Extending credit to small and medium size companies

Wong, Alfred; Lü, Wei; Tjosvold, Dean; Yang, Jie

doi:10.1108/ijcma-06-2015-0034

Cited by 19 publications

(16 citation statements)

References 87 publications

(110 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Similarly, the Industrial and Commercial Bank of China (ICBC) points out that the non-performing loan ratio of China's SMEs in the ICBC is more than 5%. Thus, FIs generally consider China's SMEs to be of low credit quality, and they usually refuse and fear financing China's SMEs (Chen et al, 2010, Wong et al, 2016. To overcome this problem, the Chinese government has attempted to improve the current financing situation of SMEs by enforcing financial policy.…”

Section: Introductionmentioning

confidence: 99%

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

You

Zhou

Xie

et al. 2019

International Journal of Production Economics

266

170

View full text Add to dashboard Cite

In recent years, financial institutions (FIs) have tentatively utilized supply chain finance (SCF) as a means of solving the financing issues of small and medium-sized enterprises (SMEs). Thus, forecasting SMEs' credit risk in SCF has become one of the most critical issues in financing decision-making. Nevertheless, traditional credit risk forecasting models cannot meet the needs of such forecasting. Many researchers argue that machine learning (ML) approaches are good tools. Here we propose an enhanced hybrid ensemble ML approach called RS-MultiBoosting by incorporating two classic ensemble ML approaches, random subspace (RS) andMultiBoosting, to improve the accuracy of forecasting SMEs' credit risk. The experimental samples, originating from data on forty-six quoted SMEs and seven quoted core enterprises (CEs) in the Chinese securities market between 31 March 2014 and 31 December 2015, are collected to test the feasibility and effectiveness of the RS-MultiBoosting approach. The forecasting result shows that RS-MultiBoosting has good performance in dealing with a small sample size. From the SCF perspective, the results suggest that to enhance SMEs' financing ability, 'traditional' factors, such as the current and quick ratio of SMEs, remain critical. Other SCF-specific factors, for instance, the features of trade goods and the CE's profit margin, play a significant role.

show abstract

Section: Introductionmentioning

confidence: 99%

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

You

Zhou

Xie

et al. 2019

International Journal of Production Economics

266

170

View full text Add to dashboard Cite

show abstract

“…However, SMEs have less access to formal sources of external financing despite these contributions (Rostamkalaei and Freel, 2016), and their resources are limited, which hinders their ability to grow and to draw up and implement strategies that can quickly take advantage of rapid technological changes (Wong et al , 2016; Eriksson et al , 2017).…”

Section: Introductionmentioning

confidence: 99%

Customer loyalty approach based on CRM for SMEs

Galvão

Carvalho

Oliveira

et al. 2018

JBIM

View full text Add to dashboard Cite

Purpose The purpose of this paper is to explore marketing analysis by developing a customer loyalty approach for small- and medium-sized enterprises (SMEs) in the service sector, based on customer relationship management (CRM) in the context of an emerging economy. Design/methodology/approach This study analyzes different models of CRM in the literature and defines a loyalty approach for SMEs in the service sector. It is characterized as exploratory research, with a qualitative approach. A systematic review of the literature is undertaken as the starting point for achieving the main objective of the study. Findings The need to develop a framework for an approach to customer loyalty for SMEs based on CRM and to make it operational. Research limitations/implications It is a qualitative and exploratory study, applied only to a company’s two main clients. Practical implications The contribution of this study is that it offers SMEs consistent guidelines on how best to conduct relationship management with their customers by using a systemic methodology that can be adapted to the requirements of more effective interaction between a company and its customers. It also provides coherence to the purpose of meeting customers’ needs. This is an action strategy that focuses especially on the use of tools, such as SERVQUAL, Kano and SWOT analysis, and thereby increases the competitive profile of organizations in this sector. Originality/value This study is a new approach to customer loyalty for SMEs in the service sector, which integrates elements of CRM strategy and implementation.

show abstract

“…A credit management strategy is essential to optimally manage accounts receivable. For example, Wong et al (2016) confirm that rational explanations and social encouragement could be essential credit management strategies. The latter could be used to provide credit and to convince borrowers that their transaction costs will be reasonable if the debtor complies with the credit strategy.…”

Section: Credit Management Policymentioning

confidence: 75%

Assessing the relative appropriateness of the credit management policies of South African universities of technology

Maseko

Oberholzer

Middelberg

2019

J. econ. financ. sci.

View full text Add to dashboard Cite

Orientation: The study explores credit management on the South African higher education landscape in the context of the institutional theory.Research purpose: To calculate a best practice frontier to assess the extent to which universities’ credit management policy as an institutional resource is appropriate for its environment.Motivation for the study: The study was undertaken to investigate how the institutional environment influences the development of formal university structures.Research design, approach and method: A parallel mixed-method research design was followed to collect both qualitative data and quantitative data: document analysis to assess five universities of technology’s credit management policies and quantitative data testing 1392 senior students’ perspectives on the credit management policies of these five universities of technology.Main findings: The lesson learnt from this study is that the more aggressive the credit management policy, the more the students rated it as appropriate (fair, understandable and accurate). Furthermore, contrary to extant literature, no evidence was found that a stringent or aggressive credit management policy is experienced as rigorous.Practical/managerial implications: Universities of technology may apply aggressive credit management policies without the fear that they will be perceived as rigorous.Contribution/value-add: Policymakers should note that students desire a credit management policy that: (1) is well communicated to them; (2) encourages them, by granting discounts, to do early settlements of debt; (3) is strictly implemented; and (4) is strict with regard to the collection and recovery of (deferred) debt.

show abstract

Extending credit to small and medium size companies

Cited by 19 publications

References 87 publications

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

Forecasting SMEs' credit risk in supply chain finance with an enhanced hybrid ensemble machine learning approach

Customer loyalty approach based on CRM for SMEs

Assessing the relative appropriateness of the credit management policies of South African universities of technology

Contact Info

Product

Resources

About