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The Impact of Artificial Intelligence and Big Data on the HRM Practices of Sterling Bank in Nigeria
No full textTechnological innovations are changing the fundamental aspects of organisations across industries in the contemporary business landscape. There are transformative intersections of business operations and technology enabled by the integrative vehicles of artificial intelligence and Big Data technologies. These technologies provide strong capabilities which enable machines to mimic or demonstrate human intelligence and make informed decisions based on such advanced knowledge, thereby changing the way organisations approach their functions, including HRM practices. This study investigated the impact of artificial intelligence and big data on the HRM practices (recruitment and selection, performance management, learning and development and employee engagement) of Sterling Bank in Nigeria. A comprehensive investigation of how the HRM function of Sterling Bank has been impacted by the amalgamation of AI and Big Data technologies has been lacking. Also, there was no study, to the knowledge of the investigator, which has explored the challenges and opportunities these technologies presented to the organisation’s HRM function. This study attempted to close these gaps. The study utilised a quantitative survey research, collecting data from 187 employees of the bank. The collected data were analysed using the correlational design to measure the significance and direction of the relationship between AI and big data technologies and HRM practices. Rejecting the four null hypotheses and accepting their alternate forms, the study finds that there is a positive and significant relationship between AI and big data technologies and the HRM practices (recruitment and selection, performance management, learning and development and employee engagement) in Sterling Bank. The study also establishes that AI and big data technologies are useful in enhancing the effective and efficient implementation of recruitment and selection, performance management, learning and development and employee engagement in Sterling Bank. However, despite these positive outcomes, these technologies still require human intervention especially in areas such as performance management and employee engagement. The issues of data privacy and protection also remain of critical importance while using these technologies
A study on the impact of Artificial Intelligence in Human Resource Management
Get PDFThe primary aim of the study is to evaluate the role of AI in Human Resource Management while the secondary aim is to analyse the obstacles faced by organisations due to the implementation of AI in Human Resource Management practices. Positivism philosophy, deductive approach, mono-quantitative choice, descriptive design, and primary quantitative strategy has been included to conduct the analysis. Purposive sampling has been used to manage samples while implementing a cross-sectional time horizon in the study for time management. AI has emerged as a powerful tool that enhances efficiency and decision-making in talent management, recruitment, and workforce planning. Data privacy concerns robust measures to comply with regulations and secure sensitive employee information with employee resistance, job displacement, transparent communication, and training programs. The development of AI technological practices is significant in reducing errors via declining manual labourers that enhances the speed of working procedures intensifying organisational performance. AI has a significant impact on improving the work efficiency of the HRM process through involving organisational performance quality
Performing Keyword Research for Updating Website Content using ChatGPT, Bard and Bing GPT: A Comparative Study between the Three, Limitations and Future Scope
Get PDFA prominent AI chatbot, ChatGPT of OpenAI, attracted a lot of attention and raised $11.3 billion. Two competitors with different characteristics have emerged such as Google Bard and Bing GPT. The purpose of this study is to evaluate their effectiveness in updating website material so that content management decisions are well-informed. It has addressed the gap in understanding their comparative strengths and limitations, emphasising user experience through interviews with digital marketers.
The literature review underscored the impact of Bing GPT on strategic marketing and content optimisation, the influence of Bard on content production and the revolutionary position of ChatGPT in digital marketing and SEO. However, the review identifies gaps in addressing real-time issues and emphasises the need for nuanced understanding. The integration of the Technology Acceptance Model (TAM) provides a structured approach. The research gap highlights the need for comprehensive exploration of real-time challenges.
Moreover, the study utilises a qualitative research approach and investigates challenges in keyword research using ChatGPT, Bard, and Bing GPT. Participants, purposefully chosen, comprise professionals with 2-3 years of website content management experience. Seven individuals from Ireland are recruited via LinkedIn, undergoing phone interviews. Employing a narrative research design with an interpretivism philosophy and inductive approach, thematic analysis is employed for data interpretation. Ethical considerations align with the Data Protection Act of 2018. The study aims to offer insights into the efficacy of Open AI models in keyword research for managing website content. Lastly overall findings from the study have been stated in the conclusion section and the limitation and recommendation to improve study has also been provided. The study could have underscored opportunities for enhancing the efficacy of these platforms. Additionally, the research findings would be valuable for upcoming researchers exploring security challenges encountered by organisations when utilising ChatGPT, Bard, and Bing GPT. The study is poised to offer future researchers the chance to collect primary data for ongoing investigations in this domain
Impact of key influencing factors on wage outcomes and strategies for maximizing earning potential using regression analysis
No full textEmployee attrition had been an area of key concern for most organisations globally. Job satisfaction had been based on the wage an employee earns in a job. Knowledge of the worthy skill sets freshers as well as experienced people had or needed to acquire to qualify for a certain job was seen as necessary. The aim of this thesis is to predict the monthly wage in a country a job would pay based on various factors such as country, sector of employment, occupational level, education, experience, soft skills set etc. The data considered here was from a Global Survey for Adult Skills conducted by PIAAC (Programme for the International Assessment of Adult) in 2014, available on OECD (Organisation for Economic Co-operation and Development) website(Organisation for Economic Co-operation and Development, 2014). This global survey spanned over more than 40 countries and accessed the fundamental cognitive and workplace skills essential for individual’s active engagement in society and crucial for economic growth and success. Six European countries (Finland, France, Greece, Ireland, Spain) were selected for the research. Random forest, Linear and XGBoost Regression models were deployed for predicting the wage for the skill sets. XGBoost was preferred due to better performance metric’s values for R-squared and RMSE. Top five influencing factors found from the best model indicated that Denmark had the highest average wage for professionals working as legislators, senior managers, and mangers. Hence, it was suggested to work in Denmark in the above-mentioned fields for better wages
Comparative Study among ARIMA, SARIMA & XGBoost for Prediction of NIFTY IT Index
No full textNifty IT index of India stock market is one of the most important yet neglected index when it comes to research and prediction. Prediction of Nifty IT index has benefits would be able to provide foresight and informed decision making to investors, traders, policy makers, etc. as Nifty IT represents IT sector of India. This research is a comparative study between three time series prediction algorithms viz. ARIMA, SARIMA and XGBoost for the most precise forecasting of Nifty IT index. The dataset used for this study has the Nifty IT index data of last 6 years. This time frame covers the dramatic historic moments such as covid-19 pandemic, Russia-Ukraine war, India-Canada tensions and the drastic changes in prices during these events. Three models were hyper parameter tuned and then compared on the basis of three metrices- MSE, RMSE and MAE. Out of the three, SARIMA models seem to have outperformed both ARIMA and XGBoost and hence the conclusion of the study is SARIMA is the most precise algorithm to use for prediction of Nifty IT index out of three
cacDNA
No full textThe main goal of this project was an educational one to showcase what I have learned in this course. To do that I would use many technologies to design and develop an application that could be hosted in the cloud and could potentially be scaled up at any point depending on the requirements of the potential user of same. It would essentially store animal data as well as their owners which can in turn be used to facilitate a “stick” approach to resolve the issue of animal fouling – the “stick” being; if an owner’s animal fouls in a public place and the owner doesn’t remove it responsibly, then they have to pay a fine. My research suggests that there are a few similar applications in the US and the UK – but they used in privatised settings and my application would be ideally used by a government department (making the fine and adherence process easier)
Investigating the role of emotional intelligence in project leadership effectiveness: a cross-industry study
No full textThis research aims to analyse how Emotional Intelligence (EI) enhances the effectiveness of project leadership. It explores EI's critical role in the workplace, particularly in improving decision-making, team dynamics, and overall project outcomes. Additionally, the study examines the challenges professionals face in developing EI, such as recognizing and managing emotions, and understanding others' emotional cues. To achieve these objectives, both primary and secondary qualitative data were considered. The primary data was gathered through semi-structured interviews with eight project leaders, offering valuable insights into the practical application of EI
in leadership. The findings suggest that EI significantly improves a project manager’s ability to negotiate by understanding the emotional drivers behind stakeholder positions. This understanding fosters more successful negotiations and compromises, leading to outcomes that satisfy all parties
involved. Overall, the study highlights the essential role of EI in effective project leadership and workplace succes
Competitive Analysis of Embedding Models in Retrieval-Augmented Generation for Indian Motor Vehicle Law Chat Bots
No full textThis study evaluates eight embedding models in Retrieval-Augmented Generation (RAG) systems for a chatbot tailored to Indian Motor Vehicle Law. The models examined are OpenAIEmbeddings, UAE-Large-V1, all-MiniLM-L6-v2, all-distilroberta-v1, all-mpnet-base-v2, bge-large-en-v1.5, ember-v1, and gte-large. Through Cosine Similarity and ROUGE metrics, the analysis distinguishes OpenAIEmbeddings and gte-large for their superior semantic understanding. These models showed remarkable alignment with expert-generated answers, indicating their efficacy in AI-driven legal assistance. The study's outcomes underscore the importance of embedding model selection in legal chatbot development, focusing on semantic comprehension capabilities. This research is pivotal for enhancing AI legal assistance, offering insights into the effective integration of embedding models in legal technology applications
Myocardial Infarction Detection Based on ECG: A Combined Approach using Variational Autoencoders (VAE) and Transfer learning
No full textThis work introduces the use of convolutional variational autoencoders (CVAEs) to effectively decrease the dimensionality of electrocardiogram (ECG) images without sacrificing diagnostic information, addressing the critical requirement for early and accurate myocardial infarction (MI) identification. The study uses CNN, InceptionV3, VGG19, ResNet152, and ResNet50 deep learning models to examine how resolution affects model performance. Higher resolution images improve accuracy and specificity more uniformly across models, according to the findings. VGG19 performs particularly well, despite requiring longer training cycles. Notably, the study
shows that CNN is efficient and presents itself as a viable model for rapid and reliable MI diagnosis. It also reveals that CVAEs offer a balanced approach, lowering dimensionality while retaining diagnostic accuracy
Teacher stress: The impact of teacher attitudes to inclusion and teacher knowledge of autism
No full textThe aim of this study was to explore the relationships between attitudes toward inclusion, knowledge of autism, and stress (general, administrative, and competency-demand mismatch) among teachers currently working with autistic students in the Republic of Ireland. Participants (N = 125) completed an online survey comprised of the Perceived Stress Scale (Cohen et al., 1994), the adapted Autism Awareness Scale (Tipton & Blacher, 2014), the Impact of Inclusion Questionnaire (Hastings & Oakford, 2003), and the Teacher Stress and Coping measure (Forlin, 2001). Significant negative relationships were found between teacher attitude to inclusion and stress (general, administrative, and competency-demand mismatch) (p < .01), and knowledge of autism and stress (administrative) (p < .05). This suggests higher the teacher’s stress, the lower their attitude toward inclusion/knowledge of autism. Results indicate the need for national guidance and training for teachers on how best to support neurodivergence in schools