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This paper aims 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.

A real data set of 487 applicants is used consisting of 336 accepted credit applications and 151 rejected credit applications made to an Islamic finance house in the UK. To build the proposed scoring models, the data set is divided into training and hold-out subsets. The training subset is used to build the scoring models, and the hold-out subset is used to test the predictive capabilities of the scoring models. Seventy per cent of the overall applicants will be used for the training subset, and 30 per cent will be used for the testing subset. Three statistical modeling techniques, namely, discriminant analysis, logistic regression (LR) and multilayer perceptron (MP) neural network, are used to build the proposed scoring models.

The findings reveal that the LR model has the highest correct classification (CC) rate in the training subset, whereas MP outperforms other techniques and has the highest CC rate in the hold-out subset. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest misclassification cost 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.

This contribution is the first to apply credit scoring modeling techniques in Islamic finance. Also in building a scoring model, the authors' 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.

The purpose of this paper is to evaluate the impact of a foundation degree programme, delivered via a flipped university approach, on student learning, development and employability in the sector of football coaching and development.

A mixed-method design was adopted, whereby current (n=106) and graduate (n=41) students from the programme completed an online course evaluation questionnaire and then follow-up interviews were conducted with 12 of the initial sample to explore the impact of the programme in more detail.

Participants reported significant benefits of the flipped university approach on their career development, improvement in their inter- and intra-personal skills (e.g. communication, reflection, confidence) and the acquisition of industry relevant knowledge. Recommendations include a greater provision of tailored study support for individuals and broadening the coaching portfolio of students to help address the diversification in Football Community Trust remits.

This study has indicated that new approaches to student learning and development are better suited to preparing young people for the industry in which they seek to gain employment post-education. Sampling a wider range of student perspectives qualitatively would have provided a more thorough insight into their experiences. However, this provides an avenue for future research that seeks to explore the mechanisms through which such approaches to learning facilitate development.

The novel flipped university concept is one that should be considered as a way of better educating and preparing students for employment in the sports industry. It is an approach that could be explored by a wide range of sectors as an alternative to both campus-based higher education and degree apprenticeships.