The aim of this paper is to explore the challenges and opportunities of designing and delivering Degree and Higher Level Apprenticeships (D&HLAs) at levels 4-7 from a multi-stakeholder perspective namely employers, Universities, independent training organisations and professional bodies. Twenty-seven face-to-face interviews were undertaken and thematic content analysis was used to analyse the data. The following three themes emerged from the data analysis: programme design; programme delivery; and graduate attributes. We conclude that whilst there are increasing numbers of trailblazer groups developing higher level standards, the uptake of apprenticeships at these levels remains relatively low. Although stakeholders support the principle of D&HLAs, we identify a number of challenges and opportunities facing those who seek to successful introduction of these programmes. Our policy recommendations include the need for all stakeholders to work collaboratively to co-create a flexible system to support the validity and relevance of D&HLAs. This will include streamlining and mapping the variety of qualifications currently available in order to promote a platform for parity of both esteem and opportunity for those achieving degree qualifications through the apprenticeship route.

This paper uses the Problem Gambling Severity Index (PGSI) to determine differences in UK internet player responses to their motives for gambling online. It also evaluates their views relating to responsible gambling practices and behavioural factors. A three stage analysis applying Structural Equation Modelling (SEM); multiple regression; and multinomial logistic regression is used. The main research instruments is an internet based questionnaire. Our findings for the motivation factors highlight that the most significant factors which players perceive are escape and relaxation; financial motivation; and social and competition. In terms of player views in relation to responsible gambling practices and behavioural factors both self-exclusion and self-help; and game design are identified as the key factors. Other factors such as proactive responsible gambling; transparent terms and conditions; and use of player information are not acknowledged as significant factors by players. This study also suggests that the financial motive to gamble should be divided into the following submotives: 'to win money' and to 'earn income'. Our main policy recommendation includes the need for a more transparent system that places emphasis on tangible or auditable means of demonstrating ethical responsibilities, and to determine areas of improvement.

Purpose -The main aim of this paper is to distinguish whether the decision making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit; and highlight significant variables that are crucial in terms of accepting and rejecting applicants which can further aid the decision making process.Design/methodology/approach -A real data-set of 487 applicants are used consisting of 336 accepted credit applications and 151 rejected credit applications make to an Islamic finance house in the UK. In order to build the proposed scoring models, the data-set is divided into training and hold-out sub-set. The training sub-set is used to build the scoring models and the hold-out sub-set is used to test the predictive capabilities of the scoring models.70 percent of the overall applicants will be used for the training sub-set and 30 percent will be used for the testing sub-set. Three statistical modeling techniques namely Discriminant Analysis (DA), Logistic Regression (LR) and Multi-layer Perceptron (MP) neural network are used to build the proposed scoring models. Findings -Our findings reveal that the LR model has the highest Correct Classification (CC) rate in the training sub-set whereas MP outperforms other techniques and has the highest CC rate in the hold-out sub-set. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest Misclassification Cost (MC) above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision making process. Research limitations/implications -Although our sample is small and restricted to an Islamic Finance house in the UK the results are robust. Future research could consider enlarging the sample in the UK and also internationally allowing for cultural differences to be identified. The results indicate that the scoring models can be of great benefit to Islamic finance houses in regards to their decision making processes of accepting and rejecting new credit applications and thus improve their efficiency and effectiveness. Originality/value -Our contribution is the first to apply credit scoring modeling techniques in Islamic Finance. Also in building a scoring model our application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected.

Financial strength ratings (FSRs) have become more significant particularly since the recent financial crisis of 2007-2009 where rating agencies failed to forecast defaults and the downgrade of some banks. The aim of this paper is to predict Capital Intelligence banks' financial strength ratings (FSRs) group membership using machine learning and conventional techniques. Here the authors use five different statistical techniques, namely CHAID, CART, multilayer-perceptron neural networks, discriminant analysis and logistic regression. They also use three different evaluation criteria namely average correct classification rate, misclassification cost and gains charts. The data are collected from Bankscope database for the Middle Eastern commercial banks by reference to the first decade of the 21st century. The findings show that when predicting bank FSRs during the period

Ti t l e D e g r e e a n d hi g h e r lev el a p p r e n tic e s hi p s : a n e m pi ri c al inv e s ti g a tio n of s t a k e h ol d e r p e r c e p tio n s of c h all e n g e s a n d o p p o r t u ni ti e s