ERP system implementation in UK Joinery SMEs

The capabilities of an Enterprise Resource Planning (ERP) system to integrate all necessary business functions into a single system with a shared database efficiently and effectively has persuaded organisations to adopt them. Research shows that ERP implementation in both large and small to medium enterprises has been a difficult challenge for organisations throughout the years. Despite the many advantages of ERP systems, there isn t a clear and easy way of implementing them in Small to Medium Enterprises (SMEs). The motivation for the research is to investigate the barriers to ERP software system implementation in an SME using a case study approach, and to identify the steps to overcome these barriers and investigate the claim of ERP vendors that their ERP solutions improve the performance of their customers, the profitability and efficiency of work processes. This research identifies the barriers to ERP implementation in an SME, provides an overview of the traditional and current approaches of ERP implementation and discusses the effects of adopting an ERP system on the company s overall performance. The research uses a mix of methods including case study research and action research. Un-structured interviews and semi structured interviews approaches with negotiation and change management techniques were also used in order to generate knowledge concerning the problems at the case study. The case study has determined reasons for failed implementations, unlike previous research which suggests education level impact upon the implementation of the ERP system, the study demonstrates that an insufficient education level is not a necessary condition for resistance to change. It has also been shown in this research that high level management can have a direct influence on the ERP implementation in SMEs. This research suggests that SMEs need to standardize processes into business routines which will influence the introduction of a different knowledge store that helps the development of the new system; however employee s resistance to change, lack of trust of the new system and lack of knowledge has limited the implementation process by increasing mistakes and duplication of data. The ERP system has been evaluated by the end users at the case study organisation, and the results suggests that the implementation of an ERP system has improved the overall business and has increased the performance, the profitability and the efficiency of work processes. This research adds to the overall knowledge of ERP implementation in SMEs by deriving a better understanding of the problem in the body of knowledge and identifying the barriers to ERP implementation in SMEs. It provides recommendations that have been tested in the case study organisation for overcoming ERP implementation barriers in SMEs, and a financial model of the implementation costs and benefits. Finally, the recommendations presented in this thesis and suggested areas for further research set out the potential way forward to advance knowledge in this area

A Cloud Computing Capability Model for Large-Scale Semantic Annotation

Semantic technologies are designed to facilitate context-awareness for web content, enabling machines to understand and process them. However, this has been faced with several challenges, such as disparate nature of existing solutions and lack of scalability in proportion to web scale. With a holistic perspective to web content semantic annotation, this paper focuses on leveraging cloud computing for these challenges. To achieve this, a set of requirements towards holistic semantic annotation on the web is defined and mapped with cloud computing mechanisms to facilitate them. Technical specification for the requirements is critically reviewed and examined against each of the cloud computing mechanisms, in relation to their technical functionalities. Hence, a mapping is established if the cloud computing mechanism's functionalities proffer a solution for implementation of a requirement's technical specification. The result is a cloud computing capability model for holistic semantic annotation which presents an approach towards delivering large scale semantic annotation on the web via a cloud platform

Online authentication methods used in banks and attacks against these methods

© 2019 The Authors. Published by Elsevier B.V. Growing threats and attacks to online banking security (e.g. phishing, identity theft) motivates most banks to look for and use stronger authentication methods instead of using a normal username and password authentication. The main objective of the research is to identify the most common online authentication methods used widely in international banks and compare it with the methods used in six banks operating in UAE. In addition, this research will cover the current authentication threats and attacks against these methods. Two well-defined comparison matrices [15], one based on characteristics and second one on attack vectors, will be used to examine and assess the authentication methods of those six banks. This paper is different than other studies and works since it will help to identify the common authentication methods used in banks operating in UAE. Moreover, the comparison matrices will help to examine those authentication methods, define their weaknesses, and evaluate them

The Creation of an Arabic Emotion Ontology Based on E-Motive

© 2017 The Authors. Published by Elsevier B.V. There is an increased interest in social media monitoring to analyse massive, free form, short user-generated text from multiple social media sites such as Facebook, WhatsApp and Twitter. Companies are interested in sentiment analysis to understand customers\u27 opinions about their products/services. Governments and law enforcement agencies are interested in identifying threats to safeguard their country\u27s national security. They are actively seeking ways to monitor and analyse the public\u27s responses to various services, activities and events, especially since social media has become a valuable real-time resource of information. This study builds on prior work that focused on sentiment classification (i.e., positive, negative). This study primarily aims to design and develop a social sentiment-parsing algorithm for capturing and monitoring an extensive and comprehensive range of emotions from Arabic social media text. The study contributes to the field of sentiment analysis (opinion mining) and can subsequently be used for web mining, cleansing and analytics

A Cloud Computing Capability Model for Large-Scale Semantic Annotation

Semantic technologies are designed to facilitate context-awareness for web content, enabling machines to understand and process them. However, this has been faced with several challenges, such as disparate nature of existing solutions and lack of scalability in proportion to web scale. With a holistic perspective to web content semantic annotation, this paper focuses on leveraging cloud computing for these challenges. To achieve this, a set of requirements towards holistic semantic annotation on the web is defined and mapped with cloud computing mechanisms to facilitate them. Technical specification for the requirements is critically reviewed and examined against each of the cloud computing mechanisms, in relation to their technical functionalities. Hence, a mapping is established if the cloud computing mechanism’s functionalities proffer a solution for implementation of a requirement’s technical specification. The result is a cloud computing capability model for holistic semantic annotation which presents an approach towards delivering large-scale semantic annotation on the web via a cloud platform

Fandet Semantic Model: An OWL Ontology for Context-Based Fake News Detection on Social Media

The detection of fake news on social media has become a very active research area. Several approaches and techniques have been proposed and implemented to address the challenge, across diverse technological domains such as NLP (Natural Language Processing) and machine learning. While substantial progress has been made on these, it remains a daunting task due to complexities in its nature. Therefore, it has become pertinent to significantly explore and integrate other technologies to detect fake news on social media. Hence, this research focuses on further exploring and developing native semantic technology solutions for the discourse space. The initial result is a taxonomy classifying socially contextual features for news articles and then Fandet: an OWL ontology for context-based fake news detection by semantically annotating contextual features of news articles and datasets using the ontology. This provides a basis for patterns recognition, analysis, and identification of news articles on social media as either fake or not

A Framework for online social network volatile data analysis: A case for the fast fashion industry

Consumer satisfaction is an important part for any business as it has been shown to be a major factor for consumer loyalty. Identifying satisfaction in products is also important as it allows businesses alter production plans based on the level of consumer satisfaction for a product. With consumer satisfaction data being very volatile for some products due to a short requirement period for such products, current consumer satisfaction must be identified within a shorter period before the data becomes obsolete. The fast fashion industry, which is part of the fashion industry, is adopted as a case study in this research. Unlike slow fashion, fast fashion products have short shelf lives and their retailers must be able to react swiftly to consumer demands. This research aims to investigate the effectiveness of current data mining techniques when used to identify consumer satisfaction towards fast fashion products. This is carried out by designing, implementing and testing a software artefact that utilises data mining techniques to obtain, validate and analyse fast fashion social data, sourced from Twitter, to identify consumer satisfaction towards specific product types. In addition, further analysis is carried out using a sentiment scaling method adapted to the characteristics of fast fashion

Improving Academic Decision-Making through Course Evaluation Technology

The objective of this study is to offer a broad understanding of how end of course evaluations can be used to improve the academic outcomes of a higher education institute. This paper presents the key findings from a study conducted using twenty-three academic degree-programs, regarding their use of end of course evaluation technology. Data was collected from an online survey instrument, in-depth interviews with academic administrators, and two case studies, one in the US and another in the UAE. The study reveals that while historically end of course evaluations were primarily used to gauge the performance of instructors in the classroom, there are several new trends in the use of end of course evaluations that can help higher education institutions improve academic assessment, teaching and learning, and academic administration decision making. Those trends include sectioning and categorization; questions standardization and benchmarking; alignment with key performance indicators and key learning outcomes; and grouping by course, program outcome, program, college, etc. in addition to those vertical structures, higher education institutions are vertically examining a specific question(s) across. End of course evaluations are now poised as an integral tool and a key health indicators of academic programs

Blockchain-based solution for Secure and Transparent Food Supply Chain Network

The global food supply chain industry has embraced digitalization and has changed consumer’s day-to-day lives in many aspects. Efficient tracking of food products when within the supply chain ensures the safety of the end consumers. However, today’s food supply chain industry falls short of providing dependable tracing of food products due to a lack of visibility and transparency in tracking the food production, processing, distribution, transportation, and movement when with the supply chain, which poses a serious threat to the quality of processed food and the safety of consumers. In this paper, we propose a blockchain and IoT-based framework to regulate and monitor the processed poultry food supply chain industry’s functioning and improves the safety and quality of food products delivered to end-consumer. Our proposed solution utilizes Ethereum smart contracts to develop a transparent, reliable, and tamper-proof food supply chain framework, and ensure the integrity of supply chain transactions by eliminating a central authority. The smart contract regulates and monitors the transactions between the entities in the network and keeps all of the parties, within the network, well informed about transactions. This proposed aims to identify and eliminate food adulteration and contamination; enhance quality and safety in the food industry’s supply chain, improve the transparency of transactions, and legal culpability, which ultimately has a positive impact on consumer trust and the overall brand value