Past and recent events have proved that critical infrastructure are vulnerable to natural catastrophes, unintentional accidents and terrorist attacks. Protecting these systems is critical to avoid loss of life and to guard against economical upheaval. A systematic approach to plan security investments is paramount to guarantee that limited protection resources are utilized in the most effcient manner. This thesis provides a detailed review of the optimization models that have been introduced in the past to identify vulnerabilities and protection plans for critical infrastructure. The main objective of this thesis is to study new and more realistic models to protect transportation infrastructure such as railway and road systems against man made and natural disruptions. Solution algorithms are devised to effciently solve the complex formulations proposed. Finally, several illustrative case studies are analysed to demonstrate how solving these models can be used to support effcient protection decisions.

One of the most influential and often quoted content theories of human motivation is Abraham Maslow’s Hierarchy of Needs. Maslow’s theory is based on an assumption that all humans are motivated by a hierarchy of needs that are fundamental and universal. While many studies have attested to the wide relevance and applicability of this model, some other legitimate studies have argued that the theory is limited in terms of its universal applicability because of its Anglo-Saxon monoculture orientation. In view of these differing points of view, this thesis investigates and tests the extent to which Maslow’s Hierarchy of Needs model is applicable or relevant to a Saudi organisational context. The empirical study was undertaken at two Saudia Arabian universities. One of the universities has a devout religious orientation while the other is, relatively, moderately religious in its outlook. The research utilised Porter’s Needs Satisfaction Questionnaire to collect the data. The data then underwent a quantitative (e.g. Q-Sort) analysis and a qualitative (thematic) analysis, yielding a number of findings related to the research questions and objectives. The findings suggest that Maslow’s theory of motivation is not universally applicable. The research generates a hierarchy of needs that is not the same as that proposed by Maslow’s theory. Furthermore, the findings reveal differences in the order of these needs across gender and religion. Therefore, this research has generated a new, refined order of motivational drivers in the Saudi organisational context, which reflects contextual influences of gender and religion.

The use of price promotions for fast moving consumer goods (FMCG’s) by supermarkets has increased substantially over the last decade, with significant implications for all stakeholders (suppliers, service providers & retailers) in terms of profitability and waste. The overall impact of price promotions depends on the complex interplay of demand and supply side factors, which has received limited attention in the academic literature. There is anecdotal evidence that in many cases, and particularly for products supplied by small and medium sized enterprises (SMEs), price promotions are implemented with limited understanding of these factors, resulting in missed opportunities for sales and the generation of avoidable promotional waste. This is particularly dangerous for SMEs who are often operating with tight margins and limited resources. A better understanding of consumer demand, through the use of disaggregated sales data (by shopper segment and store type) can facilitate more accurate forecasting of promotional uplifts and more effective allocation of stock, to maximise promotional sales and minimise promotional waste. However, there is little evidence that disaggregated data is widely or routinely used by supermarkets or their suppliers, particularly for those products supplied by SMEs. Moreover, the bulk of the published research regarding the impact of price promotions is either focussed on modelling consumer response, using claimed behaviour or highly aggregated scanner data or replenishment processes (frameworks and models) that bear little resemblance to the way in which the majority of food SMEs operate. This thesis explores the scope for improving the planning and execution of supermarket promotions, in the specific context of products supplied by SME, through the use of dis-aggregated sales data to forecast promotional sales and allocate promotional stock. An innovative case study methodology is used combining qualitative research to explore the promotional processes used by SMEs supplying the UK’s largest supermarket, Tesco, and simulation modelling, using supermarket loyalty card data and store level sales data, to estimate short term promotional impacts under different scenarios and derive optimize stock allocations using mixed integer linear programming (MILP). ii The results suggest that promotions are often designed, planned and executed with little formalised analysis or use of dis-aggregated sales data and with limited consideration of the interplay between supply and demand. The simulation modelling and MILP demonstrate the benefits of using supermarket loyalty card data and store level sales data to forecast demand and allocate stocks, through higher promotional uplifts and reduced levels of promotional waste.

In this thesis, we propose three new computational methods to price financial derivatives and construct hedging strategies under several underlying asset price dynamics. First, we introduce a method to price and hedge European basket options under two displaced processes with jumps, which are capable of accommodating negative skewness and excess kurtosis. The new approach uses Hermite polynomial expansion of a standard normal variable to match the first m moments of the standardised basket return. It consists of Black-and-Scholes type formulae and its improvement on the existing methods is twofold: we consider more realistic asset price dynamics and we allow more flexible specifications for the basket. Additionally, we propose two methods for pricing and hedging American options: one quasi-analytic and one numerical method. The first approach aims to increase the accuracy of almost any existing quasi-analytic method for American options under the geometric Brownian motion dynamics. The new method relies on an approximation of the optimal exercise price near the beginning of the contract combined with existing pricing approaches. An extensive scenario-based study shows that the new method improves the existing pricing and hedging formulae, for various maturity ranges, and, in particular, for long-maturity options where the existing methods perform worst. The second method combines Monte Carlo simulation with weighted least squares regressions to estimate the continuation value of American-style derivatives, in a similar framework to the one of the least squares Monte Carlo method proposed by Longstaff and Schwartz. We justify the introduction of the weighted least squares regressions by numerically and theoretically demonstrating that the regression estimators in the least squares Monte Carlo method are not the best linear unbiased estimators (BLUE) since there is evidence of heteroscedasticity in the regression errors. We find that the new method considerably reduces the upward bias in pricing that affects the least squares Monte Carlo algorithm. Finally, the superiority of our new two approaches for American options are also illustrated over real financial data by considering S&P 100 options and LEAPS®, traded from 15 February 2012 to 10 December 2014.

The accounting scandals in the 2000s and 2010s have led to a number of large-scale reforms in financial reporting and corporate governance regulations around the world, and still attract a lot of public debates recently. In that context, the demand for further knowledge on earnings management is very topical. What we have known is earnings management does exist. What we have not known, however, seems still overwhelming. We need to know more about issues such as how earnings management could be detected, to what extent earnings management has an impact on investment decisions, what drives earnings management behaviour etc. The accounting research community has responded to such demand by producing a very large, and still growing, volume of publications on the topic during the last few decades. In fact, earnings management has now been one of the largest strands in the mainstream accounting literature. This thesis aims to make original and important contributions to the literature on earnings management. The main components of the thesis comprise of three empirical chapters which analyse secondary data on the United Kingdom's (the UK hereafter) stock market during the period from 1995 to 2011. The contributions are made on three important and inter-related research strands within the earnings management literature, namely the earnings management detection models, the impact of earnings management on stock market investment, and the spread of earnings management as a corporate decision through board network. The first empirical chapter constructs a signal-based composite index, namely ESCORE, which captures the context of earnings management. Specifically, ESCORE aggregates fifteen individual signals related to earnings management based on prior relevant literature. Empirical results using UK data shows that when ESCORE is higher, firms do manage earnings with greater magnitude and are more likely to be most aggressive using both accruals and real earnings management. Firms which are investigated for financial-statement-related irregularities are also shown to have significantly higher ESCORE. The composite score can be easily applied in practice as well as replicated in subsequent studies, especially in emerging market where small samples technically constrain the use of other existing earnings management detection models. The approach to construct ESCORE is innovative and it only measures the likelihood rather than the magnitude of earnings management. This aspect of ESCORE is important given the growing criticisms that none of the existing earnings management models could actually measure the magnitude of earnings management. Using ESCORE as a measure that captures the general context of earnings management, the second empirical chapter asks if investors rationally price the information contained in such context. Empirical evidence shows that firms with low ESCORE outperform those with high ESCORE by 1.37% per month after controlling for risk loadings on the market, size, book-to-market and momentum factors in up to one year after portfolio formation. The relationship between ESCORE and future returns is still significant, in both economic and statistical terms, after controlling for various other known 'market anomalies', including the size, value-glamour, seasoned equity offer, market irrational reaction to financial distress, balance sheet bloat, profitability and discretionary accruals. This finding is in line with the behavioural explanation that investors tend to ignore the observable context of earnings management under the influence of the well-documented base rate fallacy. This is an original piece of knowledge which makes significant and interesting contributions to the literature on market anomalies. The third and last empirical chapter investigates whether aggressive earnings management practices spread across firms sharing interlocked directors. The evidence shows that if a firm aggressively manages earnings (referred to as a 'contagious firm') via accruals (or production activities and discretionary expenses) manipulation in a year, any firms (referred to as 'exposed firms') which are interlocked with that contagious firm in that year and the two following years are more likely to aggressively manage earnings via accruals (or production activities and discretionary expenses, respectively) manipulation. The contagion effect is found to be more pronounced if the interlocked director is male, older, British, and charged with duties which could influence financial reporting. The contagion effect is robust after controlling for endogeneity issues and common characteristics of the interlocked firms. The evidence presented in this chapter is both original and a significant contribution to our knowledge on the impact of board networks on corporate decisions, a topic which attracts a lot of attention as it fits directly to the process of reforming corporate governance codes to enhance the efficiency and effectiveness of the boards of directors.

This research studies the p-centre problem in the continuous space. This problem is particularly useful in locating emergency facilities, such as fire-fighting stations, police stations and hospitals where it is aimed to minimise the worst-case response time. This problem can be divided into a single facility minmax location problem (1-centre) and multi-facility minmax location problem (p-centre). The solution of the 1-centre location problem can be found optimally in polynomial time by using the well known Elzinga-Hearn algorithm for both the weighted and the unweighted case. The objective of the p-centre problem is to locate p facilities (p>1) so as to minimise the radius of the largest circle. However, in this case, we cannot always guarantee optimality as the problem is known to be NP hard. The aim of the research is to develop and analyse powerful meta-heuristics including the hybridisation of exact methods and heuristics to solve this global optimisation problem. To our knowledge this is the first study that meta-heuristics are developed for this problem. In addition larger instances previously used in the literature are tested .This is achieved by designing an efficient variable neighbourhood search, adapting a powerful perturbation method and extending a newly developed reformulation local search. Large instances are used to evaluate our approaches with promising results.