Academics and professionals have paid attention mainly to performance measurement systems that have implemented financial and non financial measures. However, the majority of previous studies were conducted in developed countries, but very little had been carried out in developing countries. Therefore, this study aims to investigate performance measurement systems in developing countries in twofold. Firstly, examine the existing uses of financial and non financial measures in Libyan banking industry and, secondly, analyze the contextual factors that may affect the use of these measures. In order to fulfill this study's aim managers from top and middle managerial levels have participated to the survey. Data were collected through a series of quantitative and qualitative approaches while obtained data were analyzed by employing numerous statistical methodologies. The study findings indicate that most of the Libyan banks use a mixture of performance measurement systems that include a combination of financial and nonfinancial measures. However, the Libyan banks are still relying on more financial measures than non financial measures as important information used for various purposes. In addition, several contextual factors represent the core of the study and they are of great importance for the use of performance measures according to banks' size within the banking industry in Libya. This study contributes to bridge the gap in the literature of performance measurement by providing theoretical and empirical evidences of how performance measures could be used more proficiently in developing countries. Furthermore, the study's findings offer an overview of the performance measures used currently in Libyan banking industry and suggest the implementation of the outcome of this study that will instigate important improvements to the current performance measurement systems.

The regimes of today that regulate and protect Intellectual Property Rights are based on Western cultural and philosophical values. This realization leads to the supposition that culture may influence the notion of patents. This raised the question of whether patent valuation would underlie a cultural bias. If patents are important in international business it is evident that a cultural impact on patent valuation would have significant implications and necessitate dedicated investigation. A literature review confirmed a knowledge gap in this area. This work, therefore, aims to investigate cultural impact on patent valuation. A distinction is made between a valuation from an ethical point of view and an economic valuation. Following a mixed methods approach, this research applies semi-structured interviews to create survey items for a questionnaire that then provides data that can be analyzed statistically and qualitatively. For quality assurance, a pre-questionnaire is used as an intermediate step. The results of the quantitative and qualitative analyses are subject to a between-method triangulation, which is interpreted in the following discussion in the light of relevant theory. The findings of this investigation confirm that there is indeed a cultural impact on the notion of patents. Two cultural dimensions, “Uncertainty Avoidance” and “Institutional Collectivism” correlate significantly with ethical patent valuation. Furthermore, it is not the complete cultural dimension, “Future Orientation”, but a specific aspect of it that correlates with economic patent valuation. A relationship between standpoints towards the ethical valuation of patents and economic patent valuation could not be proven. The research questions of what cultural dimensions have an impact on patent valuation and how and why they impact are answered. In addition, this work provides a model that represents cultural impact on patent valuation.

Venture capital (VC) is widely perceived by UK policymakers to be a key requirement for the growth and development of successful and innovative early stage firms. This thesis examines how government policy has impacted the emergence of VC sectors in the UK and US. Using historical, qualitative and quantitative methods it argues that the public rationale behind UK policy has been largely framed in ways that underestimate the importance of capabilities, demand for capital, and institutional differences. The thesis examines venture capitalists' key supply‐demand relationships: with funded firms; with limited partners; and with the markets that allow exit via IPO. It argues that the US VC sector has developed unique capabilities enabling the assembly of complementary assets to bring firms to successful IPO. In the UK, policy aimed at addressing the ‘equity gap' has focused on the provision of capital rather than developing the capabilities that have characterised the US sector. We perform quantitative analysis examining the effectiveness of recent UK schemes at providing VC funding to small firms. Drawing upon two proprietary datasets, including one new hand‐collected dataset of all investments made under the Venture Capital Trust scheme, the thesis provides new quantitative evidence on the success of government policy interventions, demand for capital by firms, and investment exit opportunities. The thesis then compares principal‐agent and evolutionary framing perspectives of the VC sector, arguing the evolutionary view explains some nuances more readily than a pure principal‐agent view. It concludes by discussing the theoretical and policy implications of the research.

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Neural networks have been shown to be a promising tool for forecasting financial times series. Numerous research and applications of neural networks in business have proven their advantage in relation to classical methods that do not include artificial intelligence. What makes this particular use of neural networks so attractive to financial analysts and traders is the fact that governments and companies benefit from it to make decisions on investment and trading. However, when the number of inputs to the model and the number of training examples becomes extremely large, the training procedure for ordinary neural network architectures becomes tremendously slow and unduly tedious. To overcome such time-consuming operations, this research work focuses on using various Higher Order Neural Networks (HONNs) which have a single layer of learnable weights, therefore reducing the networks' complexity. In order to predict the upcoming trends of univariate financial time series signals, three HONNs models; the Pi-Sigma Neural Network, the Functional Link Neural Network, and the Ridge Polynomial Neural Network were used, as well as the Multilayer Perceptron. Furthermore, a novel neural network architecture which comprises of a feedback connection in addition to the feedforward Ridge Polynomial Neural Network was constructed. The proposed network combines the properties of both higher order and recurrent neural networks, and is called Dynamic Ridge Polynomial Neural Network (DRPNN). Extensive simulations covering ten financial time series were performed. The forecasting performance of various feedforward HONNs models, the Multilayer Perceptron and the novel DRPNN was compared. Simulation results indicate that HONNs, particularly the DRPNN in most cases demonstrated advantages in capturing chaotic movement in the financial signals with an improvement in the profit return over other network models. The relative superiority of DRPNN to other networks is not just its ability to attain high profit return, but rather to model the training set with fast learning and convergence. The network offers fast training and shows considerable promise as a forecasting tool. It is concluded that DRPNN do have the capability to forecast the financial markets, and individual investor could benefit from the use of this forecasting.