The Value of Customer Relationship Management in the Service Industry in Egypt

Researchers have demonstrated that customer centricity strategies, including customer relationship management (CRM), contribute to 33% of the formula for organizational success. Relationship management theory was used to frame this single case study focused on the factors contributing to successful CRM strategies used by business leaders in a multinational organization in the service industry in Egypt. This company was chosen for its successful implementation of CRM strategies, as shown by online reviews, the company website, and market reputation on its effective campaing results. The population consisted of managers working in the marketing department for more than 5 years. Data collection included semistructured interviews, review of company documents, and onsite observation. Transcribed interviews, company documents, and observational notes were coded for emergent themes. Member checking was used to increase the credibility of the findings. Findings suggested 7 themes that contributed to effective the CRM strategies of this single operation: improving the customer experience, customer segmentation and targeting, improving customer satisfaction and loyalty, organization, market differentiation, sophisticated technical capability, and increasing revenue and profitability. The results from this study may influence social change by helping to create a positive work culture for the employees in this company. Research has shown that customer empowering behaviours positively affect employee creativity, satisfaction, and trust, creating a positive work environment. In addition, these positive changes to the work enviornment may in turn strengthen this organization\u27s sustainability and ability to engage directly in community outreach

Assessment Approach of Life Cycle of Vehicles Tyres on Egyptian Road Network

Recently the problem of used vehicle tires becomes in focus in Egypt. In this paper, the scope is to study life cycle of the yearly used tires by vehicles travelling on the Egyptian road network. For the first time, Life Cycle Assessment (LCA) approach is performed for tires used by the Egyptian road transportation fleet. Global Warming Potential (GWP), health toxicity, and Aquatic acidification are the main impact categories considered. The Life Cycle Impact Assessment Methodology (IMPACT2002+) is implemented for LCA analysis to encompass the transportation process of the yearly needed amount of tires, and the usage of the tires. At the nexus, data about transport demand and activity have been collected. Also, modal split ratios have been incorporated. It was found that, Egyptian road tires contributes mainly to GWP on the midpoint effect and also contributes mainly to the damage regarding terrestrial acidification and nitrification. Regarding the normalized effect, it was found that the highest contribution of used tyres on Egyptian road network is in respiratory effects on human health with 3.52*104(person year/kg) followed by terrestrial acidification of 3.1*104(person year/kg)

Physical evaluation of a new pulp capping material developed from portland cement

Background: This study examined the effects of addition of 10% and 25% by weight calcium hydroxide on the physicochemical properties of Portland cement associated with 20% bismuth oxide in order to develop a new pulp capping material. Material and Methods: The solubility, pH value, setting time, compressive strength, and push out bond strength of modified Portland were evaluated and compared to those of mineral trioxide aggregate (MTA) and Portland cement containing 20% bismuth oxide. Results: The statistical analysis was performed with ANOVA and Duncan’s post-hoc test. The results show that the strength properties and push out bond strength of Portland cement were adversely affected by addition of calcium hydroxide especially with a ratio of 25 wt%, however, the setting time and pH were not affected. MTA showed a statistically significant lower setting time than other cements (P≤0.001). Portland cement with bismuth oxide and Port Cal I showed a statistically significant higher Push out Bond strength than MTA and Port Cal II (P=0.001). Conclusions: Taking the setting time, push out bond strength and pH value into account, addition of 10 wt% calcium hydroxide to Portland cement associated with 20% bismuth oxide produces a new pulp capping material with acceptable physical and adhesive properties. Further studies are recommended to test this cement biologically as a new pulp capping material

Diagnostic evaluation of blunt abdominal trauma scoring system (BATSS)

Background: Blunt force abdominal trauma is a typical emergency room presentation in both adults and children. Trauma is widely acknowledged as one of the primary causes of illness and mortality in poor nations, as well as the greatest cause of death in those under the age of 45.Objectives: This study aims to study the diagnostic evaluation of blunt abdominal trauma scoring system (BATSS) in patients with blunt abdominal trauma in Zagazig University Hospital.Patients and methods: This study was conducted on 48 patients suffering from blunt abdominal trauma in Emergency Department of Zagazig University Hospital from January 2021 to June 2021.Result: The mean age of patients in the study was 25.87±10.7 years (range 17–61 years). Of the forty eight patients in the study there were 13 females (27.1%) and thirty five males (72.9%).There was statistically significant difference between blunt abdominal trauma scoring system (BATSS) and types of injury p<0.001. There was no statistically significant difference between blunt abdominal trauma scoring system (BATSS) and each of patients' sex and causes of injury p>0.05. Conclusion: The BATSS score system can be used as an initial screening to predict blunt abdominal trauma outcome and can be the basis of management in patients who experience blunt abdominal trauma

An Analytical Study of the Role of Organic Agriculture in Achieving Food Security in Egypt: Using Path Analysis Approach

This research aimed to assess the ability of organic agriculture in improve the state of food security, and the stability of agricultural production compared to the current levels of production. To achieve this objective, we followed a practical method in terms of purpose, and non-experimental in terms of data collection, the research tool was a questionnaire whose validity was confirmed by experts in this field, and its reliability by the Alpha Cronbach criterion, and the results showed that there is a positive effect of organic farming on enhancing economic return and increasing profitability. Moreover, enhanced the economic impact of the producers as a result of using local inputs by about 0.95 with the possibility of opening new markets for organic products by about 0.90 which is hindered by raising the efficiency of agricultural production in the cultivated areas with low productive inputs, by about 0.55 following an environmental impact through the production of safe and healthy food of high quality and value that reflects the effects of Improve the health of consumers and individuals by about 0.83. Which means the ability of organic agriculture to achieve food security requirements with the same values referred to, and finally, the study recommended in its conclusion the need to develop policies for organic agriculture adopted by government institutions to support them, to reduce the obstacles that prevent the transition from traditional to organic agriculture

Perceived person-organization fit and turnover intention in medical centers: The mediating roles of person-group fit and person-job fit perceptions

Purpose The purpose of the paper is to fill gaps in the existing fit and turnover intention (TI) literature by investigating a more comprehensive model, in which TI is proposed to be influenced by the interplays of three multidimensional types of fit including, person-organization (P-O) fit, person-group (P-G) fit, and person-job (P-J) fit. Design/methodology/approach Participants were selected from different specializations within Mansoura University medical centers, where each medical center was represented proportionately within the sample. Data were collected using self-administered questionnaires. Questionnaires were provided to 850 employees who agreed to participate. Of the 850 questionnaires distributed, 385 were valid and complete (n=385). Partial least squares analysis was utilized for the analyses. Findings Results showed that P-O fit, P-G fit, and P-J fit were positively related to each other and negatively related to TI. Furthermore, the negative relationship between P-O fit and TI is partially mediated by P-G fit and P-J fit. Originality/value The present study simultaneously examines the multidimensional effects of different fit perceptions on TI. In doing so, we identify which of the fit perspectives influence TI more intensely. Moreover, the authors advance current insights by investigating the mediating roles of P-G fit and P-J fit in the relationship between P-O fit and TI

Water pressure optimisation for leakage management using deep reinforcement learning

In this thesis, we introduce a novel approach to pressure management using deep reinforcement learning (DRL) algorithms. Exploiting DRL algorithms to optimise pressure management in water distribution networks (WDNs) provides a more computationally efficient and resilient method to reduce background and burst leakage. Using DRL to manage pressure has proven as a valuable method to reduce leakage and carbon emissions in two case studies based on a real and benchmark water network. A cohort of eight DRL algorithms of varying natures are implemented on a benchmark test network and real network model of varying sizes to prove their scalability. An investigation on their ability to reduce both background and burst leakage is conducted to highlight their abilities with regards to different leak sizes. The application of deep reinforcement learning algorithms to control leakage in WDNs builds on from two extensive reviews of leakage management and DRL applications in the urban water systems. Collating this literature pinpoints the novelty in applying deep reinforcement learning algorithms to control pressure in WDNs and provides context to the thesis. To develop DRL algorithms fit for WDN operations, a novel python-based environment is created that can communicate the hydraulic capabilities of EPANET to the DRL agent. This involved multiple design choices including action space and observation space selection as well as formulating a reward function suitable for the multiple objectives relating to leakage reduction. Regarding background leakage, the best performing DRL algorithm resulted in 65.2% reduction in leakage in the benchmark network. However, the investigation on the real water network provided by Northumbrian Water Living has proved the strong dependency between valve locations and pressure management hence resulting in a negligible background leakage reduction. The ability of the DRL algorithms to deal with uncertainty through randomised burst nodes was investigated in the second case study. DRL policies demonstrated resilience in comparison to the standard optimisation algorithms used (differential evolution, particle swarm optimisation, and nelder mead). The best performing DRL algorithm predicted a 58.46% leakage reduction and 5650kg of reduced CO2 emissions in the benchmark water network. On the other hand, the best DRL performance optimised the real water network by reducing the leakage by 5.79% and carbon emissions by 1999kg of CO2

Water Pressure Optimisation for Leakage Management Using Q Learning

The recent global urbanization problem has set the industry and researchers sights to the importance of safe, effective water distribution due to the unprecedent demand placed on our aging water networks. Our current water practices often increase the degradation of assets through heightened pressures causing more failures and leakage. Whilst the higher network pressures ensure customer demands are met; they cause detrimental failures to the system, long-term expenses, higher carbon emissions and energy consumption. This paper uses a baseline reinforcement learning algorithm to optimize valve set point for active pressure control. Using optimized Q-learning in an EPANET-Python environment, the agent learns to modify valve set points to decrease the average pressures whilst remaining within the OFWAT mandated pressure limits of 10m. This code is tested on the d-town test network. The agent shows continuous improvement finding an optimized set point of 26m and dropping the average system pressure by 2% by making simple changes to two pressure reducing valves. The agent learns the optimal actions to take for different states however further improvements can be made through the use of deep neural networks