Search

The influence of ‘credit crunch’ on Small and Medium sized Enterprises (SMEs) has been of concern to the government, regulators, banks, the enterprises and the public. Using a large dataset of UK SMEs’ records covering the early period of the ‘credit crunch’, the influence of the ‘credit crunch’ on SMEs have been studied. It uses cross-sectional method, panel data models and GAM to provide a detailed examination of SMEs performance. Both newly established and matured SMEs, segmented by age, are considered separately. The data contains 79 variables which covered obligors’ general condition, financial information, directors’ portfolio and other relevant credit histories. The ‘credit crunch’ is a typical ‘black swan’ phenomenon. As such there is a need to examine whether the stepwise logistic model, the industries prime modelling tool, could deal with the sudden change in SMEs credit risk. Whilst it may be capable of modelling the situation alternatives models may be more appropriate. It provides a benchmark for comparison to other models and shows how well the industry’s standard model performs. Given cross-sectional models only provide aggregative level single time period analysis, panel models are used to study SMEs performance through the crisis period. To overcome the pro-cyclic feature of logistic model, macroeconomic variables were added to panel data model. This allows examination of how economic conditions influence SMEs during ‘credit crunch’. The use of panel data model leads to a discussion of fixed and random effects estimation and the use of explanatory macroeconomic variables. The panel data model provides a detailed analyse of SMEs’ behaviour during the crisis period. Under parametric models, especially logistic regression, data is usually transformed to allow for the non-linear correlation between independent variable and dependent variable. However, this brings difficulty in understanding influence of each independent variable’s marginal effects. Another way of dealing with this is to add non-parametric effects. In this study, Generalized Additive Models (GAM) allows for non-parametric effects. A natural extension of logistic regression is a GAM model with logistic link function. In order to use the data in their original state an alternative method of processing missing values is proposed, which avoids data transformation, such as the use of weights of evidence (WoE). GAM with original data could derive a direct marginal trend and plot how explanatory variables influence SMEs’ ‘bad’ rate. Significant non-parametric effects are found for both ‘start-ups’ and ‘non-start-ups’. Using GAM models results in higher prediction accuracy and improves model transparency by deriving explanatory variables’ marginal effects.

This study addresses public sector intermediaries and their role in facilitating innovation in Small and Medium Sized Enterprises (SMEs) in South Korea. The primary aim is to understand and address the informational and relational barriers that SMEs face during the innovation process and how these are resolved through interaction. Although the government has been implementing SME support policies for several decades, the Korean National Innovation System (KNIS) has been characterised with six words: ‘strong large firms, weak small firms’. Korean Government policies for R&D have not been effective in enhancing the economic performance and innovative capabilities of SMEs and the ‘low level of competitiveness’ of SMEs obstructs knowledge interaction between firms. Policies directed at SMEs mainly focus on direct support and do not reflect the interactive nature of the innovation process. This mismatch between policy and desired outcomes has led this study to go beyond examining the informational and relational constraints. It analyses the factors influencing successful (or less successful) innovation and asks whether public intermediaries have provided an effective mechanism in resolving innovation barriers (i.e. system failures). Yet, there has been a lack of research into public intermediaries and SMEs within the National Innovation Systems (NIS) framework. The NIS approach is a loosely configured framework and the intermediary literature is fragmented and has rarely been integrated with the NIS literature. Research has tended to focus on specific functions of private intermediaries and far less on the public intermediaries, which have been playing a crucial role in facilitating innovation in Korean industry for several decades. The central focus of this study is on the knowledge interaction process between public intermediaries and SMEs occurring at multiple levels of interaction in the Korean NIS. This study therefore attempts to integrate the NIS concept and the intermediary approach to provide a robust way to explore the knowledge interaction process at meso- and micro-levels. Four functions of the intermediary are constructed to explore how they might influence SME innovation: knowledge facilitation, learning facilitation, knowledge enabling and managing interfaces. Through in-depth analysis of five case studies encompassing firms in mechatronics and IT, this study explicates the knowledge interaction process and influential factors of successful innovation. The analysis addresses a series of issues that the generic NIS concept cannot fully explain: (1) knowledge interaction at meso- and microlevels; (2) multiplicity of relationships and their evolving nature; (3) the role of public intermediaries in a specific cultural context; and (4) the heterogeneity of SMEs with their pre-existing resources and routines. Sociological perspectives especially provide insights for investigating not only the dynamic nature of interactions but also micro-level factors that determine successful interactions and innovation that are largely neglected in both NIS and intermediary studies; e.g. productive combination of competing rationalities, social learning, and the importance of reflexive individuals. Focusing on a modulated NIS concept for public sector intermediaries and SMEs in a Korean context, the study opens the ‘black box’ of knowledge interaction and learning that resolves the barriers, shapes the successful innovation environment and hence strengthens the innovation system. The findings have implications for policy, including the need to establish new policy measures aimed not simply at achieving a set goal but rather at facilitating the interaction process with a long-term view. The study recommends that public intermediaries need to focus on monitoring activities that integrate and support the knowledge interaction process by facilitating ‘associativeness’ among actors. Furthermore, the heterogeneity of the local contexts and SMEs in the innovation process need to be taken into account in designing the programmes, moving away from one-size-fits-all type services.

This thesis comprehensively explores the credit risk of Small and Medium Sized Enterprises (SMEs) using transactional characteristics, financial variables and market information. It contributes SMEs credit risk modelling by exploring a range of soft features, such as management capability, industrial sectors, entity type, etc. It is the first study of investigating the concept of management capability through quantitative transactional information. Firstly, models are proposed to assess the credit risk of SMEs by identifying the significant factors. To fulfill this, two studies are carried out. In the first study, logistic regression, survival analysis and ordinal regression are used to model the relationship between transformed financial variables and probability of default. Both the traditional AUROC measure and Hand Statistic are used to evaluate the performances of the models, and they both indicate that logistic regression on weights of evidence transformed data yields the best prediction. Survival model takes an extra element of the time dimension into consideration. Ordinal regression performs poorly possibly due to impact of sample sizes. The factors appeared with highest frequencies are ratios associated with liquidity and growth. The other study predicts the credit risk (‘good’ ‘bad’ and ‘indeterminate’) of the SMEs using transactional characteristics. 35000 SMEs are clustered by different clustering algorithms. It is notably found that most ‘indeterminate’ observations are clustered with ‘bad’ observation, which is different from industry habit of combining ‘indeterminate’ and ‘good’. Logistic regression performs better than ordinal regression according to AUROC measure. In addition, some key points raised in focus group interview with bank managers are seen in the modelling process as significant variables, such as sector belonging to, entity type, region/location, time associated with bank, and account conduct. Secondly, the informational bases of two major models, which are accounting based credit scoring models and Merton type models, are explored to figure out aspects which affect SMEs’ credit risk. 33 financial variables covering nine financial categories are considered. It employs other modelling frameworks rather than the often-used linear regression, which are linear regression with interactions and the Cox proportional hazard model. It is found that weak relationship exists between these two models. The two major models capture different aspects of corporate information, it is suggested that a hybrid model, which incorporate both sources of information, might be considered to predict SMEs financial health. Thirdly, management capability of SMEs is elicited by applying principal component analysis to their transactional characteristics. Management capability is a qualitative idea, and its manifestation in quantitative variables was not explored in previous research. This study indicates some success in determining management capability. It is found that financial measure (credit turnover and debit turnover) and the performance measure (number of days in excess of the account) could be considered as reflecting management capability. Good management can identify trends at a very early stage and take action to mitigate the issue.

African countries have recently experienced an extraordinary and largely unanticipated boom in the uptake of mobile phones, and increasing rates of access to the internet. This thesis investigates how and why these information and communication technologies (ICT) are being adopted for use in Tanzanian micro and small enterprises (MSEs), and explores the changes they are bringing about to the existing business culture of marginalised economies. The study covers three sub-sectors of the Tanzanian economy: perishable foodstuffs trading, the informal construction industry and the export of African blackwood carvings. The analysis is based on fieldwork undertaken over a total of 15 months in 2002 and 2003, during which time business networks were revealed by physically following entrepreneurs and their contacts operating in different locations throughout the country. Entrepreneurs were asked to draw their own comparisons between the traditional pre-ICT situation and the improved ICT access of today. The discussion is informed by the findings of semi-structured interviews with these individuals, excerpts of which are presented in the text to give voice to the entrepreneurs. Various ingenious and at times unconventional methods of access to, and appropriation of, ICT is uncovered. Together these suggest that official indicators underestimate the thirst for digital consumption, especially for mobile phones, and help explain the flourishing informal economy of handset acquisition. Internet penetration and uptake for use in business, on the other hand, is revealed to be far slower. Nevertheless, the research does reveal that a sizeable amount of poorer entrepreneurs are using a triumvirate of hired or shared ICT – mobile phones, the Internet and the ‘old’ ICT of fax – to create what can appear to be a more formal enterprise than it may actually be. The ‘mobile office’ effectively allows poorer entrepreneurs to operate without premises, thereby saving costs on rent and allowing the enterprise to remain informal. This heralds a considerable change in the working practices of a significant branch of informal sector operators who have gone unnoticed, and calls for a reconstruction and redefinition of this crucial source of entrepreneurship in developing economies. Despite the huge uptake of mobile phones in particular, the work also cautions that some traditional pre-ICT aspects of the African business culture look set to remain for some time. It becomes clear that where entrepreneurs do decide to use ICT, reputation and recommendation are still very significant. This information is usually passed on when an entrepreneur meets in person with contacts from his very fluid informal networks of knowledge. Trust, and the need for direct, personal interaction through face-to-face contact – one of the most pervasive features of African MSE economies – emerge as a common theme across the case study industries and are likely to remain a crucial aspect of the way most MSE business is conducted. Mobile phones are seen to play a crucial role in improving the exchange of supply-anddemand information domestically, while a combination of applications (particularly e-mail) appear to act as tools with which to refresh relationships with sources of market information outside the country. It is suggested that ICT may be able to help entrepreneurs in moving from the personal to the impersonal exchange – a challenge that many other African businesses will also have to come to terms with as the Internet becomes an ever more important global trading tool.