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A critical evaluation of the impact of self efficacy theory on the management of key challenges faced by young entrepreneurs in start-up. (a case study of young entrepreneurs in Lagos State, Nigeria)
No full textThe entrepreneurial landscape is evolving rapidly, especially for young entrepreneurs embarking on startup ventures. Despite the wealth of research on entrepreneurship, there remains a significant gap in understanding the impact self-efficacy has on key challenges encountered by young entrepreneurs in start-up ventures. This dissertation addresses this gap by critically evaluating the impact of self-efficacy beliefs on how young entrepreneurs manage start-up businesses. Drawing on theories of self-efficacy from entrepreneurship and psychology literatures, this research examines how self-efficacy beliefs influence the ability of young entrepreneurs to navigate the multifaceted key challenges inherent in startup environments with qualitative methodologies which included interviews, this study delves into the nuanced dynamics between self-efficacy and entrepreneurial challenges.
The data gathered are that of ten (10) in-depth interviews that were completed with young entrepreneurs who own start-up businesses in Lagos, Nigeria and all participants are between the ages of 18-30years. This was done using the inductive approach to gain insights to the impact in which self-efficacy factors have on young entrepreneurs. Themes relevant to this research's overall findings were identified through thematic coding of the primary data. Findings from this research show the key challenge faced by most young entrepreneurs is the financial challenge of raising adequate capital. Findings also show that young entrepreneurs with high self-efficacy are more likely to handle start up challenges more effectively than young entrepreneurs with low self-efficacy.
Putting in context, the challenges faced by young entrepreneurs within the broader socioeconomic landscape and exploring the complexity of self-efficacy beliefs, this study aims to contribute to the understanding of entrepreneurial dynamics in startup environments in relation to self-efficacy beliefs
What Obstacles do English Teachers in Turkey encounter while Teaching English as a Foreign Language
No full textThe aim of this study is to investigate obstacles that English teachers encounter in Turkey while teaching English as a foreign language to adult learners. The focus of the study was learners' mental mindsets, beliefs, and expectations and other factors that affect language learning. Using qualitative research methods data was obtained from teachers teaching English in Turkey by using open-ended questionnaires and semi-structured interviews. Thematic analysis became the chosen approach for data analysis, and the themes revealed included: learners' beliefs and expectations, factors affecting thoughts, fears and difficulties, importance of self-regulation and metacognition, effectiveness of practical-life situation learning, external factors interfering with learning, and effective learning methods. The findings emphasise the necessity of knowing learners' experiences to develop an environment where learning could prosper. The study demonstrates the advantages of practicing real-world contexts in language teaching to expand learners‘ interest and their language skills
What is the relationship between psychological wellbeing, job satisfaction, and work-life balance among hospitality workers in Dublin?
No full textPsychological wellness is a topic that has become popular around the world in recent years as work-life balance has increased and companies have been able to see the positive results of having a balance, companies like Google, even Facebook. However, there is a large gap in research for other industries. To date, there has been little research focused on the hospitality sector in Dublin, despite it being a country with a high market demand. The hospitality industry is known for long working hours, uncompetitive salaries, stress, etc. The purpose of this study is to explore how the effects of psychological well-being are related to job satisfaction and whether their relationship generates work-life balance, as well as what are the results such as feeling good, being more efficient, and life improvement.
This study used a quantitative research approach to collect data from 62 participants employed in various roles within the hospitality industry in Dublin. Surveys measuring psychological well-being and job satisfaction were administered to assess participants' perceptions of their psychological well-being, job satisfaction, and work-life balance. The surveys used validated scales to ensure the reliability and validity of the data collected. Statistical analyses, including correlation and regression, were conducted to examine the relationships between psychological well-being, job satisfaction, and work-life balance.
The study findings revealed a significant positive correlation between psychological well-being and job satisfaction among participants in the Dublin hospitality industry. Employees with higher levels of psychological well-being reported higher job satisfaction and exhibited positive work behaviours. In addition, the study found that psychological well-being was associated with better work-life balance, indicating that employees with higher psychological well-being experienced higher work-life balanc
Active Social Media Users Assess Online Health Information Credibility
No full textThis study will examine the online health information’s credibility
Hybrid Intrusion Detection System for RPL IoT Networks Using Machine Learning and Deep Learning
No full textThe Internet of Things (IoT) is transforming everyday objects. However, its devices’ limited memory, processing power, and network capabilities make them susceptible to security breaches. The Routing Protocol for Low-Power and Lossy Networks (RPL) is a promising IoT protocol but faces significant security challenges. Existing research often focuses on individual attacks, utilizing various mitigation strategies, including machine learning and deep learning for detection. This paper proposes an Intrusion Detection System (IDS) using the ROUT-4-2023 dataset, which encompasses Black Hole, Flooding, DODAG Version Number, and Decreased Rank attacks. The study utilizes statistical information graphs to investigate network traffic features encompassing all four attacks. Additionally, it experiments with various machine learning models and deep learning architectures for comparative analysis, focusing on confusion matrix outcomes and computational efficiency. Results indicate that the Random Forest classifier achieves 99% accuracy, while Transformers reach 97% F1-Score with a training time of only 16.8 minutes over five epochs
Gig employees’ experience: Exploring the well-being of location-based (male) gig workers
No full textThe gig economy has grown rapidly due to digitalization and unexpected events such as financial crises and the pandemic. After Covid-19, the number of gig workers increased to 435 million and the gig economy has been studied widely since that. The reason is due to the nature of the gig economy, which creates many advantages for the gig employees’ lives such as flexibility and work-life balance. However, the literature review shows that gig workers and their employers encounter multiple disadvantages regarding employees’ well-being. Well-being covers the health of physical, emotional and financial levels of people. Focusing on employees’ well-being creates employee engagement and retention and benefits a company through employees’ performance and productivity. Thus, many studies highlighted that holistic policies, regulations and strategies will encourage gig employment. On top of that, the Irish Supreme Court has announced a new regulation for gig workers meaning they should be treated as PAYE employees.
Considering this new decision, the researcher decided to explore gig employees’ well-being, identify if there are any employers’ initiatives towards gig employees’ well-being and study if gig employees’ well-being has changed since the Supreme Court’s decision. The researcher interviewed 10 male gig workers who work in location-based gig jobs as well as analyzed data using a quantitative approach. However, the result of the study showed that gig workers struggle with their present lives and have physical and emotional tiredness. Also, the gig employees do not receive any initiatives related to their well-being and the Court’s decision has not been introduced to the gig workers. Therefore, only 10% of the participants have heard about the decision and the interviewee who heard of the Supreme Court action said that he did not want to be treated as a PAYE employee due to tax concerns. Furthermore, the study recommends that further research include a diverse sample of gig workers across different types of gig workers and industries. Additionally, focusing on the Supreme Court’s decision and its implementation and legislative actions to create an inclusive environment for gig workers. The researcher would note the Supreme Court’s decision and its implementation will support gig workers in the Irish context and attract a potential talent pool to Ireland
The Impact of Online Applications and Social Media on Recruitment for Organizations and Young Jobseekers in Ireland
No full textThe purpose of this study is to examine and investigate the impact of online applications and social media on the recruitment process for both organizations and young job seekers in Ireland. In a world where technology is constantly evolving, it’s important to investigate just how much reliance organizations and individuals have on these technologies and whether they are aware of the limitations. This study looks at how companies use social media and online platforms to find and assess applicants, as well as how young people use these tools to look for and apply to jobs. This study discusses the advantages and disadvantages of e-recruitment, while noting what could be argued as a new threat or benefit to the industry: the use of AI. The use of AI in general, let alone in the recruitment world, is an extremely new preface and currently lacks a sufficient range of accessible research, which is why it is a critical case for discussion.
To facilitate this research, a range of previous research by established authors and statistics have been conveyed to provide an in-dept discussion on the topics and arguments currently surrounding this area of study. Topics such as how organizations and jobseekers connect with each other, why these online methods are preferred and the ramifications that may arise due to these practices are investigated.
A qualitative methodical approach was adopted for this study in the form of semi-structured interviews with 9 young Irish jobseekers / graduates between the ages of 20-25 to investigate their firsthand experiences with online recruitment methods. The findings from these interviews were thematically outlined, with the themes recognized from the data being Accessibility & Convenience, Engagement & Personalization, Privacy and Authenticity. The data was further compared to other authors' research previously discussed, whilst also acknowledging new findings and areas worth further research
Street Navigation for Visual Impairment using CNN and Transformer Models
No full textThis paper addresses the challenge of street navigation for individuals with visual impairments and explores the potential of Artificial Intelligence (AI) to enhance navigation safety and effectiveness. We evaluate the performance of state-ofthe-art Computer Vision Object Detection models, focusing on accuracy and speed. The central question is whether Transformer-based Object Detection models outperform other models. We use the specialized dataset ”Walking On The Road” adapted to include only relevant classes, to compare deep learning and Transformer models in pre-trained and fine-tuned states. Metrics used include Mean Average Precision (mAP) for accuracy and Average Inference Time in milliseconds for speed. Our results show that YOLO models surpass Transformer-based models in both accuracy and speed. In Phase 1, YOLOv8x achieved the highest mAP of 0.399 with an average inference time of 14ms, while Transformer-based DETR had a lower mAP of 0.344 and a significantly longer inference time of 818.2ms. In Phase 2, after fine-tuning, YOLOv8x again outperformed with an mAP of 0.471 compared to DETR’s 0.323. These findings indicate that YOLO models are more effective for street navigation applications, providing superior accuracy and speed for visually impaired individuals
Sustainable Plant Pathogen Detection: Balancing Accuracy and Energy Efficiency in Deep Learning Models
No full textIn the realm of sustainable agriculture, accurate and energy-efficient plant pathogen detection is crucial for crop health, environmental sustainability, and economic viability. This study investigates sophisticated machine learning methods for the identification of plant pathogens from images, with a particular emphasis on deep learning models such as DenseNet121, VGG19, and Convolutional Neural Networks (CNN). In order to maintain environmental sustainability, I built and assessed these models to determine their accuracy in diagnosing plant illnesses as well as their carbon footprint. A dataset consisting of 2,025 photos that were classified as bacteria, viruses, fungus, healthy, and pests was used in the study. According to the results, DenseNet121 performs better than CNN and VGG19, obtaining the maximum accuracy of 98% with the least amount of loss (0.068). DenseNet121 is the most ecologically friendly model as it also shows the lowest carbon emissions. DenseNet121 has a rather low carbon emission of 0.0003 kg CO2 per epoch, proving the efficiency of the model for large-scale applications in precision agriculture. The results depicted DenseNet121 as the most reliable and greenest among the models studied
AI-Powered Forecasting: Revolutionizing Natural Disaster Prediction and Response Optimization
No full textThe current frequency and intensification of natural disasters, influenced by climate change, remains a significant threat to global societies; human and material losses. In view of the generally recognized need for upgraded disaster prediction and improved emergency procedures, this research aims, primarily, at application of the crucial artificial intelligence (AI) methods in the context of natural disasters prediction. The research uses ML models for numerical data and DL models, such as CNN, VGG16, and ResNet50, for image-based disaster categorization. To aid practical deployment of disaster prediction and make it real-time, an interface using Gradio is designed.
The methodological approach starts with data pre-processing where data is cleaned from anomalies, outliers and some features are engineered. During exploratory data analysis, important characteristics, including cyclical patterns and differences in the incidence of disasters by region, are identified. The analyses of the chosen model demonstrate the increases in accuracy and, therefore, the enhanced results in the classification of the disaster types and the prediction of the program declarations. The interface developed with the help of Gradio improves the available adjustments and ensures real-time operability.
The implications of this research are to show the utility of AI in disaster management if the objective is a reduction in losses. The findings lay the path for future development application in real-time integration of IoT systems and other larger data sets, thereby repositioning AI as the central tool in handling global disasters