Search

This paper investigates the variation in nonperforming loans over the economic cycle and the effect of past returns based on a nonparametric quantile analysis of the largest Islamic banks in the United Kingdom and Turkey from 2010 to 2019. The findings show a weak variation in nonperforming loans that increases with an increasing return on assets and a decreasing return on equity and decreases in an inverse scenario. As a result, the credit risk of Islamic banks is countercyclical. We suggest that the inverse relationships evidence the existence of trade-offs within bank returns and credit risk. Thus, banks' past profitability and risk mitigation are determinants of asset quality. These findings provide support for risk-taking and risk-sharing principles in which flight-to-safety mirrors the calibration of risk factors in a disruptive economy. Our estimates indicate that nonparametric quantile regression captures considerably more variation in a risk-return analysis. [ABSTRACT FROM AUTHOR]

The aim of this paper is to investigate the impact of Brexit on the dependence between European financial markets. We use the Diebold and Yilmaz (2009, 2012) approach to better map the relationship between the three main European markets and we propose an Optimal portfolio weighting to gain insights into the portfolio design dynamics in the period between 2013 and 2019, in particular any changes due to uncertainty surrounding Brexit. First, the findings show that between September 2015 and September 2016, the high level of volatility and spillover confirms the strong degree of market integration, with uncertainty surrounding the referendum outcome having a clear impact on the three main European markets. Second, the direction of spillover in the pre-Brexit period was from the UK market to the French and German markets in anticipation of uncertainty regarding the outcome of the referendum and the period immediately following it. Third, only the conditional correlation between the pair (CAC40–FTSE100) is characterized by an asymmetric effect. [ABSTRACT FROM AUTHOR]

This paper presents an application of extended goal programming in the field of offshore wind farm site selection. The strategic importance of offshore shore wind farms is outlined, drawing on the case of the United Kingdom proposed round three sites as an example. The use of multi-objective modelling methodologies for the offshore wind farm sector is reviewed. The technique of extended goal programming is outlined and its flexibility in combining different decision maker philosophies described. An extended goal programming model for site selection based on the United Kingdom future sites is then developed and a parametric analysis undertaken at the meta-objective level. The results are discussed and conclusions are drawn. [ABSTRACT FROM AUTHOR]

In this paper we present new evidence on the relation between idiosyncratic risk and mutual fund performance using asset pricing models. We use a unique data set containing monthly returns of 949 UK equity mutual funds over a 28-year period to measure fund performance. We find that idiosyncratic risk cannot be eliminated in UK mutual funds. We show that idiosyncratic risk is negatively related to returns for all funds investment style categories. We present evidence that the inclusion of idiosyncratic risk significantly increases the number of funds showing statistically significant and positive selectivity skills (alpha). Furthermore, all equity mutual funds turn to show significant volatility timing performance when idiosyncratic risk is considered. Finally, we find that idiosyncratic risk can forecast fund returns after controlling for macroeconomic variables. [ABSTRACT FROM AUTHOR]

The main aim of this paper is to derive a solution to the capacity problem faced by many perinatal networks in the United Kingdom. We propose a queueing model to determine the number of cots at all care units for any desired overflow and rejection probability in a neonatal unit. The model formulation is developed, being motivated by overflow models in telecommunication systems. Exact expressions for the overflow and rejection probabilities are derived. The model is then applied to a neonatal unit of a perinatal network in the UK. [ABSTRACT FROM AUTHOR]

We investigate how in the context of Corporate Venture Capital (CVC), the investment decisions affect the likelihood of their subsequent exit strategies. We use OLS and probit regression as well as Weibull distribution of residual values, given its reliability and validity for studying lifetime analysis. Based on a sample of 8722 VC-backed ventures with the first investment dates between 1999 and 2018 in United States (US) and United Kingdom (UK), the results show that the presence of CVCs positively affects the funding amounts and the duration of the investment. CVC funds are more generous and more patient than Independent Venture Capital (IVC) funds regarding their investments in ventures. Moreover, the findings provide evidence that the exit strategies are directly influenced by the funding amounts and the duration of the investment which are influenced, in turn, by the fund type. Greater funding increases the likelihood of IPO exit which is reduced by longer investment duration. Our results are robust to alternative estimation methods, namely two-stage treatment-effects regressions. These results help the various stakeholders (VC funds, investors, ventures) make crucial decisions regarding investment amounts and duration, and exit. [ABSTRACT FROM AUTHOR]

We introduce the BDA dynamics and explore the associated applications in renewable energy sector with a focus on data-driven innovation. Our study draws on the exponential growth of renewable energy initiatives over the last decades and on the paucity of literature to illustrate the use of BDA in the energy industry. We conduct a qualitative field study in the UK with stakeholder interviews and analyse our results using thematic analysis. Our findings indicate that no matter if the importance of the energy sector for 'people's well-being, industrial competitiveness, and societal advancement, old fashioned approaches to analytics for organisational processes are currently applied widely within the energy sector. These are triggered by resistance to change and insufficient organisational knowledge about BDA, hindering innovation opportunities. Furthermore, for energy organisations to integrate BDA approaches, they need to deal with challenges such as training employees on BDA and the associated costs. Overall, our study provides insights from practitioners about adopting BDA innovations in the renewable energy sector to inform decision-makers and provide recommendations for future research. [ABSTRACT FROM AUTHOR]

Although innovation from analytics is surging in the manufacturing sector, the understanding of the data-driven innovation (DDI) process remains a challenge. Drawing on a systematic literature review, thematic analysis and qualitative interview findings, this study presents a seven-step process to understand DDI in the context of the UK manufacturing sector. The findings discuss the significance of critical seven-step in DDI, ranging from conceptualisation to commercialisation of innovative data products. The results reveal that the steps in DDI are sequential, but they are all interlinked. The proposed seven-step DDI process with solid evidence from the UK manufacturing and research implications based on dynamic capability theory, institutional theory and TOE framework establish the building blocks for future studies and industry practice. [ABSTRACT FROM AUTHOR]

In today's dynamic and competitive business environment, it is critical for firms to share information selectively and develop organizational resilience. However, only a few existing studies examine the relationships between information management and supply chain resilience. Aiming to bridge the gaps between both domains, we propose a model encompassing information security culture, information leakage, information sharing effectiveness and supply chain resilience and to derive insights from their inter-relationships in this research. A cross-sectional survey of the multi-national corporations and small and medium enterprises among their senior managers in the United Kingdom was carried out for data collection. The data set was analysed using a structural equation modelling approach. The results obtained validate the proposed model. The findings ascertain that information security culture and information leakage are negatively correlated, which influence the supply chain resilience. Specifically, information security and information leakage affect the effectiveness of information sharing, which in turn positively and negatively influence the supply chain resilience, respectively. This study posits that information security culture is instrumental to mitigate information leakage and foster effective information sharing to strengthen supply chain resilience. [ABSTRACT FROM AUTHOR]

The impact of blockchain technology (BCT) implementation on the accuracy, reliability, visibility, incorruptibility, and timeliness of supply-chain processes and transactions, makes it attractive to improve the robustness, transparency, accountability and decision-making in risk management. Therefore, the emerging BCT can present an invaluable opportunity for the organisations in need of preparing for and responding to uncertain and complex instances. The adoption of BCT in the operations and supply chain management (OSCM) literature remains scarcely investigated, especially in the context of managing risks in emergency situations such as crises, disasters, and pandemics, which are characterised by volatility, uncertainty, complexity and ambiguity (VUCA) in the business environment. This article will contribute to the OSCM literature by developing a conceptual model that will examine the causal relationships between VUCA business environment, constructs derived from technology acceptance model (TAM), resilience and behavioural intention of the operations managers to adopt BCT for risk management. The model was tested by gathering responses from 116 operations managers in the UK (during COVID-19 pandemic) through structural equation modelling. Findings from the analysis suggest that understanding the benefits of BCT, involvement in resilient organisational practices and user-friendly implementation of the technology will have a significant and positive influence on the intention to adopt BCT for risk management in the OSCM context. Building upon these findings, we have proposed a BCT decision framework to assess the feasibility and suitability of adopting BCT in each context (such as risk management), which will have strategic implications for operations managers and the OSCM community. [ABSTRACT FROM AUTHOR]

Over the past years, studies shed light on how social norms and perceptions potentially affect loan repayments, with overtones for strategic default. Motivated by this strand of the literature, we incorporate collective social traits in predictive frameworks on credit card delinquencies. We propose the use of a two-stage framework. This allows us to segment a market into homogeneous sub-populations at the regional level in terms of social traits, which may proxy for perceptions and potentially unravelled behaviours. On these formed sub-populations, delinquency prediction models are fitted at a second stage. We apply this framework to a big dataset of 3.3 million credit card holders spread in 12 UK NUTS1 regions during the period 2015–2019. We find that segmentation based on social traits yields efficiency gains in terms of both computational and predictive performance compared to prediction in the overall population. This finding holds and is sustained in the long run for different sub-samples, lag counts, class imbalance correction or alternative clustering solutions based on individual and socio-economic attributes. [ABSTRACT FROM AUTHOR]

During the first wave of the COVID-19 pandemic, in France, people cleared the shelves of butter; in Italy, it was pasta; in Great Britain, it was chicken. While there may be cultural disagreement on what is essential, clearly, in times of crisis, consumers stockpile the 'essentials'. We address the problem of "panic buying", which is characterized by increasing demand in the face of diminishing inventory. In such cases, prices may hike and firms (retailers) selling the high-demand product are quantity takers, in terms of supply, and price setters. We consider a manufacturer who sells a scarce product to a single retailer. The retailer seeks to maximize her profit, while in contrast, the manufacturer pursues a social objective of regulating and lowering the amount that the end customer (consumer) pays (including the cost of traveling to obtain the scarce product). By analyzing the competition between the two parties, retailer and manufacturer, we find that even when the regulator (manufacturer) makes a significant social commitment, neither subsidizing the retailer nor subsidizing the consumers necessarily curbs price hikes. Furthermore, there is a threshold ratio (i.e., proportion of the end price subsidized by the regulator) that determines the minimal budget that the regulator would need to allocate in order for subsidization to make a difference to consumers. [ABSTRACT FROM AUTHOR]

Sustainability of small and medium sized enterprises (SMEs) is significant as SMEs contribute to GDP substantially in every economy. This research develops an innovative sustainable supply chain performance measurement model for SMEs. Prior researches predominantly use balanced score card (BSC) approach that presume causal relationship of criteria and Data Envelopment Analysis (DEA), which derive efficiency of units from a few input and output criteria. While DEA is effective for policymakers, BSC is more suitable for individual SME. The proposed method that uses structural equation modeling (SEM) approach to derive the relationship of criteria and criteria weights formulates regression-type models for a specific region as well as for specific SME. The SEM-based supply chain sustainability performance measurement model is beneficial to policymakers as they can determine means for improvement at a regional level. The proposed method could also facilitate managers/owners of individual SMEs with measures for improving their supply chain sustainability performance. The method has been applied to three varied geographical locations in the UK, France and India in order to demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Remuneration for chief executives in UK higher education—known as Vice Chancellors (VCs)—has been on an upward trend in recent years, and VCs have received criticism that their performance does not warrant such reward. We investigate the relationship between VC pay and performance (rooted in principal agent theory), taking into account an array of other possible determinants. Deriving measures of VC performance is difficult as VCs are agents for various principals, and each principal may be interested in a different aspect of performance. We consider three measures of VC performance here: managerial efficiency as measured by data envelopment analysis; performance in university rankings produced by the media; the financial stability of the university. We construct a comprehensive data set, covering academic years 2009/2010 to 2016/2017, a period of considerable change in the UK higher education sector including rapidly-rising undergraduate tuition fees. Our results show that, once other possible determinants of VC pay are taken into account, the main measure of performance which affects VC pay is the one based on media rankings. Thus the agents (VCs) appear to be rewarded for delivering against this performance benchmark which is likely to be of interest to a variety of principals. This result however varies by type of university suggesting that the labour market for VCs differs by mission group. [ABSTRACT FROM AUTHOR]

Systems for allocating seats in an election offer a number of socially and mathematically interesting problems. We discuss how to model the allocation process as a network flow problem, and propose a wide choice of objective functions and allocation schemes. Biproportional rounding, which is an instance of the network flow problem, is used in some European countries with multi-seat constituencies. We discuss its application to single seat constituencies and the inevitable consequence that seats are allocated to candidates with little local support. However, we show that variants can be selected, such as regional apportionment, to mitigate this problem. In particular, we introduce a parameter based family of methods, which we call balanced majority voting, that can be tuned to meet the public's demand for local and global 'fairness'. Using data from the 2010 to 2015 UK general elections, we study a variety of network models and implementations of biproportional rounding, and address conditions of existence and uniqueness. [ABSTRACT FROM AUTHOR]