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Attitudes and intellectual disabilities literacy in Ireland, examining beliefs, religion and social contact
Get PDFDespite worldwide initiatives to raise support for people with intellectual disabilities (PWID) to live a life of their choosing, research continues to report negative disability attitudes. This quantitative mixed method study, examined disability attitudes of 167 people in Ireland, aged 18-78. This was achieved through an online survey and snowball sampling. The research aimed to critically analyse if intellectual disability knowledge (IDL), religion, age, or gender, influenced disability beliefs and interventions. Willingness to have contact with PWID was also explored through social distancing measures; used with additional measures to predict attitudinal responses to statements about PWID employed in the survey. The purpose of this research was to identify significant variables effecting attitudes to enhance disability inclusion strategies, encouraging positive attitudes and equality for PWID. Findings included significant gender differences as males displayed greater desire to distance from PWID. Religion, age and intellectual disability literacy did not significantly affect disability attitudes
Influencer Marketing’s effect on purchasing decisions in the health & fitness industry from the perspective of a millennial
No full textThe types of advertising that millennials are being exposed to has altered in recent years. The rise of social media has led to influencer marketing becoming an extremely popular source of advertisement aimed at millennials. The research question : What are the desired characteristics of an influencer promoting products in the health & fitness industry to successfully impact millennials purchasing decisions? Four main objectives were devised to answer this research question. To examine the impact of influencers on millennials purchase decisions in the health and fitness sector. To investigate the key factors that determine millennials' level of trust in influencers in the health & fitness industry. To investigate the importance of attractiveness, expertise & qualification for the influencers ability to gain trust and therefore impact purchase decisions. To identify key factors / make recommendations necessary for a successful influencer strategy in the health and fitness sector, aimed at millennials.
A questionnaire was used to collect predominantly quantitative data from 100 respondents. A deductive approach was taken, and the data was analysed using a descriptive analysis. The data collected from the questionnaire was then analysed using the SPSS
software. This allowed for a deeper analysis of the data. The key results : Influencers currently do have a considerable impact on millennials purchase decisions in the health & fitness sector. Influencers who promote several brands at once are not trusted by millennials. Physical attraction, a muscular build (expertise), and a qualification are all desirable characteristics for increasing trust between the millennial and influencer. However, having a qualification is the characteristic which increases trust the most. The Micro Influencer group (10,000-50,000 followers) is the most trusted of out the influencer categories & Mega Influencers (1 million + followers) are the least trusted.
Recommendations have been made to health & fitness companies who are looking to incorporate influencer marketing with millennials as the key target audience. These recommendations are strongly backed up by the data collected. To answer the research question , the desired characteristics for an influencer promoting products in the health & fitness industry to successfully impact purchasing decisions : A Micro Influencer (1,000-10,000 followers) , has a qualification , is a logical fit with the brand and does not promote many products at once. There is further research required in this area. It is suggested that a qualitative study is conducted with the aim of discovering why certain factors lead to trust/distrust
To what extent does digital literacy service differ across public libraries in the Republic of Ireland?
No full textDigital literacy is an area relatively understudied within public libraries. This study investigated the level and types of digital literacy service in place across public libraries in the Republic of Ireland, and to ascertain whether there an urban/rural divide exists. This research entailed a census, namely a survey sent out to all public libraries in Ireland. The research was quantitative in nature to handle the large amount of data, and employed a deductive, positivist approach. Results show regions across Ireland differ greatly, though some regions such as South-East and Midlands have higher levels of informal and formal DL service respectively, whilst the West has the most wide-ranging types. Most libraries, however, did not teach other digital literacy skills such as eSafety and website evaluation. The research could also not correlate whether an urban/rural divide exists, opening future research possibilities
Predictive analysis of YouTube trending videos using machine learning
Get PDFYouTube is a world-famous video sharing interactive platform which allows its users to rate, share, save, comment, and upload the content. Unlike popular videos which get number of likes and views by the time they are stated as popular, YouTube trending videos represents the content which is gaining viewership over a certain time period and has a potential to be popular. Despite their importance YouTube trending video’s analysis have not been a well-researched area yet. This research proposes to analyse interactive features to determine correlation and importance of variables for the trendiness of a video. Study focuses on how interactive video features helps a video trend on YouTube. Research is based on YouTube trending video’s viewership statistics of more than 40000 videos over a certain time period. Since trending video statistics consists of number of Views, Likes, Dislikes and Comment counts, the research performed Linear regression model of Machine Learning for predictive analysis of number of views for YouTube trending videos. In addition, the study performs a comparative analysis of a number of classification models namely Random Forest, SVM, Decision Tree, Logistic Regression and Gaussian Naïve Bayes, to determine which model suits better for predicting the number of days a video will take to get trending from its upload time and the number of days a video will trend on the trending list. Research achieved maximum accuracy of 62.53% for predicting YouTube’s trending video’s lifecycle. Cross Validation method have been used for statistical significance testing and the performance evaluation matrix has compared and determined the most useful classifiers. Furthermore, this research follows CRISP DM methodology design with correlational quantitative research method. Study will bring objectivity towards the popularity constraint of YouTube trending videos
An Irish experience of the effects of social isolation and social media use during COVID-19
No full textThe research study investigated the relationship between social isolation and social media use during COVID-19 on stress, anxiety, and coping-self efficacy using a quantitative mixed methods survey. A sample of 180 participants were accessed through snowball and convenience sampling and completed an online survey with measures comprising of The Friendship Scale (Hawthorne, 2006), Social Media Use Integration Scale (Jenkins-Guarnieri et al., 2013), The Depression Anxiety Stress Scale 21 (DASS-21, Lovibond & Lovibond, 1995), Coping-Self Efficacy Scale (Chesney et al., 2006), and Perceived Stress Scale 14 (Cohen et al., 1983). Analysis revealed social isolation and social media use was positively associated with anxiety and stress scores. There was a weak positive relationship between social isolation and social media use. There were also gender differences between social media use, stress, and anxiety. Future research was presented, and the implications of the current study were discussed
Recommender systems for product sales in the banking industry
No full textRecommender systems can be found nowadays in a variety of different domains and
industries, its popularity depends on the proven exponential enhancement of streaming
services and e-commerce businesses on recent times. However, most of the recommender
systems rely on the ratings given by users to the items. The major challenge for using
recommender systems in the banking industry is the absence of ratings. It is very unlikely that
a bank institution will ask customers to rate their products. To solve this problem, the
research implemented a rating algorithm that gives a weight depending on the number of
transactions and product usage from every customer of the dataset. Customer segmentation
was used for targeting the customers that were more prone to open and accept an investment
from the banking institution. PyTorch helped to develop a Neural Collaborative Filtering
model that was capable to predict the interaction between the customer and future banking
products. Finally, PySpark was used for the development of an ALS recommender system that
generated banking products recommendations to the customers. This research contributes
to future studies by experimenting with multiple recommender systems applied to the
banking industry that will increase the value of customers and support decision making on
implementing customer marketing campaigns
Comparison of machine learning V/S deep learning model to predict ICD9 code using text mining techniques
Get PDFHealthcare information is usually collected and stored in form of numbers, texts or images. This data consists of important details such as their visits, symptoms, prescriptions, notes or vital statistics of the patients. Most of these documents are huge in amounts and difficult to maintain or access, hence most of the health institutions maintain such details in the form of Electronic Health Records (EHR) in order to avoid manual error and avoid redundancy. This dissertation uses text mining techniques on textual notes from a real time EHR database (MIMIC – III); to identify the most effective vectorization technique to retrieve meaningful information. A comparison among machine learning models alongside of deep learning model is made using the novel H2O framework and Rapid Miner to predict the ICD9 code based on the extracted data
Cookies ‘n’ Consent: An empirical study on the factors influencing of website users’ attitude towards cookie consent in the EU.
No full textSince GDPR came into enforcement in 2018, various firms have been found violating or circumventing the ePrivacy Directive known as the Cookie Law which lays out the cookie consent guidelines for websites. To improve the GDPR compliance rate, several conversations are going on between EU commission, Data protection agencies, business & websites owners and ad vendors regarding their cookie policy, obtaining user consent for data collection and its usage. One of the key stakeholders who are the website users, whose privacy is in question seems to be left out from the discussions. The study aimed to understand user perception towards website cookie banners, which are mandatory under GDPR, and the influence of factors like awareness of cookies, user experience, consent banner design, privacy risk, brand trust on user’s willingness for accepting all cookies, to develop recommendations to improve customer’s motivations to give consent. Using a quantitative approach, the primary data was collected from 132 internet users residing in the EU region through an online survey questionnaire shared in social media networks. The results showed that the (i) majority of respondents had more than moderate level of awareness about cookies (ii) they are more likely to accept cookies for quick access or task completion, (iii) acceptance of cookies was varied across different categories of online activity and (iv) given a choice they are more likely to opt-out of 3rd party cookies which are widely used for targeted advertising. Since 3rd party cookies will be phased out in the near future and are likely to be replaced with more advanced customer tracking technologies which are harder to opt-out of, this study proposes a framework for Consent for Advertising Directive (CAD) to go beyond the existing Cookie Law, which will improve user data protection regardless of the tracking technology used, and help brands to improve transparency about their data collection and avoid GDPR violations
Volunteering friendship: Exploring the impact on the Befriending Volunteer’s mental wellbeing
No full textCurrent research demonstrates the positive impact of befriending interventions on older vulnerable adults. However, volunteer befriender experience has been overlooked. This study aimed to gain a deeper understanding of befriender’s attitudes and experiences of how befriending may impact one’s mental wellbeing. Qualitative research was carried out with five befrienders describing their experiences in semi-structured interviews. Quotes from transcripts were categorised. Five themes reflecting befrienders’ experiences were taken from the data using thematic analysis: (a) Volunteer, friend, or carer? (b) ALONE support and communication, (c) Volunteer feedback is essential, (d) Our mental health is just as important as theirs, and (e) Impact of covid-19. Implications of these themes highlight the need for regular supervision and support with a specific focus on the befriender mental wellbeing in befriending programmes across a range of social care settings
Identify opportunities to optimise energy consumption and propose strategies for energy management on an International Sports Campus in Ireland
No full textEnergy management is a well-established business practice for increasing efficiency, reducing costs and improving environmental sustainability. This mixed methods case study identified opportunities to optimise energy consumption and proposed energy management strategies for an international sports campus in Ireland. The study investigated how employees can be engaged to ensure strategy success and how human resources can be balanced between energy management and primary duties. Monthly energy use and cost data was analysed to identify opportunities to reduce consumption. The results showed the swimming/diving
facility had the highest energy usage/costs. To gain insight into energy management best practice 7 industry experts were interviewed. Recommendations are to establish an energy management team, install pool covers, retrofit LEDs, optimise the building management system and implement an energy management system at the campus. The results indicate employees can be engaged through incentive programs while organisational culture and including energy management in job descriptions can help balance resources between energy
management and primary duties