yu’E Bao’s further development. Our study contributes to the literature on Information Technology-enabled (IT-enabled) empowerment, empowerment, and FinTech. Critical implications to various stakeholders, such as government and policy makers, as well as FinTech practitioners and product designers are also drawn.

Empowerment of Grassroots Consumers: A Revelatory Case of a Chinese Fintech Innovation

Winning over Grassroots Consumers: An Empowerment Perspective of Yu’E Bao

Credit-worthiness Prediction in Microfinance using Mobile Data: A Spatio-network Approach

This research investigated the potential for improving Peer-to-Peer (P2P) credit scoring by using "private information" about communications and travels of borrowers. We found that P2P borrowers' ego networks exhibit scale-free behavior driven by underlying preferential attachment mechanisms that connect borrowers in a fashion that can be used to predict loan profitability. The projection of these private networks onto networks of mobile phone communication and geographical locations from mobile phone GPS potentially give loan providers access to private information through graph and location metrics which we used to predict loan profitability. Graph topology was found to be an important predictor of loan profitability, explaining over 5.5% of variability. Networks of borrower location information explain an additional 19% of the profitability. Machine learning algorithms were applied to the data set previously analyzed to develop the predictive model and resulted in a 4% reduction in mean squared error.

Tianhui Tan

Papers

Social Media-Driven Credit Scoring: The Predictive Value of Social Structures

Empowerment of Grassroots Consumers: A Revelatory Case of a Chinese Fintech Innovation

Winning over Grassroots Consumers: An Empowerment Perspective of Yu’E Bao

Credit-worthiness Prediction in Microfinance using Mobile Data: A Spatio-network Approach

Private Information, Credit Risk and Graph Structure in P2P Lending Networks