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How can a global organisation address the challenges of maintaining a cohesive globalised approach across a diverse international organisation and ensure consistency in its global standardisation?
No full textThe aim of this dissertation is to conduct an in-depth examination aimed at understanding the complexities involved in standardizing operations within a global organisation. The primary objective is to identify and analyze the critical factors, challenges, and opportunities associated with achieving operational consistency in functional departments. This study will focus specifically on a global organisation concentrating on the Ireland business operations. Through this research, the aim is to develop actionable strategies and best practices that can facilitate the effective standarisation of processes, procedures, and practices, ultimately enhancing efficiency, coordination, and performance within the organisation's global framework. The outcomes can be rolled out to other geophraphical regions and departments.
The finding of this project suggests that global organisations focus on strategies relating to IT, communication, feedback, training, development, organisational culture and leadership traits. Another suggestion for organisations which was evident during the findings is adopting a global IT system for each region and branch. Understanding employee needs and adopting the strategies which are included in this study will help an organisation address the challenges of maintaining a cohesive globalized approach across a diverse international organisation and ensure consistency in its global standarisation
Differences in Optimal Management Practices between Public and Private Sector Organisations: Focusing on Performance Management and Motivation
No full textPublic and Private sector organisations exist for differing reasons and carry out different functions. For the most part, private, and by extension all for-profit organisations, exist for the sole purpose of providing financial gain for those who own and run them. Shareholder value and profitability are paramount, and this drives every interaction that organisation has with its stakeholders and with the society it operates in. Public sector, or not-for-profit organisations, generally exist to provide a service of some kind for society or a particular portion of society. This means that the missions of these organisational types are fundamentally different. The current study investigated these differences, with particular focus on; Motivation – motivation is a key component in management and the current study seeks to explore aspects of motivation with reference to both the public/not-for-profit and private/for-profit sectors. Performance Management - performance management forms a vital part of any management role. Appraising performance drives accountability, allows for a defined goal setting process and helps to train staff in their roles and develop their careers. Assessing any differences in this process between sectors can help managers and leaders understand how best to approach it with their workforce. Organisational culture – culture is a broad topic and encompasses a multitude of factors that can be assessed in a working environment. The current study investigated 23 variables focusing on the working environment across both sectors. Data from these variables was analysed to assess whether there are significant differences in employee engagement, happiness in the workplace, feelings of being valued and assistance with career progression, all with respect to the differences presented between public/not-for-profit and private/for-profit organisations.
The current study found that finance-based goals are much more prevalent in the private sector than in the public sector. Staff across both sectors are equally motivated by money, but the biggest motivators across both sectors are, how challenging the work is, the clarity of the goals that are set, the perceived fairness of the reward systems and the pathways provided for career development. No statistically significant detrimental impact was found from the financial pressures of a profit motive and the overall performance of the organisation. The implications of these findings are that managers in the public sector must find ways to motivate staff without financial rewards. They must ensure that the motivators outlined above are emphasised and that adequate support is provided. Public sector managers are also required to evaluate performance using a wider variety of metrics due to the unavailability of financial targets. It is imperative that from the findings that managers across both sectors ensure that the performance management process is fair as any perceived unfairness of this process will have an impact on its usefulness
The role of Quality Management System in Organizational Performance of Service Providers in the Philippines
No full textThe aim of this study is to investigate the role of Quality Management System (QMS) on the organisational performance of service providers in the Philippines. Through conducting in-depth interviews with personnel in the managerial position in various sectors, specifically property management and construction, this research explored the impact, contributors, challenges and strategies associated with the implementation of QMS. The study addresses three research questions: the effects of QMS on organisational performance, the contribution of QMS to performance metrics, and the challenges encountered during the implementation process. The findings of this study reveals a positive effect of QMS on the organisational performance in terms of operational efficiency, financial performance, branding, and customer-satisfaction. It also highlighted process standardisation and organisational culture as critical factors influencing QMS adaptation. The challenges encountered during QMS implementation were also identified such as time management and workload, resistance to change, high attrition rate and unfamiliarity or lack of knowledge of the management system. To overcome these challenges, the findings of this study identified continuous improvement, regular process reviews and workshops or trainings as effective strategies.
This study is valuable to organisations, policymakers and researchers seeking to explore the role of QMS on organisational performance. It provides practical recommendations to instill a qualitycentred culture and enhance organisational outcomes through QMS implementation. For further research within the same context, it is recommended to conduct a longitudinal study, explore the impact on different sectors other than property management and construction, and to analyse cultural and behavioural influence in terms of QMS adaptation
Artificial Intelligence Technology Acceptance: A survey based perception study using UTAUT
No full textArtificial intelligence (AI) technology is garnering a lot of attention from academics as well as businesses. The use of AI is increasing within business management, whilst it continues to be an important topic of discussion within the tech world. While a lot has been researched around the advantages and disadvantages of using AI, and the challenges that AI technology adoption brings, current literature is scarce when it comes to people’s attitude around AI technology, is it being accepted by professionals or not? Technology adoption models and theories have been researched for decades now, since for any technology to be successful, it must be accepted and used by people. This study aims at evaluating the overall attitude, of professionals working within Ireland, while further finding the factors that impact this perception, and presents a model grounded in the UTAUT (Unified theory of acceptance and use of technology). The descriptive research uses a quantitative method of research, gathering primary data through an online survey, replicating a questionnaire from a global study with similar research intent. The results show overall positive attitude towards AI technology acceptance, in line with the global study, while a strong correlation was identified between UTAUT constructs Performance expectancy, Effort expectancy, Social Influence, Facilitating Conditions, and proved that “trust” is a significant factor that impacts the behavioural intention and actual use behaviour, while the data analysis showed the moderators, Age and Gender, did not have a significant impact on the attitude, but income level impacted the construct “Trust”, among the Irish professionals, participants in the survey. A simplified model was proposed based on the hypotheses testing, correlation testing, factor analysis, linear regressions, and cross-tabulation, where these factors, constructs, and moderators were brought together
How the role of influencer marketing drives participation amongst Generation Z in GAA in Ireland
No full textIn recent years a number of well-known GAA players has begun to post content and promoting brands on Instagram. The purpose of this dissertation is to explore the role of Influencer Marketing in the GAA drives participation amongst Generation Z in the GAA. The use of qualitative research design was adopted with the data being collected using six semi-structured interviews in order to investigate the experience of people use of Instagram and experience with GAA Influencers. The participants for the study were chosen due to their involvement of the GAA and use of social media platforms. The subsequent findings for the study have proven to be interesting and prove to be a great beginning into the further research on the area
Cryptocurrency Price Prediction Using Ensemble Methods and Sentiment Analysis
No full textThis study focuses on constructing the intersection of financial analytics and machine learning in predicting price movements of the world's most popular cryptocurrency, Bitcoin. In this effort of creating a robust predictive model that considers quantitative and qualitative measures, we will turn to historical price data and sentiment analysis from news headlines. Start with preprocessing the data to align the dates and fixing missing values. Then compute some indicators, such as Bollinger Bands, Relative Strength Index (RSI), Simple Moving Averages, and Exponential Moving Averages. Further, sentiment scores are extracted from relevant news feeds to quantify the market sentiment by using a model pre-trained, the so-called cryptobert. Random Forest, XGBoost, Long Short-Term Memory networks (LSTM) and finally, Auto Regressive Integrated Moving Average (ARIMA) were the four predictive models developed. All these models offer a rather unique insight into the pattern of price movements. These predictions were consolidated using an ensemble method, which aims to integrate the strength of each individual model. The results show that there is evidence machine learning can increase cryptocurrency price forecasts. Especially, the accuracy through this approach is way above that using an individual model. The importance of integrating market sentiment and traditional indicators in the previous study provides a step toward developing a framework of financial analytics for the future
Deep learning approach to analyze Sleep & Apps usage pattern to predict Problematic Smartphone Usage (PSU)
No full textProblematic Smartphone Usage (PSU) and its relationship with poor sleep quality has been the rising concerns on mental and physical health. This study aims to develop deep learning techniques to analyze sleep duration and apps usage pattern to predict PSU. The objective is to compare all the models built to accurately predict PSU. Two distinct datasets have been employed in this research, one for analysing the sleep quality and another for apps usage. Deep learning models like Feedforward Neural Networks (FNN), Convolution Neural Networks (CNN) and Recurrent Neural Networks Long-Short Term Memory (RNN-LSTM) were developed and evaluated. Hyperparameter tuning is used for the sleep data to optimize the model performance. For the sleep quality dataset, the RNNLSTM and CNN model with hyperparameter tunning has outperformed the other models with highest accuracy of 94%. On the other hand, FNN has slightly less accuracy with high level of precision. For the app usage dataset, FNN and RNN has achieved 99.87% & 99.80% accuracy. The CNN was slight less in accuracy 98% and showed lower precision. RNN-LSTM model emerged as a consistent model for both the dataset by offering balanced approach to predict PSU. Hyperparameter tuning has helped to increase model performance only for the CNN model
Redefining Public Safety: A Comparative Analysis of RTDETR and YOLOv8 – Unveiling The Future of Real-Time Handgun Detection
No full textThis study raises a very fundamental challenge: the improvement of public safety by integrating advanced handgun detection systems. It concerns a comparative analysis between two leading technologies in object detection techniques, which are RT-DETR and YOLOv8. The research targets proving which model has better performance when considering accuracy, robustness, and adaptability concerning real-time handgun detection in public spaces.
Models implemented using RT-DETR and YOLOv8 were tuned on a comprehensive dataset of 15,579 handgun images with heavy data augmentation applied. Results are measured in terms of mAP, precision, and recall for different classes. This was achieved through rigorous tuning of hyperparameters by running the experiment several times to squeeze out better performance from the model.
The summary of key results shows that RT-DETR performed very marginally better when it came to peak performance on all metrics: mAP50-95, 0.728; precision, 0.940; recall, 0.883; while YOLOv8 had an mAP50-95 of 0.7073, precision of 0.9010, and recall of 0.8560. However, YOLOv8 proved to be steadier and more robust among different hyperparameter settings, hence it gives better adaptability to diverge operational conditions.
This comparative analysis thus illustrates the contribution of effective and reliable AI-driven security solutions and further provides insights to enhance academic research as well as practical applications of public safety and surveillance systems
Advanced Predictive Modelling of E-Commerce Customer Behaviour: Integrating Machine Learning and Deep Learning Techniques
No full textAs the sector of e-commerce have been evolving drastically in recent years, understanding and predicting customer behavior has become essential for business owners. This research aims to overcome the issues involved in e-commerce through predicting and analyzing purchases, cart abandonment, understanding the seasonality impact on conversion rates, carrying out an in-depth clickstream analysis, customer lifetime value (CLV) estimation and lastly, predicting future monthly sales. These concerns have a huge impact on income generation and customer retention. However, they are challenging because of the sophisticated and dynamic nature of online buying behaviours. This research is motivated by the critical requirement to improve predictive analytics in e-commerce. Doing so can lead to more personalised and successful marketing strategies, resulting in higher conversion rates and increased consumer success. The dataset used for this study is "Online Shopper's Intention", provided on the UCI Machine Learning Repository. In this research, supervised learning methods such as Random Forest, XGBoost, and Logistic Regression were used for purchase prediction, and deep learning models including an ensemble of Long Short-Term Memory (LSTM-RF) model, and Bi-LSTM were developed for predicting and analysing cart abandonment. These models were improved using hyperparameter tuning, and then it proceeded to test for performance based on the metrics including accuracy, precision, recall, F1 score, and ROC-AUC. Checking if seasonality affects the conversion rates involved analyzing weekends, special days like bank holidays, months and other factors effect on revenue. An in-depth clickstream data analysis was performed to see if the time spent on a particular page has an effect on the conversion rate. Customer Lifetime Value (CLV) analysis was performed to understand about customer retention and Time Series Analysis was performed to predict future monthly sales. The results of these analysis provide business owners good insights to better understand the intricate nature of customer behaviour, to carry out personalised marketing strategies that will increase customer satisfaction and the overall revenue generation
Transfer Learning and Fine-Tuned Faster R-CNN for Improved Insect Detection in Agriculture
No full textOver the years, various insect pests have posed challenges to the agricultural sector with serious off-takers to the losses. Correct identification of insects and pests are important steps in pest control, while existing solutions for this problem can be imprecise and inhibit scalability. Traditional methodologies are gradually losing its effective role in terms of identification of insects due to its incapability in processing large amount, and versatility of data and real time detection. To this end, this research seeks to apply the advanced deep learning method to improve insect detection in agricultural environments where the pest issue is prevalent. In particular, the examined architecture is based on the Faster R-CNN model, which follows the transfer learning approach where the base networks are trained on the pre-collected datasets, and then adapted to the authors’ custom collection of dangerous farm insects sourced on Kaggle. Various species of insects and temperature conditions are incorporated in this dataset making it rich for any training and testing of models. The primary innovation of this study lies in the development of a custom training pipeline that incorporates detailed accuracy calculations tailored for object detection tasks. This approach ensures the evaluation metrics accurately reflect the model's performance in detecting and localizing insects. The methodology also involves significant data augmentation to address the class imbalance inherent in the dataset, thereby improving the model's generalizability and robustness. Upon implementation, the fine-tuned Faster R-CNN model achieved a detection accuracy of 91%, demonstrating significant improvements compared to baseline models such as ResNet50V2, ResNet152V2, MobileNetV2, Xception which achieved accuracies of 72%, 63%, 70% and 53% respectively. Also after hyperparameter tuning efficiently, the best baseline model emerged to be the Xception model with an impressive accuracy of 78% on the validation data. These results highlight the superior performance of the Faster R-CNN and the Xception model in real-time pest monitoring and management. This enhanced detection capability can lead to more targeted pest control interventions, thereby reducing pesticide usage and promoting sustainable farming practices. This research contributes to the field of agricultural technology by providing a scalable and efficient solution for insect detection