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Determinants of Financial Inclusion in Argentina and Ireland: A Comparative Perspective
No full textA comparison of the factors influencing financial inclusion in Argentina and Ireland, at both macro and micro levels over a ten-year time period (2012-2022) is conducted through this study. By using Fixed Effects Regression method, with the objective to discover which socioeconomic factors from World Bank's World Development Indicators (WDI) database along with other demographic indicators like Health Development Index (HDI) have impact in both countries. The level of financial inclusion in these countries has been calculated using a two-stage principal component analysis (PCA), based on metrics of financial access-ownership, availability and usage sourced from International Monetary Fund's Financial Access Survey (FAS) as well as Global Findex Database (Findex). The results show that Ireland, with better economic freedom and a more digitized environment, always leads to higher levels of financial inclusion than Argentina. Still, Argentina has been successful in achieving financial inclusion in terms of traditional banking. The study illustrates a contradictory inverse relationship between HDI and financial inclusion, showing complex dynamics that need further scrutiny. Policymakers need to develop tailored strategies to enhance financial literacy and adoption of fintech in both countries, with particular attention to bridging the digital gap in Argentina
The Effects of Mobile Payments on Financial Inclusion in Nairobi Metropolitan Region
No full textThis study sought to examine the effect of mobile payment adoption on financial inclusion among Boda Boda riders in the Nairobi Metropolitan Region. Cross-sectional survey was used to collect data from randomly selected participants(n =77) from the study site. Statistical Package for Social Science (SPSS.v28) was used to conduct the analysis such as descriptive, Pearson’s correlation analysis and binary logistic regression analysis. Descriptive analysis was used to model the level of financial inclusion, while correlation and logistic model were used to examine the association between adoption of mobile payment applications and financial inclusion, including moderating variables in stepwise manner. Results show a statistically significant positive association between mobile payment adoption and financial inclusion (r = 0.33, p < 0.001). Convenience of mobile payment, trust in mobile apps, cost of service and financial literacy shows moderate association to both adoption of mobile payment as well as financial inclusion. Results show that trust in mobile payments application is the most significant variables between adoption and financial inclusion, overshadowing convenience which has traditionally been considered as a critical factor. The study concluded that while adoption of mobile payment has a direct positive effect on financial inclusion, trust in the new technology is emerging as a key consideration among users. Trust in technology manifests through concerns such as data security as safety of money. The study recommends that to foster financial inclusion through digital solutions should take user’s security concerns more seriousl
A study of Graph Neural Networks and Graph Attention Networks for Node Classification
No full textGraph representations have received considerable attention as they can capture complex data relationships represented as nodes and edges, such as molecular structures, social network analysis, healthcare, and citation networks, etc. Due to this diversity of application areas, finding a suitable network representation to effectively handle complex structures, such as high dimensional term document matrices where the relationship between the nodes and edges is complex is difficult. In this study, the authors have focused on two prominent neural network representations, namely Graph Neural Networks (GNNs) and Graph Attention Networks. These two well-known neural graph architecture are designed to improve the performance of baseline GNN architectures. A range of models are proposed, and were trained and tested on the CORA dataset which consisted of research papers and citations. While the two type of neural models can handle graph based representations, it is not known a priori which one is the most suitable one for node classification. It was found that Graph Attention Networks based on attention mechanisms outperformed all proposed architectures with an accuracy of 73.8%. An accuracy that is even better than the model found in Keras. The major findings of this study show that the graph neural network models based on attention mechanisms were better than the simple GNN node classifiers and also set new targets for effectively handling complex graph-structured data in various applications such as social network analysis and citation networks
An Analysis of The Impact of Digital Marketing Strategies on Customer Loyalty: The Supermarket Sector in Ireland
No full textThis research explores the impact of digital marketing strategies on customer loyalty in Ireland's supermarket sector. As digital marketing continues to expand in this evolving environment, understanding its influence on customer loyalty becomes essential. The specific aims were to look at the effects of social media marketing, mobile marketing, content marketing and email marketing on customer loyalty and suggest some of the best digital marketing strategies in improving customer loyalty. The use of quantitative research was made for the study because data was collected, using online surveys, and structured questionnaires. To capture the sample, 100 respondents who are aged from 18 to 40 years who are in Generation Z in living in Ireland who use Instagram, Facebook, and email frequently to shop and interact with brands were administered with a survey through a convenience sampling technique. All data collected in this study were analyzed mathematically, via descriptive and inferential statistical methods, such as correlation and regression tests and were processed by the application of SPSS software. Consequent research findings highlighted that social media marketing, mobile marketing and email marketing had a positive correlation with customer loyalty whereas content marketing did not. The study suggests that supermarkets should focus more on engagement strategies that employ the use of social media platforms, mobile application marketing and e-mail marketing. Also, consumer insights that regulate the approaches of content marketing must be rebuilt. The recommendations of this study offer guidance on how digital marketing can be used to improve customer loyalty within the supermarket industry in Ireland
Examining the Relationship between Leadership Styles and Levels of Job Satisfaction, Motivation, Stress, and Productivity in Remote, Hybrid, and On-site Working Employees in a Post-Pandemic Environment
No full textThe onset of the Covid-19 pandemic has inspired revolutionary change in the way occupational duties are now being fulfilled. The pandemic and its consequential lockdowns forced many organisations worldwide to impose remote working arrangements on their employees, handing the predominant share of those in managerial positions with the unfamiliar task of leading subordinates in a virtual environment for the first time. Although the pandemic and its accompanying restrictions have since passed, remote and hybrid working arrangements have remained. This series of abrupt changes in a short period of time would suggest that managers have been required to make considerable adaptations to their behaviours and approach to leadership.
An abundance of research exists relative to the influence that specific styles of leadership have on the various work-related measures of employees, however the vast sum of which had investigated this relationship prior to the pandemics occurrence and were inclined to only include employees participating in on-site work. As a result, the impact of the pandemic and the differing work arrangements that gained prevalence since its emergence have remained unexplored in regard to the relationship between leadership styles and employee work-related measures.
This study administered a self-completion questionnaire to one hundred and three participants consisting of remote, hybrid, and on-site working employees to obtain quantitative data for the purpose of examining the relationship between leadership styles and the job satisfaction, motivation, stress, and productivity of these employees. Despite theory suggesting that transformational and transactional leadership are dissimilar in nature, their impacts on employees are comparable. The findings of this study indicate that whilst transformational leadership provides a greater benefit to the work-related measures of employees, the primary working arrangement of employees has no significant bearing on their job satisfaction, motivation, or levels of stress
The impact of the reward system on teachers’ performance in public schools in Russia
No full textThe influence of the reward system on the employees’ performance has been an important topic. The relation between these two concepts has been studied in different fields including teaching. The researchers highlight the significance of this type of analysis among teachers since they are the main element in the process of knowledge delivery which forms the global level of education. The previous studies were conducted in different countries and different types of educational institutions. This research aimed to analyse the relationship between the existing reward system and the performance of teachers working particularly in Russian public schools in order to understand how the current system works, what types of rewards are preferred and what could be improved in this area. A semi-structured interview was used as a principal data collection tool in combination with the volunteer self-selection technique of sampling. Participants’ responses were studied through thematic analysis. Based on the findings, the study explored the relationship between rewards and performance, which types of monetary and nonmonetary rewards are in favor, which of them influence teachers in public schools the most, and what current system is lacking. The research made additions to the existing works on a similar topic by uncovering other factors that impact teachers’ performance besides traditional monetary and non-monetary bonuses. Finally, based on the findings, the study suggested possible improvements to the current system which include a recommendation to focus on particular rewards mostly preferred by respondents such as monetary bonuses, additional time off, organised activities and learning opportunities for teachers
Examining female millennials’ purchase intentions for dining in Irish independent restaurants through content engagement on Instagram
No full textGeneration Y, also known as ‘digital natives’, was exposed to technology from a very early age. The Web 2.0 and Millennials traits on social media such as hyperconnectivity, cocreation, and self-broadcasting have led to the shift from disruptive marketing communications to tradigital marketing strategies – two-way dialogue communication between brand and consumer, fostering content engagement. Corresponding with it, marketing specialists in the restaurant industry have no choice other than to invest resources in developing a content strategy on Instagram, that can attract female Millennials, using several types of content: user-generated content, firm-generated content, photo, video, and text-based content.
The main objective of this research is to investigate the topic of purchase intentions through content engagement on Instagram of female millennials in the sector of Irish independent restaurants. After the defined gap in the literature is filled, the author aims to contribute to independent restaurant industry marketing by developing a list of recommendations of best practices usage content engagement for purchase decisions.
The research methodology is interpretivism in epistemology, supported by an inductive approach with a qualitative method of collecting data. The way of collecting data is semi-structured in-depth interviews conducted with three female Millennials residents of Ireland, active Instagram users.
The findings of this study propose that Instagram content has a great impact on the purchasing decisions of female Millennials in the independent restaurant industry in Ireland. Because of Instagram's constant development and updates of features, future research in the field can be done to monitor the consumer behaviour of the platform
Irrational vs rational spending within the fashion industry: Are Generation Z spending irrationally on fashion products?
No full textThe fashion industry has had to adapt to the changing consumption behaviors of Gen Z, a generation known for being informed, conscious, and having distinct values. This research is focused on the main question that was explored: "Irrational vs. Rational Spending in the Fashion Industry: Are Gen Z Spending Irrationally?" A contrast has been drawn between rational and irrational spending behaviors for this generation pertaining to fashion. Literature related to current factors affecting the purchasing behavior of Gen Z will be reviewed, forces that include social media, impulse buying, sustainability, and financial literacy influencing purchase decisions.
Moreover, irrational spending is also a feature of Generation Z, basically due to the surge in social networks and high-tech gadgets. Lim et al. (2020) highlight the role of the social media influencer, in which endorsers are more authentic, producing positive emotions and thus stimulating impulse buying. In fashion, Priporas, Stylos, and Fotiadis (2019) describe intelligent retail environments where recommendations are tailor-made, and offers are real-time; all these arouse an irrational response that generates impulsive purchases (Goldring & Bolger,2022). These behaviors can be explained by the Affect Infusion Model, which suggests that emotions are very influential on decision-making and thus could lead to impulsive buying.
In contrast, it is the rational factors—sustainability and ethical consumption—that are the drivers of spending behaviors for Gen Z. McNeill and Moore (2019) agree on the same point: this generation will pay a premium for environmentally and socially responsible brands, proving that values drive purchase decisions. Similarly, Lusardi and Mitchell (2018) posit that Gen Z has a higher level of financial literacy. Thus, they are wiser spenders and avoid reckless spending and impulsive buying. On their side, Gentina et al.,2018) indicate what makes Gen Z consumers loyal: value for money and quality is the reason they seek durable goods and long-term benefits characteristic of rational consumption.
The findings of this study prove that Gen Z consumers do both impulsive and deliberate consumption; these factors are not mutually exclusive. For example, a Gen Z consumer could be driven by the impulse to buy a fashionable product endorsed by an influencer but at the same time be concerned with the values of the company and the quality of the product. Feelings and reason thus influence their decisions. Such insights brim with the paradoxical light of Gen Z consumption behavior, marked by the coexistence of present gratification and futuristic considerations, beaming against traditional categorizations of youth consumption as either purely impulsive or rational.
The findings have shown that interactions between irrational and rational processes affect the consumer behavior of Generation Z in the fashion industry. This calls for the need for brands targeting their audience to develop marketing messages in a way that appeals to these consumers based on their irrational and rational needs. From the marketing perspective, this study offers insights to marketers and fashion brands about the need to identify and deal with various factors affecting consumption behavior among Gen-Z populations in the fashion industry
Combining Clustering and Classification methods for Galaxy Morphology Identification
No full textGalaxy Morphology is the study of galaxies based on their shapes and structures. Traditional research primarily uses classification or clustering techniques to categorize galaxies into distinct groups such as Elliptical and Spiral Galaxies. Currently as more telescopic surveys are planned to be launched, the challenge that is faced by astronomical scientists, is to classify these huge amounts of data for further research, although supervised techniques do work well, scientists have shown concern when working with human labelled data due to potential biases. Thus, this research proposes a new machine learning framework to potentially reduce human annotations in data by combining Image Classification and Clustering techniques. The framework combines a Hierarchical Clustering technique using the Entropy, Gini and Gradient Moment (EGG) coefficients, with Neural Networks. The research will conduct two tests, by implementing the Hierarchical Clustering using HDBSCAN, paired with the classification model EfficientNetB0, and for the second test combining Self Organising Maps along with a CNN architecture. The SDSS catalog containing approximately 670,000 galaxy jpeg images and FITS data, out of which approximately 13,452 of both, comprising nearly 50% of each class, will be used to conduct this research, pertaining to the limited availability of resources and time constraints. The results show the hdbscan+efficientNetb0 and Som+CNN frameworks giving an accuracy of 90% and 93% respectively
Uncertainty Analysis in Earthquake Prediction using Deep Learning Methods for Improved Risk Management
No full textPredicting earthquake magnitude remains a challenging aspect of natural disaster management. This study explores deep learning techniques to improve earthquake predictions, with a focus on uncertainty in seismic forecasting. The experiment includes Bayesian Long Short-Term Memory (LSTM), Bayesian Convolutional Neural Networks (CNN), Bayesian Temporal Convolutional Networks (TCN), and a Hybrid Bayesian CNN/LSTM model integrated with the Monte Carlo Dropout Method to enhance the reliability of the predictions by effectively quantifying uncertainty. All models underwent a training process of 1000 epochs. The Adam optimizer and Stochastic Variational Inference (SVI) were used to adjust parameters and control uncertainty, improving the learning process. The models were evaluated using various metrics such as Standard Deviation, Uncertainty Estimate, MAE, RMSE and R2 to assess their accuracy and uncertainty. The results indicated that the Bayesian LSTM model was the most effective, delivering the precise forecast while maintaining well-calculated uncertainty, with the lowest Mean Absolute Error (MAE) of 0.0322 and Root Mean Squared Error (RMSE) of 0.0453 and an R-squared score of 0.9903 indicating that it accounted for nearly all the variability in data. Also, the hybrid Bayesian CNN/LSTM model performed well, showing a good balance between accuracy and uncertainty. This research highlights the importance of deep learning methods and their potential in helping to manage the risk of natural disasters, saving lives, and reducing economic losses caused by earthquakes