3830 research outputs found
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Examining the effects of birth order on personality, self-esteem, and perfectionism
No full textThe aim of this study was to examine the effects of birth order on personality traits, self-esteem scores and levels of perfectionism. Participants (n = 198) completed an online survey which used the following measures, the Big Five Inventory – short version, the Rosenburg Self-Esteem Scale, and the Big Three Perfectionism – short form, as well as three demographic questions surrounding gender, age, and birth order. For the purpose of analysis, the participants were split into four birth orders, eldest, middle, youngest, and only. All three hypotheses were analysed using a Kruskal Wallis test, as none of them met the assumption of normality. The results of this study found no significant differences between any of the psychological variables and the different birth orders, therefore, all the null hypotheses were accepted
Customer segmentation approach for efficient targeting in retail
No full textCustomer segmentation is becoming popular nowadays, with companies increasing their product base. It is very tough to reach out to each customer in the company portfolio; hence, one can introduce segmentation to target the customers in the best possible way. This will help in reaching out to the customers in the best possible manner and the targeting can be done very quickly. The study performs stats-based and ML-based analyses on the dataset to provide a profile report to segment the customers. The study has used the RFM model and K-means clustering using a standard Data Mining Approach to get the analysis done on the retail dataset. Concluding that based on Purchase behavior, 3-4 cluster solutions are made, and for the K-means model, the following can be done only with 2 cluster solutions. Also, at the end, the profile report is generated to validate the clusters as per business
Exploring the underrepresentation of women in the fintech industry in Ireland
No full textThe study explores the underrepresentation of women in the fintech industry in Ireland, focusing on the impact of workplace culture, recruitment practices, and career progression opportunities on gender diversity. Despite global advancements in gender equality, the fintech sector in Ireland continues to exhibit significant disparities, with women underrepresented, particularly in leadership roles. To address this issue, a quantitative survey was conducted with 259 respondents from various fintech companies across Ireland. The survey assessed perceptions of workplaceculture, recruitment processes, and career advancement opportunities concerning gender inclusivity. Data were analysed using descriptive statistics and Pearson correlation coefficient to examine the relationships between these factors and women's representation in the industry. The findings reveal that while there are efforts to promote gender diversity, significant gaps remain. Approximately 65% of respondents believed that their workplace supports gender diversity, yet many reported inconsistencies in policy implementation, recruitment practices, and career advancement opportunities. The hypothesis testing confirmed that positive workplace culture, inclusive recruitment practices, and robust career progression opportunities are significantly correlated with improved representation and retention of women in the industry. This study contributes to the existing literature by highlighting the persistent challenges in achieving gender equality in the Irish fintech industry. It emphasises the need for more effective and consistently applied diversity initiatives to bridge the gender gap. In conclusion, while progress has been made, substantial work remains to ensure that women have equal opportunities in fintech. The study calls for future research to explore these dynamics further and to consider intersectionality and longitudinal impacts on gender diversity
Exploring job satisfaction amoung employees in Irish startup tech companies: a comprehensive analysis
No full textThe present study focuses on the overall level of job satisfaction among employees in Irish startup tech companies. It intends to identify key factors contributing to job satisfaction in Irish startup tech companies. The study also emphasises evaluating the impact of company culture on job satisfaction within the startup tech sector in Ireland. Another objective of this research is to examine the relationship between job satisfaction and employee retention rates in Irish startup tech companies. The purpose of this study is to investigate job satisfaction among Irish startups in the tech industry to contribute to the advancement of academic research and provide useful recommendations for managers and policymakers. These objectives of the study have been attained by collecting primary data from the staff of tech companies based in Ireland. An online survey has been conducted involving a total of 100 staff of Irish Tech companies to obtain primary data for this research. The outcome of this research indicates that employee satisfaction is dependent on various factors in the Irish tech industry such as work environment, company culture, compensation package, opportunity for career advancement, and inclusive and fair policies. Further study can focus on utilizing a larger sample to analyse the issue considered for this research
An exploration of the relationship between sustainable practices by luxury fashion brands and consumer purchasing behaviour in Europe
No full textThis study investigates the relationship between the communication by luxury brands of their sustainable practices and the impact on consumer purchasing behaviour of luxury fashion brands in Europe as a result of that consumer information and awareness. Using a mixed methods approach combining focus group interviews and a quantitative survey, the research examines consumer awareness, attitudes and behaviours towards sustainability purchases in luxury fashion. Findings reveal that sustainability and ethical practices are becoming increasingly important factors in luxury fashion consumer culture and purchase uptake, but these are often secondary to style, quality and brand reputation. While consumers show high awareness of sustainability issues, they have different levels of knowledge about specific brand initiatives. Social media and digital storytelling emerge as a critical channel for both information seeking and brand communication on sustainability. The study reveals that consumers are willing to pay a premium for sustainable luxury products and most of them accept a price increase of 5-10%. The research finds that the impact of sustainability on purchase decisions is often dependent on the specific style or brand. Ethical labour practices and material sourcing are seen as highly important with the potential to significantly influence brand loyalty and purchasing behaviour. The research also reveals the interplay between emotional attachments, aesthetic preferences and sustainability concerns in luxury fashion consumption. When valuing sustainability, this study demonstrates it is often balanced with other factors such as design and brand heritage
To review the Impact of Brexit and the COVID-19 pandemic on logistics in the sourcing of marble for the luxury commercial development sector in Ireland, specifically tier-three construction firms
Full text linkThis dissertation critically evaluates the dual impact Brexit and the COVID-19 pandemic had on the sourcing of marble for the luxury commercial development sector in Ireland, specifically on tier-three construction firms (for which the vast majority of construction firms in Ireland fall within). The direct impact of both the geopolitical fallout from Brexit and the health crisis induced by COVID-19 had on the macroeconomic environment for tier three Irish construction companies in sourcing marble resulted in complexities around new trade barriers, uncertainties disrupting established supply chains, changes to governmental policies brought about in rapid succession due to successive lockdowns, global lockdowns worldwide, labour shortages, amongst others.
This study employs a qualitative research approach providing valuable insight from industry leaders within three construction companies utilising semi-structured interviews conducted with mid to senior management and subsequent findings analysed, intending to paint a comprehensive picture of these impacts. The research revealed a significant strain on tier-three construction firms during this period. Negotiating powers of these firms were impacted due to market fluctuations and volatility brought about by Brexit and COVID-19. It revealed the strategies adopted by these firms including diversification of supply sources, employing procurement specialists as a means to mitigate against these market events. The findings contribute to a deeper understanding of the resilience and adaptability of smaller players in the construction sector facing global challenges and offers insights into potential policy and managerial responses to mitigate such impacts in the future.
In conclusion, the research served as a microcosm for studying the broader implications of global disruptions on niche supply chains such as marble, providing valuable lessons in adaptability and strategic foresight in an increasingly unpredictable global market and highlights how these firms can learn lessons to adapt in a post-Brexit and post pandemic world
The impact of investor's behavioural factors on investment decisions in financial markets in Ireland
No full textThis research investigates the impact of investor behavioral characteristics such as fear, stock market volatility, and herd behavior on decision-making in Irish financial markets. Furthermore, the study proposes to look at the function of risk perception as a mediating component in the connection. This study collected data from investors in Ireland's financial markets using a quantitative questionnaire. The convenience sampling approach was used to choose participants. The findings revealed that their fear, stock market volatility, and herding behavior significantly affected investors' investing decisions. Furthermore, risk tendencies were shown as a critical mediator in this association. This study adds to the existing body of information and provides investors with valuable insights about behavioral biases that may impact their investment decisions. Furthermore, it is crucial to recognize that other factors may affect investors' decision-making processes. The findings will be precious to stakeholders, including government officials, lawmakers, financial advisers, and investors
Comparative analysis of ML Algorithms for effective phishing URL detection
No full textCybersecurity, an increasingly critical field, is under constant threat from evolving cyberattacks. Phishing websites, a prominent method for attackers to deceive users and steal sensitive information, require effective detection systems. This study, focusing on developing a highly accurate ML model to predict the phishing nature of websites, is a significant contribution to the field. The study highlights the importance of balancing accuracy with real-time applicability, emphasising the need for quick response times in practical phishing detection systems. Future improvements may include integrating incremental learning and hybrid models to enhance detection capabilities further
Predictive analytics of CO2 emission from agri-food activities using aachine learning
No full textGlobal warming, Climate change, and Human health are getting impacted due to excessive agri-food emissions. Hence, the predictive analysis of CO2 emissions from agri-food activities is important for policymakers and researchers to develop strategies for sustainable agricultural practices. This study collected and explored secondary historical data on agri-food CO2 emissions in various countries around the world for a time span of 30 years (1990–2020) with machine learning techniques. Since previous research studies left a gap in predicting emissions from the agri-food sector and corresponding temperature rise, this project explores this area by implementing the four predictive models Linear Regression, Decision Trees, Random Forests, and Neural Networks. As a result, exploratory data analysis helps to understand the descriptive statistics, and data visualizations on agri-food activities, emissions, temperature rise, and their relationships. The four predictive models are trained and measured with metrics like MSE, RMSE, MAE, and R-squared. The Linear Regression model emerged as the best model with the highest predictive accuracy, with the lowest RMSE (1.55e-11), MAE (8.37e-12), and highest R2-score (1.00) for CO2 emissions. The study concludes that Linear Regression can serve as a robust tool in predicting CO2 emissions from agri-food activities and helps the policymakers, government bodies, and sustainable environment by providing useful insights and strategies to reduce the environmental impact of agriculture
The impact of remote working environments and virtual teamwork in the manufacturing industry
No full textThe dissertation aims to investigate the challenges associated with virtual cooperation in the industrial sector, such as maintaining effective interactions, ensuring data security, and overcoming any barriers to synchronization and teamwork. The research adopts a quantitative research design where primary data has been collected through a survey.
The survey includes around 17 questions that are answered by industry professionals who are part of remote teams. The collected data has been analysed using IBM SPSS and the findings are presented graphically.
The findings of the study indicate that there are various challenges faced by remote team members of the manufacturing industry. The communication barrier is found to be the major problem for the members of the remote team. Moreover, they also face challenges related to maintaining team cohesion and time zone differences.
There are also issues related to the lack of access to necessary resources for the team members. Moreover, technical issues also can lead to problems for the team members. Virtual team meetings are conducted mostly weekly as per the findings of the survey. To assess the performance of the remote team members both qualitative and quantitative methods are adopted. Various measures are being taken by companies to ensure data security in the remote teams