Effect of gratification, utilitarian, and trust elements on the use of retail mobile banking app in Africa:a comparative study

Abstract Information and communication technology has ushered in an era where business organisations are striving to create value for their customers to ensure retention. Thus, mobile banking flexibility is one of the reasons for developing mobile apps for customers. Ubiquitous nature of mobile devices provides an opportunity to run retail banking apps where many people can use at anytime and anywhere to engage in banking transactions. Using these apps, we investigate comparatively, the effect of gratification, utilitarian, and trust elements towards the use of retail mobile banking apps in Ghana, Nigeria, and South Africa. The study conveniently collected data from mobile banking app users in three African countries. By analysing the data with SmartPLS, the results show a minor, major and no variations in the effect of Gratification, Utilitarian, and Trust elements towards the use of retail mobile banking app in the selected countries. These findings suggest that technology innovation inclusive should be encouraged in the development of retail mobile banking app in order to improve customer experience

Predictive model and feature importance for early detection of type II diabetes mellitus

Background: Accurate prediction and early recognition of type II diabetes (T2DM) will lead to timely and meaningful interventions, while preventing T2DM associated complications. In this context, machine learning (ML) is promising, as it can transform vast amount of T2DM data into clinically relevant information. This study compares multiple ML techniques for predictive modelling based on different T2DM associated variables in an African population, Ghana. Methods: The study involved 219 T2DM patients and 219 healthy individuals who were recruited from the hospital and the local community, respectively. Anthropometric and biochemical information including glycated haemoglobin (HbA1c), body mass index (BMI), blood pressure, fasting blood sugar (FBS), serum lipids [(total cholesterol (TC), triglycerides (TG), high and low-density lipoprotein cholesterol (HDL-c and LDL-c)] were collected. From this data, four ML classification algorithms including Naïve-Bayes (NB), K-Nearest Neighbor (KNN), Support Vector Machines (SVM) and Decision Tree (DT) were used to predict T2DM. Precision, Recall, F1-Scores, Receiver Operating Characteristics (ROC) scores and the confusion matrix were computed to determine the performance of the various algorithms while the importance of the feature attributes was determined by recursive feature elimination technique. Results: All the classifiers performed beyond the acceptable threshold of 70% for Precision, Recall, F-score and Accuracy. After building the predictive model, 82% of diabetic test data was detected by the NB classifier, of which 93% were accurately predicted. The SVM classifier was the second-best performing classifier which yielded an overall accuracy of 84%. The non-T2DM test data yielded an accurate prediction score of 75% from the 98% of the proportion of the non-T2DM test data. KNN and DT yielded accuracies of 83% and 81%, respectively. NB had the best performance (AUC = 0.87) followed by SVM (AUC = 0.84), KNN (AUC = 0.85) and DT (AUC = 0.81). The best three feature attributes, in order of importance, were HbA1c, TC and BMI whereas the least three importance of the features were Age, HDL-c and LDL-c. Conclusion: Based on the predictive performance and high accuracy, the study has shown the potential of ML as a robust forecasting tool for T2DM. Our results can be a benchmark for guiding policy decisions in T2DM surveillance in resource and medical expertise limited countries such as Ghana

Social Media Usage for Computing Education : The Effect of The Strength and Group Communication on Perceived Learning Outcome

Social media has become an important platform where users share, comment, discuss, communicate, interact, and play games. Aside from using social media for personal, social, and business purposes, the use of social media has gained attention, particularly for collaborative learning in the educational sector. This paper examines the role of social media in computing education based on the use of WhatsApp social media group. Additionally, the study explores how social media usage by students influences their perceived learning outcomes. Given these aims, the study formulated four research hypotheses and tested using Partial Least Square Structural Equation Modelling. With the participants of three hundred and thirteen (n=313) students, the study found a positive relationship between social media usage for computing education and perceived learning outcomes. In addition, the study found a linear relationship between communication in- group and perceived learning outcomes. Finally, the study revealed that social media positively relates to tie strength, and that tie strength influences in-group communication.peerReviewe

Social media usage for computing education:the effect of tie strength and group communication on perceived learning outcome

Abstract Social media has become an important platform where users share, comment, discuss, communicate, interact, and play games. Aside from using social media for personal, social, and business purposes, the use of social media has gained attention, particularly for collaborative learning in the educational sector. This paper examines the role of social media in computing education based on the use of WhatsApp social media group. Additionally, the study explores how social media usage by students influences their perceived learning outcomes. Given these aims, the study formulated four research hypotheses and tested using Partial Least Square Structural Equation Modelling. With the participants of three hundred and thirteen (n=313) students, the study found a positive relationship between social media usage for computing education and perceived learning outcomes. In addition, the study found a linear relationship between communication ingroup and perceived learning outcomes. Finally, the study revealed that social media positively relates to tie strength, and that tie strength influences in-group communication

Investigating students’ perception towards the use of social media for computing education in Nigeria

Abstract This study examined the use of a social media platform — WhatsApp — by computer science students for learning computing education in the context of a Nigerian education institution. Nowadays, a large community of students in higher education institutions has embraced the WhatsApp platform for social interactions which makes it a useful tool in education. In this study, students formed three closed groups, and each group had a specific computing topic they discussed. Their discussions were in the form of posting questions, providing answers to questions, or expressing knowledge on the group topic. A questionnaire was used to collect data from the participants regarding their experiences. We conducted a descriptive analysis of the students’ learning outcomes. The results show that the use of social media contributes positively to students’ learning achievement, and they are motivated to acquire more knowledge about different computing topics

Unemployment, personality traits, and the use of Facebook does online social support influence continuous use?

Abstract Different personality traits respond differently to unfavourable life situations. Unemployment can have several negative social, economic, and domestic consequences. Many people use social media for a variety of reasons. The aim of this study is to examine the way different personality traits respond to Facebook in the period of unemployment. Data was obtained from 3,002 unemployed respondents in Nigeria. The study used regression model to analyse the data. Among the five personality traits, results indicated that the relationship between neuroticism and online social support was negative. However, the relationship between online social support and satisfaction was positive. The study highlights several theoretical and practical implications