Investigating enterprise resource planning adoption and implementation in service sector organisations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityThis thesis investigates Enterprise Resource Planning (ERP) adoption and implementation in Service Sector Organisations (SSOs). ERP is a business management system that has emerged to support organisations to use a system of integrated applications to enhance their Information Technology (IT) infrastructures, enhance business processes and deliver high quality of services. Regardless of the fact that several other sector organisations have adopted and implemented ERP systems, its application in SSOs is rather inadequate. Among other reasons, two core rationales can be attributed to the latter fact – firstly, SSOs lack the sufficient knowledge, expertise and training to implement such sophisticated integrated systems and secondly, the top management lacks the ability to take appropriate decisions for ERP adoption and implementation. However, merely focusing on a number of factors influencing ERP adoption and implementation may not be suffice, as there is a need for a systematic decision-making process for adopting and implementing ERP systems in SSOs. The limited number of ERP systems’ applications in SSOs has resulted in inadequate research in this area with many issues, like its adoption and implementation requiring further exploration. Despite, the implications of ERP systems have yet to be assessed in SSOs, leaving ample scope for relevance and producing a unique piece of research work. Thus, the author demonstrates that it is of high importance to investigate this area within SSOs and contribute towards successful ERP adoption and implementation. This thesis makes a step forward and contributes to the body of knowledge as it: investigates factors influencing the decision-making process for ERP adoption and implementation in SSOs, prioritises the importance of factors influencing ERP adoption and implementation, evaluates ERP lifecycle phases and stages, maps the ERP factors on different phases and stages of the ERP lifecycle, and in doing so, to propose a model for ERP adoption and implementation in SSOs. The author claims that such an ERP adoption and implementation process in SSOs is significant and novel as: it extends established norms for ERP adoption and implementation, by including Analytical Hierarchy Process (AHP) technique for prioritising the importance of factors, thus, facilitating SSOs to produce more robust proposals for ERP adoption and implementation. The author further assess the proposed ERP adoption and implementation model by using a qualitative, interpretive, multiple case study research strategy. Findings from two case studies demonstrate that such a systematic approach contributes towards more robust decisions for ERP adoption and implementation and indicates that it is acceptable by the case study organisations. The thesis proposes, assesses and presents a novel model for ERP adoption and implementation in SSOs and contributes to the body of knowledge by extending the literature.King Abdulaziz University and Saudi Arabia Cultural Burea

Housing, Housing Finance and Credit Risk

This paper investigates the determinants of credit risk from a broad perspective. Particular attention is given to the role of housing affordability and household indebtedness. However, the impact of credit market developments and regulations is also closely examined. Using a large panel of countries it is found that housing affordability and household fragility significantly affect the risk of banks’ loan portfolios. In addition, an analysis of the conditional quantiles of non-performing loan ratios reveals that financial institutions in countries with greater levels of financial liberalization and less regulated markets also experience greater credit risk

Credit default and the real estate market

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonEvidence from various countries over the past two decades proves that swings in house prices have been concomitant with financial instability. The history of financial crises shows that the six biggest banking crises in advanced economies were accompanied by housing busts. Despite the abundance of literature on the forces behind the financial crisis, and in particular studies investigating the connections between financial stability and disturbances in the real estate market, fundamental questions still wait for convincing answers, such as: (i) To what extent is regional heterogeneity in property price increases reflected in dissimilarity in the evolution of credit default? (ii) What role do borrower-related factors such as housing affordability and household indebtedness, and financial market-related factors such as financial developments, play on the growth of bad loans as a main concern for banking sector? (iii) To which extent do banks’ lending behaviour and property prices undermine the stability of the banking sector, and what are the directions of causality between credit defaults, property prices and banks’ lending behaviour? The goal of this thesis is to investigate these issues and explain the practical implications of the findings. This thesis contains three empirical essays. The first essay explores the nexus between house prices and non-performing loans (NPLs), concentrating on the extent to which geographical variations in house prices are translated into regional variations in credit defaults. The stochastic dominance approach has been used for this purpose, with 372 individual US banks. The stochastic dominance analyses disclose symmetric behaviour between NPLs and the scale of house price increments. The essay is further extended by employing Arellano and Bond’s (1991) GMM model to explore the effect of GDP, unemployment rates, lending interest rates and house prices on the growth of NPLs. The outcomes of the GMM estimations reveal a high explanatory power of economic growth, unemployment and lending interest rates on NPLs. In an additional analysis, a generalised panel threshold model is estimated to check for the presence of a threshold point, above which different impacts of house prices might be found. The threshold model specifications provide a threshold point, in relation to which two different impacts of house prices on the evolution of NPLs are estimated. A general consensus in the literature attributes credit defaults to a wide-ranging spectrum of drivers that take into consideration borrower-related factor, lender-related factors and factors related to financial and real estate markets. The second essay attempts to answer the second question mentioned above, by investigating the impact of borrower-related factors, lender-related factors and financial market-related factors in driving NPLs. The impact of these factors on the evolution of impaired loans is explored by estimating fixed effect models then the analysis is extended to dynamic models using the GMM procedure on an annual balanced panel dataset. Household vulnerability, financial developments and housing affordability are found to be significant contributors to the growth of NPLs. The interaction mechanism between the real estate market and the financial system has often been blamed for being the root of financial crises, through the accumulation of housing market bubbles that leads to the ultimate collapse of the financial markets. The third essay, using the Autoregressive Distributed Lag technique, looks for the presence of cointegrating relationships between mortgage defaults, property prices and bank lending in Hong Kong. Our findings reveal evidence of cointegrating relationships between bank lending, property prices and mortgage defaults in the long term, which governs the correction mechanism between these variables. These outcomes call for more effort to be devoted to maintaining a balanced relationship between these factors. The essay also finds evidence of short-term dynamics between these variables. Importantly, loan-to-value is found to play the most effective role in curbing mortgage default risk in the portfolios of the Hong Kong banking sector

Identification of a non-stationary system using the Multi-Model approach

In this paper, we will present a real-time identification of a dynamic system with time-varying parameters: the Steam Generator (Precisely the Industrial Boiler), which is located at the LAGIS (Laboratory of Automation, Computer and Signal Engineering). ) of the Lille University. The approach used to identify the system is the Multimodel, which is an identification technique of Non-linear systems

Exploratory Analysis of Outpatient Visits for US Adults Diagnosed with Lupus Erythematosus: Findings from the National Ambulatory Medical Care Survey 2006–2016

The study aims to assess office-based visit trends for lupus patients and evaluate their medication burden, chronic conditions, and comorbidities. This cross-sectional study used data from the National Ambulatory Medical Care Survey (NAMCS), a survey sample weighted to represent national estimates of outpatient visits. Adult patients diagnosed with lupus were included. Medications and comorbidities that were frequently recorded were identified and categorized. Descriptive statistics and bivariate analyses were used to characterize visits by sex, age, race/ethnicity, insurance type, region, and reason for visit. Comorbidities were identified using diagnosis codes documented at each encounter. There were 27,029,228 visits for lupus patients from 2006 to 2016, and 87% them were on or were prescribed medications. Most visits were for female (88%), white (79%), non-Hispanic (88%) patients with private insurance (53%). The majority of patients were seen for a chronic routine problem (75%), and 29% had lupus as the primary diagnosis. Frequent medications prescribed were hydroxychloroquine (30%), prednisone (23%), multivitamins (14%), and furosemide (9%). Common comorbidities observed included arthritis (88%), hypertension (25%), and depression (13%). Prescription patterns are reflective of comorbidities associated with lupus. By assessing medications most frequently prescribed and comorbid conditions among lupus patients, we showcase the complexity of disease management and the need for strategies to improve care

Russia-Ukraine conflict and COVID-19:a double burden for Ukraine's healthcare system and a concern for global citizens

The conflict between Ukraine and Russia significantly influences the healthcare sector. The ongoing COVID-19 pandemic and the armed conflict have badly devastated the established healthcare system. Only 36.08% of the Ukrainian population has received the COVID-19 vaccination, with the majority receiving two doses, and currently, Ukraine records the highest mortality rate in the world. In addition to the conflict injuries, increased susceptible deaths to COVID-19 can be found due to inadequate vaccination rates for the disease. To save their lives and for their well-being, many individuals have been relocating to the underground metro stations, other cities, nearby towns and countries. In these settings, social distancing, hand sanitation and wearing masks are not prioritised. In the current circumstances, the broken healthcare system needs to be rebuilt, and the Non-Governmental Organizations (NGOs), doctors and all the front-line workers should extend their humanitarian support to the Ukrainian population. Conclusion: It is an arduous task for healthcare organisations to supply vaccines and medicines in this ‘armed conflict’ between Russia and Ukraine. This can only happen when both parties extend their support to rebuild the shattered healthcare infrastructure

Improving access to emergent spinal care through knowledge translation : an ethnographic study

Background: For patients and family members, access to timely specialty medical care for emergent spinal conditions is a significant stressor to an already serious condition. Timing to surgical care for emergent spinal conditions such as spinal trauma is an important predictor of outcome. However, few studies have explored ethnographically the views of surgeons and other key stakeholders on issues related to patient access and care for emergent spine conditions. The primary study objective was to determine the challenges to the provision of timely care as well as to identify areas of opportunities to enhance care delivery. Methods: An ethnographic study of key administrative and clinical care providers involved in the triage and care of patients referred through CritiCall Ontario was undertaken utilizing standard methods of qualitative inquiry. This comprised 21 interviews with people involved in varying capacities with the provision of emergent spinal care, as well as qualitative observations on an orthopaedic/neurosurgical ward, in operating theatres, and at CritiCall Ontario’s call centre. Results: Several themes were identified and organized into categories that range from inter-professional collaboration through to issues of hospital-level resources and the role of relationships between hospitals and external organizations at the provincial level. Underlying many of these issues is the nature of the medically complex emergent spine patient and the scientific evidentiary base upon which best practice care is delivered. Through the implementation of knowledge translation strategies facilitated from this research, a reduction of patient transfers out of province was observed in the one-year period following program implementation. Conclusions: Our findings suggest that competing priorities at both the hospital and provincial level create challenges in the delivery of spinal care. Key stakeholders recognized spinal care as aligning with multiple priorities such as emergent/critical care, medical through surgical, acute through rehabilitative, disease-based (i.e. trauma, cancer), and wait times initiatives. However, despite newly implemented strategies, there continues to be increasing trends over time in the number of spinal CritiCall Ontario referrals. This reinforces the need for ongoing inter-professional efforts in care delivery that take into account the institutional contexts that may constrain individual or team efforts

Contribution on the tele-monitoring of networked control systems : application to robotics systems

Dans cette thèse, nous avons abordé la problématique liée au diagnostic des systèmes contrôlés en réseau. L’approche de diagnostic proposée est basée sur les modèles dynamiques de la partie opérative et la partie de communication. Par le fait que les deux parties distinguées du système contrôlé en réseau, ont un fonctionnement différent, deux approches de diagnostic à base de modèle ont été élaborées et appliquées à un système de robotique.La première utilise le principe des observateurs stochastiques par rapport à la nature du système de communication, permettant de distinguer un défaut physique d’un retard induit, en estimant les états non mesurés. La seconde utilise le principe de redondance analytique, appliquée au système à contrôler, permettant de détecter et d’isoler des défauts capteurs et actionneurs, en comparant les mesures avec le modèle mathématique du système étudié. Les résultats expérimentaux réalisés sur un robot mobile miniature, contrôlé à distance ont permis de valider notre approche de diagnostic dans le cas d’un système de communication filaire en série (RS232).Une seconde contribution sur le diagnostic des systèmes contrôlés en réseau a été développée sur un robot manipulateur. Cette dernière consiste à détecter et localiser des défauts actionneur en utilisant le principe de redondance analytique, par contre, en lançant la procédure de diagnostic non pas sur le système réel, mais sur son simulateur virtuel connecté à lui à travers un réseau industriel.In this thesis, we investigate the problem of diagnosis of Networked Control Systems (NCS). The main considered components of the NCS namely the network system and controlled system are completely decoupled according to their operation characteristics. The diagnosis proposed approach is based on the dynamical models of the controlled system and the network system.Two model based fault diagnosis approach are proposed and applied to telerobotics system. The first concerns a discrete and stochastic observer applied to the network system in order to detect and isolate system faults from delay fault on the network channel by estimating the non measured states. The second is based on the Analytical Redundancy Relations (ARR) allowing detecting and isolating the input and output system’ faults. Experimental results applied on a mobile robot system, show the performance and the validity of the proposed hybrid fault diagnosis approach.A second contribution on the fault diagnosis of Networked Control Systems are developed and applied to a manipulator 6 DOF robot. It consists to detect and isolate system faults by using the Analytical Redundancy Relations approach on a robot model based real-time simulator connected to the system through an industrial network

Enhancing the Performance of SQL Injection Attack Detection through Probabilistic Neural Networks

SQL injection attack is considered one of the most dangerous vulnerabilities exploited to leak sensitive information, gain unauthorized access, and cause financial loss to individuals and organizations. Conventional defense approaches use static and heuristic methods to detect previously known SQL injection attacks. Existing research uses machine learning techniques that have the capability of detecting previously unknown and novel attack types. Taking advantage of deep learning to improve detection accuracy, we propose using a probabilistic neural network (PNN) to detect SQL injection attacks. To achieve the best value in selecting a smoothing parament, we employed the BAT algorithm, a metaheuristic algorithm for optimization. In this study, a dataset consisting of 6000 SQL injections and 3500 normal queries was used. Features were extracted based on tokenizing and a regular expression and were selected using Chi-Square testing. The features used in this study were collected from the network traffic and SQL queries. The experiment results show that our proposed PNN achieved an accuracy of 99.19% with a precision of 0.995%, a recall of 0.981%, and an F-Measure of 0.928% when employing a 10-fold cross-validation compared to other classifiers in different scenarios