3830 research outputs found
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Early indicators of the challenges faced by irish government agencies in adopting the ‘cloud first’ approach.
No full textThis paper digs into the difficulties faced by Irish government organizations while carrying out the "Cloud First" methodology. Key obstacles incorporate the shortfall of cloud-centered methodologies, hierarchical strategies, and a cloud-situated culture. Cloud computing's appeal of upgraded proficiency, adaptability, and cost-adequacy has driven key part like Google and Amazon Web Services to offer cloud-based arrangements around the world. This study intends to explore these difficulties deliberately, distinguish their main drivers, and propose commonsense answers for laying out successful cloud-arranged procedures and societies inside government elements
Strategies governing successful internal marketing communication for employee engagement in remote and hybrid work
No full textThis dissertation explores the impact of internal marketing communication strategies on employee engagement in remote and hybrid work environments. The rapid transition to these work models, accelerated by the COVID-19 pandemic, has presented both opportunities and challenges for organizations striving to maintain a connected and motivated workforce. Through qualitative research, including semi-structured interviews with professionals in marketing, internal communications, team leaders, and employees, this study investigates the effectiveness of various communication tools and strategies. Key findings highlight the critical role of leadership support, tailored communication approaches, and mental health initiatives in fostering employee engagement. The research also emphasizes the importance of segmenting employees based on demographic and geographic factors to enhance the relevance and impact of internal communication efforts. These insights contribute to a deeper understanding of how companies can successfully engage employees in increasingly digital workplaces, ensuring that they remain aligned with organizational goals despite the physical distance. The findings are discussed in relation to existing theoretical frameworks, offering practical recommendations for organizations seeking to optimize their internal communication strategies
Exploring preoperative expectations and postoperative outcomes in adults who have undergone deep brain stimulation surgery
No full textThe purpose of this study is to explore the pre-surgical expectations of adults undergoing deep brain stimulation (DBS) surgery for a neurological disorder and what the psychosocial and wellbeing impacts experienced by this population are afterward. Participants were six individuals who underwent DBS in the past five years. The study was qualitative in nature and conducted through semi-structured interviews. Data from the interview transcripts was analysed and coded in NVivo and used to generate themes related to the two distinct time periods pre- and post-surgery. Broad themes identified pre-DBS are the participants life limiting circumstances and their personal hopes and expectations related to the surgical outcome, while those evident post-DBS include the transformative nature of the experience and the impacts of rehabilitation and recovery, rediscovering life, and psychological outcomes. This study supports the identification of a key relationship between realistic, optimistic expectations and positive psychosocial outcomes. It should be noted that no negative experiences were captured potentially due to the pre-screening that took place through the recruitment process
Traditional Machine Learning Algorithms and Deep Learning for ODI Cricket Prediction
No full textA comparative study between traditional machine learning and a deep neural network approach is presented for predicting winning teams in for One Day International (ODI) cricket games. Data is extracted from the espncricinfo website covering the years 1971 to 2022 for model training. Features include team performance and match conditions. Model performance is evaluated on 2023 match results. Both small (2010–2022) and large datasets (1971-2022) are used for training for comparative purposes. The deep neural ANN achieves an accuracy of 85.4%, outperforming the conventional techniques including ensemble techniques such as random forests and gradient boosting. The deep neural ANN model is shown to outperform in identifying nuances and intricate patterns, demonstrating an ability to use large amounts of historical data to increase accuracy. This study builds upon earlier work to add significant insights to improve ODI cricket result predictions
Customer Segmentation and Churn Analysis: A Case Study on a Global Fashion Retailer
No full textEmerging in the 1990s, fast-fashion is a business model characterised by offering affordable clothing to the mainstream consumer that follows the latest trends or imitates designer brands. Key features of this business model include; affordable pricing, frequent product turnover and regular purchases of low- to medium-value. Rising population, increased disposable income and technological advancements are some key market drivers.
However, in recent years, this industry has faced mounting criticism regarding unethical manufacturing practices, textile waste and overconsumption (Joy et al., 2012). Most global fast-fashion retailers have therefore incorporated sustainability initiatives in an effort to mitigate their environmental impact. The industry also faces growth constraints due to heightened competition and market saturation. Namely, ultra-fast-fashion companies like Temu and Shein have grown in popularity in recent years.
In the context of this project, understanding the purchase behaviours of customers, and the customer segmentation and churn analysis that follows this, is crucial to devise innovative customer retention strategies to maintain growth
The causes of remote work along with the benefits and challenges faced by both companies and employees while working from home
No full textThis study looks at how working from home affects people's physical and mental health and how productive they are in their new settings. This study used an explanatory research method and both primary and secondary sources of data to look into the topic of working from home in great detail. The research method used in this study is abductive, which blends theory ideas with real world data to give a complete picture of the subject. Scholars surveyed 30 people who do most or all of their work from home to find out what they thought about the pros, cons, and general feel of working from home. Visualising the survey data in the form of graphs allow to fully explore patterns and trends in the people who answered the questions. The results of theme analysis are also put together from secondary sources such as trustworthy websites, papers, and peer reviewed books. This method adds theoretical views and background knowledge to the study to make it stronger.
Two important parts of doing research in an ethical way are getting full consent from survey subjects and using correct citations to show appreciation for earlier researchers' work. The rights and privacy of people are protected by ethical rules like openness, privacy, and respect. There are some good things about working from home, like being able to set own hours and not having to worry about getting to and from work. But there are also some bad things about it, like having to work alone, having trouble speaking, and having personal and professional lives mix. There are many factors that affect how well working from home goes, such as the level of social support, the company's structure, and the ease of access to technology. Along with what is already known, this study looks at a lot of facts from the real world and academic points of view. It is very important to deal with problems before they happen in order to make the good things about working from home even better. It talks about how important it is to spend money on technology tools, improve communication, and make group rules stronger
Machine learning approach to predict building energy ratings of dwellings in Ireland
No full textThe machine learning models have been employed for the BER prediction of a dwelling in Ireland. Various machine learning models have been run on the BER data by researchers. This research focuses on which machine learning model provides an optimal performance. The research also provides an answer to the framed research questions. The prediction of BER using machine learning models will reduce the time required to measure the significant features of the dwelling as the current system requires 211 features to be measured for each dwelling. After running the models, it was found that logic-based techniques perform better on the BER data. For regression analysis, 35 features are highly significant whereas for classification analysis, 42 features are highly significant
Experiences from transactions: the future of traditional retail stores
No full textDue to the COVID-19 pandemic, online shopping has become increasingly important and seems to gradually replace retail stores. Therefore, this project aims to answer questions regarding the sustainability of retail stores by analyzing consumer behavior trends and market dynamics. This project employs a mixed-methods approach to achieve its research objectives. This approach combines qualitative and quantitative methods, which involves using various data collection and analysis techniques. The hypothesis that online shopping poses a significant threat to traditional retail stores is only partially supported, as most participants remain favoring physical stores. However, there is a recommendation for offline stores to expand online due to increasing competition and shifting market dynamics.
To conclude, online shopping will continue to grow in popularity. However, retail stores can remain relevant by improving their online presence and creating a unique, multi-sensory shopping experience. Given that this Capstone project is a relatively short analysis of a complex topic, further research is required to include other online and in-store shopping categories, such as groceries or furniture
Effects of declining income and improving education standards on Green Consumerism in Zimbabwe's FMCG Sector
No full textThis qualitative study in Zimbabwe’s fast-moving consumer goods sector investigates the influence of income fluctuations, educational variations, and environmental awareness on consumers’ green purchasing behaviour. Using semi-structured interviews, the research explores how declining income impacts eco-friendly product preferences and the role of education in shaping sustainable consumption during economic downturns. It examines heightened environmental awareness’s effect on consumer beliefs and purchasing commitments. Findings underscore economic challenges’ pivotal role, educational influences, and the significance of broader lifestyle factors in influencing green consumerism. Despite limitations in geographical scope, the study provides nuanced insights aligning with broader
literature. The study underlines the complex interplay between income, education, and ecoconscious consumer behaviours. Recommendations emphasise the need for improved market information and affordability of eco-friendly products, urging policymakers and stakeholders to foster a more sustainable consumer landscape
Air Pollution Visualisation, Prediction, And Automated Air Purifier Control Using Machine Learning Algorithms
No full textAir pollutants cause severe health risks to humans if their quality deteriorates. The AQI shows the Air pollution standard. This helps visualise the steep rise in AQI globally, particularly in Southeast Asia. However, accurately predicting the AQI values is difficult due to the diverse and intricate factors affecting the air quality. A complex ML algorithm simulates human knowledge to solve issues. The development and deployment of ML models in air pollution research have accelerated due to the increasing monitoring data and the rising demand for accurate and timely forecasts. Two thousand nine hundred sixty-two papers published between 1990 and 2021 were subjected to a bibliometric analysis to determine the prevalence of ML in air pollution research. After 2017, there was a notable surge in articles, accounting for almost 75% of all publications. The cluster analysis found four main study topics for machine learning applications: short-term forecasting, emission control optimisation, detection improvement, and pollutant chemical characterisation. The rapid development of machine learning algorithms has improved our ability to model scenarios, examine the chemical characteristics of different contaminants, and assess the factors that influence chemical reactions. ML models are adequate for reviewing atmospheric chemical processes and evaluating air quality management when paired with interdisciplinary data. As a result, they deserve more focus in the future. Here, a few of the regression algorithms were used to predict air pollution on the globe. Then, for India, the primary models used are LR, DTR, RFR, and SVR, which are evaluated using the root RMSE and R-squared metrics on both a validation set and a test set, providing a comprehensive assessment of their predictive performance and followed by the prototype of an Automatic Air Purifier to control Air pollution and help people protect themselves from exposure to air pollution and the health problems caused by It