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Article
Publication date: 8 May 2019

Claire Seungeun Lee

The purpose of this paper is twofold: first, to explore how China uses a social credit system as part of its “data-driven authoritarianism” policy; and second, to…

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3312

Abstract

Purpose

The purpose of this paper is twofold: first, to explore how China uses a social credit system as part of its “data-driven authoritarianism” policy; and second, to investigate how datafication, which is a method to legitimize data collection, and dataveillance, which is continuous surveillance through the use of data, offer the Chinese state a legitimate method of monitoring, surveilling and controlling citizens, businesses and society. Taken together, China’s social credit system is analyzed as an integrated tool for datafication, dataveillance and data-driven authoritarianism.

Design/methodology/approach

This study combines the personal narratives of 22 Chinese citizens with policy analyses, online discussions and media reports. The stories were collected using a scenario-based story completion method to understand the participants’ perceptions of the recently introduced social credit system in China.

Findings

China’s new social credit system, which turns both online and offline behaviors into a credit score through smartphone apps, creates a “new normal” way of life for Chinese citizens. This data-driven authoritarianism uses data and technology to enhance citizen surveillance. Interactions between individuals, technologies and information emerge from understanding the system as one that provides social goods, using technologies, and raising concerns of privacy, security and collectivity. An integrated critical perspective that incorporates the concepts of datafication and dataveillance enhances a general understanding of how data-driven authoritarianism develops through the social credit system.

Originality/value

This study builds upon an ongoing debate and an emerging body of literature on datafication, dataveillance and digital sociology while filling empirical gaps in the study of the global South. The Chinese social credit system has growing recognition and importance as both a governing tool and a part of everyday datafication and dataveillance processes. Thus, these phenomena necessitate discussion of its consequences for, and applications by, the Chinese state and businesses, as well as affected individuals’ efforts to adapt to the system.

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Book part
Publication date: 17 August 2021

Mike Hynes

Abstract

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The Social, Cultural and Environmental Costs of Hyper-Connectivity: Sleeping Through the Revolution
Type: Book
ISBN: 978-1-83909-976-2

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Article
Publication date: 24 November 2017

Zahy Ramadan

China is establishing a social credit rating system with the aim to score the trust level of citizens. The scores will be based on an integrated database that includes a…

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5623

Abstract

Purpose

China is establishing a social credit rating system with the aim to score the trust level of citizens. The scores will be based on an integrated database that includes a vast range of information sources, rating aspects like professional conduct, corruption, type of products bought, peers’ own scores and tax evasion. While this form of gamification is expected to have dire consequences on brands and consumers alike, the literature in that particular area of interest remains non-existent. The paper aims to discuss these issues.

Design/methodology/approach

A conceptual framework is suggested that highlights early on the risks and implications on brands and companies operating in that particular upcoming landscape.

Findings

The gamification of trust that the social credit system focuses on presents potential risks on brand and consumer relationships. This in turn will affect brand sustainability vis-à-vis the expected drastic changes in the Chinese business landscape. This study suggests the strategies to follow which will be of high interest to companies, consumers, as well as to the Chinese authorities during and after implementation stage.

Originality/value

This paper is amongst the first to discuss the potential effects of the Chinese social credit rating system on brands. The conceptual framework fills a sizeable gap in the literature and pioneers the discussion on potential dilemmas brands will be faced with within this new business landscape.

Details

Marketing Intelligence & Planning, vol. 36 no. 1
Type: Research Article
ISSN: 0263-4503

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Article
Publication date: 7 January 2019

Irene Bengo and Marika Arena

The purpose of this paper is to perform a critical analysis of the relationship between small- and medium-sized social enterprises (SMSEs) and banks. Based on the…

Abstract

Purpose

The purpose of this paper is to perform a critical analysis of the relationship between small- and medium-sized social enterprises (SMSEs) and banks. Based on the conceptual framework for the analysis of SME’s credit availability developed by Berger and Udell (2006), this study aims to contribute to the current debate in two ways: first, outlining the characteristics of the lending technologies currently used by banks and financial institutions to evaluate SMSEs when they apply for credit; and second, discussing, based on the results of the empirical analysis, the coherence of these systems from the social ecosystem perspective and identifying areas for possible improvement.

Design/methodology/approach

The paper develops a conceptual framework based on the model proposed by Berger and Udell (2006), which defines the characteristics of lending technologies that banks use to evaluate SMEs, and applies it to the case of SMSEs. To study the interplay of these lending technologies, the empirical analysis is based on a case study of five Italian banks. Data are collected from multiple sources to capture key dimensions of the problems analyzed.

Findings

The paper provides empirical insight about the relationship between SMSEs and banks. The Italian case shows that the current lending infrastructure must be revised to support SMSE credit availability, and government policies affect the national financial institution structure. The relationship between SMSEs and Italian banks remains underdeveloped.

Social implications

The research supports the scaling up of social business.

Originality/value

This paper fulfills an identified need to study how social enterprises credit access can be enabled.

Details

International Journal of Productivity and Performance Management, vol. 68 no. 2
Type: Research Article
ISSN: 1741-0401

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Article
Publication date: 9 July 2018

Ceylan Onay and Elif Öztürk

This paper aims to survey the credit scoring literature in the past 41 years (1976-2017) and presents a research agenda that addresses the challenges and opportunities Big…

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2758

Abstract

Purpose

This paper aims to survey the credit scoring literature in the past 41 years (1976-2017) and presents a research agenda that addresses the challenges and opportunities Big Data bring to credit scoring.

Design/methodology/approach

Content analysis methodology is used to analyze 258 peer-reviewed academic papers from 147 journals from two comprehensive academic research databases to identify their research themes and detect trends and changes in the credit scoring literature according to content characteristics.

Findings

The authors find that credit scoring is going through a quantitative transformation, where data-centric underwriting approaches, usage of non-traditional data sources in credit scoring and their regulatory aspects are the up-coming avenues for further research.

Practical implications

The paper’s findings highlight the perils and benefits of using Big Data in credit scoring algorithms for corporates, governments and non-profit actors who develop and use new technologies in credit scoring.

Originality/value

This paper presents greater insight on how Big Data challenges traditional credit scoring models and addresses the need to develop new credit models that identify new and secure data sources and convert them to useful insights that are in compliance with regulations.

Details

Journal of Financial Regulation and Compliance, vol. 26 no. 3
Type: Research Article
ISSN: 1358-1988

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Book part
Publication date: 9 July 2010

Akos Rona-Tas and Stefanie Hiss

Both consumer and corporate credit ratings agencies played a major role in the US subprime mortgage crisis. Equifax, Experian, and TransUnion deployed a formalized scoring

Abstract

Both consumer and corporate credit ratings agencies played a major role in the US subprime mortgage crisis. Equifax, Experian, and TransUnion deployed a formalized scoring system to assess individuals in mortgage origination, mortgage pools then were assessed for securitization by Moody's, S&P, and Fitch relying on expert judgment aided by formal models. What can we learn about the limits of formalization from the crisis? We discuss five problems responsible for the rating failures – reactivity, endogeneity, learning, correlated outcomes, and conflict of interest – and compare the way consumer and corporate rating agencies tackled these difficulties. We conclude with some policy lessons.

Details

Markets on Trial: The Economic Sociology of the U.S. Financial Crisis: Part A
Type: Book
ISBN: 978-0-85724-205-1

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Expert briefing
Publication date: 27 February 2019

Ant Financial's Sesame Credit rating service.

Details

DOI: 10.1108/OXAN-DB242168

ISSN: 2633-304X

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Article
Publication date: 5 October 2021

Hongming Gao, Hongwei Liu, Haiying Ma, Cunjun Ye and Mingjun Zhan

A good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to…

Abstract

Purpose

A good decision support system for credit scoring enables telecom operators to measure the subscribers' creditworthiness in a fine-grained manner. This paper aims to propose a robust credit scoring system by leveraging latent information embedded in the telecom subscriber relation network based on multi-source data sources, including telecom inner data, online app usage, and offline consumption footprint.

Design/methodology/approach

Rooting from network science, the relation network model and singular value decomposition are integrated to infer different subscriber subgroups. Employing the results of network inference, the paper proposed a network-aware credit scoring system to predict the continuous credit scores by implementing several state-of-art techniques, i.e. multivariate linear regression, random forest regression, support vector regression, multilayer perceptron, and a deep learning algorithm. The authors use a data set consisting of 926 users of a Chinese major telecom operator within one month of 2018 to verify the proposed approach.

Findings

The distribution of telecom subscriber relation network follows a power-law function instead of the Gaussian function previously thought. This network-aware inference divides the subscriber population into a connected subgroup and a discrete subgroup. Besides, the findings demonstrate that the network-aware decision support system achieves better and more accurate prediction performance. In particular, the results show that our approach considering stochastic equivalence reveals that the forecasting error of the connected-subgroup model is significantly reduced by 7.89–25.64% as compared to the benchmark. Deep learning performs the best which might indicate that a non-linear relationship exists between telecom subscribers' credit scores and their multi-channel behaviours.

Originality/value

This paper contributes to the existing literature on business intelligence analytics and continuous credit scoring by incorporating latent information of the relation network and external information from multi-source data (e.g. online app usage and offline consumption footprint). Also, the authors have proposed a power-law distribution-based network-aware decision support system to reinforce the prediction performance of individual telecom subscribers' credit scoring for the telecom marketing domain.

Details

Asia Pacific Journal of Marketing and Logistics, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1355-5855

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Article
Publication date: 15 October 2021

Akanksha Goel and Shailesh Rastogi

This study aims to formulate a behavioural credit scoring models for Indian small and medium enterprises (SME) entrepreneurs using certain behavioural and psychological…

Abstract

Purpose

This study aims to formulate a behavioural credit scoring models for Indian small and medium enterprises (SME) entrepreneurs using certain behavioural and psychological constructs. Two separate models are built which can predict the credit default and wilful default of the borrowers, respectively. This research was undertaken to understand whether certain psychological and behavioural factors can significantly predict the borrowers’ credit and wilful default.

Design/methodology/approach

A questionnaire survey was undertaken by SME entrepreneurs of two Indian states, i.e. Uttar Pradesh and Maharashtra. The questionnaire had two dependent variables: wilful default and credit default and nine independent variables. The questionnaire reliability and validity were ensured through confirmatory factor analysis (CFA) and further a model was built using logistic regression.

Findings

The results of this study have shown that certain behavioural and psychological traits of the borrowers can significantly predict borrowers’ default. These variables can be used to predict the overall creditworthiness of SME borrowers.

Practical implications

The findings of this research indicate that using behavioural and psychological constructs, lending institutions can easily evaluate the credit worthiness of those borrowers, who do not have any financial and credit history. This will enhance the capability of financial institutions to evaluate opaque SME borrowers.

Originality/value

There are very few numbers of studies which have considered predicting the credit default using certain psychological variables, but with respect to Asian market, and especially India, there does not exist a single significant study which has tried to fulfil such research gap. Also, this is the first study that has explored whether certain psychological factors can predict the wilful default of the borrowers. This is one of the most significant contributions of this research.

Details

Journal of Entrepreneurship in Emerging Economies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 2053-4604

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Article
Publication date: 25 November 2013

Sherif Omar Attallah, Ahmad Senouci, Amr Kandil and Hassan Al-Derham

The purpose of this paper is to present a methodology for assessing, in quantifiable terms, the reduction in environmental impacts achieved by applying different credits

Abstract

Purpose

The purpose of this paper is to present a methodology for assessing, in quantifiable terms, the reduction in environmental impacts achieved by applying different credits of sustainability rating systems in building construction projects.

Design/methodology/approach

Sustainability rating systems are developed in various regions to evaluate construction projects with respect to their environmental performance. Although implementation of rating systems had a recognized effect on reducing environmental impact of construction projects, there is no objective and quantifiable evidence that the approaches recommended by these rating systems to achieve the required certification lead to optimum environmental results. This paper presents a methodology that utilizes life cycle analysis (LCA) as a powerful and objective tool to validate the way rating systems evaluate project performance. The Qatar Sustainability Assessment System (QSAS), recently developed in the State of Qatar by Gulf Organization for Research and Development (GORD), is chosen as a case study to illustrate application of the developed methodology. Environmental impacts due to implementation of QSAS credits are calculated for one project in Qatar, which is currently under construction.

Findings

Results reveal possible use of LCA as a tool for evaluating the effectiveness of rating systems. For the QSAS case study, findings reveal indications of over and, in some instances, under estimation of the weights assigned to some credits and the difficulty in the quantification of the impacts of other credits, which indicates the need for reconsideration of these weights to improve effectiveness of the implementation of these credits.

Originality/value

The proposed methodology stands as a step toward the enhancement and rationalization of the currently used building sustainability ratings system.

Details

Smart and Sustainable Built Environment, vol. 2 no. 3
Type: Research Article
ISSN: 2046-6099

Keywords

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