8333 research outputs found
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Examining the challenges encountered by diverse enterprises or businesses implementing or adopting Oracle’s cloud suite to manage its human capital operations
No full textThe research explored the problematic list of challenges met via businesses using the Oracle Cloud Suite whilst coping with their core business activities, with a specific focus on Human Capital Management operations. When you agree with the idea, remember that ‘businesses’ can refer to many different types of enterprises. It could be a hospital, a small store, or even a large car company. Every business, no matter what size, would benefit from an on-premises or cloud-based system to handle its human capital related operations like Human Resources, Absence Management, Payroll and many more. The adoption of cloud-based has been increasingly accepted within the modern business environment, imparting scalable and advanced tools for corporations to streamline operations. Oracle Cloud Suite, a complete suite of cloud-based applications, has supplied the necessary operations needed for successful Human Capital Management. However, the usage or adoption of these offerings inside a cloud-based technology has its own challenges that call for an investigation.
To study the challenges faced by businesses using Oracle Cloud’s Human Capital Management services, a few individuals that work in consulting firms as an expertise in Oracle Cloud’s Human Capital Management were interviewed for this research
Challenges and Enablers of African Migrant Entrepreneurship in Ireland
No full textPurpose: African migrant entrepreneurs are increasingly contributing to the vibrant Irish entrepreneurial scene. With a focus on the interplay between resource access, support networks, and cultural assimilation, this study explores the challenges and enablers presented to these entrepreneurs.
Methodology: This study used semi-structured interviews and thematic analysis to investigate the effects of social capital, funding constraints, and cultural variations on market entry, business performance, and overall integration into the Irish ecosystem.
Findings: The findings of the qualitative analysis detailed cultural prejudice, difficulty in accessing funds as some of the challenges, while stating personal drive and Irish entrepreneurial ecosystem as some enabling factors. These findings add to our knowledge of African immigrant entrepreneurs' experiences, as well as presenting insights for policy makers and support groups to create focused initiatives promoting greater inclusivity and fostering a more dynamic entrepreneurial ecosystem.
Limitations: The major limitations are basically two: first is insufficient research work on African migrants in Ireland which could be due to African population to Ireland’s native citizens ratio (67,546 to 5.127 million). Second is the small sample size of interviewees available to the researcher.
Conclusion: African migrant entrepreneurs are often faced with challenges of poor cultural embeddedness, and for those who do not have prior entrepreneurial experience or access to funding, this could be a precursor to failure
Study how HRM practices contribute to employee well-being, including mental health initiatives, work-life balance, and stress management programs
No full textThis paper aims at understanding the effects of outsourcing of HRM practices on employee health with the main areas of interest being mental health, stresses, and work-life balance. If the well-being of the workers is beneficial for the organisation, this study aims to assess sound methods of HRM that may influence positive employee health. The main research question focuses on what records the impact of HRM practices on the health of the employees and the difficulties faced by the HR managers in the processes of implementing the practices. The study is based on the frameworks of organisational behaviour and human resource management with references to mental health issues, stress, and work-life balance.
To gather data the study applies a qualitative method of research that includes 10 HRM professionals from different organisations in Australia in a way of semi-structured interviews. Thus, this methodology allows for additional investigation of the situation, including the assessment of the effectiveness of present policies and determining obstacles to their implementation. The study shows that organisations must enhance mental health care of employees and employ flexible working arrangements to mitigate stress while improving employees' job satisfaction. Consequently, this has power implications for the existing body of knowledge in the field of employee well-being and comes up with recommendations that will help in improving HRM practices and thus efficiency in the Organisation
Perceptions and Challenges faced by Irish Tech sector employees around strict return to the office policies
No full textThe Covid-19 pandemic has changed the way we work, especially in the technological sector, where employees had to adapt to remote work. As the pandemic came to an end, many companies are reassessing the return to the office policies. This sparks debates around the effectiveness of these policies and the effects they have on overall employee satisfaction, productivity and well-being. This study investigates the perceptions of Irish tech sector employees when it comes to challenges around strict return to the office policies. Herzberg's Two-Factor Theory of Motivation is used to analyse the factors that influence employee job satisfaction and turnover.
Ten professionals from the Irish tech sector, who have been recently asked to return to the office, participated in the semi-structured interviews to provide their perception and identify key issues contributing to dissatisfaction. The interview candidate pointed out problems such as rigid company policies, strict enforcement of office attendance by managers, and the physical and mental strain of long commutes. A common feeling among them was that inflexible office policies led to a sense of mistrust and feeling undervalued. While there were positive factors such as increased company culture and pleasant office settings, these were often overshadowed by the negative aspects.
The findings highlight that many participants felt that their work-life balance is being compromised, prompting them to consider seeking more flexible job options. This emphasises the need for tech companies to adopt more flexible, employee-centric practices to enhance satisfaction and retain talent in a competitive market.
To tackle these issues, the study recommends that companies should promote more flexible work arrangements, address commuting challenges, consistently acknowledge employee efforts, and ensure fair access to career development opportunities. Future research should focus on larger and more diverse groups, employing mixed methods to gain deeper insights into the long-term effects of mandatory office attendance across various cultural contexts
Investigating the Impact of Artificial Intelligence on Application Support and Operations
No full textThis research explores the integration of Artificial Intelligence (AI) in application support and operations, focusing on enhancing observability and issue resolution. Utilizing a mixed-methods approach, the study combines quantitative data from surveys and qualitative insights from focus group discussions. Findings reveal that AI significantly improves operational efficiency by automating routine tasks and enabling predictive maintenance. However, challenges such as the "black box" nature of AI, compatibility issues, and concerns about job displacement present significant barriers to full adoption. The study also highlights the need for comprehensive training and transparent AI systems to build trust among professionals. This balanced approach ensures that the benefits of AI are maximized while mitigating associated risks
Exploring the Impact of Cultural Diversity on Financial Literacy Among Immigrant Small and Medium Enterprises (SMEs) in Dublin: A Qualitative Analysis
No full textThe research is qualitative, exploring the extent to which cultural diversity has impacted the financial literacy of immigrant SMEs in Dublin. This paper highlights that there are cultural perceptions which have a significant influence on financial practices such as risk aversion and access to finance. The study has revealed that although cultural diversity enriches the effect of financial decision making, it also challenges the effect of financial decision making among immigrant SMEs. It can be concluded that, for immigrant SMEs, the financial sustainability of the business depends on the targeted financial literacy programs and social policies which should also take into consideration the cultural context.
Employing an interpretive research philosophy that delves into the subjective experiences of individuals, this study conducted semi-structured interviews with immigrant business owners and focus group interviews to explore the personal experiences of these businesses regarding finances. The use of NVivo software facilitated a thematic analysis, revealing that cultural diversity significantly influences financial management among immigrant SMEs in Dublin. Specifically, cultural perceptions of finance, such as risk aversion, attitudes towards debt and investment strategies, and access to finance and community sources, amplify the effects of cultural diversity on financial decisions.
The presence of broad cultural diversity makes Ireland an enriching environment but the same also comes with challenges. Nonetheless, it is important to observe the fact that immigrant entrepreneurs are supported by the community, which is a positive aspect. Therefore, the evidence that immigrant-owned businesses are prospective is the fact that most people who have completed the business programme have improved their financial management skills. Additionally, a study has determined numerous factors that hinder developing one’s immigrant-owned business. These factors are the language problem, also the statement of problems included a limited use of formal financial services, and problems with bureaucratic hardness, such as long application procedures, and strict eligibility criteria.
This research conclusively demonstrates the vital role of cultural diversity in determining the financial literacy generality within immigrant SMEs in Dublin. Even though such results are not only meaningful but also enlightening, analysing the financial obstacles faced by immigrant business owners, it seems vital for Ireland to introduce more social policies. Such actions can contribute to the creation of a more inclusive environment in the country’s economy, thus fostering the conditionality of immigrant businesses. The importance and impact of the findings on future policies and practice in the field certainly suggest the actions of organizations, policymakers, and individuals to change
Hate Speech Detection on Social Media – A Practical Research using NLP and LLM Models
No full textHate speech on social media poses a fundamental problem that affects both community safety and online discourse. Conventional techniques for identifying such content, such as decision tree classifiers, frequently fail to capture the complex phrasing and context of hate speech. I used a cutting-edge Large Language Model (LLM) from OpenAI to improve the detection accuracy of hate speech to solve this. By utilizing the LLM’s sophisticated natural language processing powers, this approach allows it to comprehend context and nuances more accurately than other models. The outcomes show a significant improvement, with our model outperforming conventional classifiers with over 95 percent accuracy. This development gives social media sites considerable advantages in reducing harmful information and is in line with current advances in using deep learning for complex linguistic tasks. Still, there are issues with how well the model handles ambiguous circumstances and how to modify it to fit changing linguistic trends
Co-pilot widget for assisting the public in processing US presidential political candidate tweets from Twitter in 2024 US elections candidate choice through sarcasm and stance detection
No full textWith 50 percent of the world’s population going to the polls to elect new leaders in 2024 it was thought to be relevant to examine what can be done to lessen polarisation and improve quality of discourse online. The gap the paper intends to fill is the prior lack of combined natural language processing (NLP) techniques collected together to raise understanding of media. NLP results were 0.9960 for truth-fake detection, 0.9835 for sarcasm detection and 0.9978 for stance. Additionally, a set of two versions of graphic outcome to make understanding simpler and faster
Enhancing Wind Turbine Longevity: A Comparative Study of Deep Learning and Traditional Machine Learning Techniques for Predicting Remaining Useful Life
No full textThe aim of this study is to evaluate how effective deep learning is compared to traditional machine learning in predicting the Remaining Useful Life (RUL) wind turbine components. Various models for predicting wear of damage-sensitive gearbox bearings will be evaluated based on their computational requirements and prediction accuracy, and other relevant factors will also be considered in this research. While evaluating the model performances, the lowest value of Mean Absolute Error (MAE) achieved by Random Forest algorithm because it provides good performance and requires less computational cost. This research increases our knowledge of how to predict when machines will need repairs, and also shows how we can apply these ideas in real world predictive maintenance systems for wind turbines
Enhancing E-Learning Platforms with AI-Driven Personalization through AI Chatbots
No full textMore recently, with advances in technology and internet-borne learning, differentiated learning has emerged as a central focus for improving learning amongst students. This research examines the establishment of an AI-enhanced e-learning chatbot with a customized application for student learning profiles. The fundamental reason for carrying out this research is to overcome the shortcomings of the conventional e-learning systems, characterized by their inability to provide personalized and dynamic learning designs to accommodate a specific learner’s characteristics, hence providing standardized services. The study devised the chatbot through Python programming language with the support of rich NLP tools and integrated a rich set of educational references. The process of research was bureaucratized, including the procedure of data accumulation and data preparation, and NLP models to enhance the capabilities of the chatbot to answer a wide range of education-related inquiries. As discovered within this study, implementing an AI chatbot is effective in increasing user satisfaction and engagement due to the customized answers provided, which ultimately enriches the learning process. A series of tests and users’ feedback were used to assess the effectiveness of the proposed chatbot for delivering individualized educative assistance. The findings of this study can be valuable to the field of educational technology by providing an example of the use of AI in learning customization. The outcomes show that the discussed chatbot can become the missing link in e-learning solutions, helping personalize content delivered online. The study establishes that preserving and enhancing AI-based personalization is valuable and relevant to e-learning, as new advancements in technology proceed in the years to come